central asian countries initiative for land management multicountry partnership framework

Project Number: TA 6357 January 2010

RETA 6357: Central Asian Countries Initiative for Land Management Multicountry Partnership Framework Support Project

Prepared by: Christopher Hatten

This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents.

ADB does not guarantee the accuracy of the data included in this report and accepts no responsibility for any consequence of their use.

Technical Assistance Consultant’s Report

CACILM SLMIS Final Report (Draft) – September, 2009

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Central Asian Countries Initiative for Land Management

Land Management Information System

for Sustainable Land Use



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Report prepared by Dr. Christopher J. Hatten (SLMIS-SLU Consultant, for ADB) in Aug-Sept 09 based on work undertaken by staff of the CACILM Multicountry Secretariat (MSEC) and the implementation units (NSIUs) of the five Central Asian Countries (CAC-5) over the period Sept 08 – Aug 09. Work presented here is in MS Word, printable in A4-format; however, many of the original maps and charts to which they relate are in larger format (A3, A2, A1 etc) and these would be presented in any final paper version of this report. Accompanying the digital version of the report is a CD with all files worked on by Dr Hatten during his 117 days of input during the period Sept 2008 to Sept 2009 under the present project, including primary data collected. Primary data is also included for the preceding ADB-financed project (EMIMS-SLU) undertaken in Kazakhstan and focusing on the South Kazakhstan Oblast. Official holders of all of this material are now CACILM’s MSEC and ADB, from which permission should be sought for any future use of the data. Any technical questions outstanding can be addressed to Dr Hatten at [email protected], phone 44 1442 245 381 Disclaimer: This text has been drafted with financial assistance from the Asian Development Bank (ADB). The views expressed herein are those of the Project Staff and Consultants and not necessarily those of the ADB nor its individual staff members. Number of pages: 151 (annexes not included)

mailto:[email protected]�


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List of Acronyms and Abbreviations

AWHC Available Water Holding Capacity (% water (on volume basis) that can be held in the soil and be available for crop growth)

Bonitet Land quality rating, relating to potential production of a basket of crops, best land awarded 100 points.

BVO Basin Water Organisation CAC Central Asian Country CACILM Central Asian Countries Initiative for Land Management CEC Cation Exchange Capacity (fundamental soil property: expressed as

milliequivalents/100g soil) CGIAR Consultative Group on International Agricultural Research Cnt Centner (100kg – standard unit for crop yields in FSU countries) CSPC Centre for Sustainable Production and Consumption (local partner NGO based in

Almaty) Decade 10-day period (use in meteorological and crop water use) DEM / DTM Digital Elevation Model / Digital Terrain Model DM Dry Matter (in connection with pasture or crop productivity) ECONET Project financed by WWF-UNEP, with final report and data of 2005, covering the

entire CAC-5 area, and focussing on an ecological network of protected areas, and establishing a basic GIS with coverages printable at 1:1m scale.

EIA Environmental Impact Assessment EIMS Environmental Information Management System (of MoEP, KAZ) EMIMS Environmental Monitoring and Information Management System for Sustainable

Land Use (of Project) ESRI Environmental Systems Research Institute Inc (makers of ArcView and ArcGIS

GIS software) FAO Food and Agricultural Organisation of the United Nations FHC Forestry and Hunting Committee (of the MoEP-KAZ) FSU Former Soviet Union GE Google Earth (imagery available worldwide, free of charge, over the internet) GIS Geographic Information System GoK Government of Kazakhstan GosNPCZem State scientific production centre on land resources and land-use planning (KAZ) Ha Hectare (10.000m²) HME Hydro-Geological Meliorative Expedition (of the MoA, KAZ) IAC Information and Analytical Centre (of the MoEP, KAZ) ICARDA International Centre for Agricultural Research in the Dry Areas IFPRI International Food Policy Research Centre ISRIC International Soil Research and Information Centre KAZ Kazakhstan Kolkhoz Collective Farm (during Soviet times; now largely disbanded and privatised in

most areas, although some collective institutions and structures remain in some areas)

KYR Kyrgyzstan MIS Management Information Centre MoA Ministry of Agriculture MoEP Ministry of Environmental Protection MS Microsoft MSEC Multicountry Secretariat of CACILM NAP National Action Plan NCC National Coordination Council (set up in each of the 5 countries) NDVI Normalised Difference Vegetation Index (essentially a remotely-sensed

measurement of chlorophyll activity in large areas of vegetation) NDVImax Maximum NDVI as recorded from the imagery of any one year


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NDVImean Mean NDVI, averaged over all the imagery available for any one year NEAP National Environmental Action Plan NGO Non Governmental Organisation NPF National Programming Framework NSEC National Secretariat (set up within each of the 5 countries) NSIU National Secretariat’s Implementation Unit (set up within each of the 5 countries) Oblast Province (2nd level of administration after National/Republic level) OblVodkHoz Oblast (SKO) Hydrological Design Institute OECD Organisation for Economic Cooperation and Development OM Organic Matter Pentade 5-day period (use in meteorological records and crop water use) PSR Pressure-State-Response Rayon District (3nd level of administration after National/Republic and Oblast levels) REAP Regional Environmental Action Plan RK Republic of Kazakhstan RS Remote Sensing SALRM State Agency (of the Republic of Kazakhstan) for Land Resources Management

(‘Land Use Agency’) SelskiyOkrug Sub-district or Municipality (in Kazakhstan) (4th level of administration, usually

corresponding to former state or collective farms) SKO South Kazakhstan Oblast SLM Sustainable Land Management SLMIS Sustainable Land Management Information System (component under CACILM) SLU Sustainable Land Use SO Selskiy Okrug (equivalent to Sub-District; commonly corresponding to one or

more former state or collective farm areas; also equivalent to Aiyl Okmotu in Kyrgyzstan)

Sovkhoz State Farm (during Soviet times; now largely disbanded and privatised in most areas, although some collective institutions and structures remain in some areas)

SRPCL State Research & Production Centre of Land Resources & Land Management, (under SALRM)

TAJ Tajikistan TAMU Technical Assistance Monitoring Unit ToR Terms of Reference TUK Turkmenistan UNCCD United Nations Convention to Combat Desertification UNCSD United Nations Commission for Sustainable Development UNDP United Nations Development Program UZB Uzbekistan UZGIP WUE Water Use Efficiency (kg Crop Yield / mm water / ha / year). Marginal Water Use

Efficiency = kg Crop Yield / mm additional water applied / ha / year (used in supplementary irrigation considerations)

WUG Water User Group


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Executive summary

1. This report was prepared by the SLMIS-SLU Consultant in Aug-Sept 2009 based on work undertaken by staff of the CACILM Multicountry Secretariat (MSEC) and the implementation units (NSIUs) of the five Central Asian Countries (CAC-5) over the previous 12-month period. 2. Work undertaken in this, preliminary, phase of the Project deals almost exclusively with the ‘CAC5 Priority Area’ which is essentially the part of the CAC5 area to the south of latitude 47ºN and east of meridian 56ºE. This area includes all the CACILM National Projects and all the territory covered by multi-national rivers, most notably the Amu Darya and Syr Darya whose waters are shared between the 5 countries. It includes all the territories of Kyrgyzstan (KYR), Tajikistan (TAJ), and Uzbekistan (UZB), and most of the area of Turkmenistan (TUK). Within Kazakhstan (KAZ), four large southern oblasts are included. By taking this Priority Area, the intensity and level of detail of work of the NSIUs of Kazakhstan and Turkmenistan could be maintained at the same level as those of the other countries. Although Tajikistan was not formally included by ADB in all aspects of project activity, information was sought and obtained in several key sectors so that coverage for the CAC-5 Priority Area could be maintained. 3. Work focuses on establishment of the SLMIS and obtaining and working on a selection of geographic information and data sets on land and environmental degradation and restoration, representing all scale ranges from 1:5m down to 1:2,500. However, the major thrust of the project has been on establishing data and map coverages for the whole priority area, printable at around 1:1million scale, and extending the scope of the basemap coverages already established by the GIS of the WWF-UNEP ECONET at the same level of detail. Further important objectives of the project have been in obtaining more detailed mapping and data for the CACILM National Project areas and in assisting the NSIUs and NSECs in reporting procedures on land degradation in their respective countries. 4. SLMIS System Design and the technical and institutional background to it are described in Chapter 1. Institutions at multi-country level include the MSC (Multi-Country Steering Committee), MSEC (CACILM Multi-Country Secretariat), and of course CACILM itself. At national level there are the NPFs (National Programming Frameworks), the NCCs (National Coordination Councils), the NSECs (National Secretariats) and the NSIUs (National Secretariats’ Implementation Units), the latter being the technical counterparts at national level for the SLMIS work. 5. The technical background covering major issues of land degradation and climate change in Central Asia are further described (Section 1.3), including irrigated agriculture (and issues of water availability and water quality), rainfed agriculture (including soil fertility and water / wind erosion issues), pasture / rangeland (degradation and water use efficiency issues), the unused lands / desert lands, the mountain and hill zones (watershed management and transboundary issues), and land degradation issues due to industrial, mining and human settlement. Given all of these issues, and the prevailing institutional set-up, the aims, objectives and requirements of the SLMIS are outlined in Section 1.4. 6. Definition of the CAC5 Priority Area, and justification for taking this as the study area for this phase of the project, are outlined in Section 1.5, together with definition of Priority Data to be acquired, and issues of acquisition of this data. Discussion on the various sets of Land Degradation Indicators (and parameters) is given in Section 1.6 with further detail given in Annex B.


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7. Details of System Design and Information Flows are presented in Section 1.7 and two key figures (Figs 1.4 and 1.5). At this stage the system has to be simple and flexible, with greatest attention focused on obtaining a good selection of all the necessary map coverages and data which should finally be the basis of the SLMIS. The system is essentially a GIS based around ERSI ArcView software, and workable with both the well-established and widely-used ver.3.3 and the current ESRI ArcGIS 9.3 versions. Database files linked to the relevant ESRI shape files are in MS Excel and Access format, with duplicates also in dbf format. Major groupings of data that are currently on the system include the following: - National Coverages, 1:500.000 – 1:1m scale, covering land use, irrigation, soils, socio-economic data… - National Statistics Agencies’ rayon data: land use, crop areas, yields and production, human and livestock populations, employment, income, poverty… - CACILM National Project Area coverages, generally scale 1:200.000 and more detailed, including key information and mapping at municipalities level… - Water Management Map, covering the Syr Darya and Amu Darya Basins… - ECONET GIS coverages, printable at 1:1m scale, and including basemap layers, socio-economic data at rayon level, ecoregions, ecosystems and protected areas (existing and proposed)… - ICARDA GIS coverages, including agro-climatic and agro-ecological zoning, some land use and land cover mapping… - FAO LADA Global LUS coverages, cut out for the CAC5 area, and some further work done by individual NSIUs at national level.. - Remote Sensing Imagery, georeferenced and overlayed with over GIS coverages: these have included MODIS-NDVI-13 250m resolution monthly coverages for 2008 for the whole CAC5 area, and the higher resolution (printable at 1:100.000 scale) LANDSAT ETM+ coverage for 2004, covering the centre of the Priority Area.. - CACILM / MSEC-generated coverages, including land degradation hotspot areas, dams and water diversion structures, CACILM National Project locations, seismic activity, Google Earth imagery downloads… 8. Details of the working of the SLMIS and work undertaken in the current phase of the project are given in Section 1.8. In essence, the SLMIS as it currently stands has a full sample of all the types of required data, and the full scope of this information is presented in Chapters 2 to 9 of this report. However, there is much work still to do, and this is described in Chapter 10 of the report. 9. CACILM Land Use and Land Management Information is presented in Chapter 2, covering both FAO LADA LUS mapping - both Global (Fig 2.1) and National (Fig 2.2, for UZB) approaches – and also CACILM Land Use Mapping, presented here (Fig 2.3) for the full Priority Area. By comparison, the proportion of land cultivated in each rayon is illustrated for the same area in Fig.2.4, with data obtained from the ECONET socio-economic database file. Maps of dominant cropping in the different cultivated areas are presented in Figs 2.5 and 2.6 for UZB and for the CAC5 Priority Area respectively. Further presentation of cropping breakdown at rayon and oblast level for UZB is given in Table 2.2 and Fig.2.8, and factors determining cropping patterns discussed, particularly with respect to the land degradation processes. Increase of salinity and water table problems are having a particularly adverse affect on leguminous fodder crops essential in crop rotations in maintaining soil humus levels and soil physical characteristics. Vegetation and Ecosystems mapping at multicountry level is presented in Section 2.5, including Soviet-era mapping and subsequent ECONET derivatives of this work. 10. Socio-Economic and Land Productivity Data is presented in Chapter 3. Data sources here are mainly the national and oblast statistics agencies (collected by the NSIU teams), and the ECONET-socio database for 2000, the latter being the source of multi-country data at rayon level. Maps of the Priority Area showing total population and rural population


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densities present interesting comparisons (Figs 3.1 and 3.2), as does the density of rural populations per hectare of cultivated land (Fig 3.3): very high densities being observed in many areas of Tajikistan and some rayons of both Uzbekistan and Turkmenistan. 11. Detailed demographic and income statistics for the three baseline years – 1991, 1999, and 2007- are currently available only for KAZ (Section 3.3). Birthrates declined drastically between 1991 and 1999 and populations actually declined in many areas, although they rose in most rayons in SKO during this period. In the period 1999-2007 birthrates and population increases have more closely resumed their pre-1990 levels in most areas. Immigration from nearby densely-populated areas of Uzbekistan (and other FSU countries) as well as high birthrates in the indigenous population explain the higher population increase in SKO in contrast to the other oblasts. Income levels also present an interesting picture, with rayons which show higher concentrations of degraded land (and particularly marginal rainfed arable land) recording lower incomes, and rayons with booming mining activities (e.g. Sozak with uranium) showing much higher incomes, both in absolute terms, and also with respect to published rayon poverty / subsistence income levels. In general rayon average income statistics for the period 2003-2007 present a distinct trend of rapidly rising income, although in some rayons the rise is much more muted. 12. Again primary data covering crop areas, yields and production for the three baseline years were available only for KAZ (Section 3.4). And again a big decline was seen in the 1991-99 figures in areas cultivated in Almaty Oblast, particularly for cereals which fell from 870.000 to 552.000ha in this period, most notably for barley and maize. Other, higher value, crops increased in area, notably sunflower, soybeans, potatoes and vegetables. Also yields increased markedly over the period – not because of better management, let alone land restoration activities, but because the initial baseline year (1991) happened to be a drought year in which yields for all rainfed crops were very depressed. Cross checking with met data and local experiences is essential when interpreting these statistics. 13. More detailed data is available for 2005 from SKO (EMIMS study data) relating areas harvested for different crops with gross production and farm gate values and receipts, and relating this to rural incomes (Table 3.4). Of interest is the current pattern of land holdings: the break up of the sovkhozes and kolkhozes has meant that 57,5% of the arable land is now managed as private farms of average size just 7ha; but 27,2% of the land is still managed under ‘agricultural enterprises’ of average size 186ha, and 15,2% is managed as village and peri-urban garden areas, of average size 0,34ha. The latter holdings are highly productive, accounting for most of the vegetable, fruits & berries, and grapes production (all higher-value crops), and much of the potatoes and melons & gourds output. Together these high value crops cover only 9,4% of the total cultivated area, but they make up 33,8% of gross value of agricultural production. Cotton covers 26,6% of total area, but 45,5% of gross value. By contrast, grain, oilseeds and fodder together cover 64% of total area, but only 20,8% of gross values. Small private farms are experiencing many problems: lack of suitable workable machinery; lack of affordable short- and long-term credit; and lack of technical support and advisory services. Some co-operative/collective working of the land is thus still logical in many areas, although this is now more a voluntary association rather than imposed from above. Understanding the practical workings of the agricultural systems is considered fundamental to any programme to tackle land degradation. 14. Changes in irrigated cropping in SKO over the full transition period is presented and further discussed in Section 3.4. Irrigated areas showed declines of 24% between 1990 and 1997, but have since risen by 5,5%. However, fodder crops (particularly alfalfa) have severely declined and cotton has expanded enormously, particularly in low elevation irrigated areas with little rainfall. There are both economic (higher returns as compared to most crops) and physical (salinity build-up) reasons for this increase. A cotton monoculture


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now prevails in many areas, which is of increasing concern to both farming communities and provincial administration. 15. Land use and livestock production is presented and discussed in Section 3.5 for the four priority oblasts of KAZ. As with cropped areas, livestock numbers experienced dramatic decreases between 1991 and 1999 with livestock densities (expressed in livestock units) only 1/3 in 1999 what they were in 1991 for many rayons. Sheep and goat numbers were most badly affected. However, between 1999 and 2007 numbers have largely recovered, so that several rayons (mainly in SKO) show increases in livestock units per hectare of over 15% from 1991 levels. However, over most of the area livestock numbers are down by 25% or more (Fig 3.6). Annual statistics from SKO (Fig 3.7) show that precise years for the fall in livestock numbers (particularly sheep and goats) were 1993-1997, and that livestock numbers have again increased appreciably since 2001, although sheep and goat numbers are still well below their 1993 levels. Pasture productivity measurements made at the same time show that pasture has recovered considerably due to the fall of livestock numbers, so that the overall Water Use Efficiency (WUE) of the pasture vegetation increased from 5,5kgDM / ha / mm precipitation to 13kgDM / ha / mm, in spite of overall lower precipitation trends over this period. Regulating livestock numbers is thus vital to overall pasture productivity, as well as both livestock productivity and carbon-sequestration considerations. 16. CACILM use of satellite remote sensing is covered in Chapter 4, including GIMMS, GLOBC-EUR SAT, MODIS, LANDSAT ETM, and Google Earth imagery. GIMMS gives coarse resolution NDVI trends over a long-term period, comparing the averages of the 3 consecutive years 1982-84 to those of 2004-2006 (Section 4.2). (NDVI is essentially a measure of actively-growing green vegetation). Of interest here is that within the Priority Area large areas of negative trends (generally the more arid areas, with less than 150mm mean annual precipitation, particularly in the extreme north and west) are matched by equally large areas of positive trends (generally most higher rainfall areas and most irrigation areas). Areas of positive trends may be explained by three factors: decline of livestock numbers in the main rangeland areas (discussed above); regeneration of (mostly undesirable) bush vegetation in many higher-precipitation rainfed areas; and by most irrigation schemes, village areas, and even peri-urban areas developing more vegetative cover as the schemes mature. Several particular hotspot areas (major declining NDVI) were noted, including a large area around Turkestan Rayon, SKO (see more detailed imagery, Fig 4.8). GLOBC-EUR SAT is covered in Section 4.3, including a critique of its immediate applicability to the CAC5 priority area land use mapping. 17. Applicability of MODIS13Q1 imagery of 250m resolution to the work is covered in Sections 4.4 and 4.5. This imagery has been downloaded at monthly intervals throughout the year 2008 by Dr Ji, and made available to the project as a rectified georeferenced product for the whole CAC5 area. Figure 4.3 shows the monthly time series of this imagery, demonstrating the different nature of the spring / summer ‘green up’ period in the various distinct agro-climatic zones. NDVImax, mean and min imagery was also generated from this 2008 imagery, and examples are given for the NDVImax and NDVImean imagery for the major part of the Priority Area (displayable here at a scale of around 1:6m), and also the area around Chui Oblast, KYR, and Zhambyl and Almaty Oblasts, KAZ, at a scale of 1:1m (Figs 4.4 - 4.7). The ECONET rivers/canals coverage was overlayed with this imagery so that ground positions and georeferencing can be appreciated: the 1:1m figures include the areas of four CACILM national projects, including Ugulek Municipality, KAZ, and Susamir Valley, KYR. This imagery is of great value for routine land use / land cover interpretation work at around 1:1m scale, but this also needs to be checked and correlated, area by area, with more detailed imagery (Landsat ETM+ and Google Earth imagery), and also with some well-selected fieldwork sites. NDVI values from this imagery also needs calibrating with DM measurements, so that estimates of vegetation yields can be obtained directly from the imagery.


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18. The higher-resolution LANDSAT ETM+ imagery (Section 4.6) was also downloaded and made available to the project by Dr Ji. This 30m resolution product was captured during the peak of the green-up period of 2004, and stands enlargement to around 1:100.000 scale. Eight scenes, each covering around 18m ha of land, give almost complete coverage of the Priority Area. Only one scene (42-40), however, was made available in georeferenced format, but that covered the most central area, including Tashkent, Fergana Valley, SKO and the western part of Chui Oblast, KYR and Zhambyl Oblast, KAZ. This imagery was inspected for areas where fieldwork had previously been undertaken, and the very high degree of photo-interpretability for land use mapping confirmed (Fig 4.9). Gulley erosion hotspots (Fig 4.12), mining degradation hotspots, and homegarden areas could also be easily differentiated with this imagery, in addition to the main land use divisions. 19. The application of Google Earth imagery, now freely available over the internet, for remote sensing work is discussed in Section 4.7. The GE moderate–resolution imagery is identical to the LANDSAT ETM+ coverages, while the detailed GE sputnik imagery, now available for more than half of the more populated areas, with irrigated and rainfed arable land, can stand enlargement to more than 1:5.000 scale. Given the problems of obtaining detailed topo maps in the CAC5 countries, this detailed GE imagery can serve as a topo basemap as well as being useful in land use / land cover interpretations and in outlining land degradation and other environmental hotspots. 20. The SLMIS now has the key essential remote sensing coverages and also ideas on how these can be used for routine assessment and mapping activities. The need for lateral thinking, relating one data source to another, and incorporating met and hydromet data as well as crop and pasture performance data, is emphasized in this work, which is summarized in Section 4.8. 21. Soil Mapping, together with the related Land Quality (Bonitet) Rating System, is reviewed in Chapter 5. This includes detailed mapping at 1:10.000 scale, originally for each of the kolkhozes and sovkhozes, with a high intensity of detailed soil chemical and physical analysis, all of which was done to high technical standards. This mapping is of enormous value as baseline material, and the SLMIS has at least begun to take steps to catalogue this, with a database at municipality level (see Chapter 6). 22. Section 5.1 reviews Soil and Land Quality Mapping which has been carried out at the above detailed level (generally 10.000 for arable areas, 1:25-50.000 for pasture areas). It also reviews work at oblast level (1:200.000 – 1:500.000), and national level (1:750.000 – 1:2m). Commonalities of legends and attribute data are reviewed in Section 5.2. Although national mapping is now available on the SLMIS in ESRI vector format for all the countries, much work remains to be done in order to achieve a common map and legend at 1:500.000-1:1m scale for the whole CAC5 Priority Area, and this remains a major, and potentially very useful, task for the future. Digitisation of oblast-level mapping also needs to be extended, so that more precise mapping is entered onto the SLMIS which would be used as a base for regional bonitet and land degradation assessments. 23. Land Quality (Bonitet) Ratings and factors affecting Land Degradation are presented and discussed in Section 5.4. Box 5.1 reviews the theory (and practicalities) behind the bonitet system. Heavy emphasis was put on soil analysis of parameters such as soil organic-C, soil salinity, soil mechanical analysis, and soil structure stability. Reassessment surveys and analyses have been carried out periodically since original surveys were undertaken (mostly in the 1960s and ‘70s) and changes in bonitet ratings over time have been recorded (see Fig 9.1 for average changes at rayon level for UZB over the period 1981 to 2003). Changes can be positive (essentially land restoration) or negative (degradation). Growing alfalfa in long rotations and providing irrigation water (and adequate drainage) will probably all increase


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bonitet ratings as well as increasing soil organic-C; repeated annual cultivations, over-irrigation with inadequate drainage, and inadequate organic residue and fertiliser application will probably all decrease bonitet ratings. The effect of salinity on depression of crop yields is also reviewed in Section 5.4, and illustrated by the recent ICARDA work at Kyzyl Orda, KAZ (Figure 5.7). Soil infiltration is another key soil parameter, and work done in UZB (Figure 5.6: UZB-NSIU, quoting FAO, 2004 and Morozov, 1989) and in SKO-KAZ (Table 5.6: EMIMS, 2007), both show declining infiltration with over-use of irrigated and rainfed arable soils respectively. 24. Municipalities Mapping and Linked Databases are covered in Chapter 6. Current rural municipalities are essentially the successors of the Soviet-era Kolkhozes and Sovkhozes on which monitoring and surveys were, and still are, undertaken at the most detailed level. Any rural implementation project would have to function at this level, and thus obtaining from the SLMIS a listing of maps and information which potentially could be available in each specific municipality would be the necessary first steps for this work. However, there are several major challenges to overcome: changes of name during and after the Soviet period; major complications on municipalities’ boundaries (which mean that it is much easier to work with point data – linked to municipalities/ centres – rather than with polygon data); and problems of availability of survey data. (Topo maps at detailed scale are officially secret: – however, soils / bonitet overlays and linked attribute data are not secret). Linked databases to the municipalities point file coverage thus include: correlation of old names of municipalities / Soviet-era enterprises with new names (Table 6.2); soils, bonitets, and other surveys undertaken (dates, and types: Table 6.4); bonitet ratings (initially areas and bonitet averages for irrigated land and for rainfed arable land: Figure 6.9); land use breakdown (CACILM land use classes); crop statistics (Table 6.3; and Figures 6.8, 6.10 and 6.12 for SKO municipalities); key agro-met parameters (mean annual and monthly precipitation; mean annual and monthly temperatures; elevation). A major start on this work has been made both for both KYR and KAZ, and this work now needs to be extended to the other countries. 25. CACILM use of meteorological and hydrometeorological information is covered in Chapter 7, beginning with four key Priority Area maps: Location of the Met Stations within the CAC5 Priority Area; Mean Annual Precipitation; Growing Season (days) as limited by Precipitation and by Temperature; defined Agro-Climatic Zones (ICARDA). The applicability of met and hydromet data to land use and watershed management is given in Section 7.1.2, where met and hydromet records are taken for three contrasting watersheds within the SKO rainfed arable zone. Format of the 1991-2006 average met records on a decade (10-day) basis is presented for two of these areas, Tasaryk and Ryskulov (Table 7.1). Using the detailed daily met records and applying the Daily Soil Water Balance Model, actual evapotranspiration (ETa) and other water balance parameters were worked out, year by year, for the 1991-2006 period, for the loess-derived silt loam soils which predominate in the rainfed arable zone of this area. ETa is important as it relates very closely to crop performance and yield, and correlations were obtained between ETa figures of Ryskulov with yield figures of nearby Karamurt. Also from these simulations, drought effects could be accurately quantified year by year, and charts plotted to show this for the two stations (Fig 7.5 – picture for the full soil profile; Fig 7.6 – picture for the surface A-horizon). The poor rainfall years and the better years could be appreciated graphically in these diagrams. The soil water balance model also predicts surface and sub-surface run-off. Interestingly, for the very deep loess soil profiles, run-off coefficients are normally very low (given very high AWCs and high infiltration rates under pasture or alfalfa vegetation), but with lower rates due to land degradation, run-off coefficients and the tendency for flash run-off events increase. Shallower and more rocky soils at higher elevations, however, have much higher run-off coefficients. 26. Hydromet data is presented in Section 7.1.4 for the three stations shown in the location map (Fig 7.7). The flow hydrograph for the most recent year (2005) is given in Figure 7.8


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and Table 7.3, illustrating well the hydrograph peaks due to rainfall and low-elevation snow melt (March-April), high-elevation snow melt (May-June) and both snow and glacier melt (July). The different picture for the smaller and lower-elevation watershed on the Kokbulak river was notable: here there was only one, very pronounced, peak corresponding to rainfall and snowmelt at low elevation in March. The hydromet record was also obtained from daily records over the 16-year period 1991-2006 and compared with the precipitation records (Table 7.4) In general, higher precipitation years corresponded to higher flow years, but the glacier ice-melt component was another large additional factor, and this depended both on temperature and also antecedent precipitation and snow melting. Key hydromet parameters for the three contrasting watersheds are finally presented in Table 7.5, with monthly flows also expressed in mm of precipitation over the whole of the respective watershed, so that direct comparisons could be made across the three watersheds. Here it is notable that lower flow rates during the late-summer season will be a major constraint to irrigated agriculture, but early and peak summer season flows from the glacier-fed rivers are excellent. The low elevation watershed, however, showed a very short and very sharp peak, but this was too early in the season to be of use for irrigation. 27. Long-term trends in the Central Asian Met and Hydromet Record are given in Section 7.2. There is clear evidence of rise in average temperature over the last 75 years of records, both from UZB (Fig 7.10) and KAZ (Fig 7.12). However, trends in precipitation are less clear: the UZB record (sum of all stations - Fig 7.11) show big variations from year to year: two consecutive years of precipitation 30% below average levels being followed by two or three years of precipitation 25-30% above average being a common pattern, but an overall 10-15% rise in precipitation over the 75 years would appear to be the long-term trend. The KAZ station (Arys) however, shows a different pattern: larger year to year fluctuations (50% above or below average figures being common), but a pattern of rising precipitation is observed from the early 1930s to the late 1950s, followed by a substantial fall since that time. 28. Use of Water Management Information is presented in Chapter 8. An excellent water management map covering the basins of the two major river systems has been produced by the UZB-NSIU in both pdf and ESRI shape file formats. Appended to that map are detailed average flow records for main channel and tributary sources of the two rivers, and also capacities of the major irrigation canals and water storage reservoirs serving the area. Long-term flow records at various points along the Syr Darya and Amu Darya are presented in Fig 8.1, showing very well the period and locations when and where abstractions began along the two rivers. By 1957 annual flows on the lower reaches of the Syr Darya had been reduced to near zero in some years, while the same situation occurred on the Amu Darya only from 1974, with occasional wet years still showing some appreciable flow. Water abstractions are governed by international agreement, the UZB allocations, from the various sources totaling some 63km³/year (Table 8.1). Coinciding with decrease of water flows has been a deterioration of water quality, with the lowermost reaches of the Syr Darya reaching total salinity levels of 1,9g/l by the 1980s, while equivalent positions of the Amu Darya had rises in salinity to 1,2g/l by the same period. 29. A CACILM dams and water diversion structures data base has been established (Table 8.2) in order to bring together data from various sources on the subject, and to refer these to positions on the GIS. GE imagery has also been a major source of this information. Again the scale of presentation of this coverage is currently 1:1m (Fig.8.4), although future work at a more detailed scale (1:200.000) would soon be required. Future presentations of river flow information and plans and elevations showing reservoir operation procedures for both major river systems are also called for, and Box 8.1 shows an example of an equivalent presentation for the Mahaweli River basin cascade in Sri Lanka, showing the river flows and reservoir operational drawdowns for one typical year. For the Syr Darya and Amu Darya systems, as for the Mahaweli, the objective is integrated watershed and reservoir


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management for multi-use purposes: irrigation (with major return-flow usages), hydropower, and run-off and flood control. 30. Application of the CACILM SLMIS in defining Land Degradation Hotspots and Brightspots is covered in Chapter 9. The various sources of information are assessed: remote sensing imagery (Section 9.2), rayon and municipalities statistical data (9.3), studies and mapping at irrigation-scheme or rayon level (9.4). National rayon coverages showing declines in average land quality (bonitet) ratings are given for UZB in Figure 9.1. Changes of soil salinity status of UZB irrigated land for 1990, 2000, and 2007 are shown in Figure 9.2, while Figure 9.3 shows proportions of irrigated land affected by high water tables at these dates. Figures 9.4 – 9.6 show the same parameters, but mapping at rayon level for two rayons in Kashkadarya Oblast, UZB. 31. Use of Google Earth imagery in defining hotspots and brightspots is presented and discussed in Section 9.5. A land degradation hotspots file has been established, currently with 553 records. Currently soil erosion gullies, landslides, and sites of sedimentation are recognized (Figures 9.7 and 9.8), and in some locations – particularly where loess soils are cultivated on more steeply sloping land, large areas have been devastated. Areas of concentration of landslides have also been noted, particularly in KYR in the area to the E of Mailluu Suu where unstable underlying geology coincides with steep terrain and higher rainfall. The hotspot file has also been extended to cover mining and industrial pollution, the latter being very close to intensively-farmed irrigation areas in many cases, and thus possibilities exist for agricultural produce to be contaminated by dangerous elements, mercury, cadmium, zinc, lead and uranium mining being very widespread in the area. 32. The final section, Chapter 10, makes recommendations for CACILM SLMIS development at both Multi-country and National Levels, including the institutional arrangements under which the information flows and key findings would be disseminated. The MSEC-NSIU-NSEC arrangements under the current project have been successful in the following areas: - key data which has hitherto been difficult to obtain has been made available to CACILM for the SLMIS; - the CAC5 multi-country GIS coverage, initially established by the WWF-ECONET, has now been considerably strengthened with both national and multi-country coverages relating to land degradation and restoration, land use, physical resources, environmental hotspots, and rural livelihoods, and all countries have contributed to this; - MSEC has benefited greatly from receiving new information from the NSIUs and NSEC staff on new and important subject areas; - different countries have all shown strong points and have benefited greatly by seminar presentations and discussions among themselves, as well as with MSEC. Some of the presentations and some of the reports have included outstanding material; - major physical problem areas and major contentious issues have been openly discussed; - communications between each NSIU, NSEC and parent national ministries in most cases have been good, and good cooperation has resulted; - a promising start has been made with use of remote sensing data and other new techniques, and all of this work urgently needs further development and expansion in the immediate future. - a good inter-disciplinary approach to technical presentation and reporting has been developed. 33. Specific recommendations for future work, under existing and possibly expanded institutional arrangements, include the following: - collection of full rayon statistics, particularly on socio-economics and land productivity, for the three baseline years (1990, 2000, 2007) for all rayons in the CAC-5 Priority Area (Section 10.2);


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- definitive land cover and land use mapping updating and standardization, aiming for uniform coverage printable at a 1:500.000 scale for the whole CAC5 Priority Area (Section 10.3); - Remote Sensing Development: including multi-data-source land use / land cover mapping techniques and involving adequate ground control, and linked to known land degradation hotspot areas. Work to include pasture productivity quantification by RS (10.4); - establishment of a central CAC5 image processing facility to furnish up-dated processed imagery to the five countries (10.4); - undertake unified soil and land unit mapping at 1:500.000 scale for the Priority area, largely based on correlation and standardization with existing material obtained from national and oblast-level mapping (10.5); - development of the SLMIS to undertake land suitability evaluations and land productivity assessments, and to output tabular and map-format material (10.6); - further evaluation of the soil erosion hotspots and development of implementation proposals to tackle these (10.7); - further monitoring and evaluation of high water table and salinity hotspots (10.8); - proposals to strengthen preparedness for weather extremes associated with climate change (10.9):

Improving overall management of the water resource across the 5 countries; Improving water use efficiencies of irrigation; Improving drainage and salinity control measures, and local institutions dealing with

these; Improving use and management of organic wastes; Improving conservation measures for both soil and water; Increasing carbon sequestration from vegetation; Increasing carbon sequestration from soils; Improving advisory and information services available to farmers and rural

communities.


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Table of contents

LIST OF ACRONYMS AND ABBREVIATIONS ............................................................................3 EXECUTIVE SUMMARY ..............................................................................................................5 1 INTRODUCTION : CACILM SYSTEM DESIGN...................................................................18

1.1 BACKGROUND .................................................................................................................18 1.2 INSTITUTIONAL FRAMEWORK FOR THE SLMIS...................................................................19

1.2.1 CACILM and ADB ....................................................................................................... 19 1.2.2 Multi-Country Institutions............................................................................................. 20 1.2.3 National Institutions ..................................................................................................... 20

1.3 MAJOR ISSUES OF LAND DEGRADATION AND CLIMATE CHANGE IN CENTRAL ASIA..............21 1.3.1 Background ................................................................................................................. 21 1.3.2 Irrigated Agriculture ..................................................................................................... 22 1.3.3 Rainfed Agriculture...................................................................................................... 23 1.3.4 Pasture (Rangeland) ................................................................................................... 24 1.3.5 Unused Lands: Desert Lands...................................................................................... 25 1.3.6 Mountain and Hill Zone: Watershed Management & Trans-boundary Issues............. 26 1.3.7 Land Degradation due to Industrial, Mining, and Human settlement .......................... 27

1.4 AIMS, OBJECTIVES AND REQUIREMENTS OF THE SLMIS ...................................................27 1.5 DEFINITION OF CAC-5 PRIORITY AREA, AND ACQUISITION OF PRIORITY DATA ...................28 1.6 LAND DEGRADATION INDICATORS.....................................................................................34 1.7 SYSTEM DESIGN, AND INFORMATION FLOWS AT MULTI-NATIONAL, NATIONAL, OBLAST &

LOCAL LEVELS ...........................................................................................................................35 1.7.1 Background ................................................................................................................. 35 1.7.2 ECONET Data............................................................................................................. 36 1.7.3 ICARDA Data .............................................................................................................. 37 1.7.4 FAO-LADA Global LUS CAC-5 coverages.................................................................. 37 1.7.5 National Coverages, 1:500.000 – 1:1.500.000............................................................ 37 1.7.6 National Statistics Agencies’ Rayon Data ................................................................... 38 1.7.7 Remote Sensing Images ............................................................................................. 39 1.7.8 CACILM National Project Area Coverages ................................................................. 39 1.7.9 CACILM / MSEC / ADB Coverages............................................................................. 39

1.8 BASICS OF THE SLMIS, AND WORK UNDERTAKEN DURING CURRENT PHASE OF PROJECT ..40 2 CACILM LAND MANAGEMENT INFORMATION ................................................................43

2.1 INFORMATION SOURCES ON LAND USE, LAND COVER, AND OPERATIONAL STATUS OF THE

LAND 43 2.2 FAO LADA LAND USE SYSTEMS (LUS) MAPPING ............................................................43

2.2.1 Global LUS Mapping ................................................................................................... 43 2.2.2 National LUS Mappping .............................................................................................. 45

2.3 CACILM LAND USE CLASSES FOR CENTRAL ASIA: MULTI-COUNTRY PRESENTATION ..........46 2.4 CROPPING: DOMINANT CROP GROUPS WITHIN AGRICULTURAL AREAS ..............................56

2.4.1 General........................................................................................................................ 56 2.4.2 Multicountry Mapping: Oblast and Rayon Statistics .................................................... 56 2.4.3 National Mapping: Oblast, Rayon and Municipality Statistics ..................................... 58

2.5 VEGETATION AND ECOSYSTEMS MAPPING FOR CENTRAL ASIA: MULTI-COUNTRY

PRESENTATION..........................................................................................................................61 2.5.1 KAZ 1:2.5m vegetation map for Central Asia .............................................................. 61 2.5.2 ECONET Ecosys coverage ......................................................................................... 62

2.6 ADMINISTRATIVE AREAS AND INFRASTRUCTURE OF CENTRAL ASIA: MULTI-COUNTRY

PRESENTATION..........................................................................................................................63 2.7 UPDATING AND ENRICHING THE CACILM LAND MANAGEMENT INFORMATION ....................65

3 SOCIO-ECONOMIC & LAND PRODUCTIVITY DATA.........................................................67


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3.1 COLLECTION, INFORMATION FLOWS, AND PUBLICATIONS...................................................67 3.2 REQUIREMENT FOR THREE SEPARATE BASELINE YEARS: 2006/07; 2000; 1990 ..................68 3.3 SOCIO-ECONOMIC AND DEMOGRAPHIC DATA....................................................................68

3.3.1 Multicountry Data ........................................................................................................ 68 3.3.2 Population and Income Data for Kazakhstan .............................................................. 71

3.4 LAND USE, AND CROP AREAS, YIELDS AND PRODUCTION..................................................77 3.4.1 Comprehensive Data from Almaty Oblast, KAZ, 1991, 1999, 2007 ............................ 77 3.4.2 Data from SKO on Irrigated Areas, Yields and Production: 1987, 1990, 1995, 1997, 2004 ............................................................................................................................... 77 3.4.3 Comprehensive Data from KAZ-SKO, 2005................................................................ 80

3.5 LAND USE AND LIVESTOCK PRODUCTION..........................................................................83 3.6 UPDATING AND ENRICHING THE CACILM LIVELIHOODS INFORMATION...............................87

4 CACILM USE OF REMOTELY SENSED DATA ..................................................................88

4.1 INTRODUCTION: UNIVERSAL DEVELOPMENTS, AND WORK DONE FOR CACILM ...................88 4.2 NDVI TRENDS, 1982-2006: THE GIMMS DATASET ..........................................................88 4.3 GLOBCOVER LAND COVER VERSION 2 (GLOBC-EUR SAT).............................................90 4.4 MODIS: THE TIME SERIES FOR 2008: THE OVERALL PICTURE FOR CENTRAL ASIA ..............91 4.5 MODIS: 2008 NDVI MAX, MEAN AND MIN; AND RESOLUTION OF IMAGERY ........................95 4.6 LANDSAT: HIGHER SPECTRAL- AND PIXEL-RESOLUTION IMAGERY: LANDSAT ETM+

GEOCOVER : 2004 DATA ...........................................................................................................98 4.7 GOOGLE EARTH : MODERATE AND HIGH-RESOLUTION IMAGERY ......................................103 4.8 FURTHER WORK REQUIREMENTS FOR REMOTE SENSING IN THE CAC-5 .........................107

4.8.1 General...................................................................................................................... 107 4.8.2 Application of RS for pasture monitoring................................................................... 108 4.8.3 Application of RS in soil erosion, soil salinity, and water-table monitoring................ 112 4.8.4 Application of RS for Carbon sequestration and Climate monitoring ........................ 112

5 CACILM USE OF SOIL MAPPING, & LAND QUALITY (BONITET) RATINGS & REASSESSMENT SURVEYS ..................................................................................................114

5.1 SOIL MAPPING AVAILABLE THROUGHOUT THE 5 COUNTRIES.............................................114 5.1.1 Detailed Mapping ...................................................................................................... 114 5.1.2 Oblast-level Mapping................................................................................................. 116 5.1.3 National and Multi-Country Mapping ......................................................................... 120

5.2 COMMONALITIES OF LEGENDS AND ATTRIBUTE DATA .......................................................123 5.3 LAND QUALITY (BONITET) RATINGS AND FACTORS AFFECTING LAND DEGRADATION..........124

6 CACILM USE OF MUNICIPALITIES MAPPING AND LINKED DATABASES ...................131

6.1 SOVIET AGRICULTURAL ENTERPRISES AND LINKS TO PRESENT-DAY MUNICIPALITIES.......131 6.2 PROBLEMS OF MUNICIPALITIES BOUNDARIES MAPPING...................................................132 6.3 ESTABLISHMENT OF MUNICIPALITIES POINT FILE.............................................................136 6.4 DATA TO BE LINKED TO MUNICIPALITIES POINT FILE: .......................................................138 6.5 SUMMARY AND CONCLUSIONS: MUNICIPALITIES DATABASES...........................................142

7 CACILM USE OF METEOROLOGICAL AND HYDRO- METEOROLOGICAL INFORMATION.........................................................................................................................144

7.1 CENTRAL ASIAN METEOROLOGICAL AND HYDROLOGICAL RECORDS ................................144 7.1.1 Introduction................................................................................................................ 144 7.1.2 Met Stations, Met Records and their Applicability to Land Use Management.......... 144 7.1.3 Rainfall-Runoff Modeling and Soil Water Balance Studies using Met Data .............. 152 7.1.4 Hydromet Stations and the Hydromet Record........................................................... 153 7.1.5 Glacier Retreat and Snow-Melt Modeling.................................................................. 157 7.1.6 Institutions Responsible and Data Availability........................................................... 158

7.2 CONCLUSIONS ON TRENDS IN THE CENTRAL ASIAN MET AND HYDROMET RECORD ..........158


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8 USE OF WATER MANAGEMENT INFORMATION ...........................................................161 8.1 THE AMU DARYA AND SYR DARYA BASINS WATER MANAGEMENT MAP ...........................161 8.2 OTHER WATER MANAGEMENT INFORMATION ..................................................................163

8.2.1 The CACILM Dams Database................................................................................... 163 8.2.2 Information on River Basin Cascades: Mean Flows and Reservoir Drawdowns ...... 167

8.3 CONCLUSIONS: WATER MANAGEMENT............................................................................167 9 CACILM SLMIS APPLICATION: LAND DEGRADATION HOTSPOTS AND BRIGHTSPOTS ........................................................................................................................169

9.1 INTRODUCTION: LIVELIHOOD & LAND DEGRADATION BASELINE, AND LAND DEGRADATION

TRENDS ..................................................................................................................................169 9.2 INDICATIONS FROM REMOTE SENSING IMAGERY .............................................................169 9.3 INDICATIONS FROM RAYON- AND MUNICIPALITIES-LEVEL STATISTICAL DATA ....................169 9.4 INDICATIONS FROM RAYON-LEVEL OR IRRIGATION SCHEME-LEVEL STUDIES AND

MAPPING.................................................................................................................................170 9.5 INDICATIONS FROM GOOGLE EARTH OBSERVATIONS.......................................................176 9.6 FIELD CHECKING AND RECORDINGS, AND USE OF GPS DEVICES ......................................181 9.7 OTHER ENVIRONMENTAL HOTSPOTS ..............................................................................182

10 RECOMMENDATIONS FOR CACILM SLMIS AT MULTI-COUNTRY AND NATIONAL LEVELS ....................................................................................................................................184

10.1 SYNOPSIS: ACHIEVEMENTS TO DATE...............................................................................184 10.2 SOCIO-ECONOMIC AND LAND PRODUCTIVITY STATISTICS ................................................185 10.3 DEFINITIVE LAND COVER AND LAND USE MAPPING UPDATE AND STANDARDISATION: 1:500.000...............................................................................................................................185 10.4 REMOTE SENSING DEVELOPMENT..................................................................................188 10.5 SOIL AND LAND UNIT MAPPING CORRELATION AND STANDARDIZATION: 1:500.000. ..........188 10.6 EVALUATION OF LAND SUITABILITY AND LAND PRODUCTIVITY..........................................189 10.7 EVALUATION OF SOIL EROSION HOTSPOTS ......................................................................189 10.8 EVALUATION OF HIGH WATER TABLE AND SALINITY HOTSPOTS .........................................189 10.9 LAND DEGRADATION, AND PREPARATION FOR CLIMATE CHANGE.....................................190

10.9.1 Carbon sequestration and release from vegetation: conclusions for the CAC5 area 190 10.9.2 Carbon sequestration and release from soils: conclusions for the CAC5 area ......... 191 10.9.3 Sustainable land management, and carbon sequestration ....................................... 191

10.10 SYNOPSIS: POSSIBLE MEASURES TO ADDRESS ADVERSE AFFECTS OF CLIMATE CHANGE..192 ANNEXES:................................................................................................................................195 ANNEX A: NSIU DATASETS: SHARED DATA BETWEEN NSIUS AND MSEC....................196 ANNEX B: MSEC MULTI-COUNTRY DATASETS .................................................................197 ANNEX C: GEF PROJECT SITE DETAILS ............................................................................198 ANNEX D: DETAILS OF REMOTE SENSING IMAGERY AND TECHNIQUES .....................199 ANNEX E: DETAILED SOILS AND LAND QUALITY (BONITET REASSESSMENT) MAPPING AT OBLAST, RAYON, & MUNICIPALITY LEVELS.................................................200 ANNEX G: RAYON AND MUNICIPALITIES STATISTICS DATA: CHANGES IN LAND USE, CROPPING, PRODUCTION AND YIELDS, 1987 – 2006 ........................................................210


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ANNEX H: DEGRADATION OF LAND AND WATER RESOURCES: PHOTOGRAPHIC EVIDENCE 211 ANNEX I: DAILY SOIL WATER BALANCE AND RAINFALL / RUNOFF MODELING: A BASIC FOR CLIMATE CHANGE STUDIES. ............................................................................212 ANNEX J: BIBLIOGRAPHY....................................................................................................213


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1 INTRODUCTION : CACILM SYSTEM DESIGN

1.1 BACKGROUND

The five Central Asian Countries (CAC-5) represent the heartland of the Eurasian super-continent and together cover an area larger than all 27 EU countries combined (Figure 1.1). They include a large area of very arid terrain – desert or semi-desert - and the remaining (mostly semi-arid) areas are very sensitive to season-to-season fluctuations in climate. Overall, mean annual precipitation for the entire area is some 264mm (ICARDA GIS), but the vast majority of the area has precipitation below this figure. Furthermore the precipitation is less effective in supporting plant growth due to very low winter temperatures in most areas and high summer temperatures in all areas. Trends over the last 80 years have been for rising temperatures, and for rapidly declining extents of the high-altitude glaciers and snow-fields which feed the major rivers and form an important component of the late-summer river flows which are so vital for irrigation. Although some 60% of the total population of 68m is urban, agriculture, livestock and the associated rural economy has a disproportionately large share of both the internal and export economies of the CAC-5. Although irrigated agriculture has been practised for millennia, systematic large-scale irrigation development began in the 1920s and ‘30s, and accelerated in the ‘60s and early ‘70s, to the extent that by the mid-1970s the waters of the two main rivers were over-committed. The Aral Sea, with its originally productive fishing industry, thus declined markedly, and now covers less than 25% of its former area, being surrounded by saline wind-blown sediment materials which are contaminating surrounding, originally productive, irrigated lands. Since independence of the five countries in 1990 some improvements have been seen in many areas of the economy, but in the rural areas problems have generally become much worse. Organisation of collective agriculture has markedly declined, but individual private farmers – often farming plots of just a few hectares – have lacked the organisation and knowledge, the farm machinery, credit, extension and marketing support, in order to function as viable independent units. Most critically, for irrigation and drainage, the organisation imposed from above under the Soviet era has weakened, but has not been replaced by workable water-user groups. Irrigation and drainage management is thus generally very much worse than during the recent Soviet period. This has been made worse by the five countries working independently and not running the watersheds (and the associated power generation infrastructure) as a shared resource. Upstream countries are not getting winter electricity, gas and coal from downstream countries which are richer in fossil fuels. These upstream countries are thus pushing through too much water during the winter period to generate their own necessary hydro-electric output. This winter surplus of water causes downstream flooding and high-water table problems during the winter and spring periods, and delays salt leaching and spring planting of the irrigated summer crops. This wastes an inordinate amount of valuable water. Conversely, in late summer, there is often insufficient water available for the required irrigation. Salinity problems of soils and shallow groundwaters are generally getting worse, accompanied by an increase in cotton monoculture in many areas, cotton being the only medium-value crop tolerant of this salinity. Rainfed agriculture has also experienced major land degradation problems. Individual private farmers have lacked the credit to purchase fertilisers and the Soviet-era fertiliser provisions have almost completely stopped in most areas. Rainfed, and also most irrigated yields, have thus declined markedly over the last 15 years, and soil reserves of nutrients and organic matter have also markedly declined. Even more seriously, soil erosion problems – by both water and wind – have not been addressed either by the national or oblast


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authorities or by the farmers themselves. Even the most simple soil conservation measures – alternate strip cultivation and contour cultivation – are not being carried out. Soil gulleying problems are dramatically apparent in the latest Google Earth imagery covering many areas in the five countries (see Sections 4 and 9 of this report). Livestock rearing has also been problematic, with organisation provided under the Soviet period again having broken down and not replaced by viable alternatives. Organisation of grazing with the provision of water and alternative feeds at certain times of the year have declined, with the result that large areas (invariably near to permanent settlements) are overgrazed. Conversely, similarly large areas in remoter areas are under-exploited with the result that bush-encroachment there is becoming problematic. Accidental and uncontrolled burning of rangeland vegetation is also a major feature in many areas, and is very evident on the satellite imagery. This has mainly negative impacts on most land degradation parameters. Figure 1.1: Location Map: CAC-5 Countries and SLMIS Priority Area

Map Projection: Equidistant Cylindrical: Spheroid: sphere; Central Meridian: 69°; Ref. latitude: 41° . EU countries (27) shown for comparison (green), total 4,328,000sqKm; CAC-5 countries (yellow), total 4,685,000sqKm. 1.2 INSTITUTIONAL FRAMEWORK FOR THE SLMIS

1.2.1 CACILM and ADB

To address the multitude of problems associated with the degradation of land and water resources the Central Asian Countries Initiative for Land Management (CACILM) was set up in 2005. Through CACILM, and with support from the Asian Development Bank, National Programming Frameworks (NPFs) were set up in each country which prioritised a set of projects and established mechanisms for consultation both nationally and across the five countries. A common set of nine specific program areas were defined by the NPFs:


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- capacity building for strengthening the enabling environment; - capacity building for integrated land use planning and management; - sustainable agriculture in rainfed lands; - sustainable agriculture in irrigated lands; - sustainable forest and woodlot management; - sustainable pastureland management; - integrated resource management; - protected area management and biodiversity conservation; - remediation in the region of the former Aral Sea.

Activities and Projects embracing the five countries to date have included:

- capacity building for mainstreaming sustainable land management and ensuring that it is integrated into planning and management;

- development of a sustainable land management information system (SLMIS) (this project);

- sustainable land management research; - knowledge management and information dissemination.

In addition to these multi-country projects, a large number of national projects have been formulated. These are described in Section 1.5 and Annex C, and their locations are given in Figure 1.3 .

1.2.2 Multi-Country Institutions

MSC – Multi-Country Steering Committee The MSC is the governing agency for CACILM comprising senior representatives of the 5 countries and the key funding and implementing agencies. The meeting is co-chaired between the ADB senior representative and the senior representative of the host institution (usually a Government Minister or his deputy). The venue for the committee meeting rotates between the five countries. Committee members comprise representatives of ADB, GEF, GTZ, FAO, ICARDA, as well as NCC chairmen, and NSEC Heads (generally 10 regional staff and up to 10 international funding / implementation agency staff). Further participants (non-members) may attend the meeting together with observers, most of these being from the local country where the meeting is being held. The MSEC Head takes the minutes of the meeting, the first draft of which is discussed first with the ADB co-chair before being circulated to the committee members. The committee meets twice a year (during spring and autumn). MSEC – CACILM Multi-Country Secretariat. The Secretariat has a technical, administrative and coordinating role between ADB, GEF and the other donor agencies on the one hand, and the CAC-5 governments and technical institutions on the other, in arresting land degradation and promoting sustainable land management over the CAC-5 countries, and advancing development assistance in line with the aims and objectives of CACILM. MSEC is currently based in the premises of the Ministry of Agriculture in Bishkek, Kyrgyzstan, and comprises five full-time senior professional staff and two graduate support staff.

1.2.3 National Institutions

NCCs – National Coordination Councils


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The NCCs comprise NSEC staff, plus senior government staff from relevant ministries and institutes, plus some notable academics. Their current roles are to advise and direct the relevant NSECs and NSIUs, and to propose, approve and direct the various National implementation projects. A potentially useful enlarged future role would be to act as a national coordinating committee directing policy and activities on land and water use and environmental planning and management. Many of the individuals on the NCC would also be on other technical and executive committees directing other related activities – e.g. water policy, transboundary water issues, energy policy, agricultural policies etc. NSECs – National Secretariats NSECs have been established in all 5 countries to act as technical secretariats filtering and providing key data to Government through the NCCs on the state of land and water degradation, and existing and proposed projects on these subjects. NSECs each comprise three staff members (almost full-time) including a Head and a Project Implementation Specialist (PIS) who is national counterpart to the PIS at MSEC and has equivalent roles and responsibilities. The Head invariably is a very senior and respected academic or chief of a relevant local institute, with maximum technical experience as well as administrative and political contacts. The challenge and requirement for the SLMIS is to provide useful relevant factual information for these staff in an attractive and easily digestible format. A further challenge is that many of the older staff are not computer-literate, although most have a good level of scientific education and an intimate knowledge of their specialist subjects based on the sound scientific foundations of the former Soviet scientific and technical institutes. NSIUs – National Secretariats’ Implementation Units. NSIUs were established in September, 2008, in 4 of the 5 countries (except TAJ). The host institution here is either a Land Management Institute (KYR, TUK), a Hydrological Design Institute (UZB), or a Geographical Institute (KAZ). All institutes have good contacts within their respective countries over the fields of land, water and the rural environment. Under the present contract with ADB, most institutes have sub-contracts with local partner agencies or institutes in order to cover the full remit of the current work and obtain all the key datasets from the respective agencies and institutes. Each NSIU comprises 5 key (nearly full-time) staff: Team Leader; Land Degradation Monitoring Specialist; Socio-Economist; GIS/Remote Sensing Specialist and GIS Technician. The Main roles and responsibilities of the NSIUs include: provision of accurate and updated scientific information to the NSECs and NCCs, as well as to MSEC; hosting and establishing the SLMIS; collection of land and water degradation baseline and monitoring data for the SLMIS covering the whole of their territories at general level, and the CACILM National Project sites at much greater levels of detail; dissemination of baseline and monitoring information from SLMIS to the respective NSEC and to MSEC; and undertaking half-yearly monitoring reports. All four established NSIUs (and further representatives of TAJ) attended the project’s inception workshop, held over the period 6-10 October 2008 and hosted by the UZB-NSIU, in which direction of the project was outlined and ideas and possible challenges outlined. In particular, priorities for the provision of basic maps and data were circulated, and time schedules for the delivery of this data given. 1.3 MAJOR ISSUES OF LAND DEGRADATION AND CLIMATE CHANGE IN CENTRAL ASIA

1.3.1 Background

Very different land degradation processes affect the different major groupings of land uses, and these need to be analysed and treated separately. Major groupings include irrigated


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agriculture; rainfed agriculture; pastureland (rangeland); unused land (notably desert land); forest areas; watershed management and transboundary issues and including mountain and hill areas; industrial, mining and human settlement areas. Different intensities of monitoring, different scales of map presentation, and different reporting procedures need to be applied to these different groupings. A further major continuing factor to the land degradation processes have been government policies and actions over the last 50 or so years, and particularly events in the post 1990 period. During the latter period most of the agricultural land has moved from state or collective ownership to private ownership. While this has benefited some farming and entrepreneurial groups, it has had a major disrupting effect on both agricultural production and on sustainable land management, and there are continuing major problems which need to be resolved.

1.3.2 Irrigated Agriculture

Irrigated agriculture is by far the most productive rural sector in the CAC-5 area, contributing to more than 75% of agricultural primary GDP although altogether it makes up less than 5% of the total land area. Major crops are cotton (one of the foremost areas in the world for that crop), cereals (notably wheat), leguminous forage crops, and vegetables and fruits, with rice and other specialist crops being grown in several key areas. Major issues are efficiencies of water use and management, soil salinity and ground water table build-up, soil sodicity (high exchangeable Na), and soil erosion (‘irrigation erosion’) on some of the more sloping irrigated lands particularly in the tributary valleys. Primary irrigation water quality is not problematic by worldwide standards, salt concentrations of these waters rarely being above 1.500 mg/l and salts being dominated by Ca, Mg and SO4, rather than the more damaging Na and Cl. High-salinity periods are also confined to low-water periods in late summer when crops are less susceptible to high salt concentrations. Only on the lower reaches of the Syr Darya, below Kyzyl Orda, is primary water quality a major problem for more than a short period (see Figure 8.2). However, soil salinity caused by poor irrigation and drainage management is a major problem in many areas. Also return flows of high-salinity drainage water is often a local problem in many areas, and needs careful monitoring. Soil and shallow groundwater salinity are of the greatest problem in Maktaraal Rayon, SKO-KAZ and the adjacent Sirdarya Oblast, UZB. There major issues of land drainability occur due to too high water levels in the Chardara reservoir for much of the year. Irrigation and drainage water user groups are also not very effective in these areas. Many soils have particularly high levels of magnesium, which causes nutrient imbalances with both K and Ca cations and greatly depressed yields in the worst cases. ICARDA have been undertaking field experiments on these problem soils, and by applying cheap locally-obtained gypsum materials and leaching with fresh irrigation water have been able to restore Ca/Mg balances to more normal levels and thence improve markedly crop yields.

I. Irrigated areas show two major problems which feed on each other: cotton monoculture and build-up of soil salinity caused by poor drainage infrastructure and non-functional irrigation/drainage water users’ associations. Cotton monoculture is most problematic in Maktaraal rayon (>90% cotton) and increasingly problematic in Shardara, Otyrar, Arys, and Turkestan rayons (all around 65% cotton) and Ordabasy rayon (50% cotton), essentially all low-lying areas below 300m elevation. Cotton shows moderate salinity tolerance whereas alternative crops – particularly alfalfa, which would be excellent to have in a crop rotation – is very sensitive to salinity (see Table 6.1). Small land holdings, lack of marketing possibilities for most alternative crops, and constraints with machinery, also make introduction of alternative cropping


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very difficult. Attention to all these constraints, and particularly to control of soil salinity, would be needed before the problems inherent in cotton monoculture can be tackled.

Monitoring of irrigation areas for salinity and high (and saline) water tables is undertaken by the Hydro Geological Meliorative Expedition (HME) of the Ministry of Agriculture (MoA) in Kazakhstan, and there are parallel organisations to this in the other countries. Irrigation on more sloping lands in tributary valleys rarely shows any salinity or sodicity problems, but often there are problems of erosion and land levelling. Irrigation has been almost exclusively by surface (flood) methods, which are less expensive but much less efficient in terms of water use – efficiencies of only 40-50% are normal, as compared to 80-90% efficiencies for sprinklers. However, very recently drip irrigation has become more widely used, particularly for horticultural crops, including a major development of several thousand hectares for tomato cultivation in Tulkibas rayon, SKO. This drip irrigation is very much more efficient (90-95%), but requires good quality water in terms of low bicarbonate content and very low sediment content. In areas favoured by available surface water or groundwater within 30m depth there are excellent possibilities for supplementary irrigation for predominantly rainfed crops, using sprinkler equipment with mobile aluminium piping and diesel or electrically-operated pumps (see below, under rainfed cropping). Thus this equipment would be used for modest applications on winter cereals in October and late April / early May, and then moved for use elsewhere for full irrigation of higher-value vegetable, fruit or forage crops from mid-May to late September. Automatic mobile sprinkler equipment in the form of centre pivots could also be tried in favoured areas, although they would be more suitable in areas where soils are more sandy (rather than silty) and have higher bearing capacity when wet and higher infiltration rates. With experienced management these centre-pivot irrigators can give uniform water applications and extremely high yields, and their labour requirement is much less than for conventional surface irrigation methods. Modifications are also possible with this equipment (drop arm sprayers, backward-directing spray) to deal with the problems of the silty soils which predominate in many areas of the CAC-5 countries. Within irrigated areas attention needs to be given to wind breaks, as strong, dry winds encountered in many areas in the summer add greatly to the irrigation water requirements of the crops.

1.3.3 Rainfed Agriculture

Rainfed agriculture within the CAC-5 Priority Area covers no more than 2% of the total land surface, although in the most favoured rayons, generally spanning the 400-800mm mean annual precipitation isohyets, it may make up 30-35% of the total land surface. Most of this rainfed land occurs in a belt spanning the three southern oblasts in Kazakhstan (SKO, Zhambul, and Almaty), located in the northern toeslopes of the Tien Shan mountains where the soil parent materials comprise a mixture of aeolian loess materials, colluvium, and coalesced alluvial fan materials. Much of this land is moderately to quite steeply sloping and water erosion is a major and continuing problem: as much as 50% of such land is classified as ‘water eroded’ (SALRM, 2003). The erosion problem is made worse by inappropriate land management practises (cultivation by large machinery up and down the slope; lack of contour hedges or grass strips; lack of manure and fertiliser applications; lack of crop rotations). Much of this land is clearly under much physical stress (and economic pressure)


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as cultivated areas have declined markedly. In SKO, the decline was from 694.000ha in 1991, when they comprised 5,9% of the SKO area, to less than 3,5% today. For all rainfed areas decline of soil organic matter and soil nutrient reserves are major problems due to lack of adequate fertiliser and manure applications since 1990. However, monitoring of these lands (as with irrigated lands) continues to be undertaken by GosNPCzem in Kazakhstan and by equivalent organisations in the other countries, with 1:10.000 soils and bonitet reassessment surveys being undertaken at the municipality (Selskiy Okrug) level (see Section 6). Deterioration of soil physical properties, and most notably infiltration rates, is associated with continuous cultivation of these poorly-structured predominantly silty soils. Sprinkler infiltration rates were measured by the TA 4375 project and were found to be 42mm / 2-hour simulated rainstorm for pasture, but only 18mm / 2hours for cultivated land (excluding alfalfa). For alfalfa rates were 35mm / 2hours, indicating the beneficial effects of alfalfa on physical properties (as well as chemical properties) in a crop rotation. Remedial measures to improve these rainfed lands would include the following: (i) conversion of rainfed agriculture into more permanent pasture, particularly for rainfed areas with below 500mm mean annual precipitation; (ii) introduction of soil conservation measures (most simple contour-related measures such as contour cultivation and grass strips and hedges); (iii) introduction of tree-belts to act as wind-breaks, high windspeeds being a major contributing factor to high evapotranspiration rates and hence low yields; (iv) introduction of firewood / charcoal wood plantations, to substitute for burning of manure which is the current major rural fuel and which should be conserved for increasing the fertility and organic matter status of the land; (v) introduction of supplementary irrigation for cereal crops by mobile sprinkler equipment, with small irrigation applications being made in Autumn (to facilitate planting) and in Spring (ear-initiation stage). These small irrigation applications (average of <100mm/year) would guarantee yields and have extremely high marginal water use efficiencies; (vi) use of organic wastes and sewerage sludge to increase organic matter status, fertility and infiltration characteristics of the most degraded soils; (vii) attention to crop rotations, with maximum integration of alfalfa and livestock systems into the overall farming system; (viii) selective payment of any Government subsidies to support good land management practises; (ix) consideration by Government of a national system of carbon taxation and carbon credits (rainfed farmers potentially benefiting from introduction of C-fixing measures on their land). All of the above measures should all be considered for this area, and good information is vital for the implementation of this. All of these measures would lead to higher and sustained yields in the long-term, and also to increased stocks of soil organic carbon.

1.3.4 Pasture (Rangeland)

Rangeland and desert lands cover some 80% of the CAC-5 area, with most of this land being of very low productivity and mostly spanning mean annual precipitation (MAP) isohyets of some 100-400mm/year. Productivity of the land varies considerably: from 100 – 400 kgDM / ha / year being common, although much higher productivity can be seen under good management in the more favourable areas. Productivity is limited both by extreme cold in the winter months (end-November to early March in the low elevation areas in the south: mid-October to late April in the north) and by low rainfall which in most areas is less than 1 / 5 of potential evpotranspiration during the remaining warmer months. In general, only 3-8 weeks of active growth is observed in the Spring ‘green-up’ period for most areas, these


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being concentrated in the period mid-March to mid-May in warmest areas in the south to early May to end June in the coolest areas in the north. Rainfall during the potential growing period in the Autumn is much less than in the spring, and rainfall in the Autumn period is normally much less effective (more being lost to direct evaporation) although 1-year in 10 may show appreciable Autumn rainfall and a significant Autumn green-up period. Variations in soil properties and micro-relief – quite often these being very localised – can also have a big effect on localised rangeland productivity, with run-off and run-on areas being observed in close proximity. Areas of shallow wind-blown sand (commonly 20-200cm depth) over more impermeable soil parent materials may often show much higher rangeland productivities, particularly in the contact areas between these two materials. Such variations usually show up well in the remote sensing imagery at an appropriate scale when this is captured at the optimum time (usually at the end of the spring green up period). ‘Free-for-all’ grazing and complete lack of fencing and windbreaks are characteristic of the rangeland. Overgrazing and inadequate rangeland management techniques are contributing to range degradation, reduction in soil infiltration rate, increased run-off, and both water and wind erosion, leading to further declines of range productivity. Overgrazing varies considerably from area to area and is closely related to distance from settlements and watering points, and these areas of overgrazing are again very commonly observed on the remote sensing imagery (see Fig 4.17). Fire is also a big factor in gross rangeland productivity and large contiguous areas can be observed to have been burnt in many of the images captured in late summer and autumn. Overgrazing intensity is also related to overall livestock numbers which in turn is related to national economic conditions. Interestingly, livestock numbers declined markedly over the period 1991 – 2000 following the break-up of the Soviet Union and decline of the collective land management. This period thus saw a significant recovery of the rangeland in many areas. Water Use Efficiencies of the rangeland thus actually increased over this period from an average of 0,6kg DM / mm precipitation to 1,2kg DM / mm (see EMIMS Guidelines, Annex G). However, subsequent trend of livestock numbers (post-2000) has been upward, and thus information on existing and likely future rangeland degradation and on current stock pressures is essential for planning necessary remedial measures. Current rainfall patterns and available soil water status will have a large bearing on this, and need to be integrated into the system. Remedial measures may include subsidies for effective land management agreements; payments for alternative feeds (e.g. crop residues) at certain times of the year (see feed optimisation spreadsheets, EMIMS Guidelines Tables D.4 a and b); subsidies for early sales of livestock for fattening yards to relieve pressure on rangeland, payments for fencing off range areas to act as range reserves, and possibly registration of livestock and introduction of a tax on excessive livestock numbers, etc. Information on rangeland ecosystems is given in the ECONET ecosystems map and accompanying voluminous legend table, this being an important layer on the SLMIS.

1.3.5 Unused Lands: Desert Lands

As with other arid areas of the world, there is no clear dividing line between rangeland and desert lands, although a pragmatic division would be between lands which are grazed by herds of domesticated animals on the one hand, and those lands which are completely uneconomic to graze on the other. For the CAC-5 areas, the latter would invariably be areas of less than 150mm mean annual precipitation and include areas of mobile sand and areas of bare rock and very shallow soil which would further make rooting of any vegetation extremely difficult. However, areas of shallow sand – the so called ‘sand sheet’ areas, can be surprisingly productive, particularly where they lie in contact with underlying impermeable


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materials. Similarly local slope factors can greatly enhance the effects of either run-on or run-off, and lead to huge variations in vegetation productivity across short distances. Also, particularly where there is variation in underlying lithology, superficial formations, and slope, and particularly where sand sheet materials also occur, biodiversity of plants and animals in desert ecosystems can be very high, and these areas need conserving because of these reasons. Inspection of the NDVImax imagery shows that there is a very sharp gradient corresponding to the transition zone between poor pasture land and true desert land, and thus this imagery could potentially be used as an objective indicator to mark this boundary. Work on this – particularly in getting useful quantitative correlations between pasture productivity and NDVI – should be an important part of the work to be done in a future phase of the project.

1.3.6 Mountain and Hill Zone: Watershed Management & Trans-boundary Issues

Mountain and Hill Zone areas may be less important in terms of direct agricultural production per sq km of land, but they are of enormous importance for biodiversity conservation, for tourism and eco-tourism, and for generation of both run-off water and hydro-electricity. Spring snow-melt and spring, summer and early autumn glacier melt contribute as much as 80% to this overall run-off (UZGIP modelling, Uzbekistan), but run-off from spring and early summer rainstorms may be important in local catchment areas. (Such heavy rainstorms might also generate landslides and mudflows). The headwater areas of both the Amu Darya River (Tajikistan; small area in Kyrgyzstan) and the Syr Darya (Naryn Oblast, Kyrgyzstan) have enormous hydro-electricity potential, only half of which is currently tapped. (See the new Dams.shp coverage on the SLMIS, described in Section 7). Current power shortages in both upstream countries mean that there is pressure to proceed with more hydropower development, in spite of environmental concerns and huge conflicts between upstream and downstream water users in the different requirements between optimum hydropower generation scheduling, irrigation water scheduling, and also flood protection and water storage scheduling. The main problem here is with release of water from hydroelectric generation in the upstream countries during the winter and early spring period. Not only is this water not required for irrigation during this period, but it causes flooding and periods of high water tables well into the Spring cultivation period, and worsens the salinity problem on large areas of floodplain land, much of which is under irrigation infrastructure. Summer irrigation is later constrained by low availability of water, and salt content of this low-flow water is often a problem for sensitive crops. On the Syr Darya, possible construction of a large reservoir at Koksarai on the lower Arys river, and transbasin diversion of Syr Darya water into this reservoir, will partly alleviate the latter problem for downstream areas (mainly in Turkestan rayon, South Kazakhstan oblast, and in Kyzyl Orda oblast). Transboundary issues on the Syr Darya River received much comment and discussion from SKO participants to the EMIMS Project’s first workshop in December 2006, and clearly this is a major issue of concern for the oblast administration. Currently, under existing hydropower regimes, some 0,9 km³ of potential irrigation water is wasted as spillage into the Arnasai depression during the winter, and another 1,5km³ is wasted as downstream depression spillage (Syr Darya floodplains). With construction of the Koksarai dam, and continuation of the existing hydropower regime, these figures would be reduced to 0,3km³ and 1,1km³ respectively. Even so, the 1,4km³ that would be wasted because of Kyrgyzstan’s requirement for winter hydropower would be equivalent to some 1,8m tonnes of wheat (when the water is used under a supplementary irrigation regime on wheat grown under largely rainfed conditions).


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1.3.7 Land Degradation due to Industrial, Mining, and Human settlement

Mining and Metal Processing. Problems here are disfigurement of the landscape over large contiguous areas by previous and present activities; pollution of water courses with heavy metals and other toxic substances; and toxic dust fall-out on extensive surrounding areas, much of it under agricultural uses. Toxic elements of interest include: lead (and associated cadmium and mercury); zinc; copper; uranium, arsenic, and fluoride. Phosphate rock is also present and mined locally, as is brown coal and associated minor elements. Soil pH and management practises (e.g. application of organic matter; application of acidifying fertilisers; use of rainwater for irrigation, especially on horticultural crops) may affect the toxicity of these substances as far as plant uptake is concerned, although most soils have neutral or slightly alkaline pHs which mitigate these problems. However, areas of accumulation of heavy metals and other toxic substances need to be recorded even if these metals are currently largely unavailable for plant uptake under prevailing soil pH values. Intensive land use (e.g. for vegetable cultivation) in such areas needs to be monitored, particularly if soils are sandy, heavy N-fertiliser application is made, or areas suffer also from heavy sulphur fall-out. Such areas may well occur close to industrial centres, particularly the Shymkent area in SKO, in the industrial and mining areas to the S of Tashkent, and in various sites in the periphery of the Fergana Valley. Large areas of land have been adversely affected by open-cast mining activities dating back to Soviet times and well before environmental laws were enacted. Many of these mines have been abandoned, with toxic spoil materials disfiguring the landscape. Such sites are readily interpretable on the Google Earth imagery, and these positions have been recorded in the environmental hot-spot coverage of the GIS (see Section 9). In general, however, these areas together comprise less than 1% of the total landscape, although in some municipalities the figure can rise to almost 10%. Contamination of water courses by industrial and domestic sources of nutrients and organic matter. Problems here are contamination from domestic sewerage, agricultural point sources (stock yards, slaughter houses, chicken farms etc) and industrial sources of contaminants containing both organic matter and nutrients (notably N and P) and thus source materials for eutrophication. Such eutrophication problems are much worse during the late summer period when streamflow is at its lowest, and most of the smaller rivers and streams draining the more heavily-populated areas are most badly affected. Where streams from these areas enter static water – for example in ponds and reservoirs – bad algal growth and eutrophication problems rapidly become apparent. 1.4 AIMS, OBJECTIVES AND REQUIREMENTS OF THE SLMIS

The immediate practical objectives of the SLMIS are fourfold: - to provide baseline agricultural and rural land use and management information, and livelihood information, across the CAC-5 priority area; - to identify land degradation and other environmental hotspots and bright spots of significance at national and multi-country level; - to assist in monitoring of CACILM National Projects, particularly in terms of recording of key land degradation and restoration indicators. - to assist the national institutions (NSIUs, NSECs, NCCs) in reporting and presentation activities on land degradation and restoration.


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The overall long-term practical objectives of the SLMIS will be to aid promotion of the following activities: - cooperation between sister institutions across the 5 countries, and particularly on transboundary issues; - data sharing, particularly for data at a general mapping scale (initially 1:500.000 – 1:1.500.000 for the entire area, together with 1:200.000 for the CACILM National Project rayons); - use of the SLMIS as Metadata, essentially an ‘index map’ for locations of detailed mapping and studies on relevant subject areas, particularly the soil and land quality (bonitet) reassessment mapping at municipality level; - integrated environmental and land use planning activities, using the data generated on the SLMIS; - integrated watershed management activities, with adequate monitoring of key catchment areas under rehabilitation, and in particular a major focus on soil conservation in the worst soil erosion hotspot areas (see Section 9); - integrated hydropower and irrigation development, coordinating international efforts to assist in alleviating the current problems in these two subject areas, of crucial importance economically; - integrated water pollution monitoring and control measures, liaising with the relevant national institutions and the EU project currently assisting in these areas; - contribution to arresting CO2 build-up and consequent global warming. (Within the CAC-5 environment, these activities can sequester up to 0,5t C / ha / year, or release a similar quantity: the difference between good and bad land management is thus up to 1t C / ha / year for much of this terrain and agro-climate, and considerably more in the higher-rainfall areas and particularly in the forested areas); - provision of vital data for project monitoring; - provision of vital data for formulation of new projects; - provision of data which, as far as possible, would meet the guidelines from UNCCD and their listing of Core Indicators for the periodic monitoring of land restoration and degradation. 1.5 DEFINITION OF CAC-5 PRIORITY AREA, AND ACQUISITION OF PRIORITY DATA

In order to meet the above objectives within the very limited (12-month) time-frame of the current project, concentration on particular geographic areas and subject areas was required. It was clear that more attention had to be paid to CACILM National Projects (formerly termed ‘GEF Projects’) and their immediate surrounding areas. Also more attention was called for on transboundary issues, especially with shared watersheds, particularly for the Amu Darya and Syr Darya watersheds which run across all five countries. The project thus decided that it was essential to concentrate activities in a defined CAC-5 Priority Area, this being the area enclosed within the oblasts of the five countries which were south of the 47ºN latitude and east of the 56ºE meridian. This area includes all the National Projects and all the trans-national watershed areas. In Kazakhstan, it includes four large oblasts: Kyzyl Orda, South Kazakhstan Oblast (SKO), Zhambul, and Almaty. The location of the Priority area is outlined in Figure 1.1, and shown in detail in Figure 1.2 together with detail of major towns and linear infrastructure. (Minor areas in the N, S, and E extremities of the Priority Area, being of unused desert land, are excluded in this and other related figures so that these figures can be presented at maximum scale at A4 format). The Priority Area is also shown in Figure 1.3 in relation to Oblast boundaries, major watershed boundaries, major areas of irrigated land within the Amu Darya and Syr Darya Watersheds, and CACILM National Project locations. Further basic statistics on these National Projects are given in the accompanying legend table to that figure.


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By taking this Priority Area approach, both Kazakhstan and Turkmenistan were then able to provide mapping, data and fieldwork at the same level of detail as the other three countries, and this has been an important aspect of the current work. From experience on the ADB-financed EMIMS-SLU project, which took SKO-KAZ as a pilot area, the present project was able to appreciate what Priority Data, related to land degradation, should be acquired for the SLMIS. This data includes:

- mapped coverages, particularly those at around 1:1m scale, on a wide range of themes;

- remote sensing imagery (GIMMS, MODIS-250, LANDSAT-ETM+, Google Earth); - statistical data at oblast, rayon, and municipality level covering crop areas,

production and yields, livestock numbers, demographic data, and income and poverty data;

- special studies, consultancy projects etc. related to agricultural and rural development, irrigation and drainage rehabilitation, rangeland ecological studies;

- detailed soil and land quality (bonitet) assessment mapping; - routine meteorological and hydrometeorological data; - routine monitoring by Government Agencies of salinity and water tables (HME),

pasture (various institutes), long-term soil fertility. However, the major (negative) lesson from the earlier ADB projects was that acquisition of much of this data would be a major problem, and that the formal institutional arrangements for the acquisition of such data would be the ‘make or break’ issue for the success of the future SLMIS. The emphasis for the present phase of the project has thus been on development of the NSIUs and their links with sister agencies and staff in their own countries in order to obtain this key data, and, to a reasonable extent, this has been successful. In all of the above areas the project now has some excellent data, at least for a part of the CAC-5 area. The project also has a workable model on which the remaining data can be added to the system.


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lFigure 1.2: Location Map: CAC-5 Priority Area


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Figure 1.3: CACILM National Project Sites in relation to Watershed Boundaries, Water Bodies, and Oblast / International Boundaries


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Figure 1.3: CACILM National Project Sites: Locations and Basic Statistics


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Fig 1.4: SLMIS System Components


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1.6 LAND DEGRADATION INDICATORS

The project has been obliged to align itself to the sets of global indicators of land degradation, and also to seek to rationalise the regional set already formulated but not yet fully put into practice. GEF Global Indicators for land degradation comprise the following: 1. Land Cover 2. Land Productivity 3. Water Stress 4. Rural Income 5. Organic carbon (soil carbon plus vegetation biomass) Organic carbon is under discussion on its possible inclusion. On the one hand, analyses and calculations can be complicated, particularly for countries where basic information is limited. On the other hand, organic carbon assessments are absolutely vital to C-sequestration analyses and climate change projections: it would thus be good if they could be included in the indicators list. Also, the CAC-5 countries generally have excellent detailed soils information (when it is made available) and we have a good idea at oblast level of the areas of each soil type and their organic-C contents (see SKO – EMIMS reports). We also have excellent municipalities mapping with detailed analyses, which provide a further check on this data, area by area. This municipalities mapping is also being updated, and total soil carbon changes can thus also be monitored, area by area. Vegetation net primary productivity and total biomass (for rangeland vegetation) is obtainable from remote sensing techniques, supported by limited ground control. Maps of rangeland vegetation at 1:200.000 scale also show these parameters, and also form a basis for field checking and updating. Sources of information for the first four of the global indicators are as follows (data from KM Land Initiative): 1. Land Cover: GLC 2000 2. Land Productivity: a. NDVI corrected for RUE: using MODIS NDVI, GLADA trend data derived from GIMMS and VASCLIMO annual rainfall b. RESTREND: using a joint product by LADA and ISRIC. 3. Water Stress: Water model developed by the Water Systems Analysis Group at the University of New Hampshire 4. Rural Poverty Rate: % population below poverty line based on the Millennium Development Goals Dataset. UNCCD have identified seven core indicators for land rehabilitation (reverse of land degradation) and these are listed as follows: 1. Decrease in numbers of people negatively impacted by the processes of desertification / land degradation and drought. 2. Increase in the proportion of households living above the poverty line in affected areas. 3. Reduction in the proportion of the population below the minimum level of dietary energy consumption in affected areas. 4. Reduction in the total area affected by desertification / land degradation and drought. 5. Increase in net primary productivity in affected areas. 6. Increase in carbon stocks (soil and plant biomass) in affected areas. 7. Areas of forest, agricultural and aquaculture ecosystems under sustainable management. Within each of these core areas, UNCCD are seeking from individual countries specific impact indicators that are currently in use in the country, and any further indicators, not currently in use, which the recipients of the UNCCD questionnaire would consider might be useful to include in the future. For each of the specific impact indicators, the recipients are then requested to provide information on Definition / Description; Initial Date when indicator was used; Level of Implementation; Measurability; Reliability; Simplicity, Applicability, and Cost Effectiveness.


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CAC-5 Multi-Country Indicators have essentially taken the GEF Global Set and added six ‘Indicators of Institutional Change and Performance’ to this list. Importantly, the land productivity indicator has been further defined as follows: ‘increase in average productivity by land management class’ considering independently pasturelands; irrigated lands; rainfed arable lands; forest lands. (This independent assessment of the four groups of land uses is essential as trends may be moving in opposite directions: for example, pastureland may show declining productivity while irrigated land may show an increase.) A further indicator ‘increase in average financial returns by privately managed lands’ has also been added. (This again is important as more profitable crops (e.g. vegetables, oilseeds) may replace less profitable ones (maize, cereals) as the farming systems adjust to the market economy, rather than policy dictates from national or union level). 1.7 SYSTEM DESIGN, AND INFORMATION FLOWS AT MULTI-NATIONAL, NATIONAL, OBLAST & LOCAL

LEVELS

1.7.1 Background

The initial emphasis for the SLMIS has been to establish reliable basemap and baseline data at a general level for the entire CAC-5 priority area as quickly as possible (and certainly within the 12-month period of the project). The level of detail of this information should be consistent with map print-outs at approximately 1:500.000 – 1:1.500.000 scale. This general information – not at a scale likely to cause security or confidentially concerns – should be shared between the CAC-5 and MSEC (and relevant international partners) and between the CAC-5 countries themselves. Overall format of the SLMIS and information flows, as applying to the CACILM SLMIS and to the whole of the CAC-5 area, are shown in Figures 1.4 and 1.5. The latter figure also summarises the main groupings of data going onto the system, the current status of which is briefly described in the following sections (1.7.2 to 1.7.9). A further important role for the SLMIS is to present information on trends on land degradation or rehabilitation changes over recent time, and to devise formats for monitoring similar changes in the future. This information has to link with the required international land degradation indicators (or groupings of indicators), and also to make best use of what existing data is available at the various applicable levels of scale (from regional and national, through oblast and rayon, to municipality level). This information is of fundamental importance to the NSECs and NCCs, and, the more detailed of that information will also be of great importance to the individual CACILM National projects.


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Figure 1.5: Overall System Design and Information Flows: MSEC and CAC-5 Level

SLMISMSEC

FAO-LADA Global LUS (CАС-5)- Soils 1:5.000.000 – 1:8.000.000- Land Use / Land Cover, etc.

REMOTE SENSING IMAGES (CAC-5)- MODIS- LANDSAT, etc.

ECONET (CAC-5) (1:1.000.000)- Basemap layers- Socio-economic data (rayon level)- Eco-regions / Eco-systems / Land use- Protected areas, existing & proposed, etc.

National Coverages1:500.000 – 1:1.500.000- Soils- Land Use / Land Cover / Agricultural Systems- Agro-climate (rainfall & elevation zones)- Irrigation- Socio-Economic / Poverty data- etc.

ICARDA (CAC-5) (1:1.000.000)- Agro-Climatic layers…- Agro-ecological zoning- Land Cover / Land Use- Digital Terrain Model, etc.

National Statistics Agency Rayon Data- Land Use, Crop Areas- Crop Yields, Crop Production- Population- Employment, income, poverty, etc.

CACILM / MSEC / ADB Coverages (CAC-5)- Major River Basin Boundaries- Dams & Water Diversion Structures- Hotspot / brightspot areas- Meteorological & Agro-met Stations - Hydro-met stations- ADB, WB, EU, GTZ project locations;- Google Earth imagery downloads- photos, reports, etc.

GEF Project Area Coverages (NSIUs)- 1:200.000 (Rayon + Oblast)- 1:50.000- 1:20.000 (Municipalities)- 1:10.000, & monitoring data etc

Remarks: (i) above shows only major groupings of coverages / data layers (shape files), with database files (dbf / xls format) appended. (ii) above coverages emphasise data layers at general scale (especially 1:500.000 – 1:1.500.000 scale range) showing commonality over national boundaries, with data which can be shared across the entire CAC-5 area. (iii) for CACILM National Project area coverages and for MSEC-generated coverages more detailed data is included.

1.7.2 ECONET Data

Data was received officially from the Turkmen NSIU team on 10Oct08 as ArcView3.x shape files, created / digitized in the period up to January, 2005. Permission and protocols on use of the data have also been verified with the WWF office in Moscow, officially in charge of this data. Data included comprises the following: Basemap Layers: Topographical contours; Hydrographic data (rivers, streams, canals, major drains, water bodies); Towns and villages / hamlets; Roads and railways; National, Oblast and Rayon boundaries; Socio-economic Data (rayon level): Rayon statistics covering total population, rural population, livestock numbers (cattle, camels, horses, sheep & goats) major land use divisions (arable; pasture; forest; other land) (see Figure 2.4 and Table 2.1); Ecoregions, Ecosystems, Land Use data: The ecosystems coverage is valuable but categorically very complex (literally thousands of individual units and subunits) and the full legend table prints out on 70 pages (about 45 pages alone for the CAC-5 priority area)! Cartographic accuracy of agricultural areas (rainfed and irrigated arable) needs checking: it is suspicious in some areas, with unit boundaries cutting across contrasting geomorphic units. Map detail in intensively-studied areas (e.g. the Aksu-Zhabagli Protected Area) is also much higher in this coverage than for other areas.


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Protected Areas, existing and proposed: proposed ECONET-protected areas are very extensive and represent a major consideration on any land use planning activities. The ECONET data is particularly valuable in many respects, not least because it covers the CAC-5 area to a uniform methodology and level of detail with multi-layer maps printing out very well at an optimum 1:1million scale. It thus represents an excellent basemap for the project. Several of the most important files have now been translated from Russian into English by the project, including the Socio-Economic data attached to the rayon polygon file, the Ecosys file, and the file covering cities, towns and villages. The Windows link with English version of the ArcView software has furthermore been modified so that Russian characters are supported and the original Russian version of the database can now be read in this software.

1.7.3 ICARDA Data

A list of 66 different digitised / derived layers has been provided to MSEC, and the research and 6-monthly progress reports produced by ICARDA have given interesting print-outs from the ICARDA GIS. Much of this data is at the level of detail consistent with map print-outs at 1:1million scale (although some is at a more general scale). The main groups of map layers are as follows: Climatic and agro-climatic data: 42 themes, some with up to 12 further layers. Includes temperature, annual and monthly potential evapotranspiration; annual and monthly precipitation; crop-growing periods; climatic biomass productivity indices under rainfed and irrigated conditions etc. Land Use / Land Cover: 8 themes, period 1982 -1999; Landforms / Elevations: 2 themes; Soils: 13 themes (data at more general level: 1:5million); Agro-ecological Zones. Delivery of the digital data (in DVD format) was kindly provided by ICARDA to the Project in the first quarter of 2009.

1.7.4 FAO-LADA Global LUS CAC-5 coverages

Global material cut-out for the CAC-5 (dated 12Sep08) with an excellent general explanatory file (dated 31Oct08) was provided. Material covers many themes: soils, land use/land cover; irrigation areas; urban areas; protected areas; livestock density; poverty etc. The material is in gridded format, and consistent with print-outs at 1:5m – 1:8m scale, providing good but very general single-theme maps of the entire CAC-5 area at A4 or A5 formats. (A JPEG of the Global Land Use Systems (LUS) map is presented as Figure 2.1, below). Originals of this GIS material needs to be opened with the Spatial Analyst add-on to the ESRI ArcGIS GIS software package. Key attribute data now needs to be added. A further key process is the substitution of these Global layers by more detailed national layers (these being both cartographically and categorically much more detailed – see Figure 2.2 for the example for UZB). The completed detailed compilation of national LUS maps would have represented the key FAO-LADA deliverable for this stage of the project, but only minimal on-the-job training was provided by FAO staff to the 8 NSIU GIS staff and the MSEC SLMIS specialist and no follow-up activity was provided by FAO. The very short on-the-job training was held in Bishkek and included: use of Spatial Analyst for the mapping process; addition of required attribute information; substitution of more detailed national layers for the general global layers.

1.7.5 National Coverages, 1:500.000 – 1:1.500.000

A long list of national coverages available at the National level at a general scale was prepared by each of the NSIUs (see Annex A). The Project then requested that these coverages be made available to MSEC in three priority groupings, the top priority being required for the FAO LADA National LUS mapping exercise – see above – second priority being those themes essential for transboundary land use planning and watershed management activities.


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In general, the highest priority themes requiring to be delivered first included the following: - Land Use / Land Cover / Agricultural Systems; Irrigation areas (& sub-classification); Urban areas; Protected areas. (See Section 2 for details). - Socio-economic / poverty status (See Section 3 for details). - Soils and Land Units (See Section 5 for details). - Agro-climate (Precipitation; temperature regime/elevation zone). For the above data it was stipulated that data should be eventually provided in ESRI shape file format. However, it was requested that if these ESRI.shp files were not immediately available then JPEG scans and paper-format materials should be delivered first so that legend formats and map detail could be inspected by MSEC staff. Further point file data was also sought on two further themes: - Municipalities: locations (and sub-villages, already denoted in the ECONET data, under each municipality), correlation of old and new names for these municipalities (old names often corresponding to state and collective farms under the Soviet period), dates of soil and bonitet surveys and reassessment sampling surveys for the municipalities. (See Section 6 for details). - Met, Agro-Met and Hydro-met stations: locations, runs and types of data, quality, etc. (See Section 7 for details).

1.7.6 National Statistics Agencies’ Rayon Data

Rayon data is compiled annually by each country’s statistics agency covering a wide range of data. Most relevant for both National and MSEC purposes is the following: Land Use and Crop Areas Crop Production and Crop Yields Population (Total) and Rural Population Employment / unemployment, Income, Poverty Livestock population and production National statistics data is generally available for any year by August or September of the following year. Agricultural statistics cover separately irrigated cropping and production from total figures on cropping and production (total including both irrigated and rainfed agriculture). Statistics are commonly further broken down according to type of holding: enterprises (state and collective farms, or their current successors); individual (family owned) farms; and small private horticultural / garden plots. Unsurprisingly, the enterprises have declined markedly in importance since 1990, having been split into small privately-owned family farms. Formats for publication of the statistics have changed over time, years before 2000 invariably being in hard copy (printed) format only, with more recent years being in digital format (MS Word, Excel, Foxpro and dBaseIV having been used in different offices). Some printed data has been shown to have included occasional mistakes (numbers not adding) or some printing not being clear (poor quality of printing). Also livestock data may be particularly erratic. However, most data is deemed to be reliable and to be of great value to the project in giving particularly relevant and immediate information on any land degradation trends. For most countries or projects a single baseline year (using taken as the most recent, typical year, for which statistics are available. However, for the current project it is deemed that three baseline years need to be considered in order that all figures can be interpreted in a satisfactory way. The three key baseline years are: - 1990: the last year of the Soviet Union; - 2000: the end of the post-Soviet re-adjustment period (in most areas big declines in production were seen, with minimum production levels being reached between the period 1997-2001); - 2006 or 2007: the latest year for which published statistics are available, and average years as far as rainfall during the cropping season are concerned (2008 being a bad year)..


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Output of the statistical data needs to be made in a common format across the CAC-5 countries and norms as used on the Kazakhstan-EMIMS project have been circulated to the NSIUs to this effect. The rayon data across the full area is best appreciated as shaded polygons (see Figure 2.4); for smaller geographic areas (e.g. one Oblast; one river basin area), representation as pie-charts or histograms positioned on the rayon centre is more appropriate (Figure 3.4). Here attention to cartographic presentation is very important: selection of the shading should follow a logical order (in the latter case, the higher-value crops are shown with a more intense colour).

1.7.7 Remote Sensing Images

A large number of original downloads of remote sensing imagery (primary data) have been made available to the project from Dr Ji, but many of these require image processing software in order that directly readable or interpretable output is generated. Imagery which was fully processed and delivered in georeferenced (Geotiff) format includes: - MODIS NVDI for 12 individual months of 2008 and compilations covering NDVImax, NDVImean and NDVImin for that year: this is available for the entire CAC-5 area and enlargeable to 1:1m scale; - LANDSAT ETM+ GeoCover: 2004 imagery, and captured at the approximate time of maximum NDVI: this is enlargeable to 1:100.000 scale but is available in Geotiff format for the central part of the Priority Area only. However, this imagery is used extensively by Google Earth in the latter’s moderately detailed coverage. Section 4 covers use of remotely sensed imagery, both the work undertaken by Dr Ji, and the work subsequently undertaken by the project, based on the imagery supplied by Dr Ji.

1.7.8 CACILM National Project Area Coverages

CACILM National Project Areas are currently recorded on the SLMIS as a point data file (see below and also Figure 1.3) but many of these sites cover appreciable areas, some of which are entire rayons or oblasts. For full rayon or oblast coverage delineation of boundaries is obtainable from the ECONET coverages, and a new GEF project polygon coverage can be generated (by the respective NSIU or by MSEC) from this. For other projects, maps covering specific project areas need to be obtained from the NSIUs, and the boundaries digitised. In due course NSIUs should obtain the key maps necessary to delineate these project areas, and to provide further information within the specific areas. The main scales of mapping required would be the following: -1:200.000: topographic, land use, soils and other coverages; -1:50.000: rayon coverages, cultivated and irrigated lands, village settlement areas, soils and bonitet assessment compilations, -1:20.000: municipalities mapping, again with the above themes; -1:10.000: municipalities mapping and detailed irrigation layouts, again with the above themes. Published maps at scales more detailed than 1:100.000 may be under security restriction. For this 12-month phase of the project it would be expected that only full lists be compiled for these materials (format as in Annex A) with dates of mapping and name of agency holding the mapping being clearly delineated.

1.7.9 CACILM / MSEC / ADB Coverages

A number of new coverages have already been generated by the MSEC staff and consultants, and a number of further coverages are planned to be initiated over the next few months. Coverages include: CACILM National Project Locations (point data file completed: attribute data mostly complete- see Figure 1.3 above) Major River Basin Boundaries (major boundaries completed – smaller watersheds to be delineated) Dams and Water Diversion Structures (point data file partly completed: attribute data only just started) Hotspot and Brightspot Areas (point data file linked to JPEGs downloaded from Google Earth imagery or terrestrial photos)


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Google Earth imagery: polygon file: location of high-resolution imagery, enlargeable to 1:20.000 scale or more detailed) Seismic events over the last 100 years (point data file obtained from US Geological Survey, earthquakes classified on intensity and depth) Glacier extents and rates of melting (planned coverage: data from GTZ projects) Other Donors (Non-GEF) and NGO Project Locations (planned coverage: data from various sources) 1.8 BASICS OF THE SLMIS, AND WORK UNDERTAKEN DURING CURRENT PHASE OF PROJECT

The format of the SLMIS and the major groupings of data on the system are shown in Figures 1.4 and 1.5. The core of the system is a GIS running under ESRI ArcView 9.3 software, with database information under MS Excel and Access software. All files are also compatible with, and duplicated to, the earlier and more freely-available ESRI ArcView3.x software with database information in dBaseIV (dbf) format, in turn operable with the 1997-2003 versions of MS Excel. The various components of the system, outlined in Figure 1.4, are further described as follows: Primary Data Primary data, the property of the NSIU or original owner, has been made available in copy to MSEC as part of the ADB contract. Much of this information is in its original format, often as paper maps dating back 20-30 years. Although dated, most of the mapped information is still very valid, particularly that relating to physical phenomena – geology, geomorphology, soils, topography, hydrology etc. Much of this mapping has furthermore been at an ideal scale-range: national mapping at 1:1m -1:2m scale (ideal for combining with the existing ECONET and ICARDA GIS files), and oblast-level mapping at 1:100.000 – 1:300.000 scales (ideal as base material for the CACILM National Projects). The approach taken by MSEC has been to obtain scanned copies of all of this Primary Data as quickly as possible (often in small JPEG files) so that map legends can be inspected and the applicability of the map and its accuracy can be assessed. For valuable or priority coverages MSEC has then requested that it receives the digitized map in ESRI format in due course: for less valuable coverages the original raster format would be sufficient for the SLMIS in the current period. For the latter raster-format material it may be convenient simply to keep the mapping as scanned images, but to undertake the necessary georeferencing so that vector files (e.g. CACILM hotspots; CACILM National Project sites) can be superimposed. Some maps and data have been sensitive material and not available from the NSIUs in ESRI format: MSEC holds some very valuable mapping, normally very difficult to obtain, which now needs digitizing. Primary data has also been obtained from the previous ADB project KAZ EMIMS-SLU, most of which was material from SKO, the trial oblast. This material is valuable in that it covers a full selection of material which is available at oblast level – some of it again difficult to obtain – and it is relevant for considerations at both national level and project level. Some of the material (particularly on detailed soils mapping, and also on hydromet data) was not fully reported in the EMIMS reports, as it was delivered only in Oct-Nov 2007, too late in that project to be processed and finally reported on. Four further advantages with SKO as a pilot project location is that it represents the geographic centre of the CAC-5 Priority Area; it has a significant component of rainfed agriculture, much of it very marginal and very sensitive to weather fluctuations associated with climate change; it has major downstream irrigation water issues with neighbouring upstream countries; and finally, it is geographically very close to Tashkent, the biggest city in the CAC-5 and the major centre for activity in the region. In digitizing some mapped material and overlaying with other coverages some mistakes and anomalies have been discovered. In some statistical data there has been some clear mistakes (numbers not adding, decimals being in the wrong position), but in most cases it has been possible to make the due correction without resort to the original papers. Some digital material has furthermore been made available in unique map projection systems which have defied any conversion programmes. Where such data has been particularly valuable, it has had to be redigitised. Processed data, digital format Processed data, in digital format, is in four different forms:


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- databse files: xls and dbf format (easily inter-convertible with MS Excel 1997-2003); - map files: vector format: shp format (ESRI shape file format); - map files: raster format, georeferenced: Geotiff format (suitable as basemap ‘underlay’ on

which vector files can be superimposed) - map and image files, not yet georeferenced: JPEG format.

The format of the map files has been unprojected geographic coordinates (degrees N, degrees E). Maps printed out under this format are convenient in that lines running N-S run parallel across the map, but the scale EW is some 35% greater than the scale NS (one degree of latitude at 41º N being much greater in distance than one degree of longitude). Maps showing the entire CAC5 Priority Area are best displayed under the following map projection: equidistant cylindrical, spheroid sphere, central meridian 69º; reference latitude 41º. This projection has two advantages: NS lines again run parallel; NS and EW scales are the same along the NS line 69º and the EW line 41º which bisect the area. External Tools Often much detailed data requires processing for which an already-established programme or interactive Excel spreadsheet is available. Thus, rather than re-programming these procedures into AMLs attached to the GIS, it is more convenient to run the already established programmes and spreadsheets, many of which are quite sophisticated. Those already developed and widely used include:

- FAO Cropwat; - Climate / precipitation data reformatting; - Rainfall-runoff and Daily Soil Water Balance Modeling; - Soil Water Infiltration calculation; - Irrigation water quality assessment; - Land Evaluation Assessment; - 1-ha Crop Model Economic spreadsheet; - Rangeland feed optimization spreadsheet.

These are described briefly in Figure 1.4, and much further explanation is given in the EMIMS Design Guidelines, Annex D. Report Formats and On-Screen Outputs The system to date has been set up for internal users with some GIS training. Within the ArcGIS and ArcView software in-built procedures for project (apr) files enable a series of map themes to be stored together in a particular order so that one theme (e.g. point file) is essentially overpaid over another (e.g. polygon file, e.g. with polygon shading). Each of these themes, in turn may follow a particular theme legend, itself stored as a legend (avl) file. The operator thus has considerable scope for rapid print-outs of maps based on revised database material – e.g. crop areas and yields for the next year of rayon data that may become available. Single parameter data (e.g population density, on a rayon basis) can be best displayed as a series of polygon shades (e.g. Figure 2.4). Multi-parameter data (e.g. areas of different crops, within individual rayons) is best displayed either as pie charts (where data conveniently adds up to 100%) of as histograms (where data is open-ended). These pie charts or histograms are then automatically positioned in the centre (or label-point) of the polygon (e.g. Figure 3.4). Within ArcView there are inbuilt procedures for the creation and printing of maps in standard format, with legends and ancillary information accompanying the map detail in a series of standard designs. Alternatively, one may take screenshots in JPEG format, and attach legend boxes through cut-and-paste facilities with the simple Paint software. The latter approach has been used extensively with the maps in this report, as this is more flexible and much more economical on the use of paper. Further possibilities exist to use the GIS to select particular points or polygons within particular areas (e.g. rayons within precipitation zones of more than 400mm, a limit likely to be sufficient to support rainfed farming). These polygons can be automatically labeled, and a new database file created from which charts or histograms can be produced using the facilities within the spreadsheet (i.e. Excel software - e.g. Figure 2.7).


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Website (for external users) This would be for future development, once all the required baseline data is available and the due monitoring data is also being obtained on a routine basis. Conclusions The emphasis of the present work has been on acquisition of all relevant data and on simplicity and flexibility in the SLMIS designed to use it, rather than sophistication of formats and procedures. Both at MSEC, and also in each of the NSIUs, the SLMIS has been operated by GIS-trained staff, most with much experience. Contact between the NSEC staff and the SLMIS has thus, so far, been through these GIS staff. In a future phase of the project development of the SLMIS might include training of the NSEC staff so that they themselves could extract data directly from the system, or alternatively, there could be development of a simple menu-based system by which non-technical users could obtain directly standardised information required. The latter could also be built into interactive website-based menu-driven procedures. In the current phase of the project MSEC have not had in-house facilities for data capture (map digitizing), and thus any such work has had to be done by the individual NSIUs or by local contract agencies. For map production and printing, local contract agencies have also been involved as in-house map production has been limited to an A3 format. Again, future development of the system would firstly include provision of a GIS assistant whose main functions would be for data capture and cartographic output.


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2 CACILM LAND MANAGEMENT INFORMATION

2.1 INFORMATION SOURCES ON LAND USE, LAND COVER, AND OPERATIONAL STATUS OF THE LAND

For the rural and agricultural environment a range of different map types exist which deal with the various aspects of land use and management. These include the following: - Land Use (see Section 2.3); - Land Cover; - Land Use Systems (Section 2.2); - Vegetation and Ecosystems (see Section 2.5) - Land Ownership; - Land Allocation; - Operational Status; - Land Valuation; - Land Quality (Bonitet) Status; - Structural Plans; - Potential Land Use; - Land Management Plans. The theory and practical use behind some of these map types is further discussed in Box 2.1. Information sources which have been used in the current work have included: - Remote Sensing (see Chapter 4), including MODIS (1:1m); LANDSAT-ETM (1:100.000); Google Earth: 1:100.000-1:50.000; Google Earth: 1:10.000-1:2.500; - ECONET coverages, including socio-economic coverage & databases, vegetation/ecosystems mapping; topographical basemap layers; - Rayon Planning Maps (general); - Municipalities mapping (see further information in Chapter 6); - Statistics Agency Stats: land use, cropping, yields (see further information in Chapter 3). Present work has aimed for uniform multi-country coverage at a level of cartographic detail consistent with print-outs at a scale range of 1:1m (multi-layers) to 1:2m (single layer), this being the same level of detail as that presented by the ECONET coverages. 2.2 FAO LADA LAND USE SYSTEMS (LUS) MAPPING

2.2.1 Global LUS Mapping

FAO-LADA was intended as an early input to the project, in the form of its Global Land Use Systems (LUS) mapping, and global coverages covering many themes (irrigation; urban areas; soils; …). The format of these coverages is ERSRI ArcGIS Grid (raster) files, with pixel size approximately 5 – 8 km, equivalent to reasonable printed map output at around 1:8million scale. Originals of this GIS material needs to be opened and analysed with the Spatial Analyst add-on to the ESRI ArcGIS GIS software package. Figure 2.1 presents the CAC5 cut-out of the global LUS map for the 5-countries area. The material provided by FAO also included other themes at general level, soils, land use / land cover; irrigation areas; urban areas; protected areas; livestock density; poverty etc. The material is in gridded format, and consistent with print-outs at 1:5m – 1:8m scale, providing good but very general single-theme maps of the entire CAC-5 area at A4 or A5 formats. Further attribute data is appended to each of the individual grids, the level of categorical detail of course being much greater than that provided in the LUS Map Legend. This information is useful at global level, particularly in presenting comparisons between the CAC-5 area and equivalent areas elsewhere. However, for


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considerations within the CAC-5 area, the following are considered to be the main limitations and main criticisms with respect to the use of this map: Box 2.1. Maps relating to Use and Management of the Land Land Ownership / Cadastral Mapping. This is effectively a map, or series of maps, covering legal ownership (i.e. who holds the freehold to the land). With Cadastral Mapping there is normally much attention paid to map detail and precision, particularly with respect to boundaries and boundary points. Links are also made to a property register. In most countries there is normally a system of smaller-scale index maps, often including a series of maps at different scales ranging from detailed individual houseplot plans at 1:1000 scale (individual property, with boundaries details (posts, fences), and information on immediately adjacent properties) through to a series of index maps at 1:10.000 to 1:50.000 scale. Land ownership mapping is normally covered by well-defined legislation and land administration procedures, usually under specific land administration offices, most often at District (Rayon) level. The mapping and registers are now invariably computerized. Legal land titles and accompanying papers also usually have great value as collateral for loans. For agricultural land, some information on agricultural land value (bonitet ratings) and further potential uses of the land may be given, and in some cases this may be quite elaborate (e.g the land ‘passport’ scheme under GosNPCZem in Kazakhstan). Land Allocation Mapping covers what uses(s) the land has been allocated for, and who is legally entitled to use the land. Usually this is produced at a general scale (1:250.000 – 1:50.000) and covers groups of owners, rather than individuals, with broad categories used. Operational Status Mapping deals with how the land is actually being used and who is actually using / operating the land. Quite often this may also include encroachments used for agricultural or for housing purposes. Again these maps are usually undertaken at a general scale (1:250.000 – 1:50.000), with broad categories defined. These may include forestry management categories (e.g. production forest; limited production forest; protection forest, etc). They may also include wildlife protection status categories (national park; wetland reserve; strict nature reserve; ‘zakaznik’ and ‘zapovednik’ etc in FSU countries). They may also include watershed management categories (e.g. catchment areas for potable water; catchment areas for irrigation etc). It is important to note that land allocated for a specific use may often be put under a very different land use category in practise: operational status / land allocation maps need to be carefully checked against actual land use. Often a matrix table of actual use vs land allocation / operational status is very useful in clearing any confusion on this issue. Land Valuation Mapping. Covers potential sale valuation of the land at a given date, for a specified feasible land use. Usually rural land uses (no building allowed) are valued very differently from cases where building (existing or future) is permitted: in many developed countries, the ratio of building use value / rural use value is greater than 100:1. Land Quality (Bonitet) Status Mapping. Well defined system within FSU countries whereby agricultural land is assessed according to potential productivity for a basket of possible crop types, best land being awarded 100 points and other land parcels rated accordingly. Irrigated land and rainfed land are rated independently – average ratings for irrigated land on an enterprise may be say 59 points, and for rainfed, 28 points. System is based on detailed (1:10.000) soil mapping and much chemical and physical soil analysis, including soil organic-C content, salinity etc. (see Section 5.4, and Box 5.1 for details). In some countries rural land taxation is based heavily on this system. Potential Land Use: Structural Plans are legal documents specifying broad categories of potential land use. Structural Plans are often used to restrict building to certain well-defined areas, and preserve the best


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agricultural land for future agricultural uses. In many heavily-populated areas much of their intended value would be to preserve ‘green belt’ land round towns and cities, and prevent ribbon development. A further function is to protect value of good residential areas, and to restrict industry – particularly noisy and polluting industry – to particular areas where it can be better monitored and where damage to surrounding areas would be minimized. Potential damage from natural hazrds also have to be considered in devising structural plans – e.g. floods, landslides, tsunamis, earthquakes, wind-damage, etc. Development Control Document. Within certain categories of a structural plan, more specific mapping & guidelines for planned development and specific buildings may be given in this particular document (e.g. heights of buildings, earthquake regulations, foundation regulations etc). Land Management Plan. Usually mapping and appended tables and text relating to future land use and management proposals for a defined area of (rural) land. Ideally this should be based on a land suitability evaluation for a number of potential land utilization types (LUTs), and further financial and work scheduling analysis. Land Utilization Type (LUT). Usually a potential crop type and specified level of physical and management inputs (e.g. winter wheat, largely rainfed, but up to 100mm of supplementary irrigation applied at ear initiation stage by mobile sprinkler irrigation under electrical pumping; fertilizer up to 70 units N/ha; expected yield 3.900kg/ha grain, 3.200kg/ha straw; harvesting by combine; planning by minimal tillage techniques). Unit on which land suitability evaluation by FAO Framework for Land Evaluation is based. - the main irrigation areas are split between two classes, ‘Agriculture, Large scale Irrigation’ (17), and ‘Agro-pastoralism, moderately intensive or higher, with large scale irrigation’ (16). Field inspection shows that most of these areas are identical, (i.e. are dominated by large-scale irrigation schemes, with land being intensively used) and should be in the same class (i.e.17); - representation of class 17 (more intensive) is a less vibrant colour than 16 (less intensive): this is the wrong way round as the most intensively used areas should be shown with the stronger colours; - intensity of use / weaker colours also applies to the other land use divisions (classes 6, 11, 15 are all intensively used, yet are shown in weak colours) - attribute data covering cropping did not include the crops grown in the CAC5 area, most notably cotton For CACLIM work, and particularly any two-countries transboundary work, much more detailed coverage is needed, with work at around 1:1m scale. This was the intended work of the national LUS mapping teams, replacing the global datasets with the national coverages and then adding the required attribute data to these. Only limited FAO input was provided during the course of the study, and thus this part of the work progressed much less rapidly than was intended. (Discussion on this, and on the lessons to be learnt from work associations across the project, are presented in the Consultant’s Final Report.)

2.2.2 National LUS Mappping

National Mapping, replacing the general Global LUS Mapping with a much more detailed national dataset, has progressed furthest in UZB, and their work is briefly reviewed here. Figure 2.2 presents a detail of the mapping for the major part of the country (both Central and eastern Uzbekistan), together with the LUS legend, as modified by UZB-NSIU. The mapping is based on earlier land use mapping, with recent updates having been made by remote sensing. The detail and precision of the mapping is consistent with print-outs within the scale range of 1:1m – 1:2m. The main limitations and criticisms with respect to use of the map are the following:


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- colour differentiation between classes 8, 17 and 32 is poor. This is important, as these colours cover a vast area. It is presumed here that the huge areas of this in the W part of the country are class 32 (sparsely vegetated areas – extensive pastoralism). - a similar (but less severe) problem also applies for classes 15 and 16. - again, many of the main irrigated areas deemed to be only moderately intensive, while others are intensive. However, inspection by Google Earth imagery shows both these areas have >>50% of the total land surface covered by large scale irrigation, with most of the land managed intensively. - intensively managed areas are shown in a lighter / less vivid colour. It would be better if this were reversed (i.e. these should be shown in stronger colours). (Again, this is a fundamental FAO legend problem.) ,- urban areas (class 25) cover extensive areas in E Uzbekistan and usually in association with irrigation schemes. Inspection of detailed Google Earth imagery show that these areas include much peri-urban and rural village land, with more than 2/3 of groundcover as intensively managed home-gardens (see Figures 4.15 and 4.16 for an example at detailed and general scale, respectively). This is a quite separate land use from ‘Urban Areas’ and is essentially very intensively used agricultural land. At 1:5m scale it is appreciated that urban and periurban and village homegarden areas are difficult to separate. However, at 1:1m scale (i.e. the original scale of this map) the two should be separated, in a colour which would not clash with other colours in a rather crowded map. A possibility here (given the limitations of an already well-established FAO LADA legend palette) would be to show the periurban / village homegarden areas in grey, and the pure urban areas in black. Nevertheless, in spite of the above limitations, the UZB LUS Map represents a major advance and has application both nationally and regionally (CAC5) as well as further afield in filling an important gap in the FAO LADA global coverage. It would be advantageous if the other NSIUs could follow the example of the UZB-NSIU in pursuing this work, in order to devise an upgraded LUS map at 1:1m scale for the Priority Area. 2.3 CACILM LAND USE CLASSES FOR CENTRAL ASIA: MULTI-COUNTRY PRESENTATION

Delays in the production of the National FAO LADA LUS Mapping put an immediate premium on devising very rapidly an updated land use map for the entire CAC5 Priority Area, consistent with cartographic print-out within the 1:1m – 1:2m scale range. This was undertaken by MSEC staff, in liaison with the individual NSIUs who undertook much of the primary work for their respective countries. The latest, but still preliminary version of this map, is shown in Figure 2.3, at a vastly reduced scale, for the core of the Priority Area. Eight classes of land use are displayed: irrigated arable cultivation; rainfed arable; forest; urban; wetlands; pasture; and unused areas, with protected areas shown as a cross-hatched overlay over the other classes, which are all shown in solid colours. The more ‘valuable’ land uses, from either an economic or an environmental point of view, are shown with vivid colours. The two least valuable classes, covering by far the biggest areas overall, comprise pasture land (very light yellow) and unused land (very light pinkish grey), and are shown by very muted colours. The map shows the most important land use classes, and also usefully includes data of Tajikistan (not yet fully integrated into most of the CACILM activities). However, some outstanding problems remain, and these are listed as follows: - excessive areas of rainfed arable land are still shown in KAZ, especially in mountainous areas (SE of Almaty; E extremities of SKO; S parts of Zhambul Oblast). All of these areas would have high NDVI values during peak spring green-up periods, and these probably represent high-productivity pasture areas. They now need to be investigated (by comparisons with other imagery: LANDSAT ETM+ and Google Earth high-resolution sputnik imagery) and re-classified on a case-by-case basis;


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- the Shymkent area of SKO-KAZ shows an excessive concentration of irrigated land. In reality, there is a mixture of irrigated (35%), rainfed arable (40%), and pasture (25%) and these can be separated for final representation at a scale of 1:1m. - pasture areas include a large range of productivity classes, ranging from <10cntDM/ha/year in many of the most arid areas to >100cntDM/ha/year in some high-elevation high rainfall summer pasture areas and to >200cntDM/ha/year in the natural haylands in some of the Syr Darya floodplain areas. There is a strong case at a later stage of the Project to reclassify these areas based on combinations of NDVImax and NDVImean values from MODIS imagery from an ‘average precipitation and productivity year’ (e.g. 2005, 2006) and perhaps also divide the main pasture grouping (10-100cntDM/ha/year) into 2 or 3 further subclasses. - a thorough rechecking of all areas needs to be made on the basis of the LANDSAT ETM+ coverage., and a working compilation needs to be made at 1:500.000 scale prior to final cartographic print-out at 1:1m scale.


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Figure 2.2: FAO Land Use Systems Map for Central and Eastern UZBEKISTAN. (Source: UZB-NSIU Final Report, 2009)


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Figure 2.3 CACILM Land Use Map, CAC5 Priority Area (Preliminary)


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Figure 2.4: CAC-5 Transboundary Priority Areas: Proportion Land Cultivated in each Rayon


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Figure 2.5: Dominant Crop Groups within Agricultural Areas within each Rayon: Central and Eastern UZBEKISTAN. (Source: UZB-NSIU Final Report, 2009)


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Figure 2.6: CACILM Cropping Map, CAC5 Priority Area (Preliminary)


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For KAZ, the recent (c.2005) farmlands map (Figure 2.7) shows fairly accurately the main areas of both irrigated and rianfed arable land (and other land uses), and shows considerable cartographic detail, even though the published scale is only 1:5m. The techniques that might be used in updating this land use mapping largely by remote sensing are demonstrated in Chapter 4, and the final proposals for this work are outlined in Chapter 10. Figure 2.7 KAZ Priority Area: Farmlands, 2005. E areas (top); W areas (bottom)


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Source: KAZ-NSIU. IG mapping, scale 1:5m, c.2005. Dark orange brown: Irrigated arable; Light orange brown: rainfed arable. 2.4 CROPPING: DOMINANT CROP GROUPS WITHIN AGRICULTURAL AREAS

2.4.1 General

Any changes in Land Quality or Land Degradation status is likely to be reflected in changes of cropping; conversely changes of cropping over the long-term may have beneficial or adverse affects on Land Quality or Land Degradation Status. In general, soil with a high Land Quality (Bonitet) rating will be able to grow a wide variety of crop types. If that land were to become degraded so that bonitet ratings fall, the choice of crops that would produce a profitable yield may become successively more restricted. For example, horticultural crops may be highly remunerative, but most require deep soil of high fertility status and very low levels of salinity or sodicity in order to give good yields. As salinity increases, the range of such crops that can be grown profitably would become more restricted (see Table 5.1 for examples of salinity tolerance), until only the most salt-tolerant crop would eventually dominate the cropping pattern for that area. Such is the case with cotton in Maktaraal rayon, SKO-KAZ, where almost a cotton monoculture has developed because of run-down of irrigation and drainage infrastructure and build up of soil salinity (see Figure 3.5 for increase in proportion of cotton in irrigated areas between 1987 and 2004). For any further increases of salinity, and accompanying rises in water table, total abandonment of the land might then occur. Monitoring of cropping composition and crop yields on a routine basis is thus a key initial requirement of any SLMIS-SLU. In addition to Land Qualilty (bonitet) status, crop performance and yield is also determined by agro-climate, with specific crops having a specific temperature or humidity requirements at different stages of their growth cycle. Within the CAC5, rice and cotton have high temperature requirements (long growing seasons) and are thus limited to the lowest elevations. Conversely, many horticultural crops prefer somewhat cooler summer temperatures and thus do much better at higher elevations. A further factor in cropping patterns is the interplay of tradition and marketing patterns. Oilseeds and sugarbeet, for example, need access to processing factories and thus both products tend to be grown within a relatively short distance of central processing factories. Horticultural crops, being mostly very perishable, require easy access to markets, and thus tend to be grown within a short distance of major towns.

2.4.2 Multicountry Mapping: Oblast and Rayon Statistics

Available from the collection of ECONET GIS coverages is the file Socio-14-01-05.dbf which is linked to a polygon (shp) file showing all 497 rayons (districts) which make up the entire CAC5 area. The characteristics of the dbf file, the rayon socioeconomic database of the Year 2000/2001, is given in Table 2.1. Figure 2.4 is a GIS output from this file and shows the proportion of the total land in each rayon which is cultivated, bright blue showing the areas with the highest proportion of cultivated land, and very light yellow the lowest proportion. (The legend for this is represented by the file landuse.avl, created by MSEC.) Classes here are as follows: Very light yellow: < 0.5% cultivated Light yellow: 0.5 - 1% cultivated Greenish yellow: 1% - 2% Light green: 2% - 5% Green: 5% -10% Light blue: 10% -30% Mid blue: 30% -60%


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Bright blue: >60% This rayon coverage as presented by ECONET is very useful but may not be fully up-to-date, and may also have some cartographic errors in some locations, although the coverage checked very well with known rayon maps (e.g. for South Kazakhstan Oblast). However, Rayon boundaries did not completely match with Oblast boundaries in several areas, and inspection of both boundaries with respect to natural features (ridge-tops, rivers, and stream lines) suggested that the oblast boundaries were the more accurate in most cases. Rayon boundaries commonly showed displacement of 1km or so and were particularly inaccurate for the following areas: - Tajikistan (many areas) - Kyrgyzstan (NW part of Chui Oblast: rayon boundary missing, and oblast boundaries over-generalised) - Kazakhstan: (NW edge of SKO, W edge of Almaty oblast). Checking and updating of these areas, by the respective NSIUs, are thus required. The file Socio-14-01-05.dbf has now been translated into English by MSEC so that the file is readable with the standard English versions of the ESRI ArcView software. Table 2.1: Major Charactersitcs of Rayon Socioeconomic Database Name: Socio-14-01-05.shp, dbf etc (ESRI shape file) [under directory:

C:\CACtopo\Topo\..] Type: Polygon file Established: Jan, 2005 (original fields); amendments & addition of 9 further fields: Oct

2008 – Apr 2009 Created by: ECONET staff & consultants (original); MSEC staff and consultants

(amendments) Sources: Official statistical data at rayon level from CAC5 for 2001 (or 2000) Records (current): 497 Representation on map:

i. Set of polygon shades for key parameters: rural population density, grazing pressure, etc ii. Set of pie charts, centred on each rayon, showing land use breakdown (cultivated, pasture, forest, other uses)

Fields (current): Objectid ID number, automatically generated by GIS Numb ID number, as given by stats listing Strana Name of country Oblast Name of oblast (province) Rayon Name of rayon (district) Adm_centre Name of rayon centre (town or village) Rayon,000ha Rayon area (000ha) – numerical field Population,000pe Total rayon population (000persons) – numerical field RuralPopl,000pe Rural population of rayon (000persons) – numerical

field RrlPpDn.p/1000ha Rural population density (persons/1000ha) – numerical

field Cultiv.000ha Cultivated area (000ha) – numerical field Cult/TotalArea Ratio of cultivated to total rayon area (0,000)–

numerical field Pasture,000ha Pasture area (000ha) – numerical field Pasture/TtlArea Ratio of pasture to total rayon area (0,000) – numerical

field Forest,000ha Forest area (000ha) – numerical field Forest/TtlArea Ratio of forest to total rayon area (0,000)– numerical

field Cattle Number of cattle – numerical field Ctl/1000haPastr Number of Cattle per 1000ha pasture – numerical field Goats&Sheep Number of goats & sheep – numerical field Gt&Sp/1000haP Number of goats & sheep per 1000ha pasture –


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numerical field Horses Number of horses – numerical field Horss/1000haP Number of horses per 1000ha pasture – numerical

field Camels Number of camels – numerical field Caml/1000haP Number of camels per 1000ha pasture – numerical

field Shape_leng Length of perimeter of polygon – numerical field Shape_area Area of polygon – numerical field Grzpresr Grazing pressure (no. animal units / 1000ha pasture–

numerical field) Fields (possible future):

Many further fields illustrating land use or cropping statistics at national and multicountry levels can be attached to the original shp file, and statistics displayed graphically as polygon shades (polygon coverage) or as pie charts and histograms attached to label points for each polygon.

Remarks: -1 animal unit defined as 1 horse or 1 camel or 1 head of cattle or 5 sheep. - Some of rayon boundaries are not accurate (EW displacement of 1km for many rayons); one rayon crosses KYR-KAZ border; - Some doubts expressed on accuracy of some of the livestock statistics (but land and crop statistics considered more reliable)

It is clear that there is much further rayon level data which is common throughout the CAC5 and which can be linked to this shp file coverage. However, obtaining statistical data at rayon level has continued to be a difficult process in some countries, although much information has been provided in the last few weeks of the Project.

2.4.3 National Mapping: Oblast, Rayon and Municipality Statistics

Dominant crop groups within agricultural areas within each rayon have been presented by UZB NSIU. Here crop statistics are available at rayon level, and the rayon coverage has been overlaid onto the cultivated area coverage to give the information as presented in Figure 2.5. The figure also displays quite well the dominant crops, which are reflected in major groupings of colour. Barley-dominated crop combinations, for example, are given in shades of purple; cotton in shown light green, wheat in light yellow, sunflower in light brown, and grapes in various alternative stronger colours. Figure 2.6 is represents an extension of the approach taken in Figure 2.5 for the whole of the CAC5 Priority Area. This map is a first draft and still in preliminary format pending revisions which need to be made in three areas: - rainfed arable areas in KAZ (as shown in Fig 2.3, which was used to generate this map) are far too extensive, particularly in hill and mountain areas, and these need urgent revision; - many crop combinations are shown, and some appear to occupy only minor areas. These should be combined with other, almost similar units. For example, ‘wheat, sunflower’ and ‘sunflower, wheat’ should both be under the heading ‘wheat, oilcrops’ (areas around Shymkent actually have little sunflower and much safflower, another oilcrop, but wheat is still the dominant crop for that area). A total of 26 combinations could usefully be reduced to around 15. - legend colour scheme should follow an overall format, with most valuable crops shown in the strongest colours, least valuable in more muted colours. An overall scheme needs to be devised, for example wheat and wheat combinations could be shown in shades of light yellow to light orange; grapes and fruit in shades of purple; legumes (probably dominated by alfalfa) shades of light green; cotton in black or grey tones. Table 2.2: % of Crop Groups within Irrigated Land, UZB : Oblast Averages, and Rayons within Sirdarya Oblast Oblast Cotton Cereals Rice Vegetable Forage Garden Andijan 43,0 35,7 0,3 4,0 4,1 12,9 Bukhara 52,2 28,5 0,0 1,1 9,3 8,8


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Jizak 26,4 41,9 0,0 4,0 11,2 16,5 Kashkadarya 35,6 30,0 0,0 7,7 16,5 10,3 RKK 41,5 31,9 11,0 11,7 0,0 3,8 Namangan 41,0 35,1 1,3 6,9 0,0 15,8 Navoi 29,2 43,2 0,0 3,7 14,6 9,2 Sirdarya 47,1 38,9 2,7 3,5 5,3 2,5 Surkhandarya 44,6 38,0 0,0 0,1 2,0 15,3 Tashkent 25,8 37,0 0,7 6,8 15,1 14,6 Khorezm 45,2 17,2 0,0 4,8 26,5 6,4 Fergana 33,0 39,7 0,4 7,0 5,9 14,0 Samarkand 23,3 27,3 0,0 22,9 13,4 13,1 Obl. Mean 37,5 34,2 1,3 6,5 9,5 11,0 Obl. Max 52,2 43,2 11,0 22,9 26,5 16,5 Obl. Min 23,3 17,2 0,0 0,1 0,0 2,5 Sirdarya Obl., rayon figures (example for 1 oblast): Sirdarya Obl mean: 47,1 38,9 2,7 3,5 5,3 2,5 Bayaut 50,9 34,6 2,2 3,3 6,1 2,8 Gulistan 48,7 38,9 0,0 0,9 8,4 3,1 Mirzaabad 41,3 33,0 0,0 16,4 7,3 1,9 Akaltyn 53,2 40,3 0,0 1,4 4,3 0,8 Sardoba 51,0 45,0 0,0 1,3 1,3 1,5 Saikhunabad 45,2 39,3 7,8 0,9 5,5 1,3 Syrdarya 42,5 33,5 11,3 0,7 6,6 5,5 Khavast 43,8 46,5 0,0 3,1 3,2 3,4 All 158 rayons: Rayon mean 37,0 34,0 1,3 7,0 9,2 11,5 Rayon max 70,9 74,9 29,9 52,5 87,1 91,2

Source: Calculations & formatting by MSEC, on data of UZB-NSIU Crop statistics are available at both rayon and oblast level for both irrigated and total cropped areas throughout the CAC5, and Table 2.2 presents this for irrigated cultivated land in the different rayons in Sirdarya Oblast, together with averages for the 13 oblasts of the country. Six main crop groups are differentiated: cotton; cereals; rice; vegetables; forage (mainly leguminous crops, alfalfa and clover) and garden (the village homegarden areas, sometimes dacha plots and peri-urban areas). In terms of proportion of cropped areas, averages for the entire country are: cotton: 37,5%; cereals 34,2%; rice: 1,3%; vegetables: 6.5%; forage: 9,5%; and garden (generally growing high value crops) 11,0%. Figure 2.8: Contrasting Cropping of Irrigated Areas between Eight of the Uzbek Oblasts


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Source: Rayon and Oblast Crop Statistics from UZB-NSIU; Cartography: MSEC. Of the crop groups rice is the most localised, being grown in only 27 of the 158 rural rayons for which data is available. Unsurprisingly RKK is the area with the major concentration of rice, where it is grown in six specific rayons, making up 18-30% of the total cropped area for these rayons. Rice is also important in some rayons of Namangan and Sirdarya Oblasts, but is a very minor crop elsewhere. By contrast, cereals are grown almost everywhere: just three rayons did not have an appreciable proportion of their areas under cereals. Cotton is also very widely grown, although several of the rayons in each of the oblasts of Tashkent, Samarkand and Jizak had little or no cotton. For gardens, fodder and vegetables, much bigger variations were seen in the areas of these groups between the rayons than for cereals and cotton, with maximum rayon areas of 91%, 87% and 53% respectively for these crop groups. Cropping patterns and crop rotations are extremely important and the present danger is that with small privatised farms adequate crop rotations will not be maintained. The lack of forage crops in some areas, and the low percentages of these crops in most areas, is particularly worrying in this respect. Leguminous forage crops enrich the soil with organic matter and nitrogen, improve surface infiltration characteristics and deep subsoil permeability, and generally improve land quality status. However, they can be difficult to establish well; they need also to grow for several years to be fully effective, and they are somewhat less remunerative than most competing crops. A further and related worry is that insufficient plant nutrients are being applied to all crops as fertiliser or organic manures to compensate for crop removals, and thus there is a rundown of soil nutrient status.


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Land quality (bonitet) status is discussed in Section 5, and maps are given showing bonitet rating declines in Uzbekistan between 1990 and 2003 at a rayon level. Contrasting cropping patterns of the irrigated areas between eight of the 13 oblasts of Uzbekistan are given in Figure 2.8 illustrating these variations at oblast level. In this diagram exploded pie charts are given for four of the Syr Darya watershed oblasts on the left, and four of the Amu Darya Oblasts on the right. The top of the diagram marks the lowest elevation oblasts (and thus the ones with the worst water quality), bottom of the diagram the highest (best water quality). 2.5 VEGETATION AND ECOSYSTEMS MAPPING FOR CENTRAL ASIA: MULTI-COUNTRY PRESENTATION

2.5.1 KAZ 1:2.5m vegetation map for Central Asia

Figure 2.9: Vegetation Map for Central Asia, c. 1985 (Detail for central part of Priority Area)

Source: JPEG scan of original paper map, (KAZ-NSIU). Map projection is non-cylindrical: (NS lines hence run at different angles) A scanned copy of the Vegetation Map of Kazakhstan and Central Asia (1995), scale 1:2,5m, was provided by the KAZ-NSIU. The map (co-incidentally) covers the whole of the CAC-5 Priority Area, and although printed at a scale of 1:2,5m, it is cartographically as well as categorically detailed, so that display at about 1:1,5m scale is optimum for most non-arid areas (i.e. mean annual precipitation of 250mm or more). The map is categorically very detailed, with numbers and symbols referring to a voluminous legend. The units are fundamentally agro-ecological zones, being defined on natural vegetation communities which are grouped according to physiography (plains, piedmonts, mountains), 12 major ecological / agro-climatic zones, soil groups and soil parent materials. Figure 2.9 shows map detail at approximately original (1:2,5m) scale, covering the central part of the Priority Area, between Turkestan, Bishkek, Tashkent and the Fergana Valley. The map represents a very thorough piece of scientific work, following a uniform methodology throughout the whole area, and is the definitive work on botanical survey, with detailed information


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on natural plant communities throughout the area. The mapping has been heavily influenced by the Russian concepts of climate-soil-vegetation zonation. Anthropgenic influences are marked with subsymbols to the main map units. With respect to base material for land degradation work, the three weaknesses of this mapping are: - coverage of arable land (irrigated and rainfed) is both poor and imprecise: these areas should have been taken out of the total area and considered quite separately; - information on climate, geomorphology and soil is not given in the legend: this information would be most useful, and needs to be obtained from other sources; - map units, being vegetation communities, by nature have rather diffuse boundaries. This fact needs to be considered when overlaying this material with other (much more precise) coverages.

2.5.2 ECONET Ecosys coverage

The ECONET Ecosis coverage is essentially a digitized version of the 1:2,5m Central Asian vegetation map, but with three major differences: - mapping extends to the northern and north-eastern extremities of Kazakhstan, whereas the Central Asian vegetation map was restricted, more or less, to the Priority Area; - some protected areas are mapped at much greater detail (e.g. Aksu-Zhabagli); - legend information has been added, notably sub-division of units according to geographical areas. Table 2.3: Major Charactersitcs of ECONET Ecosis Database Name: Ecosis 2005.xls, ecosis.shp, dbf etc (ESRI shape file) [under directory:

C:\CACtopo\Topo\..] Type: Polygon file, covering the whole of the CAC-5 Established: Veg Map, 1995; updates to 2000; digitized 2004-2005;( translation March,

2009) Created by: ECONET staff (translation: MSEC staff) Sources: Veg Map for C.Asia, 1985; revisions in certain protected areas (Aksu-

Zhabagli, etc). Records (current no.):

6597

Representation on map:

Polygonshades, legend (avl) file created by MSEC, generally following veg.map

Fields (current): OBJECTID (automatic polygon no. created by ArcView) INDEX_ Unique number polygon number, 0-7516 (Numerical

field) NUM_ECO Ecosystem number, 1-424; 992-999 (Numerical field) GEOGR_VARI Geographic variant code number, (0-92) (Numerical field) ANTR_INDEX (Anthropegenic Index); Five grades: 0, 1, 2, 3, 4, 9;

(Numerical field) TEXT_ECOSI Ecosystem number & geographic variant code no., as

character field ZONE_ Major zone (one of 12) TYPE_ECO Major ecosystem type (Text) SUB_ECO Ecosystem subtype (Text) VARIANT Geographic variant description (Text) ECOSYSTEMS Vegetation associaition ZON-NUM Zone number (0 – 35) ECOREG Ecoregion NUM_REG Ecoregion number (0 – 113) GEO_NAME (used only for some areas) REG_BALL 1-7 Shape_Length Length of perimeter of polygon Shape_Area Area of polygon Fields (possible i. Mean annual precipitation range (separate fields: upper & lower limits);


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future): ii. Elevation range (separate fields: upper & lower limits); iii. Slope range (separate fields: upper & lower limits); iv. Major soil characteristics.

Remarks: Areas of arable land use show generalized boundaries.. The ECONET ecosis mapping would potentially be very useful for land capability and land suitability characterization of land units (small mappable homogeneous areas) when linked with other physical data. This would include mean annual precipitation ranges, elevation (temperature zone) ranges, major soil properties, characteristics of soil parent materials, and slope ranges. Further extension of the ecosis database file (i.e. characteristics of the map legend) would thus be a potential major area for future work for the SLMIS. The existing uniform mapping methodology, and the ecosystems / vegetation communities information, would both greatly facilitate this work. The mapping is also useful as a basis for quantification of above-ground biomass, important in C-accounting for both vegetation and soils. 2.6 ADMINISTRATIVE AREAS AND INFRASTRUCTURE OF CENTRAL ASIA: MULTI-COUNTRY

PRESENTATION

Mapping covering administrative areas and infrastructure for the CAC-5 include ECONET basemap layers and printed, generally Soviet-era, topomaps at scales 1:100.000, 1:200.000, 1:500.000 and 1:1m. Mapping at scales more detailed than 1:100.000 is under security restriction in most FSU countries, although derived maps (e.g. vegetation, soils) using detailed basemap material may not necessarily be restricted (e.g. Figure 6.2). ECONET basemap layers were invariably derived from 1:1m topo maps. The more important of the many individual layers are as follows: - Oblast boundaries: (file Politiko_administrativnye_granicy .shp, dbf..) Line file, 1485 records. Boundaries between adjacent oblasts. Fields include: Class_id, Shape_leng. - Oblast areas: (file Politiko_administrativnye_edinicy .shp, dbf..) Polygon file, 757 records. Includes many oblasts in adjacent areas in Russia and other republics (hence large number). Fields include: Objectid, Name_cntr, Name_adm1 (oblast name), Name_adm2 (alternative oblast name), Shape_leng, Shape_area. Final field ‘No.’ added by project in order to denote oblasts within Priority Area (1=inside area; 0=outside area). - Rayon areas and boundaries: (file Socio-14-10-05.shp, dbf..) Polygon file, 497 records. Fields include Rayon name, Admin. Centre, population etc (see Table 2.1 for full details). Names translated by MSEC. On SLMIS shown in two forms: i. polygon file, with polygons shaded according to population density, livestock density, proportion of arable land in total land area, etc; ii. Outline of polygons (showing rayon boundaries only), with transparent polygonshade revealing underlying information. Inspection shows that some of these boundaries are not accurate (positional errors of up to 1km being observed), and thus checking and re-capturing of boundary positions are thus required. - Towns and cities: (file Naselenye_punkty_polygony.shp, dbf..). Polygon file, 1884 records. Fields: Class_id, Name, Name2, Adm_sts (useful data), Pop_range (useful data, classifying village size), Population (no data yet), Function_ (little data), Condn (v. Little data), Shape_leng, Shape_area. Names translated by MSEC. On SLMIS shown as bright red polygons. Inspection with detailed GE imagery shows that most of these areas are urban (but often with considerable tree-cover, most notably Almaty) but a considerable proportion of the area would also qualify as ‘peri-urban areas’ or ‘village homegarden areas’. - Village settlements: (file Naselenye_punkty_tochki.shp, dbf..). Point file, 22.668 records. Fields: Class_id, Name, Adm_sts (useful data), Pop_range (useful data, classifying village size), Population (no data yet), Function_ (little data), Condn (v. Little data). Names translated by MSEC. On SLMIS shown as gradated bright red dots (various sizes based on Pop_range, size


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proportional to population). Inspection of more detailed (1:200.000) mapping, and detailed GE imagery shows that not all settlements are included, although most are. Also most of these would qualify as ‘village homegarden areas’, with average individual plot size of around 1/3ha. - Roads: (file Avtodorogi.shp, dbf..) Line file, 38.533 records. Fields: Class_id, Road_sts (road status: classes 0, 1, 2, 3, 4); Road_id (Highway number, for roads of status 1-4), Cover_t (little data), Shape_leng, and various other fields, none of which show data. - Mountain passes (file: Soorujeniya_pri_avtodorogah.shp, dbf..) Point file, 529 records. Fields: Class_id, Name, Elev, Period (months of the year during which passes are open); - Railways: (file .shp, dbf..) - Railway stations: (file Soorujeniya_pri_zheleznyh_dorogah.shp, dbf..) Point file, 1674 records. Fields Class_id, Name; - High-voltage electricity grid: (file Linii_svyazi_i_elektroperedach.shp, dbf..). Line file, 175 records. Fields: Class-id, voltage, N-items (little data), Width (little data), caption (little data), Shape_leng. File does not appear to be complete: coverage appears good for TUK and southern UZB, but not good elsewhere. Updating and inclusion of lines down to 11kV is important: provision of mains electricity is a major boost for development activities. Also trans-boundary power sharing will facilitate running of hydropower dams more to suit irrigation requirements. Given the relatively good ECONET basemap coverages, requirement for further topomapping at more detailed scales was not considered a high priority during the present phase of the project, (other than for location of municipalities centres - see Chapter 6 - and for more detailed coverages on CACILM National Project Areas). However, a large volume of scanned topomap material was made available at 1:500.000 scale by the TUK-NSIU (Figure 2.10). This mapping in JPEG format comprised 21 sheets, which also covered large areas of surrounding countries, notably southern UZB and western TAJ. Dates of this mapping span the years 1963-1989: generally original surveys would have been undertaken in the 1960s, with updates on much of the infrastructure undertaken in the 1980s. This material shows greater detail and much more precise boundaries (e.g. rayon boundaries, which are not accurately captured with some of the ECONET coverage). This material will thus be very valuable for any future upgrade in the SLMIS basemap layers. Figure 2.10: Scanned Toposheet coverage, scales 1:200.000 – 1:1m


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Source: SLMIS output. Hydrology, major cities and oblast boundaries from ECONET basemap coverages; topomapsheet areas, major watershed boundaries from MSEC-generated coverages. Scanned mapsheets at 1:500.000 scale from TUK-NSIU; at 1:200.000 scale from KAZ-EMIMS. Scanned 1:200.000 topomaps were also available from the KAZ-EMIMS study for 12 sheets covering the more populous SE part of SKO and adjacent parts of W. Zhambul oblast (Fig. 2.10). This mapping shows very much more detail and precision than the ECONET basemap layers. Again, this mapping would be of immediate application for any upgrade of the SLMIS basemap material, and would be particularly valuable for CACILM National Projects covering extended areas. 2.7 UPDATING AND ENRICHING THE CACILM LAND MANAGEMENT INFORMATION

A good start has been made on creating a CACILM multi-country coverage on Land Use and dominant crop groups, but both of these coverages need some further refinement. This includes corrections for over-mapping of arable lands in the Kazakh part of these maps, and also better and more precise definition of unused desert lands in comparison to low-productivity rangelands. Remote sensing, using imagery for a typical-precipitation year, and specifically use of MODIS-250 NDVImax would be most useful in this definition. NDVImax needs to be correlated with actual pasture measurements made in the field during the late March-early June period so that this can be done. Pasture productivity at 500kgDM / ha / year and 2000kgDM / ha / year would separate the three classes. Low productivity pasture would be separated from unused (desert) lands at around 100-200kgDM / ha / year productivity levels. The existing cropping mapping needs further checking against the Statistics Agency crop areas data, and a reorientation of the legend needs to be undertaken, with revised colour coding (e.g. cotton-dominated areas in shades of grey/black; cereals in dull yellows, oilseeds in bright yellow, rice in bright green, high-value crops in bright violet colours etc). Ideally, this land use and cropping mapping should be thoroughly rechecked using multi-source imagery (LANDSAT ETM+ as the base, at 1:100.000scale, routinely cross-checked against MODIS-250 NDVImax and NDVImean, and further checked against detailed GE imagery in some key sample sites where this imagery is available). A more precise coverage, finally printable at 1:500.000 scale should be aimed for. This mapping ideally should follow the main existing CACILM Land Use Classes, but further sub-classes could be added as follows: - irrigated arable: degraded and low intensity use areas (see Fig 4.8 covering irrigation areas near Turkestan, SKO) to be separated from moderate and high-intensity areas; - rainfed arable, degraded and marginal areas to be differentiated; - pasture areas: high productivity, moderate productivity and low productivity areas (defined by MODIS NDVImax and mean values); - peri-urban and village homegarden areas (see Figs 4.15 and 4.16) – these should be mappable on ETM+ imagery at 1:100.000, and, if shown in bright colour, then printable and readable at 1:500.000 scale. Although ECONET basemap layers have been generally fairly accurate and very helpful for the current work at around 1:1m-1:2m scale, an immediate upgrade on checking and correction of rayon boundaries needs to be made. Other, less urgent requirements would include: - georeferencing of existing scanned topo (and other) mapping (so that these could act as a base on which other layers could be superimposed); - updating the high-voltage electricity grid coverage; - correlating the village settlement file (Naselenye_punkty_tochki.shp, dbf..) with the listing of municipality centres, and adding a further field to this database to denote these centres; - for areas with a concentration of arable land (large contiguous blocks with, say, >35% arable) upgrade the basemap layers with 1:500.000- (or even 1:200.000-) scale material.


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The 1:2,5m CAC-5 vegetation map and the associated ECONET ecosis coverage could be upgraded by addition of climate, geology, geomorphology, and soil information to the legend which should further be revised (particularly in the case of the ecosis mapping) to make it simpler and clearer. The resultant work would then be of great value for potential land use recommendations and zoning. Any further work with FAO-LADA needs better contractual stipulations on time commitments that FAO-LADA staff would make in the region. The staff commitment actually made under the existing (flexible) contract was insufficient by a factor of 10 for the work contemplated. There are also some fundament problems with use of FAO-LADA methodology when applied to the local situation in the CAC-5, and these become more critical when the Global material (printable at 1:8m scale) is upgraded to national level (with intended coverage at around 1:1m scale). These problems aside, the overall concepts of FAO-LADA appear sound, although the specific techniques required necessitate both considerable on-the-job training and close follow up which were in very short supply in this phase of the project.


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3 SOCIO-ECONOMIC & LAND PRODUCTIVITY DATA

3.1 COLLECTION, INFORMATION FLOWS, AND PUBLICATIONS

National Statistics Agencies, usually through their offices at Oblast level, have continued to collect official statistics at rayon level on a wide range of data. Statistics are collected and collated annually, and officially published by around each August for the preceding year. Printed statistical reports are available for purchase (generally at a reasonable price) by the public. Older printed reports dating back well into the Soviet period are also available for purchase. Since around 2004 soft copy versions of the statistical reports are also available for purchase (albeit at a higher price). Statistics available at rayon level in general include the following data:

- demographic: population, total and rural, births, marriages, divorces, deaths, natural increase;

- income: total and rural income; poverty levels; - employment / unemployment; - livestock: cattle, sheep & goats, horses, camels, pigs, poultry; - land use: total areas; agricultural; pasture; rainfed arable; irrigated arable; - operational status of land: areas of private farms; homestead gardens; agricultural

enterprises (various types, including cooperatives); protected areas (various categories); state forest fund;

- crop areas (sown and harvested), breakdown according to farms, enterprises and homestead gardens: cereals & pulses (breakdown on specific crop – wheat, barley, maize, rice, etc); fodder (alfalfa, clover, annual grasses / hayland, etc); oilseeds (safflower, sunflower, soybeans); cotton; potatoes; sugarbeet; tobacco; vegetables; melons & gourds; berries & fruits, vines; tobacco;

- crop production (tonnes) for all of the above; - crop yields (centners / ha) for all of the above; - further breakdown on total cultivated area statistics, and irrigation area statistics

(deductions can thus be made on rainfed arable statistics). Statistics are collected on a sample area basis (not a 100% census), but procedures at least meet EU statistical requirements for agricultural statistical data. The EMIMS-SLU study looked at SKO statistics for various dates between 1987 and 2006 and was favourably impressed by apparent accuracy and consistency of data, the only problems being:

- livestock statistics were generally deemed to be less reliable than the land and crop statistics (this was for various reasons, not least because the animals move from area to area and that the owners may be in one area, and their animals may be grazing some distance away);

- quality of printing of some of the Soviet-era statistical reports was poor, so that 3s, 8s and 0s could sometimes be confused;

- calculation of totals and checking against published totals was necessary to check for possible errors;

- decimals were occasionally in the wrong place: on yields, there was common confusion between centners/ha and tonnes/ha (a factor of 10 applying here)

- no distinction was made between winter and spring cereals (important because yield potential of the two is vastly different, and the proportion of the two can differ from year to year).

For SKO, since 2005 statistics have also been available at municipality (selskiy okrug) level. This is potentially extremely useful for land degradation studies, as a land degradation hotspot area can represent a major fraction of one municipality’s land (but a very minor fraction of a total rayon area). However, municipality statistics do not have a breakdown according to irrigated / rainfed arable cropping, nor do they separate cotton (included as ‘other crops’ together with fodder, etc).


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Collection of primary statistical data at rayon level for the whole of the Priority Area was requested from the NSIUs for their respective countries. That request has largely been met by KAZ, but not by the other NSIUs. UZB overall have presented good data, but that has been more at oblast level, and of data in mapped form (taken from other available publications) rather than vital primary data. Thus the best single source of data for the entire Priority Area (indeed, the entire CAC5 area) remains the ECONET file Socio-14-01-05.dbf/shp, linked to the rayon polygon coverage, 497 rayon polygons covering the entire CAC5 area. Statistical data given in that file was from the year 2000 (or 2001). Characteristics of that file are given and discussed in Table 2.1 above. KAZ data, both from KAZ-NSIU and from the KAZ EMIMS Study (2007) (both published and unpublished) is presented in the following sections. 3.2 REQUIREMENT FOR THREE SEPARATE BASELINE YEARS: 2006/07; 2000; 1990

As discussed above, taking a single baseline year for land degradation work would present a very distorted picture. The rural economies of the five countries suffered catastrophic falls over the period 1990-2000 as the countries went from a centralized command system to a free-market system. These changes dwarf any long-term changes due to land degradation or restoration trends. Three baselines are thus required: - end of Soviet period (ideally 1990); - end of readjustment period (ideally 2000); - most recent typical year (ideally 2007, or 2006). Both 1990 and 2000 represent dates for which freely-available LANDSAT TM imagery is obtainable. However, both were rather dry years over most of the Priority Area. 2008 was a 1-in-20 dry year (and thus far from typical for a baseline). Conversely, 2009 would appear to be a very wet - perhaps a 1-in-20 wet year. Both 2006 and 2007 were average years: 2007 would thus appear to be the optimum year for the recent baseline. 3.3 SOCIO-ECONOMIC AND DEMOGRAPHIC DATA

3.3.1 Multicountry Data

Total population density for the Year 2000, viewed on a rayon basis, is given in Figure 3.1. Units here are number of persons per thousand hectares of total land area. The figure shows that more than one third of the area has extremely low population densities (less than 20 persons/1000ha, or less than 2 persons per km²). A further third has a very low population density – 20-50persons/1000ha. Only in the rayons where irrigation is a major feature in the total land use is the population density above 500persons/1000ha, i.e. >50persons/km², although some of these areas (especially in the Fergana valley) have densities over 3000persons/1000ha. The rayons with the highest population density showed figures of over 10000 persons/1000ha, but for these rayons agriculture and the rural economy would comprise only a small proportion of the total economy. For rayons where rainfed agriculture is the predominant source of primary income, population densities are in the range 200-500persons/1000ha.


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Figure 3.1: CAC5 Priority Area: Total Population Density, Year 2000, Rayon Basis (Persons / 1000ha)

Source: SLMIS: Data from ECONET, cross-checked with NSIU data. Rural population density for the Year 2000, viewed on a rayon basis, is given in Figure 3.2. Units here are number of ‘rural persons’ per thousand hectares of total land area. Overall similarities with Figure 3.1 are notable, indicating that for most areas rural population is more important than urban population. However, rayons where mining towns are notable (periphery of Fergana Valley, Central Wastern Uzbekistan, Turkestan rayon, KAZ) there are significant differences between the two figures. However, where irrigated agriculture is the major feature of the economy, overall rural population densities of around 1000-5000 / 1000ha seem to prevail, whereas for rainfed agricultural rayons the figure is 200-500 / 1000ha. Figure 3.2: CAC5 Priority Area: Rural Population Density, Year 2000, Rayon Basis (Persons / 1000ha)


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Source: SLMIS: Data from ECONET, cross-checked with NSIU data. Figure 3.3: CAC5 Priority Area: Rural Population per 000ha Cultivated Land, Year 2000, Rayon Basis.

Source: SLMIS: Data from ECONET (plus new fields from MSEC), cross-checked with NSIU data. The density of rural population per ha of cultivated land is shown in Figure 3.3. Here rayons with a predominance of marginal rainfed cultivated land (e.g. Baidebek, SKO) show a density of 400-700 / 1000ha; better rainfed land with some irrigation (Tulkibas, Tolebi) show 1000-2000 / 1000ha, while areas noted for a mixture of rainfed and irrigated land, with a major component of higher value crops (Sairam) show densities of 2000-5000 / 1000ha. For UZB, where irrigated cultivated land


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predominates, densities of rural population per ha of cultivated land are generally in excess of 5000 / 1000ha (i.e. >5persons / ha) which is a high density for a relatively well developed country. But the highest densities are seen for the more remote and mountainous areas of Tajikistan where rural population densities of more than 10000 / 1000ha of cultivated land predominate over large areas.

3.3.2 Population and Income Data for Kazakhstan

Population and income data for the four priority oblasts of Kazakhstan are given in Table 3.1. Total population figures, given for 1991, 1999, 2007 and 2008, show a very variable picture, with overall static or slightly declining populations in Almaty and Zhambul Oblasts, a slightly rising population in Kyzyl Orda (8% rise in 17 years) and an appreciably rising population in SKO (23% rise). Within SKO populations rose most steeply in Shardara (26% increase) and Saryagash (24,6% increase): the former showed recent irrigation development; the latter is effectively on the northern limits of the Tashkent conurbation. Conversely, populations rose by only 10,1% in Kentau (declines in zinc and lead mining activities) and 12,1% in Baidebek (very marginal rainfed arable land with big water erosion problems). Between 1991 and 1999, over the economic adjustment period, populations actually fell by 6% in Almaty and 5% in Zhambul, but rose over this period by 2.6% in Kyzyl Orda and 4.6% in SKO. Rise of population in SKO can be explained both by natural increase, and by immigration from nearby countries – especially workers arriving from Uzbekistan, but also whole communities returning to their former villages after the break-up of the Soviet Union (e.g Fogelenko, whose inhabitants moved from Siberia, to which they were expelled during the 1930s). Current natural rates of increase in population vary from 1,1% per annum for Almaty, 1,6% for Zhambul, to 1,77% for Kyzyl Orda (figures for SKO are not obtainable). Birth rates for 2007 (as a % of total population) vary from 2,1% per annum for Almaty, 2,53% for Zhambul, 2.57% for Kyzyl Orda to 2,99% for SKO. Within Almaty Oblast, birth rates are as low as 1,81% for Sarkandsky Rayon (declining population area) and 1,93% for Taldykurgan (heavily urbanized). Within SKO, maximum birthrates are seen in Turkestan (3.27%). Although birth rates generally appear to be related to urbanization: - the higher the degree of urbanization, the lower the birth rate – this does not appear to be the case in many Kazak-dominated urban areas, as Turkestan, Symkent, Taraz and Kyzyl Orda all show relatively high rates for 2007. Changes of birth rates over the period 1991 – 2007 are also notable, very sharp declines having been experienced over the period 1991 to 1999. Birth rates by 2007 have subsequently risen to just below 1991 levels for Almaty and Kyzyl Orda, and to just above 1991 levels for Zhamyl Oblast. For SKO birthrates for 2007 considerably exceeded those for 1991. Infant mortality (for infants of <12months) again shows big variations throughout the area, although rates are well above normal rates in developed countries. In Almaty Oblast rates are 0.91%; for SKO they are 1,9% and for Shymkent they are 3,6% - a shockingly high figure for urban area of a potentially rich country, reflecting the poor state of health facilities in relation to overall income and state of material development of the country. Average monthly income figures are available for both Almaty Oblast and SKO for the years 2003, 2004, and 2007. Minimum subsistence level figures are also available for the same locations and same periods. Average monthly income for 2007 was equivalent to US$138 in Almaty Oblast and $132,7 in SKO. Within both oblasts, however, average incomes varied considerably from rayon to rayon: $84,2 (Zhambyl rayon) to $203,1 (Kapchagay) in Almaty, and $99,5 (Saryagash and Kazygurt) to $263,7 (Sozak) in SKO. In relation to minimum subsistence, these figures are 76% above subsistence for Almaty Oblast and 86% above the subsistence figures for SKO, with the variation between rayons being 11% to 140% for Almaty and 39% to 269% for SKO. Separate statistics on income of rural workers is available for Kyzyl Orda Oblast. These show actual income paid to people in work rather than average monthly income for the entire population,


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and thus are somewhat higher than the above figures, with the average for the oblast standing at $154,2 for 2007. Within the oblast, however, there was a considerable variation, the minimum being for Aral rayon at $105,2 while the maximum was for Kazalinsk at $232,9.


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Table 3.1: KAZ: Four Priority Oblasts: Population and Income, 1991-2008. 1991 1999 2007 2008 2008 1991 1991 1999 1999 2007 2007 1991 1999 2007 2007 2007 1991 1999 2007 2003 2004 2007 2003 2004 2007 2003 2004 2007

Population,Total (000) Density Urban

Rural

Urban

Rural

Urban

Rural ______Births______

Deaths NatlIncr ___Infant Deaths___

Avg Income (KZT) / Mo SubsistenceMinimum/Mo

RatioIncome / Susbsistence

ALMATY OBL 1655.3 1556.5 1620.7 1643.3 7.3 488.2 1132.5 38656 23005 34220 15826 18394 934 368 312 6594 8193 16838 4973 5189 9556 1.33 1.58 1.76 Aksu 47.8 45.3 37.0 36.3 2.9 10.6 26.4 1713 711 819 420 399 51 7 9 5344 6633 14048 4489 5111 8514 1.19 1.30 1.65 Alakol 83.7 79.9 76.2 76.3 3.2 18.6 57.6 2078 1156 1395 691 704 44 19 14 4527 5679 14967 4647 5493 9325 0.97 1.03 1.61 Balkhash 31.3 31.0 30.2 30.1 0.8 0.0 30.2 1151 499 596 256 340 22 5 4 3948 4552 12602 4838 5044 9308 0.82 0.90 1.35 Enbekshikazahsky 210.2 202.5 207.0 211.5 25.5 31.5 175.5 4757 2884 4538 1908 2630 120 66 39 5875 7080 13421 5126 5423 10130 1.15 1.31 1.32 Eskeldinsky 51.7 48.4 50.2 50.1 11.7 15.0 35.2 1408 665 957 509 448 29 7 6 4872 5435 11328 4385 4685 9086 1.11 1.16 1.25 Zhambyl 107.6 105.7 116.3 120.0 6.2 2.8 113.5 2641 1470 2511 1035 1476 49 21 19 4159 5197 10272 4748 5153 9261 0.88 1.01 1.11 Ili 131.0 121.5 141.3 145.5 18.7 53.6 87.7 2250 1801 3353 1522 1831 49 29 33 8875 13383 18992 5099 5447 10252 1.74 2.46 1.85 Karasai 162.9 153.2 160.0 160.2 70.9 34.3 125.7 2862 2100 3909 1719 2190 70 29 36 8181 11170 20382 5239 5501 9416 1.56 2.03 2.16 Karatalsky 51.1 46.7 49.3 48.9 2.0 24.4 24.9 1122 629 767 475 292 21 7 3 4624 5505 15236 4649 4752 7894 0.99 1.16 1.93 Kerbulak 56.3 53.6 51.1 51.4 4.5 12.0 39.1 1663 902 1008 444 564 37 11 10 4695 4960 12610 4904 4902 8974 0.96 1.01 1.41 Koksusky 42.6 40.2 39.4 39.7 5.6 12.1 27.3 1022 594 774 372 402 22 4 6 4642 5478 14144 4633 4837 8657 1.00 1.13 1.63 Panfilovsky 119.8 112.9 117.7 118.5 11.2 33.1 84.6 3125 2126 2772 830 1942 108 45 32 4376 4722 10722 4640 4877 9407 0.94 0.97 1.14 Raiymbeksky 84.3 83.2 79.7 78.7 5.5 1.4 78.3 2941 1520 1535 634 901 87 24 20 3641 4366 11487 4952 5196 9940 0.74 0.84 1.16 Sarkandsky 51.6 47.9 42.6 42.1 1.7 16.6 26.0 1389 757 772 549 223 33 15 7 3808 4191 11246 4594 4807 8716 0.83 0.87 1.29 Talgar 141.7 132.6 146.7 150.7 40.1 47.9 98.8 2706 1645 2930 1519 1411 64 21 29 6505 7436 17933 4967 5043 10311 1.31 1.47 1.74 Uyghur 63.7 63.0 63.8 64.2 7.3 0.0 63.8 1728 1120 1412 464 948 39 20 13 3577 4459 11645 4660 4983 9660 0.77 0.89 1.21 Kapchagay 52.5 46.3 51.7 53.1 14.5 37.4 14.3 773 637 1125 601 524 22 14 5 9472 11847 25513 4874 5045 9440 1.94 2.35 2.70 Taldykurgan 135.7 117.5 134.3 138.7 1981.6 110.7 23.6 2727 1521 2673 1456 1217 56 22 25 8736 12330 22633 4969 5121 9450 1.76 2.41 2.40 Tekeli 29.8 25.1 26.2 27.3 455.0 26.2 0.0 600 268 374 422 -48 11 2 2 7622 7772 15080 4666 4907 9065 1.63 1.58 1.66 ZHAMBYL OBL 1041.9 988.8 1009.2 1018.8 7.1 499.4 542.5 452.0 536.8 454.0 555.2 24765 16048 25505 8850 16655 791 366 350 Bayzaksky 62.7 68.7 78.8 80.2 17.8 62.7 0.0 0.0 68.7 0.0 78.8 1621 1193 1992 541 1451 54 18 18 Zhambyl 69.2 70.0 76.2 77.4 18.0 69.2 0.0 0.0 70.0 0.0 76.2 1903 1358 2124 590 1534 63 23 23 Zhualynsky 46.4 48.4 51.5 51.5 12.3 46.4 0.0 0.0 48.4 0.0 51.5 1248 1088 1298 382 916 30 15 11 Kordaysky 107.1 104.6 109.9 111.3 12.4 93.5 13.6 5.3 99.3 5.9 104.0 2627 2044 3512 1016 2496 71 30 43 T. Ryskulov 61.3 61.5 61.1 61.3 6.7 51.2 10.1 9.9 51.6 9.3 51.8 1704 1129 1671 502 1169 41 21 22 Merkenskiy 76.5 73.7 72.6 73.4 10.3 65.1 11.4 5.1 68.6 5.1 67.5 2112 1183 1918 677 1241 51 16 19 Moyynkumsky 51.3 34.6 32.1 32.1 0.6 34.5 16.8 8.5 26.1 7.1 25.0 1273 561 716 268 448 35 12 8 Sarysu 80.7 48.6 44.3 44.6 1.4 27.4 53.3 25.9 22.7 23.4 20.9 839 957 1058 293 765 46 17 12 Talas 76.8 53.4 50.9 50.7 4.2 76.8 45.0 29.0 24.4 28.7 22.2 945 958 1065 427 638 37 12 18 Shusky 101.4 95.2 93.3 93.8 7.8 60.7 40.7 38.2 57.0 36.0 57.3 1644 1587 2302 907 1395 39 34 31 Taraz 308.5 330.1 338.5 342.5 3425.0 308.5 0.0 330.1 0.0 338.5 0.0 6082 3990 7849 3247 4602 253 168 145 Rural Average Income / Month 2003 2004 2005 2006 2007 KYZYLORDA OBL 580.1 595.5 625.1 632.2 2.8 372.1 253.0 17825 13527 16156 4920 11236 582 410 331 8685 10430 13253 15995 18827 Aral 70.4 68.3 71.5 72.2 1.3 43.7 27.8 2389 1605 1810 643 1167 92 54 41 5565 6368 8076 11019 12832 Zhalagashsky 37.0 39.5 41.4 41.6 1.8 14.6 26.8 1306 847 837 304 533 42 27 23 8966 10822 12529 15042 17205 Zhanakorgansky 62.4 67.5 71.3 72.4 4.6 21.9 49.4 2002 1640 1915 416 1499 94 56 46 8527 10757 16662 24423 24101 Kazalinsk 71.5 68.7 72.6 73.3 1.9 42.2 30.4 2293 1866 1661 591 1070 84 52 41 6853 6847 19382 24846 28417 Karmakshinsky 47.5 45.4 48.9 49.4 1.6 26.6 22.3 1262 1056 1049 393 656 42 22 24 8895 10716 11995 13948 15668 Syrdarya 75.2 39.1 39.7 39.8 1.4 8.6 31.1 2406 864 881 292 589 79 27 20 8839 9752 12847 15351 18885 Shieliysky 62.0 73.7 75.5 75.6 2.3 27.7 47.8 1995 1829 1809 516 1293 51 48 28 7203 11064 13864 16861 17904 Kyzylorda 154.1 193.5 204.2 207.9 85.1 186.8 17.4 4172 3820 6194 1765 4429 98 124 108 10376 12488 17634 21444 24787 1991 1999 2007 2008 2008 2007 2007 1991 1999 2007 1991 1999 2007 2003 2004 2007 2003 2004 2007 2003 2004 2007

Population,Total Density Urban

Rural ______Births______ ___Infant Deaths___

Avg Income (KZT) / Mo SubsistenceMinimum/Mo

Ratio Income/Susbsistence

SOUTH-KAZAKHSTAN 1889.3 1975.6 2282.5 2331.5 19.9 875.1 1407.4 57420 45232 68287 1758 881 1300 5288 6433 16184 4258 4691 8681 1.24 1.37 1.86


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Shymkent 400.5 419.7 544.3 554.6 1848.7 544.3 0.0 9571 7258 15189 280 162 548 7956 9944 22090 4258 4691 8845 1.87 2.12 2.50 Arys 56.7 59.1 64.3 65.0 10.3 37.7 26.6 1887 1506 1948 59 23 28 5373 7196 16530 4331 4381 8663 1.24 1.64 1.91 Kentau 79.4 82.5 84.6 85.8 143.0 72.0 12.6 2491 1323 2039 55 48 35 3756 4702 12940 4403 4640 9084 0.85 1.01 1.42 Turkestan 164.4 172.6 193.5 197.7 26.7 113.0 80.5 4128 4324 6326 131 85 104 4320 5424 12647 4350 4554 8373 0.99 1.19 1.51 Baydibek 49.1 50.5 54.7 55.1 7.7 0.0 54.7 1786 1153 1376 82 18 11 3697 4333 12376 4428 4464 8885 0.83 0.97 1.39 Kazygurt 83.1 87.6 100.8 102.5 25.0 0.0 100.8 2892 2280 2982 91 42 43 3379 4281 12155 4306 4320 8333 0.78 0.99 1.46 Maktaaral 226.7 236.4 268.7 272.4 151.3 29.5 239.2 7713 6415 8096 216 101 97 5591 6649 12824 4692 4930 8832 1.19 1.35 1.45 Ordabasy 77.5 80.6 92.9 96.6 35.8 0.0 92.9 2534 1815 2657 83 35 37 3771 4419 12444 4239 4321 8512 0.89 1.02 1.46 Otyrar 51.0 54.0 56.4 56.8 3.1 0.0 56.4 2012 1249 1388 63 24 24 3887 4747 13260 4370 4584 8614 0.89 1.04 1.54 Sairam 207.8 216.8 240.7 249.1 147.0 0.0 240.7 6120 5864 8526 198 127 112 4178 4817 12591 4429 4539 8819 0.94 1.06 1.43 Saryagash 204.0 212.5 246.7 254.1 33.0 26.8 219.9 6902 5416 8499 235 96 126 3584 4435 12142 4213 4673 8275 0.85 0.95 1.47 Sozak 45.4 47.2 52.1 53.2 1.3 0.0 52.1 1754 1137 1590 51 21 27 7579 10069 32171 4465 4432 8716 1.70 2.27 3.69 Tolebi 100.1 105.6 112.8 114.9 37.0 21.4 91.4 3082 2223 3228 89 48 47 4979 5549 12372 8656 - - 1.43 Tulkibas 83.1 86.3 95.4 97.5 42.4 0.0 95.4 2243 1787 2262 59 31 29 4234 5077 12538 4481 4433 8727 0.94 1.15 1.44 Shardara 60.5 64.2 74.6 76.2 5.9 30.3 44.3 2305 1482 2181 66 20 32 4924 5764 12864 4493 4709 8631 1.10 1.22 1.49 Note: Most Data from KAZ-NSIU of KAZ Statistics Agency, checked, corrected and reformated by MSEC.


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Table 3.2: KAZ: Almaty Oblast: Crop Areas, Production and Yields in 1991, 1999 and 2007.

CropArea (000ha) Yr2000 Yr1999 Yr99/00 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007

Arable TtlCrop OtherCrps Cereals, incl.Maize Wheat Barley R i c e Maize(grain) Other Cereals Sugar Beet Soyabeans Sunflower Tobacco Potatoes Vegetables

ALMATY OBL 1035.554 632.801 402.753 870.4 552.1 494.4 303.7 328.4 229.4 465.0 167.2 185.8 16.5 10.7 13.6 65.9 39.8 61.1 19.3 6 .0 4 .5 23.1 14.7 13.2 15.0 0 .3 48.7 3 .1 15.6 43.3 2 .9 3 .8 3 .2 20.2 29.5 34.7 14.4 16.8 25.0

Aksu 50.941 35.400 1 5 . 5 4 1 75.1 29.6 30 .2 46.7 23.0 18.3 28.9 6 . 5 11.9 0 . 2 0 . 1 -0.7 0 .0 0 .0 4 . 4 3 . 5 3 . 3 0 .2 0 .0 3 .1 0 .5 1 .0 4 .2 0 .0 0 . 7 1 . 1 1 . 6 0 . 2 0 . 2 0 . 4

Alakol 69.355 45.300 2 4 . 0 5 5 70.9 40.2 36 .9 33.8 34.3 19.1 32.9 5 . 1 16.3 1 . 9 0 . 1 0 . 9 2 .3 0 .7 0 .6 5 . 8 0 . 5 1 .4 0 .0 8 .9 0 .5 2 .8 12.1 0 .0 1 . 5 1 . 4 1 . 6 0 . 7 0 . 4 0 . 5

Balkhash 28.747 16.500 1 2 . 2 4 7 20.6 15.5 18 .0 7 . 3 3 . 0 8 . 3 0 . 9 5 . 4 12.3 7 .3 9 .6 0 .0 0 .0 0 .0 0 . 0 0 .0 0 .2 0 .1 0 .0 0 . 1 0 . 4 0 . 6 0 . 2 0 . 4 0 . 6

Enbekshikazahsky 92.427 58.309 3 4 . 1 1 8 39.2 42.7 44 .3 10.3 21.2 16.9 15.1 7 . 4 6 . 8 11.8 13.5 20.2 2 .0 0 .6 0 .4 0 . 4 0 . 2 0 . 2 2 .0 0 .0 7 .4 0 .7 1 .8 2 .6 2 .9 3 .7 3 .1 2 . 4 5 . 2 5 . 2 2 . 8 4 . 7 6 . 9

Eskeldinsky 62.018 36.170 2 5 . 8 4 8 51.4 28.9 26 .6 19.1 18.5 12.7 16.7 9 . 6 12.7 1 . 0 0 . 4 0 . 8 14.6 0 .4 0 .4 2 . 8 1 . 8 1 . 8 1 .4 0 .2 6 .6 0 .6 2 .5 1 .1 0 .0 2 . 0 2 . 0 2 . 0 0 . 6 0 . 8 0 . 9

Zhambyl 153.164 74.000 7 9 . 1 6 4 119.5 72.5 47 .2 32.3 37.6 28.8 85.4 34 .4 18.3 1 . 4 0 .4 0 .5 0 .1 0 . 4 0 . 3 0 . 1 0 .2 0 .0 1 .9 0 .1 0 .0 0 .1 0 . 4 0 . 6 1 . 6 0 . 4 0 . 5 0 . 9

Ili 67.892 59.700 8 . 1 9 2 65.0 55.0 35 .4 21.5 25.9 14.9 41.8 27 .8 15.2 0 . 9 1 . 0 5 . 0 0 .8 0 .3 0 .3 1 . 1 1 . 0 0 . 6 1 .2 0 .0 0 .7 0 .1 0 .5 0 .3 0 . 0 0 . 9 2 . 5 1 . 4 0 . 6 0 . 7 3 . 0

Karasai 49.683 29.912 1 9 . 7 7 1 23.8 25.0 22 .4 6 . 9 13.7 15.0 14.7 10 .9 6 . 0 0 . 8 1 . 0 1 .4 0 .4 0 .4 0 . 3 0 . 8 0 . 2 0 .9 0 .0 0 .7 0 .0 0 .0 1 . 0 2 . 9 3 . 9 1 . 7 1 . 2 2 . 0

Karatalsky 24.337 13.031 1 1 . 3 0 6 27.1 8 . 8 9 . 5 6 . 2 4 . 0 3 . 2 14.6 1 . 4 2 . 0 4 .2 3 .4 4 .0 1 . 9 0 . 3 0 .2 0 .0 0 .0 0 . 5 0 . 7 1 . 2 1 .6 0 .0 0 .9 1 .3 0 .9 0 .0 0 . 5 1 . 0 1 . 1 1 . 3 1 . 2 1 . 5

Kerbulak 134.981 72.400 6 2 . 5 8 1 91.6 69.9 79 .1 22.0 34.3 28.9 69.9 34 .3 49.3 1 . 1 0 . 5 0 . 4 -1.4 0 .8 0 .5 0 . 0 0 .0 0 .1 0 .1 0 .0 2 . 8 2 . 1 2 . 2 0 . 1 0 . 3 0 . 4

Koksusky 31.472 20.914 1 0 . 5 5 8 27.8 15.3 12 .9 14.9 12.7 6 . 7 11.8 2 . 5 6 . 0 1 . 2 0 . 1 0 . 3 -0.1 0 .0 -0.1 2 . 9 3 . 6 2 . 7 1 .0 0 .0 4 .2 0 .8 0 .6 0 .0 0 . 4 0 . 8 0 . 9 0 . 5 0 . 4 0 . 6

Panfilovsky 43.230 34.200 9 . 0 3 0 41.8 31.8 22 .7 5 . 6 13.6 2 . 4 0 . 6 1 . 1 0 . 3 31.8 17.0 20.0 3 .8 0 .1 0 .0 0 . 1 0 . 0 0 .0 0 .0 0 .1 1 .1 1 .8 0 .0 0 . 3 0 . 7 1 . 3 1 . 6 0 . 6 1 . 2

Raiymbeksky 79.358 33.400 4 5 . 9 5 8 67.9 29.7 19 .1 16.4 22.3 12.2 51.3 6 . 9 7 . 0 0 .2 0 .5 -0.1 0 . 0 0 .0 0 .2 0 .0 0 .0 3 . 9 3 . 5 5 . 1 0 . 1 0 . 2 0 . 5

Sarkandsky 86.401 45.300 4 1 . 1 0 1 74.6 40.3 45 .6 47.5 33.9 28.3 26.3 5 . 1 15.6 0 . 1 0 . 2 0 .8 1 .2 1 .5 4 . 3 1 . 4 2 . 4 1 .2 0 .0 6 .0 2 .3 18.7 0 .0 0 . 7 0 . 6 0 . 9 0 . 3 0 . 7 0 . 6

Talgar 39.054 33.328 5 . 7 2 6 53.0 28.8 24 .0 15.8 16.0 12.1 35.2 10 .6 9 . 0 1 . 0 1 . 7 2 . 6 1 .0 0 .5 0 .3 0 . 1 0 . 5 0 . 4 3 .9 0 .0 3 .5 0 .2 0 .2 0 .0 1 . 9 2 . 7 3 . 6 2 . 6 1 . 1 2 . 2

Uyghur 22.494 14.400 8 . 0 9 4 15.2 13.1 14 .0 3 . 2 7 . 6 3 . 8 1 . 4 0 . 2 1 . 3 10.9 5 . 3 8 . 9 -0.3 0 .0 0 .0 0 . 1 0 .0 0 .1 0 .1 0 .3 0 .8 0 .1 0 . 1 0 . 2 0 . 3 0 . 4 0 . 6 0 . 8

Kapchagay 1 . 6 4 . 6 0 . 6 2 . 5 1 . 1 1 . 6 0 . 4 0 .0 -0.1 0 .1 0 . 1 0 . 2 0 .0 2 .4 0 .0 0 .0 0 . 6 0 . 3 1 . 5 0 . 9

Taldykurgan 5 . 3 3 . 0 1 . 5 1 . 5 1 . 8 0 . 4 7 . 9 1 . 2 0 . 9 0 . 1 -4.1 0 .0 0 .1 0 . 2 0 . 1 0 .0 2 .3 0 .4 0 .0 0 . 4 0 . 9 0 . 9 1 . 2 1 . 2 1 . 1

Tekeli 0 . 6 0 . 4 0 . 4 0 . 2 0 . 4 0 . 2 0 . 2 0 .2 0 .2 0 .0 0 . 0 0 .0 0 .1 0 .2 0 .0 0 . 2 0 . 3 0 . 2 0 . 1 0 . 1

Production (000t) 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007

Cereals, incl.Maize Wheat Barley R i c e Maize(grain) Other Cereals Sugar Beet S o y a b e a n s Sunf lower Tobacco Potatoes Vegetables

ALMATY OBL 694.6 865.7 1111.5 240.0 483.1 432.5 189.1 229.2 323.6 79.0 31.9 49.9 174.9 115.9 298.6 11.6 5 .6 6 .9 335.2 255.7 341.2 14.6 4 .3 82.9 1 .9 11.9 36.6 3 .9 6 .2 6 .5 236.6 346.0 551.9 168.6 278.7 589.0

Aksu 36.9 47.5 55 .3 27.5 33.7 33.0 12.3 13 .6 22.3 0 . 1 0 . 2 0 . 0 -3.0 0 .0 0 .0 78.1 31.0 60.0 0 .1 4 .5 1 .1 5 .6 6 . 5 8 . 8 23.2 2 . 6 2 . 5 5 . 5

Alakol 51.5 47.9 54 .4 26.9 40.7 25.8 19.1 6 . 9 25.0 1 . 0 0 . 2 2 . 8 4 .5 0 .1 0 .8 71.4 4 . 5 0 . 0 0 .6 14.5 0 .2 1 .5 8 .7 20.7 15.0 19.8 6 . 4 4 . 1 6 . 1

Balkhash 70.9 32.4 53 .9 11.0 5 . 4 11.6 1 . 0 9 . 8 61.1 20.4 38.5 0 -1.8 0 .0 0 .2 0 .0 0 .1 0 .1 0 . 8 4 . 3 7 . 0 1 . 1 3 . 9 6 . 6

Enbekshikazahsky 54.6 103.9 159.5 22.4 40.0 42.8 8 . 7 11 .2 14.9 22.8 51.6 101.2 0 .7 1 .1 0 .6 3 . 7 3 . 5 5 . 6 1 .9 0 .1 13.9 0 .7 1 .9 3 .2 3 .9 5 .9 6 .4 27.5 52.6 65.5 24.0 85.2 174.3

Eskeldinsky 43.2 51.9 56 .5 28.0 32.8 27.3 7 . 4 16 .7 25.1 3 . 8 1 . 9 3 . 5 4 .0 0 .5 0 .6 35.4 39.1 56.0 2 .8 2 .6 9 .9 0 .3 2 . 0 1 .3 0 .2 30.9 30.3 32.3 7 . 8 11.5 15.1

Zhambyl 27.2 79.0 65 .3 13.1 44.9 33.2 15.8 34 .0 32.1 1 . 5 0 . 0 -3.2 0 .1 0 .0 1 . 2 5 . 5 17.3 0 .2 2 .7 0 .0 0 .1 3 . 9 10.1 29.3 1 . 6 6 . 1 18.4

Ili 16.4 67.7 71 .6 9 . 4 33.4 23.4 7 . 0 31 .2 19.7 2 . 2 2 . 8 28.0 -2.2 0 .3 0 .5 14.5 26.8 18.0 1 .0 0 .3 1 .4 0 .3 0 .5 0 .4 7 . 5 26.7 23.6 5 . 3 11.4 67.7

Karasai 12.7 45.0 55 .6 4 . 7 25.0 36.0 7 . 0 19 .2 13.0 0 . 4 0 . 2 6 . 1 0 .6 0 .6 0 .5 5 . 3 22.4 6 . 0 0 .9 0 .2 1 .7 0 .0 11.8 35.4 84.6 15.7 23.2 50.0

Karatalsky 32.6 17.7 22 .5 5 . 8 4 . 8 6 . 2 5 . 2 1 . 3 3 . 0 17.9 11.5 11.4 1 . 7 0 . 1 1 . 9 2 .0 0 .0 0 .0 9 . 5 15.8 32.2 1 .3 0 .4 0 .8 0 .4 1 .1 6 . 8 13.9 17.6 19.4 24.1 31.2

Kerbulak 20.2 98.6 130.3 6 . 4 53.2 54.9 11.9 53 .8 73.6 2 . 9 1 . 0 1 . 3 -1.0 -9.4 0 .5 0 . 3 0 . 6 0 .0 0 .1 36.6 31.5 42.0 0 . 5 3 . 8 10.9

Koksusky 16.8 22.1 28 .5 12.9 18.9 14.3 1 . 8 3 . 1 13.3 1 . 3 0 . 1 0 . 9 0 .8 0 .0 0 .0 46.6 64.5 91.7 1 .0 0 .1 8 .4 0 .5 0 .7 5 . 6 8 . 3 17.5 6 . 2 11.1 17.5

Panfilovsky 126.4 65.5 102.4 6 . 0 23.6 5 . 4 0 . 5 1 . 5 0 . 5 111.1 40.3 96.6 8 .8 0 .1 -0.1 0 .1 0 .0 0 .1 0 .5 2 .2 3 . 2 8 . 1 18.4 7 . 7 1 . 3 24.6

Raiymbeksky 86.2 32.8 26 .1 27.5 25.4 15.6 58.5 7 . 3 10.5 0 .2 0 .1 0 .0 0 .1 0 .0 36.1 31.5 67.3 0 . 7 6 . 7 7 . 8

Sarkandsky 50.3 74.3 105.7 36.2 58.8 65.4 13.6 14 .3 37.1 0 . 1 0 . 1 0 . 6 0 .4 1 .1 2 .5 68.5 23.1 30.1 1 .3 10.0 2 .2 11.6 10.8 6 . 2 14.3 3 . 3 26.4 15.2

Talgar 14.4 49.4 58 .9 6 . 3 32.2 27.9 5 . 5 10 .1 16.0 1 . 9 6 . 2 14.3 0 .7 0 .9 0 .8 1 . 0 14.8 14.0 3 .4 0 .5 6 .5 0 .1 0 .3 0 .0 19.4 36.1 62.4 27.5 6 . 0 63.8

Uyghur 32.6 23.0 49 .5 6 . 6 11.8 8 . 3 1 . 8 0 . 4 2 . 4 24.1 10.9 38.9 0 .1 -0.1 0 .0 1 . 1 0 . 0 0 .3 0 .1 0 .2 1 .4 0 .2 0 . 4 2 . 1 5 . 1 2 . 2 30.6 17.6

Kapchagay 2 . 3 11 .5 0 . 4 6 . 5 1 . 6 3 . 2 0 . 3 1 . 8 0 .0 0 .0 0 .0 0 . 6 6 . 8 4 .7 0 .0 11.0 5 . 9 16.9 15.9 29.4

Taldykurgan 2 . 1 4 . 3 3 . 1 0 . 3 2 . 4 0 . 9 1 . 7 1 . 8 1 . 6 0 . 6 0 .1 0 .1 0 .0 2 . 7 2 . 9 0 .1 3 .6 0 .4 0 .1 6 . 2 10.3 13.3 19.1 2 . 2 25.6

Tekeli 0 . 4 0 . 9 0 . 1 0 . 4 0 . 1 0 . 2 0 . 5 0 . 0 -0.1 0 .1 0 .0 0 .1 0 .3 0 .0 1 . 9 3 . 8 2 . 8 0 . 6 1 . 7

Yield (centn/ha) 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007 1991 1999 2007

Cereals, incl.Maize Wheat Barley R i c e Maize(grain) Other Cereals Sugar Beet S o y a b e a n s Sunf lower Tobacco Potatoes Vegetables

ALMATY OBL 8 . 0 16.0 23 .2 7 . 9 15.0 20.1 4 . 1 14 .1 17.6 47.8 29.9 39.1 26.5 30.0 48.9 145.0 189.0 275.0 9 .7 14.3 17.1 6 .0 7 .6 9 .0 13.6 16.2 20.5 117.5 116.6 159.0 118.0 171.0 235.0

Aksu 4 . 9 16.1 18 .3 5 . 9 14.7 18.0 4 . 3 21 .0 18.7 5 . 8 28.0 178.0 113.0 214.0 7 .5 15.0 0 .9 11.0 13.3 93.0 80.0 145.0 130.0 100.0 156.0

Alakol 7 . 2 11.9 16 .3 7 . 9 11.8 16.5 5 . 8 12 .0 15.4 2 . 8 21.8 32.6 123.0 98.0 3 .8 16.3 4 .5 5 .4 7 .2 138.0 111.0 128.0 91.0 109.0 130.0

Balkhash 34.4 23.0 30 .0 17.9 18.6 13.6 15 .7 18.3 49.0 27.9 40.0 6 .6 10.0 80.0 95.0 124.0 55.0 100.0 126.0

Enbekshikazahsky 14.0 24.3 36 .0 21.7 18.9 25.4 5 . 8 15 .4 21.7 17.4 38.2 50.1 93.0 213.0 281.0 7 .0 11.3 18.6 9 .6 10.3 12.3 13.6 16.0 20.5 115.0 101.0 126.0 86.0 181.0 253.0


76

Eskeldinsky 8 . 4 18.0 21 .3 14.6 17.7 21.5 4 . 4 17 .6 19.7 55.1 48.4 43.8 126.0 230.0 330.0 19.6 15.3 15.1 4 .4 7 .9 11.6 154.0 151.0 161.0 130.0 149.0 167.0

Zhambyl 2 . 3 12.0 16 .1 4 . 0 12.6 14.4 1 . 9 11 .3 18.3 32.1 30.0 203.0 287.0 5 .4 14.0 14.6 9 .0 0 .1 30.0 20.1 98.0 185.0 181.0 40.0 152.0 204.0

Ili 2 . 5 12.3 20 .2 4 . 4 12.7 15.7 1 . 7 11 .4 14.1 35.4 27.3 55.6 132.0 268.0 300.0 8 .7 18.0 3 .4 10.1 13.0 83.0 110.0 168.0 106.0 166.0 223.0

Karasai 5 . 3 18.0 24 .8 6 . 8 18.3 23.9 4 . 5 17 .7 21.7 8 . 7 25.3 61.0 177.0 283.0 301.0 9 .7 17.0 24.0 118.0 112.0 217.0 92.0 196.0 250.0

Karatalsky 12.0 20.1 25 .9 9 . 3 11.8 19.2 3 . 4 9 . 8 15.1 41.1 34.2 36.4 31.3 36.2 55.0 190.0 282.0 280.0 9 .1 13.1 12.0 9 .1 3 .0 11.6 136.0 137.0 160.0 149.0 215.0 215.0

Kerbulak 2 . 2 14.1 16 .6 2 . 9 13.0 19.1 1 . 7 15 .2 15.0 27.2 19.5 37.0 158.0 150.0 2 .4 12.9 130.0 153.0 189.0 50.0 100.0 242.0

Koksusky 6 . 0 14.5 22 .1 8 . 7 14.9 21.4 1 . 5 12 .5 22.3 12.9 11.0 39.2 161.0 180.0 348.0 10.1 7 .3 20.1 6 .1 6 .5 13.2 140.0 100.0 194.0 124.0 100.0 312.0

Panfilovsky 30.2 21.6 45 .1 10.7 17.3 22.3 8 . 3 13 .5 17.0 40.0 26.0 48.3 125.0 30.0 3 .5 9 .8 5 .1 12.8 22.0 107.0 116.0 141.0 128.0 185.0 205.0

Raiymbeksky 12.7 11.8 18 .0 16.8 12.2 19.5 11.4 10 .7 16.1 93.0 89.0 133.0 70.0 80.0 149.0

Sarkandsky 6 . 7 18.3 23 .2 7 . 6 7 . 2 23.1 5 . 1 27 .4 23.8 12.6 14.0 32.2 159.0 155.0 177.0 11.1 16.6 2 .9 9 .4 7 .2 154.0 104.0 164.0 110.0 92.0 243.0

Talgar 2 . 7 17.8 24 .5 4 . 0 20.8 23.1 1 . 6 10 .2 17.8 19.9 34.5 55.3 100.0 303.0 311.0 8 .7 17.6 19.0 12.1 14.5 10.0 25.0 102.0 136.0 173.0 171.0 235.0 293.0

Uyghur 21.6 17.5 35 .7 20.6 15.6 22.3 12.9 13 .8 18.1 22.9 20.5 43.9 147.0 3 .3 17.1 6 .0 17.4 17.7 40.0 85.0 169.0 55.0 91.0 214.0

Kapchagay 13.4 25 .3 7 . 9 25.5 15 .5 20.5 17.9 40.9 136.0 319.0 19.1 190.0 199.0 248.0 309.0

Taldykurgan 4 . 0 14.2 21 .2 2 . 0 13.7 21.0 2 . 2 14 .9 17.2 55.8 181.0 261.0 9 .8 15.9 8 .8 12.8 155.0 115.0 143.0 159.0 140.0 220.0

Tekeli 9 . 9 20 .6 9 . 2 20.6 2 . 5 9 . 6 20.6 39.5 15.9 16.3 13.0 95.0 124.0 140.0 60.0 175.0 275.0

Note: Most Data from KAZ-NSIU of KAZ Statistics Agency, checked, corrected and reformated by MSEC. Total Arable Areas (Yr2000) from WWF-UNEP ECONET data. File: CAC5 \ V41 \ Copy of StatData KAZ1.xls


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3.4 LAND USE, AND CROP AREAS, YIELDS AND PRODUCTION

3.4.1 Comprehensive Data from Almaty Oblast, KAZ, 1991, 1999, 2007

Comprehensive crop data was made available by KAZ_NSIU from Stats Agency data for all rayons in the Almaty Oblast covering the years 1991, 1999, and 2007 (Table 3.2). The data shows interesting and major changes in cropping over this period. Crop areas figures (covering both irrigated and rainfed crops together) show that cereals are the predominant crop group in the oblast, in which wheat and barley are both much more important than the other cereals. However, major falls in crop areas were recorded for these crops over the period 1991-1999, cereals declining from 870.000ha to 552.000ha. Big falls particularly occurred for barley (falling from 465.000 to 167.000 ha) and maize (falling from 66.000ha to 40.000ha). A further fall in cereal areas also occurred from 1999 to 2007, when total cereal areas fell to 494.000ha. However, rises were recorded for other, higher value, crops over the full period, particularly for sunflower (3.000 to 43.000ha), soybeans (15.000 to 49.000ha), potatoes (20.000 to 35.000ha) and vegetables (14.000 to 25.000ha). All of the higher value crops showed a tendency to be concentrated in a few specific rayons, perhaps due either to crop processing or marketing requirements. Thus of the 19 rayons, 80% of the sugarbeet production is concentrated in just 5 rayons. Sunflower shows 70% of production in just 3 rayons; soybeans 70% in 5 rayons; potatoes 50% in 4 rayons (70% in 7 rayons) and vegetables 60% in 4 rayons. Statistics unfortunately do not cover other crops, particularly fodder (both alfalfa and grass leys). However, total arable areas are available on a rayon basis from the ECONET data for 2000, and total 1.035.500ha for the whole oblast. Total crop areas from the Stats Agency data total 632.800ha for 1999, which would imply that other, mainly fodder, crops might total around 402.700ha for the period 1999-2000. Within the rayons, Talgar would appear to have the smallest area of other crops at 5.700ha, while Zhambyl had the largest area at 74.000ha. In general other crops comprise between 25% and 50% of the total cropped area for most rayons, implying that rotations might be practised to a satisfactory degree for those areas. Only in Talar and Illi did other crops areas fall below 15% of the cropped area total. Falls in crop area were counterbalanced by big increases in crop yield, due to the fact that 1991 was an abnormally bad year due to drought. Cereal yields for 1991 averaged only 8cntr/ha, as compared to 16,0cntr for 1999 and 23,2cntr/ha for 2007. Cereal yields in 1991 showed enormous variation: three of the rayons showed appreciable yields (20-35cnt/ha) which would have been boosted by at least an irrigation component, while most of the other rayons showed yields of below 8cntr/ha (clearly all rainfed land). By contrast, yields for 2007 show rayon averages mostly in the range 18-26cnt, which is a normal yield for areas combining both rainfed winter cereals (yields 23-29cnt) with spring cereals (16-22cnt). Rayons with total cereal averages of over 26cnt for 2007 invariably had important components of (irrigated) maize or (irrigated) rice, where average yields were near 50cnt/ha and 40 cnt/ha respectively. Both of the latter two crops were concentrated in just a few areas, maize in Enbekshikazahsky, Raimbekskiy, and Uigur rayons, and rice in just Balkash and Karatalskiy rayons.

3.4.2 Data from SKO on Irrigated Areas, Yields and Production: 1987, 1990, 1995, 1997, 2004

Data was available for SKO from the EMIMS study, covering crop areas, yields and production for irrigated land on a rayon basis for several years over the period 1987 – 2004. This covers the standard list of crops and crop groups. The primary statistics were obtained from Oblast Statistics Agency annual reports, retyped into Excel and rechecked by the Consultants. Data for five key


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years over this period are shown in summary form for the oblast in Table 3.3. Changes in the irrigated crop areas are shown in Figure 3.4. Statistics show that cropped irrigated land in the oblast declined from 420.000ha in 1990 to 326.000ha in 1997, thereafter rising to 344.000ha by 2004. Even more major changes in cropping were seen over this period, with fodder maize declining from 33.900ha in 1990 to just 1.700ha in 2004. Fodder maize is a very nutrient-demanding crop and issues of fertiliser cost and availability have clearly affected this crop more than most others. Cereals, however, showed little changes in area from 1987 to 1997, but have more than halved in area since that time. Cotton areas have changed dramatically: gradual declines from 127.000ha in 1987 to 104.000ha in 1997, thereafter a sudden rise to 215.000ha by 2004. Areas of ‘other’ crops (mainly alfalfa and other fodder crops) also changed greatly over this period, falling from 126.000ha in 1990 to 80.000ha in 1997, and thereafter to just 33.000ha in 2004. Of the higher value crops, all the seasonal crops (potatoes, vegetables, and melons & gourds) recorded a big increase in area (15.700ha to 26.600ha) over the full period, while the perennial crops including fruits, berries and grapes recorded a big decrease (17.000ha to 8.200ha). Fig 3.4. SKO, Changes in Irrigated Crop Areas (000ha), 1987 – 2004.

Source: SLMIS output, based on data from KAZ EMIMS study, 2007, in turn based on Oblast Statistics Agency figures.


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Table 3.3: SKO, Irrigated Agricultural Areas, Production and Yields, 1987 – 2004.

Source: SLMIS output, based on data from KAZ EMIMS study, 2007, in turn based on Oblast Statistics Agency figures. Yields and production have also seen big changes, which is surprising considering that these are all irrigated crops. Yields were very low for grain and oilseeds in 1997 and particularly 1995, 1997 being an average to wet year with average flows of irrigation water, while 1995 was a very dry year with very low flows. 2004 yields were much better: much higher for oilseeds, and approaching 1990 levels for cereals. Figure 3.5: SKO, Rayon Statistics: Changes in Irrigated Cropping, 1987 (inner pie) – 2004 (outer pie)


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Source: SLMIS output, based on data from KAZ EMIMS study, 2007, in turn based on Oblast Statistics Agency figures. Pie charts are positioned on rayon centres. Crop groupings shown are: Vin: vines/grapes; B_f: berries & fruits; M_g: melons & gourds; Veg: vegetables; Pot: potatoes; Oil: oilseeds (mainly safflower, some sunflower); Fmz: fodder maize; Grn: grain, (mainly wheat, barley, some rice, some maize grain); Oth: other crops, (mainly fodder, alfalfa); Ctn: cotton. Contours elevations shown in colour - for legend see Fig 7.7. Small grey circles show municipality (selskiy okrug) centres. Tabular insets: database with crop areas (ha) accessed through the ArcView GIS for Sairam Rayon (top), Maktaraal Rayon (bottom). Figure 3.5 shows the geographic distribution of the relative changes in crop areas between 1987 and 2004, showing strikingly different patterns in different rayons. In SKO each rayon has an almost unique combination of the different detailed agro-ecologicial zones, and different ease of access to local markets. Sairam, Tolebi, and Tulkibas are close to Shymkent while Kazigurt and Sariagash are close to Tashkent and thus benefit considerably in proximity to these markets for high-value horticultural produce. Rises over the 1987-2004 period in such crops in Tolebi, Kazigurt and Sariagash have been particularly notable. Cotton has expanded enormously in the lower-elevation and drier rayons, particularly for Maktaraal, Shardara, Arys and Otyrar, all of these showing decreases in both cereals and particularly ‘other’ crops (mainly alfalfa and other fodder crops).

3.4.3 Comprehensive Data from KAZ-SKO, 2005

Socio-Economic and Agricultural Production & Productivity Baseline Data and Recent Trends Out of a total oblast area of 11.726.000 ha agricultural lands comprise 10.268.000 ha of which a total of 833.000 ha is classified as arable (the remaining land being classified as pasture). Total irrigated lands amount to 423.000 ha, the balance 410.000 ha being rainfed arable. (All figures here are for 2005 data, the last full year for which complete data was available). Of the 833.000 ha of available arable land 758.300 ha were planted in 2005 and 755.000 ha were harvested. Some 62.450 Individual private farms accounted for 436.400 ha (average size 7.0ha); 1112 agricultural enterprises (including state enterprises, agricultural associations, joint stock companies and producers’ cooperatives) comprised 206.600ha (average size 186ha); and 343.165 village and peri-urban garden areas (or ‘personal subsidiary plots’) comprised the remaining 115.300ha (average size 0,34 ha). In spite of their small size, subsidiary plots are very important


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for high-value produce, and in 2005 were responsible for over 75% of the production of fruits and berries, over 65% of the potatoes, 50% of the vegetables and 33% of the grape production. Nearly all of these small plots are irrigated, although water shortages are common for many during the latter part of the summer season, particularly in 2005 which was a 1-in-5 dry year. Total agricultural production in 2005 was valued at KAZ Tenge (KZT) 90.359 million (US$740,6million), comprising KZT 55.495m (US$454,9 m) for crop sales and KZT34.864m (US$285,7 m) for sales of animal produce. Crop sales involved 8 main crop groups, including grain (mainly wheat; some rice and barley also grown), cotton (both for fibre and seed), oil crops (safflower and sunflower), potatoes, vegetables, melons and gourds, fruits and berries, and grapes (for fruit, wine, and raisins). Remaining areas, making up the 755.000ha harvested, were under forage crops (mainly alfalfa and cultivated grasses). Statistics for crop areas, production and crop values in the oblast for 2005 are given in Table 3.4. Table 3.4: Crop Areas, Production and Values for SKO-KAZ for 2005 Crop Group: Harvested

Area (ha) Gross

Production (tonnes)

Farmgate Value ($ /

tonne)

Total Value ($m)

Remarks

Grain 238.509 400.635 120 48,1 Mainly rainfed Cotton

200.969

464.386

490 227,5

Irrigated: recent big increase; crop rotation concerns

Oil seeds

71.331

56.001

180 10,1

Mainly rainfed: big increase for safflower; govmt.subsidy

Potatoes 9.594 148.891 115 17,1 Irrigated Vegetables 24.320 480.050

200 96,0 Irrigated: homegardens

Melons & gourds

17.853 294.501 46 13,5

Irrigated

Fruits & berries

14.425 62.075 400 24,8

Largely homegardens

Grapes 4.230 30.354 550 16,7 Largely homegardens Others (forage)*

173.755 850.000

55 46,8

Big decreases: important in crop rotations

TOTALS 754.986 500,7 Note: 1KZT = US$122 (as of Jan.2006); farmgate prices taken from EU-TACIS Kyzyl Orda Study, 2005, cross checked with SKO Study, 2006. Forage would be used internally on the farm, its value (some $47m) contributing to the total value of animal sales (some $286m). Total crop value sales from above table would be some $454m, almost identical to the above gross survey value ($455m). Of the above areas, most of the grain, oil seeds and the forage crops are grown under rainfed conditions in the more favourable areas (mean annual precipitation 350-900mm). The balance of these crops, and virtually all of the other (high value) crops are grown under irrigation. Rural population for 2005 would have totalled around 1.380.000, of which around 600.000 would have been actively engaged in the farm sector. With total gross farm sales of US$741 million, gross average value of agricultural production per worker would amount to some $1240. In addition to these ‘official’ figures, it is estimated that a total of some $36m is the value of home-consumption produce, not entering the official sales figures, bringing the total annual agricultural production per farm worker to some $1300. Local labour shortages in some areas has meant that influx of seasonal labour – most notably agricultural workers from Uzbekistan – is an important feature of the economy in those areas.


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Changes over the period 1987 – 2005, and Districts (Rayons) most badly affected by land degradation Statistics for the years 1987, 1990, 1995, 1997, 2004 for irrigated areas, plus the present statistics for all areas (rainfed and irrigated) for the years 2004-6, show that there have been major changes in cropped area and crop type over this 20-year period. Further inspection of these figures, inspection of the remote sensing imagery (imagery provided by Dr Ji plus Google Earth imagery), plus fieldwork in selected areas, have indicated that these changes have been as a result of three separate factors: i. re-structuring following the break-up of the Soviet system (affecting all areas); ii. response of farmers to widely-fluctuating crop prices and contrasting market conditions, varying widely between different areas; iii. as a result of land degradation processes (affecting particularly some hot-spot areas). From a maximum irrigated area in the SKO of 405.000 ha in 1990, total irrigation areas declined by 25% by 1997 (the the worst year economically in the post-adjustment period). Declines were seen for most crops, and particularly for forage crops (alfalfa, cultivated grasses) and, most severely, for fodder maize. However, since 1997 areas cultivated have again increased, most notably for cotton, the most profitable of the major crops. Inspecting the crop composition data at rayon level (see Rayon&SOlanduseRepr.doc) increase of cotton has been most dramatic in low-lying dry irrigated rayons, most notably Maktaraal and Shardara, where almost a cotton monoculture has developed, and fodder crops, particularly alfalfa, have declined alarmingly. These changes have been promoted by increase in soil salinity following degradation of drainage infrastructure: cotton is fairly tolerant of high salinity, whereas alfalfa and most vegetables are very sensitive. These land use changes which are not sustainable, are of major concern to the oblast agricultural authorities. A further area of concern is the area of marginal rainfed land to the north and northeast of Shymkent where water erosion and run-down of soil fertility are major problems. This area includes much of Baidebek rayon and several further selskiy okrugs in adjacent rayons. These areas deserve much closer monitoring, particularly at selskiy okrug level, as they represent further land degradation hotspot areas. Cross-checking with the NDVI 1983-2005 GIMMS dataset (Figure 4.1) shows that there are two major areas of long term degradation in the oblast – these area large areas of intense red and orange colouration denoting that NDVI (active green vegetation) was much less in the recent the 3-year period (2004-06) than in the earlier period (1982-84):

i. Area in and around Turkestan rayon, in the west of SKO, on the border with Kyzyl Orda Oblast. This large area (some 180*90km) includes a large area of apparently partly-developed and abandoned irrigated land, as well as natural lower-floodplain areas of the Syr Darya river. Field inspections have shown that salinity, sodicity and seasonal high watertables are all major problems in these areas.

ii. Area comprising most of the northern 1/3 of the oblast, including nearly all of Sozak rayon, with the exception of the season riverine area bisecting this large area, in an E-W direction. Almost all of these areas are under low-productivity pasture, with less than 150-200mm mean annual precipitation.

Further cross-checking with the Google Earth imagery has shown the extent of irrigation and drainage infrastructure in the area (i) above and the degrading natural vegetation in these and surrounding areas. The Google Earth imagery has brought up further (but somewhat smaller) hotspot areas for land degradation, notably areas affected by severe gulleying in Baidebek rayon in the east of the oblast. This area of large gullies has not shown up on the GIMMS dataset, partly because the base of the gullies are now naturally being re-vegetated with perennial bush vegetation, masking the overall NVDI signature of the large pixels of the GIMMS imagery.


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3.5 LAND USE AND LIVESTOCK PRODUCTION

Livestock census data is available for all 53 rayons of the four CAC-5 Priority Area oblasts within KAZ for the periods 1991, 1999, and 2007 for 3 of the oblasts, and 2008 for Almaty Oblast. Livestock included were cattle, sheep & goats, horses, camels, and pigs. Analysis here includes all grazing livestock – i.e. all livestock except pigs. In order to assess ‘livestock pressure’ the concept of ‘livestock unit’ was used: one livestock unit representing one head of cattle, or one horse, or one camel, or 5 sheep / goats. Full livestock data is presented in Table 3.5.

Table 3.5 KAZ: Four Priority Oblasts: Livestock Numbers (‘000) in January 1991, 1999 and 2007*

Source: Data from KAZ-NSIU, collected from Oblast Statistics Agencies, 2009. Further checking, corrections & reformatting by MSEC. *2008 data for Almaty.


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In January, 1991, total livestock units were 2,36m in Almaty Oblast, 1,40m in SKO, 1,16m in Zhambul and 0,63m in Kyzyl Orda. However, by January, 1999 numbers had fallen dramatically, to 1,11m (only 47,0% of the 1991 total) in Almaty; 0,86m (61,3%) in SKO; 0,41m (35,1%) in Zhambul, and 0,63m (52,5%) in Kyzyl Orda. Declines were steepest for sheep and goats, numbers in both Almaty and Zhambul Oblasts being less than one-third in 1999 than they were in 1991: corresponding figures for Kyzyl Orda and SKO were 34,3% and 45,9% respectively.


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Figure 3.6: KAZ Changes of Livestock Numbers 1991-2007 (as measured in Livestock Units): Top: Ratio 1997 / 1991 (%); Middle: 2007 / 1997 (%); Bottom: 2007 / 1991 (%).


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By 2007 (2008 for Almaty) livestock numbers had recovered, but to a variable degree, across the whole area, livestock units totalling 1,53m; 1,48m; 0,84m; and 0,47m, these being 64,7%; 105,9%; 72,1% and 74,6% of 1991 figures respectively. For SKO, the actual rise of 5,9% in livestock unit numbers disguised a fall in sheep & goat numbers (only 79,2% of 1991 numbers), but this was more than counterbalanced by a rise in cattle numbers (to 145,7%) and horse numbers (to 149,4%). Zhambul and Kyzyl Orda Oblasts also showed a rise in cattle numbers over the whole period (1991-2007) but these were matched by even bigger falls in sheep and goat numbers. Geographic distribution of falls (and rises) of Livestock Units over the three periods are given in Figure 3.6. The upper part of this figure shows the Livestock Units in 1997 as a factor of the 1991 numbers. Intensity of red in this figure is proportional to fall in numbers: the worst hotspot areas of livestock decline are seen in major areas of southern Zhambul Oblast and Otyrar Rayon in SKO, but major falls were seen in most rayons. The middle part of the figure shows the rise of livestock numbers in the subsequent period (1997-2007), while the lower part of the figure shows changes over the full period (1991-2007). In the latter map green areas denote increasing livestock numbers. Increases are most notable in the higher rainfall areas of SKO, but also include the area around Kyzyl Orda town, and Aral Rayon in Kyzyl Orda Oblast. Red areas again denote the steepest falls over the full period, in this case 2007 numbers representing less than 50% of 1991 numbers. Although these were ‘hotspots’ with respect to livestock numbers, they were very much ‘brightspots’ as far as recovering pasture vegetation was concerned (see below). Changes in livestock numbers in SKO over the period 1985 – 2005 were also analysed during the EMIMS-SLU Study in 2007 (Guidelines, Annex G). Figure 3.7, taken from that study, shows livestock numbers (again measured on the same parameter of livestock units), on a yearly basis for SKO. Of interest is the fact that sheep / goats numbers rose slightly to 1993 – the maximum year – but fell very rapidly to 1996, finally reaching a minimum in 1997. From 1997 to 2000 numbers were still depressed, although they rose very slightly, after which they rose more rapidly to 2005. In SKO, most of the sheep and goats were reared in Sovkhozes which fell into a state of collapse after 1993, although low precipitation figures in 1995 and 1996 would have accelerated the decline. Conversely, 1993, the maximum year, was also a very good year for precipitation.

Figure 3.7: KAZ-SKO: Changes in Livestock Population (in Livestock Units) over the period 1985 – 2005


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Source: KAZ EMIMS Final Report, Nov. 2007. Guidelines, Annex G. Interestingly, EMIMS Guidelines, Annex G, also reports on pasture productivity measurements at three pasture monitoring sites in SKO over the period 1991-2000. The three sites (Syzgan, Nalibay, Birlik) had mean annual precipitation of around 170mm, 220mm, and 370mm respectively, straddling the met station of Arys which had a mean annual precipitation of around 230mm. Although there was an overall decline in precipitation over the period (as measured at Arys), the trend in pasture productivity registered an overall increase. Water Use Efficiency (WUE) thus recorded a substantial increase, from an average of around 5,5kgDM / mm precipitation / ha for the period 1991-1994 to around 13kgDM / mm / ha for the last 2 years of the period (1999-2000), an improvement which would be attributed to the much lower livestock pressure during the latter period. These figures are broadly consistent with figures obtained on WUEs from moderately degraded and protected rangeland respectively in Jordan (Cranfield University, UK / University of Jordan / ICARDA, JAZZP, 2001), under a mean annual rainfall range of 150-200mm (but significantly higher Winter Growth Potentials than prevailing in Kazakhstan).

3.6 UPDATING AND ENRICHING THE CACILM LIVELIHOODS INFORMATION

The good KAZ Statistical data for the three key baseline years (and indeed for all years over the transition period) presents an interesting picture and correlates well with other data sources for the same time and same areas (RS, GE, ECONET, met data, MoA data, GosNPCzem data). This primary data now needs to be obtained from the Statistical Agencies of the other countries for these key years. The data can be added to the ECONET rayon coverage shape file and graphics as per Figures 3.1-3.4 and printed out for a wide range of themes. Statistics for a single year needs to be viewed with caution, and always related to the met and hydromet data for that year, particularly for the rainfed arable and livestock statistics. The factors underlying the major changes over the period 1990 – 2005 need to be appreciated, particularly the problems of agricultural restructuring with respect to the small private farmer. The KAZ statistical data shows considerable detail in differentiation of very specific crops, and also of specific land holding types, and this could be useful for future work, particularly at national project level. Statistical data at municipality (selskiy okrug) level since 2005 has become available for Kazakhstan, again through the oblast statistical agencies. This data will be even more useful for monitoring any effects of land degradation and extreme weather associated with climate change. Municipality databases are further covered here in Chapter 6.


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4 CACILM USE OF REMOTELY SENSED DATA

4.1 INTRODUCTION: UNIVERSAL DEVELOPMENTS, AND WORK DONE FOR CACILM

Due to the enormous size of the CAC5 area, the predominance of pasture and the well established vegetation-climate-soil associations, and the relatively good weather conditions for much of the year, studies and mapping by satellite remote sensing must form an indispensable part of any land degradation monitoring work in the region. Work undertaken during this inception phase of the project included intensive study and routine use of a number of remotely-sensed imagery sources, and these are as follows:

- GIMMS (reported here under Section 4.2); - GLOBC-EUR SAT (Section 4.3); - MODIS (MOD13Q1, 250m) (Section 4.4 and 4.5); - LANDSAT ETM+ (Section 4.6); - Google Earth, moderate- and high-resolution imagery (Section 4.7 and also Section 9.4).

Work on the first four of these sources was undertaken by Dr Ji over the period July 2008 to March 2009. This included production of two major reports – an inception report of September, 2008, and a final report of April, 2009 – practical on-the-job training to the UZB NSIU staff and some ground-truthing over the period 10-17October, 2008, plus delivery of a large volume of remote sensing imagery in digital format. The most important of the latter was the MODIS 2008 monthly time series and the LANDSAT ETM+ 2004 coverages: these formed complete and excellent quality coverages, presentable as maps at scales of 1:1million and 1:100.000 respectively. As most of these materials were georeferenced, other GIS coverages – ECONET basemap materials, soil and bonitet assessment mapping, land use and land cover assessments – could be overlaid with this imagery on the SLMIS and relationships observed with land cover features. 4.2 NDVI TRENDS, 1982-2006: THE GIMMS DATASET

Figure 4.1: CAC5 Priority Area: Changes in NDVI between 1983 and 2005: Hotspots (Red) and Brightspots (Green)


89

The Global Inventory Modeling and Mapping Studies (GIMMS, Tucker et al, 2003) is the only long-term satellite continuous observation dataset available at the global scale, useful for long-term evaluation of changes in land cover and land use. The GIMMS dataset was downloaded from the Goddard Space Flight Centre of NASA (http://gimms.gsfc.nasa.gov/). This dataset was created using AVHRR data collected by NOAA 7, 9, 11, 14 and 16 sensors which operated every 15 days over the period July, 1981 to end 2006. Although some corrections such as satellite drift and inter-calibration of NDVI from all instruments made, atmospheric effect corrections were not performed, and most seriously, the imagery is of coarse resolution (8km) only. In order to show macro-vegetation changes, as manifest by average NDVI over a 3 year window, the GIMMS dataset for the period 1982-84 was analysed and compared to that for 2004-06, and the results shown as Figure 4.1 for the Priority Area. This shows quite effectively the areas of vegetation degradation (the ‘hotspots’) – orange and red in this figure - and those areas where vegetation restoration (the ‘brightspots’) – green in the figure - would appear to be occurring. Areas of vegetation degradation (the ‘hotspots’) include the following: - very large area (c. 150,000km²) in the northernmost parts of the Priority Area, crossing four oblasts in Kazakhstan (most of this area shows mean annual precipitation of 150mm / year or less); - large area (c. 30,000km²) N. of the Syr Darya River in the border area of SKO and Kyzyl Orda oblast (near, and generally to the W of Turkestan, including a large proportion of the Karatau Mountains); - large area (c. 25,000km²)S of lower reaches of the Chu River in Zhambul oblast, KAZ, this representing most of the upland areas here; - large area in W. UZB (c. 60,000km²), to the W and SW of the Aral Sea; - significant area in Tajikistan, centred on Rogun damsite; - large area in Tajikistan in the Parmirs, SE corner of country; - significant areas in Kyrgyzstan, each of about 5,000 – 8,000km²: in Kara Kyldyzinskiy rayon, in the extreme E of the country, and in the area to W and SW of Lake Issuk Kul.

http://gimms.gsfc.nasa.gov/�


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Very extensive areas also show positive trends, most notably in the southern part of Turkmenistan, the SW half of Uzbekistan and adjacent areas in both Kazakhstan and Kyrgyzstan. But these positive trends may not necessarily coincide with increases in useful vegetation. There is much evidence that vast quantities of early-spring floodwater permeates over large areas of alluvial floodplains and desert flats where it contributes very little to the rural economy. The 3 years taken around the start year (1983) and around the end year (2005) balance out the year-to-year fluctuations of rainfall. 2004 was average, 2005 was dry, and 2006 rather wet; 1982 was average; and both 1983 and 1984 were slightly dry. Further, more sophisticated corrections for seasonal fluctuations in agro-met parameters (most importantly rainfall) will be required in future assessments of long-term NDVI trends. Nevertheless, Figure 4.1 represents an excellent starting point in outlining at a coarse level some of the main land and vegetation hotspots and brightspots. 4.3 GLOBCOVER LAND COVER VERSION 2 (GLOBC-EUR SAT)

The possibility for Automatic Land Cover Mapping directly from satellite imagery has long been a driving force for remote sensing programmes, and GlobCover Version 2, released to the users’ community in October 2008, is perhaps the best of the products available for the CAC5 Priority Area. This shows a resolution of 300m and is based on imagery captured over the period 2004-2006. Mapping shows an overall assessed accuracy of 73% for the 22 different land cover types differentiated (Fig 4.2). Ji, 2009, pp20-24 analyses this in detail. However, both Ji’s work, and some further analyses, have shown a number of clear inaccuracies in this coverage, and these are listed as follows: - clear areas of artifacts: straight and other regular lines cross the mapping in a number of areas, the clearest examples being a) the area NE of Lake Balkash (reviewed by Ji, p.22), b) the area in the SW corner of Tajikistan, and c) the areas to the E and S of the Fergana Valley. Large differences in the classifications are seen on either side of these lines, giving the impression that the image was compiled by different teams working independently and with insufficient coordination and correlation work between the individual workers; - too extensive areas of irrigation: irrigated croplands (bright red in the figure, p21, of Ji, 2009). Many of these areas are natural floodplains, receiving spring floodwaters, and under natural pasture and bush vegetation. Inspection of the LANDSAT ETM+ imagery, with further correlation with Google Earth detailed imagery, is needed to verify and differentiate these areas. - too extensive areas of rainfed croplands: rainfed croplands were shown in areas that were of managed pasturelands, being in areas too dry for rainfed crops. - conversely, the main areas of rainfed croplands (eastern extremity of SKO, southern areas of Jambul and Almaty Oblasts) showed much less of the croplands than was observed in the field and in the detailed imagery. Fig 4.2: GlobCover Version 2, Land Cover, for entire CAC-5 area


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In spite of these problems, GlobCover remains perhaps the best single source for land cover mapping and perhaps the best single individual basemap for constructing a more definitive land cover map. The latter work needs to be done by comparing this material with MODIS, LANDSAT ETM+ and any detailed Google Earth imagery, area by area, and updating and correcting the GlobCover classification in any necessary areas. 4.4 MODIS: THE TIME SERIES FOR 2008: THE OVERALL PICTURE FOR CENTRAL ASIA

A large number of MODIS products are available from the Year 2000 onwards. Of these the MODIS VI products are the most relevant for project use, of which the MOD13Q1 has the best temporal (16-day) and spatial (250m) resolutions. A total of eight 10° granules cover the entire CAC5 area, of which four cover the Priority Area (H23-V04, H23-V05, H22-V04, H22-V05). One image for each of the months of 2008 were downloaded and processed, each containing composites of Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) as well as reflectance data in Red Blue, Near Infra red (NIR) and Mid Infra Red (MIR) bands. During the course of the study, Dr Ji undertook a large amount of image processing, using his proprietary software, and delivered composite materials covering NDVI and EVI for each month in standard, geo-referenced and geo-referenced / masked formats. In addition, Max, Mean and Min composites were produced for both NDVI and EVI imagery, again in GEO TIFF format (see Annex D, Appendix, and Ji, 2009, for full list of materials delivered). ArcGIS project files were further produced, in order to directly access this material (which can be enlarged at least to 1:1million


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scale before pixelation begins to occur). Figure 4.3 shows the NDVI time series for 2008, showing very clearly the vegetation green-up periods, area by area across the CAC5 countries, throughout the year.


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Figure 4.3: NDVI Time Series of 2008(MODIS MOD13Q1)


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From Figure 4.3, relative NDVI across the different parts of the CAC5 and for the different months of the year could be analysed, and this is presented in Table 4.1. Of interest is the very wide growing season, varying from area to area, and the fact that the maximum green-up period for the irrigated areas – even at low elevations – occurs so late in the season (especially in August, September) when availability of irrigation water is generally at its most critical. Table 4.1: Relative NDVI for growing-season months across main zones of the CAC-5 Mont

h TUK

S-most slopes,

Mari

KAZ-rainfed areas: SKO,

Zhambul

Low valleys: S.UZB & S.W.TAJ

Fergana Valley: rainfed areas

Mountain slopes

Irrigation areas: KAZ, KYR, UZB.

KAZ: Northern rainfed

arable areas

Mar ++ ++ ++ Apr + +++ +++ ++ May - +++

(lowest parts: ++)

+++ +++ + + +

Jun ++ (lowest parts:

+)

+ + +++ +++ +++

Jul ++ +++ +++ Aug + +++ + Sep ++ Oct +

Key: +++major green-up activity; ++moderate activity; +minor activity. 4.5 MODIS: 2008 NDVI MAX, MEAN AND MIN; AND RESOLUTION OF IMAGERY

All coverages processed and delivered by Dr Ji are of last year (2008). Although this is advantageous in that this is the most recent complete year, 2008 was not typical in that it was very dry – perhaps a 1-in-10 dry year. Also statistical data is not yet available for 2008 (annual reports are generally published about 8-9 months after the end of each year, after all the relevant data is collated and checked). In retrospect, use of 2007 or 2006 (both average to rather good years) perhaps would have been better choices for the ‘most recent baseline year’: taking 2008 will produce a low baseline. MODIS 2008 coverages were enlarged to 1:1m scale on the GIS (to overlay with other coverages – ECONET, Water Management Map, etc) and registration of the various sources was observed to be good, once the geographic coordinate system was converted from GCS-Krasovky-1940 to WGS-1984. Units on the legend were statistical groupings and did not directly correlate with quantitative units of ‘standing active vegetation’ say in kg/ha. Nevertheless, what was displayed was considered extremely useful: most irrigated areas could clearly be differentiated, and a further class of ‘rainfed arable lands plus better pasture/forest land’ could also be demarcated. Within the main irrigated areas, ‘good’, ‘moderate’ and ‘marginal’ areas could again be differentiated. This imagery was of greatly superior quality to that presented by Dr Ji in his final report (which tended to focus on screenshots covering the entire area CAC-5 area). Various screenshots have now been taken to illustrate various phenomena for presentation in this MSEC final report. Indeed the three original coverages could be enlarged to 1:500.000 scale before obvious pixilation occurred, and this makes them extremely useful as basemap and baseline layers in the CAC-5 SLIMIS. NDVI-Max images show very sharp gradients between ‘productive’ and ‘non-productive’ vegetation areas. This demarcation could well be extremely useful for pasture management purposes, separating quite good vegetative growth (albeit over a short vegetative season) from very poor growth (and not worthwhile to graze). This could provide a key indicator effectively separating areas of pasture use from areas which are effectively unused.


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NDVI-Min images reflect very well ‘winter growth potential’ – i.e. potential for vegetative growth during the coldest months of the year. This is a major indicator and parameter for pasture production in Middle-East countries (Jordan, Israel, Syria, Saudi..), and may well be also important in the southern and lower-elevation areas in the CAC-5. Climate-change (specifically increased average winter temperatures) may also affect this particular parameter quite favourably. Figs 4.4 and 4.5 show respectively max and mean NDVI for the major part of the Priority Area, with the coverage of hydrographic features (rivers and canals from the GIS) superimposed on these images, indicating that georeferencing was excellent. In addition, the dense network of canals and drains indicates irrigated areas and shows that many (but not all) of the high-NDVI areas in the NDVI Mean image are irrigated lands. Figs 4.6. and 4.7 show the area around Bishkek enlarged to approximately 1:1million scale, including appreciable areas of Chui Oblast (KYR) and adjacent areas in KAZ. Fig. 4.4: NDVI Max, 2008: Major Part of Priority Area, in relation to Rivers and Canals: Scale on screen: c. 1:6million

Fig.4.5: NDVI Mean, 2008: Major Part of Priority Area, in relation to Rivers and Canals: Scale on screen: c. 1:6million


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Fig.4.6:NDVI Max, 2008: corner of Chui Oblast, KYR, Zhambul & Almaty Oblasts, KAZ: Scale on screen:c.1:1million


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Fig.4.7:NDVI Mean, 2008: corner of Chui Oblast, KYR, Jambul & Almaty Oblasts, KAZ: Scale on screen:c.1:1million

Figs 4.6. and 4.7 also include the two CACILM National Project areas focusing on pasture management: Susamir Valley, in the SW of the figure, and Ugulek Selskiy Okrug in the NE. The relative location of these projects, in relation to the total area covered by Figures 4.6 and 4.7, is given in Figure 8.4. At Ugulek, the site is at lower elevation (800-1000m), with mean annual precipitation of around 300-400mm, and here low NDVI areas (orange and yellow colours) correspond to poor pasture on sloping land with shallow soils (retaining little water). Moderate NDVI areas (light green colours) correspond to better pasture and to rainfed arable areas, both on deeper soils on less steep slopes, while high NDVI areas (bright green) correspond to irrigation areas and to concave colluvial footslopes benefiting from increased influx of water. (See also Figure 4.14, a composite from recent Google Earth imagery, at 1:100.000 scale, for further details.) In the Susamir Valley elevation ranges from around 2000m to the high mountain areas at 4000m, with mean annual precipitation at around 250mm at lower elevation rising to perhaps around 500mm at high elevation. (Susamir valley is in a rain shadow as compared to equivalent areas above Tasaryk, further W in KAZ – see Section 7). The NDVImax image shows a very steep, altitude-related gradient, with very low NDVI at high elevation (>3500m) quickly passing to high NDVI at moderately high elevation (3000m) thereafter falling slowly but steadily to low NDVI within the large, dry valley areas. Susamir shows a larger area of relatively high NDVImax (green) than does Ugulek, but corresponding areas show lower NDVImean, implying that the green-up period at Susamir is very short but quite intense, while at Ugulek it is less intense but much longer. 4.6 LANDSAT: HIGHER SPECTRAL- AND PIXEL-RESOLUTION IMAGERY: LANDSAT ETM+ GEOCOVER :

2004 DATA

LANDSAT ETM+ GeoCover is a 15m-resolution true-colour composite product in MrSID format, with each scene representing a square of side 5° (area of about 180.000km²). This product is further


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digitally enhanced from original 30m-resolution material, and, in MrSID format, stands enlargement to around 1:100.000 scale before pixilation becomes obvious. The location index sketchmap for the imagery delivered by Dr Ji, covering virtually all of the CAC-5 area plus substantial areas of surrounding countries, is as follows:

42 – 50 KAZ - NW

43 – 50 KAZ - N

44 – 50 KAZ - NE

39 – 45 N.CASPIAN

40 – 45 ARAL-NW

41 – 45 ARAL-NE

42 – 45 KYZYL ORDA

-N

43 – 45 BALKASH-NW

44 – 45 BALKASH-

NE 39 – 40

C.CASPIAN 40 – 40

ARAL-SW 41 – 40

UZB-NW 42 – 40

KAZ-SKO * ** 43 – 40

KYR-CHUI

39 – 35 S.CASPIAN

40 – 35 TUK-SW

41 – 35 TUK-E

42 – 35 TAJ – C, SW

43 – 35 PARMIRS

The format of imagery provided for each scene comprises four main files with approximate sizes as follows: JPEG browse: 360kB; JPEG preview:16kB; SID: 207MB; tif browse:4.9MB. For the Scene 42-40 (perhaps the most important single scene of the ‘CAC-5 Priority Area’ in terms of population and agricultural production), the following imagery products are also given: ex-geo:12.9MB*; ex:14.1MB**. (Apparently these are the only georeferenced products delivered for these LANDSAT scenes.) Scene 42-40 is centered on KAZ-SKO and covers also Tashkent and the substantial irrigated area to the SW of Tashkent; also most of the Fergana Valley, the W part of Chui Oblast (KYR) and the W part of Jambul Oblast (KAZ). The 8 scenes highlighted in bold above together cover almost all the CAC-5 Priority Area. Only the southern fringes (maybe less than 10% of each image) is required of the four images just to the north of these (i.e. 40-45, 41-45, 42-45, and 43-45), these covering the northern extremities of the four southern Kazakh oblasts included in the CAC-5 Priority Area. Only a very minor area is missing completely: the eastern tip of Kyrgyzstan (well to the east of Lake Issyk Kul), and the south-eastern extremities of Almaty Oblast, Kazakhstan. These missing areas are well outside any areas covered by the GEF projects. They would be covered by image 44 – 40. All the JPEG browse images were investigated in a rapid study covering the whole CAC-5 area. Each image filled the computer screen and could be enlarged by a further 60-70% before pixilation began to be obvious. This happened at a scale of about 1:1.500.000 (as viewed on the computer screen). Spectral differentiation of this imagery was excellent, and this was clearly captured at the period of maximum vegetation green-up in 2004, although all the images were dated August-September, 2004. The MrSID-format file for Image 42-40 was also opened in both ArcView3.x and also in ArcView9.3. For ArcView3.x Mr SID image support had to be activated first, in the File~Extensions~ box. In ArcView9.3, ArcMap, the MrSID file extension could be opened directly. The quality of the MrSID imagery was excellent, these images being enlargeable to 1:100.000 scale before any visible pixilation begins to occur, (see Figures 4.8 to 4.14). Fig.4.8: LANDSAT ETM, 2004: Degraded area (drainage, salinity, water shortages) SW of Turkestan, KAZ. 1:100.000.


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Fig 4.9: LANDSAT ETM, 2004: Area to N of Shymkent : N. Sairam Rayon, SKO, KAZ. Scale c. 1:100.000.

Figure 4.8, covering the area between Turkestan (SKO,KAZ) and the Syr Darya River, is the centre of a major hotspot area as recognized in the NDVI Trends, 1982-86, from the GIMMS Dataset (see Section 4.2 above). This area suffers from ‘tail-enders’ problems, being at the end of irrigation canals which are invariably short of water at the end of the summer period. Nearby low-lying backswamp areas conversely have drainability problems, and adjacent low-lying lands show major problems of salinity and sodicity. During the spring period much of this land is flooded (or suffers very high water tables) from overspill from the swollen Syr Darya river. Altogether these are text-book examples of problems of watershed management and irrigation and drainage management, and these have got worse over the late-Soviet and post-independence periods.


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Fig.4.9 shows the potential of the LANDSAT ETM imagery for land use differentiation and mapping over large areas. The area shown is that to the N of Shymkent, SKO- KAZ, with the Arys River (a major tributary of the Syr Darya) flowing from E to W across the N part of this image. Four major N-flowing sub-tributaries of the Arys River can also be clearly seen .The scale here again is approximately 1:100.000. Land uses differentiated include the following: - irrigated arable (bright and dark green, uniform coloration), - rainfed arable (high-reflectivity crop stubble and dark, recently cultivated soil surface), - irrigated homegarden areas (dark green, speckled), - urban areas (also roads / infrastructure) (speckled, but with more high-reflectivity, light blue-grey, colouration), - rainfed pasture (v.light brown and v.light green, variable tones, signs of sloping lands), - water: fishponds and rivers (black); The area of Figure 4.10 is a few kilometers upstream of the area in Figure 4.9, entering a landscape of more rolling terrain. Here (in the N half of this figure) rainfed arable land is much steeper (8-16% cultivated slopes being common) and water-induced soil erosion much more of a problem. Loess cover has been removed in most sloping areas in the north of this image, exposing subsoils derived from underlying palaeozoic rock materials (and hence the red coloration of the exposed soil surfaces). However, the same land use differentiation as was made for Fig 4.9 also applies here. Figure 4.10: LANDSAT ETM, 2004: Upper Arys River Valley, SKO-KAZ, scale c.1:100.000.

Figure 4.11 covers the same area as in the central part of Fig.4.10 (above), and is imagery taken from Google Earth and reproduced here at a scale of approximately 1:40.000. This imagery is from two sources. The Western part of this image is of very detailed sputnik imagery, enlargeable, in this case, to 1:5.000 scale. The Eastern part is of the same Landsat ETM source as that applying for Fig 4.10, the fields being in exactly the same status of cultivation and cropping. This part of the image is grainy, as it has been enlarged to more than the 1:60.000 – 1:70.000 scale limit at which pixilation begins to occur. For the W part of the image, enlargement to 1:5.000 gives much useful further information on land use and management. Irrigation (and drainage) canals are visible; details of land cultivation, cropping, and soil and other environmental problems can be inferred (if not directly seen); and land use divisions as made at 1:100.000 in Fig. 4.9 and 4.10 can be confirmed. Figure 4.12: covers the area a few km to the NE of Fig. 4.11, and shows areas of major land degradation caused by water erosion on sloping rainfed arable land, in Baidebek Rayon, SKO-KAZ, at


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again a scale c. 1:100.000. Figure 9.8 shows a detailed image from GE, covering the area in the central part of Fig 4.12, showing the severity of the gulleying problem here. Figure 4.11. Google Earth imagery, same area as the central part of Fig.4.10 (above), scale c. 1:40.000.

Fig 4.12: LANDSAT ETM, 2004: Areas of major land degradation through water erosion (severe gulleying), Baidebek Rayon, SKO-KAZ, scale c. 1:100.000.


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Areas of severe gulleying such as occur in Figures 4.12 and 9.8 appear to be related to weakly-structured (but potentially very fertile) loess-derived soils on slopes steeper than 6%, where cultivation has been practiced in a slopewise direction, usually with heavy cultivation machinery. Usually the gulleying has occurred in the past, and the land is currently only used for grazing, but many areas of current gulleying associated with slopewise cultivation or with cattle tracks (as in Figure 9.4) are still observed. Figure 9.7, GIS map output at 1:1m scale, shows such hotspot areas. In this figure the land devastated by gulleying (both past and present) is shown with a unique dot symbol (magenta flashes). For the initial recognition of these areas, the detailed Google earth imagery has been essential, but once these are confirmed for any particular area the 1:100.000 Landsat ETM+ can be used to extrapolate these areas out to cover the full extent of the affected areas. Also of note is the fact that these areas do not show up in imagery at more general scale (e.g. MODIS-250m, presented at 1:1m scale): the high NDVI vegetation at the bottom of many gulleys balancing the adjacent very low NDVI bare soil areas and giving an overall moderate NDVI signature. Fig 4.13: LANDSAT ETM, 2004: Karamurt Selskiy Okrug (Municipality), Sairam Rayon, SKO-KAZ, scale c.1:100.000.

Figure 4.13 shows an area of very mixed land use, including irrigated arable, irrigated homegardens, dryland arable (stubble and recently-plowed land), pasture, and bare lands (river beds). From the ADB-financed EMIMS study, we have detailed soils and land quality (bonitet) assessment mapping for this municipality, at 1:10.000 scale, together with detailed analysis of crop composition and yields for the period 2002-2005. This information is given in Section 5.1 with mapping given in Figures 5.1 and 5.2. This area appears to be in a rain-shadow, with mean annual precipitation around only 520mm Detailed study of Karamurt and similar areas is essential as yields are well below potential, and with abundance of irrigation water at the early part of the summer supplementary irrigation in the period mid-April to mid-June could vastly improve yields for (mainly) rainfed winter crops. 4.7 GOOGLE EARTH : MODERATE AND HIGH-RESOLUTION IMAGERY

Google Earth imagery now gives excellent coverage for the Priority Area, with approximately 60% of the areas of irrigated and rainfed arable farming now covered with the very detailed sputnik imagery, enlargeable to 1:5.000 scale or more detailed. Outside these areas, approximately 20% of the land is covered by this very detailed imagery. This imagery has been captured at a very wide variety of dates, and the most recent version of the Google Earth software now provides the specific dates for most of the respective sections of this detailed imagery. Generally, the very detailed imagery has


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been captured at the end of the summer dry season and spectral resolution in some cases is not particularly good, although clarity of imagery is usually excellent, with the best imagery able to stand enlargement to a scale of 1:1.000. For all remaining areas, outside the coverage of the high resolution imagery, moderate-resolution imagery (invariably the 2004 LANDSAT ETM coverage) is available. As was seen in Section 4.6, this can stand enlargement routinely to 1:100.000 scale with no problem, and to about 1:60.000 before graininess begins to be problematic. Spectral resolution of this moderate-resolution imagery is invariably very good. Figure 4.14 is an example of a collage of this imagery for the Ugulek Selskiy Okrug CACILM National Project Area, Zhambul Rayon, Almaty Oblast, KAZ. On-screen imagery (and A3 print-outs) are at a scale of around 1:100.000: the A4 section of this, given here, covers the NW part of the total map coverage. Figure 4.14: Ugulek CACILM National Project , Almaty Obl., KAZ: Moderate-resolution GE Imagery: Scale c1:100.000


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Fig. 4.15: Example of Very Detailed GE Imagery. Homegardens Area, Talas Oblast, NW KYR. Scale c. 1:2.500.


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Fig. 4.16: Example of Very Detailed GE Imagery. Homegardens Area, Talas Oblast, NW KYR. Scale c. 1:20.000.


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From Figure 4.14 the following land use types can be differentiated on this 1:100.000-scale imagery: - Homestead gardens and village areas - irrigated cultivated land; - rainfed cultivated land; - improved pasture and haylands: rainfed; - higher productivity pasture / haylands under natural flooding, or high water tables, or artificially

irrigated; - unimproved, low-productivity pasture, mostly on poor soil areas and sloping, eroded lands

(maybe with further subdivision, according to pasture productivity); - natural water courses and river beds.

A preliminary land use overlay with the above legend could be produced as a photo-interpretation for this and adjacent areas. This interpretation would then need cross-checking with the NDVI-mean and NDVI-max imagery (Figs 4.6 and 4.7) and any other imagery available for this area. For similar areas nearby also covered by the very detailed sputnik imagery (e.g. as in Figure 4.11), checking in the contact zone between the two types of imagery will give further useful information – for example, to differentiate the rainfed cultivated land from the irrigated cultivated land on the one hand, and from the improved pasture and haylands on the other. Fieldwork is then required for checking of this work, one full day with a GPS and notebook/field data recorder being required for an area of this size (100.000ha). GPS points should then be downloaded to imagery (see Fig 9.11) and interpretation confirmed or amended, as required. Figures 4.15 and 4.16 present an example of the very detailed Google Earth imagery now available, in this case for an area of village homegardens in the NW extremity of Talas Oblast, KYR, very close to the KAZ border. Figure 4.15, presented at about 1:2.500 scale (the limit of enlargement) shows the detail observable for these individual homegardens, varying in size between 0,2 and 0,6ha. A homegardens allotment plan, together with road, canal and drainage infrastructure, would ideally be produced at this scale, following field-maps (and cadastral plans) which could be produced at a scale as detailed as 1:1000. This work is well beyond the level of detail envisaged for CACILM-MSEC, but could well be within the remit of project implementation work that could be envisaged as part of the CACILM National Projects field implementation activities. Figure 4.16, presented at 1:20.000 scale for the same area, shows the total village homegarden area delineated on this imagery. This area shows also irrigated agricultural land, relatively intensively managed. One area, however, in the NW of the image, indicates that some land degradation has occurred, the boundaries of this degraded areas with the adjacent irrigated areas being rather diffuse and thus reflecting some natural phenomenon. Fieldwork is needed to determine the cause of this degradation: it may be due to salinity, sodicity, poor drainage, or simply structure deterioration of the surface soil. Use of the detailed imagery at this scale would be ideal for the land use differentiation and demarcation. Although captured at 1:20.000, investigations would be undertaken, area by area, at around 1:5.000 scale to determine specific land uses. Also, after the overlay is produced at 1:20.000, it would be digitized and a final GIS map output could be in the range 1:20.000 (if additional topo details were to be added) to 1:100.000 (if land use information alone were to be displayed). 4.8 FURTHER WORK REQUIREMENTS FOR REMOTE SENSING IN THE CAC-5

4.8.1 General

The immediate and most pressing need is for a high quality Land Cover and Land Use Mapping for the full priority area following a uniform methodology, aiming for field print-outs at around 1:500.000 scale and high-quality cartographic print-outs at 1:1m scale. For this satellite remote sensing using LANDSAT ETM+ as the base, but with cross-checking with MODIS NDVImax and NDVImean imagery, and with Google Earth detailed imagery, would be required, together with some well-selected fieldwork. Some revision and refinement of the CACILM land use legend needs to be made for this, including some rational subdivisions for pasture productivity. This work would be at the top of the priority list for any future phase of the project.


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The imagery already prepared by Dr Ji and delivered in May / June 2009 has already been shown to be very useful for this purpose. The 2004 LANDSAT ETM+ imagery, although beginning to be a little dated, is of very high quality and of the correct degree of detail for the above work. The 2008 imagery, covering the full CAC5 area, is useful and interesting, but it has the limitation of representing the 1-in-10 or 1-in-20 dry year. Any imagery that might be obtainable for 2009 would conversely represent the 1-in-10 or 1-in-20 wet year. Both 2005 and 2006 would appear to have been more typical years (see Section 7). One lesson of the NSIU work 2008-09 is that all areas need checking with multiple sources of data: relying on only one source of RS data does not provide for a good result. Close cross-checking with the (free) Google Earth imagery provides excellent information, very quickly. A further lesson is that some fieldwork is essential. Even land cover mapping (let alone land use mapping) can not be undertaken on remote sensing alone. More attention must be given to these two aspects, rather than an increased focus on ever-more sophisticated (and expensive) image processing. Lateral thinking, relating one data source to another, is more important than expecting a perfect answer from a single data source. However, some image-downloading and processing facility is clearly needed in the CAC-5, and CACILM could most cost-effectively provide this facility centrally (i.e. at MSEC), rather than supporting efforts split between the five centres.

4.8.2 Application of RS for pasture monitoring

Ji (2009) provides good examples of RS application for pasture monitoring, showing areas where pasture has clearly degraded within close proximity to watering points, giving a clear spotted affect on the imagery. Ji’s key illustration of this is further reproduced here, as Figure 4.17. This figure usefully shows degraded spots, but absolute pasture productivity in terms of cnt / ha of DM still has to be devised, and yield figures obtained over larger areas. Look-up tables relating monthly or half-monthly NDVI taken from the MODIS-250m imagery have to be calibrated with stations where accurate pasture measurements have been made, and these applied both for the active growing season and for the full year. This work also needs to be related to the well-established pasture mapping (‘geo-botanical mapping’) undertaken at rayon level during the Soviet period, and now being updated in some areas (most actively by GosNPCZem in Kazakhstan). Fig 4.17: Grazing gradient detected using annual maximum NDVI, 2008 for the area north of Nuratau Lake (UZB)


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Source: Ji, (2008/09) Approaches in this work were further reviewed in Annex G in the Design Guidelines of the EMIMS-SLU Final Report, November, 2007, and possible applications of GIS and Remote Sensing were outlined. Of key long-term interest would be to be able to use satellite remote sensing, combined with existing geo-botanical mapping and limited current fieldwork, to devise figures, season by season, for the three key parameters which are currently measured from fieldwork tied to the existing mapping: - palatable DM production (cnt / ha / season); - fodder units (cnt / ha / season); - digestible protein production (kg / ha / season). Much further development work needs to be done to get to this stage. Finally RS work needs to be undertaken to correlate precipitation estimates obtained by remote sensing with estimates of potential pasture productivity, obtained from the above geo-botanical mapping and remote sensing study, in order to come up with estimates of expected pasture productivity in the forthcoming 2-3 months growing season. Again, this is a potential useful development activity for CACILM to implement, with applicability to all five countries. But before that is able to happen, free sharing of information between different institutions needs to occur, and that is still the limiting factor to this work at the moment. Figure 4.18(a): Bare / abandoned lands in Fergana Valley, Uzbekistan: scale c. 1:1m (as viewed on screen)


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Source: Ji, (2008/09) Figure 4.18(b): Fergana Valley, Uzbekistan: 2008 NDVImax with superimposed rivers, canals, and reservoirs.

Source: CACILM SLIMIS: 2008NDVImax (imagery prepared by Dr.Ji) with superimposed hydrographic layers from ECONET basemap information. Approximately same area as above, at same scale (c. 1:1m as viewed on computer screen, full page width). Figure 4.18(c): Fergana Valley, Uzbekistan: Water Management Map (NSIU-UZB). Scale c.1:1m.


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Source: NSIU-UZB, (2009), with appropriate layers selected by SLMIS. Figure 4.18(d): Fergana Valley, Uzbekistan: 2008 NDVI day 183-213, & Irrigation Boundary (Water Management Map)

Source: CACILM SLIMIS: 2008NDVI day 193-213 (imagery prepared by Dr.Ji) with superimposed river layer from ECONET basemap information, and irrigation area boundaries (gold) from Water


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Management Map of NSIU-UZB. Approximately same area as above, at same scale (c. 1:1m as viewed on computer screen, full page width).

4.8.3 Application of RS in soil erosion, soil salinity, and water-table monitoring

Figure 4.18(a) shows the MODIS-250 2008 NDVImax image for most of the UZB portion of the Fergana Valley, with areas of low / moderate NDVI shown in yellow, and very low NDVI shown in bright red. These are clearly areas that had little or no vegetation at any time during the growing season in 2008. This image, processed by Dr Ji to reflect 3 discreet classes of NDVI, was derived from the standard image, now on the SLMIS, shown in Fig.4.18(b) in relation to hydrographic features (given in the ECONET basemap coverages). Also of the same area is Fig.4.18(c) showing the water management map coverages. Irrigation command areas are shown in light green. Fig.4.18(d) shows the 2008 NDVI for July (Day183-213) with boundary of irrigated areas shown in gold outline. Lastly, the Google Earth coverage of the whole area was inspected, more than 70% of the area covered by the very detailed imagery and 30% by the moderate-detailed LANDSAT ETM+ imagery. The observations and conclusions from this work are the following: i. most of the red and yellow areas of Fig.4.18(a) were areas out of irrigation command, and many of these were at higher elevation (elevation being obtained by the DTM data given in the detailed GE imagery). Many were also clearly of shallow or eroded soils (not alluvial / colluvial / and aeolian soils which comprise most of the irrigation areas). Some of these areas also correspond to urban, industrial or mining land uses. ii. the cropping pattern in the valley is quite complex, with crops being at different stages of growth at any one time. Higher NDVI areas in Fig.4.18(d) thus show quite a variation in tone. Most of these areas are green in Fig 4.18(a) but some of these correspond to some of the yellow areas. Some wetland rice is also being grown, further complicating the picture. iii. some of the yellow areas shown in Fig.4.18(a), however, do seem to be related to some land degradation phenomena (poor crop growth due to soil salinity or sodicity, soil structure breakdown, poor and uneven germination, or high water-tables). However, to investigate these areas imagery needs to be enlarged and overlayed with topographical coverages. Comparisons then have to be made with all other imagery and mapped information. iv. Remote Sensing (RS) can never completely substitute for field work: some field checking is essential. But RS can indicate which areas should be prioritized so that field work may be carried out most efficiently. v. RS needs to be undertaken across multiple sources of data. Use of a single source can lead to misleading results. The presence of wetland rice in the cropping pattern in several key areas is a further complicating factor, as high water tables, by definition, are a key feature of this crop. Wetland rice is an important crop in the lower elevation areas of both the Syr Darya and Amu Darya floodplains, but is extremely water-demanding. Although crop yields per ha can be high, water use efficiencies are invariably very low. However, another feature of the cropping pattern which includes wetland rice is that it is a good crop for both salinity tolerance, and salt removal – salt simply is transported into the surrounding drainage ditches (the ideal situation), or, very commonly, to the surrounding areas growing other crops requiring low water tables (a highly undesirable situation). Use and management of land for wetland rice in an area where other (dryfoot) crops are grown represents a large complication, made more difficult now by the fact that the land is owned and managed by small individual farmers rather than large enterprises.

4.8.4 Application of RS for Carbon sequestration and Climate monitoring

Wider aspects of carbon sequestration, climate change, and climate monitoring are discussed in Section 10.7. Remote Sensing can have four specific inputs in this work:


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- monitoring of active vegetation, and current rates of C-fixation and release from this vegetation;

- monitoring of soil cultivation and degree of exposure, and consequent rates of C-fixation and release from the soil;

- direct, large-area monitoring of meteorological parameters: sunshine, cloud cover, upper cloud temperature, and, most importantly, precipitation.

- Monitoring specific activities which are designed to mitigate any negative affects of climate change – e.g. implementation of improved irrigation and drainage practices, planting of windbreaks, etc.

Again Remote Sensing requires detailed field work so that adequate correlations are established between Vegetation Indices and other RS parameters between different dates, and precise measurements made on the ground. This work has so far not been undertaken in Central Asia.


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5 CACILM USE OF SOIL MAPPING, & LAND QUALITY (BONITET) RATINGS & REASSESSMENT SURVEYS

5.1 SOIL MAPPING AVAILABLE THROUGHOUT THE 5 COUNTRIES

5.1.1 Detailed Mapping

During the Soviet period excellent soil and land quality studies and mapping were undertaken in all republics, coordinated by staff of the soil science institutes at republic level and by agricultural ministry staff at both Republic and Union (Moscow) level. From the late 1950s a program of detailed soil mapping was undertaken throughout the Union for each of the agricultural enterprises (kolkhozes and sovkhozes). Field and laboratory work, mapping and reporting were all undertaken by technical staff of the national or oblast offices of the agricultural ministries, usually with technical input and supervision by staff of the local national soil science institutes. Mapping was usually at 1:10.000 scale for all intensively cultivated areas, but at more general scales (1:25.000 or 1:50.000) for enterprises with a predominance of less intensively-used (e.g. pasture and forest) land. The mapping at 1:10.000 scale was also categorically detailed, as well as being cartographically detailed, with soil phases being mapped. Attribute data for each mapping unit usually included the following: soil texture (topsoil and subsoil); soil parent material; rooting depth; slope gradient and position; drainage; depth to mottling and gleying; depth to water table; stoniness, salinity etc…Areas of each mapping unit (ha) and bonitet ratings (see Section 5.3) were also included. Technical memoirs accompanying the maps included land use, climate and yield data for the main crops for the preceding years, plus detailed descriptions of the main soil types and detailed chemical and physical analytical data.

Fig. 5.1: Detailed Soil & Bonitet Reassessment Mapping: 1:10.000 scale, Karamurt SO, Sairam Rayon, SKO-KAZ

Source: EMIMS-SLU study, EMIMS Design Guidelines, November, 2007. Original mapscale: 1:10.000; this figure photoreduced to c. 1:25.000.


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Note: Bonitet ratings assigned to each mapping unit based on assessment of parameters made at each sampling point (crosses on this map). In this case, soil organic-C content and A-horizon (topsoil) depths and related slope gradients & positions are key determinants, leading to a huge variation in Bonitet across a relatively small area. Note that some of the areas are irrigated (served by canals), others are rainfed, and ratings will vary accordingly. In spite of this work being done to very high technical standards very little of it has been formally published. Usually only a few hand-coloured copies of the maps were produced, with the soil institute of the republic, the counterpart office in Moscow, and the office of the enterprise itself receiving copies. Obtaining access to this data is usually difficult and time consuming, but not impossible. Getting all of this information into digital format, and using the resultant information for practical use, should now be top priority in any SLMIS-SLU development. In Kazakhstan, GosNPCzem, under the State Agency for Land Resource Management, currently holds this mapping and has a long-term program for digitizing this, mostly under their local oblast offices. This detailed mapping is hence potentially available to both public and private users, although current policy is both to restrict usage and to charge a (high) commercial price for the data made available. This policy facilitates finance for GosNPCZem activities, but is a major impediment for overall rural planning and development in the country, where rapid access to all the digitized information is required by the planners and administrators. The Presidential Decree on free exchange and sharing of official data across ministries and public agencies is being blatantly ignored: narrow commercial interests of the semi-privatised agencies diverging from National interest. A review of the Detailed Soil and Bonitet Assessment Mapping and Reporting for Karamurt Selskiy Okrug, one of the municipalities of Sairam Rayon, SKO-KAZ, is presented in Annex B of the EMIMS-SLU Final Report (November, 2007). Mapping from this survey, at a greatly reduced scale, with map legends edited and enhanced (by the Consultant) in order to better reflect land quality ratings, is shown in Figure 5.1. Status of mapping, reporting, and digitizing for one of the rayons (Sairam) is given in Table 6.4. Data on other rayons in SKO are held, in a similar format, on the SLMIS. A not dissimilar situation prevails in the other republics. In Kyrgyzstan the local Soil Institute holds the detailed mapping, and, as in KAZ, has an on-going programme of bonitet reassessment surveys in its 441 Aiyl Okmotu (municipality) survey areas, this programme under (restricted) local finance. Generally, the surveys are undertaken in groups under respective rayons – the dates of original and latest surveys for these are given in Table 5.1. For any implementation project, acquisition of these surveys must be a high priority, and it would be of mutual benefit if the funding agency for the project would cover the relatively modest cost of bonitet re-assessment surveys if these had not been undertaken in the previous 10-15 years. Table 5.1: KYR: Detailed Municipalities Soil and Bonitet Assessment Surveys: Dates of Original & Latest Surveys


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Source: KYR-NSIU, 2009. A key requirement for the SLMIS now would be to obtain the average bonitet ratings (irrigated, and rainfed arable) for each survey area for each date of survey, and to plot these in either pie format (Figure 6.9) or polygon format (Figure 9.1) in order to show clearly any changes in bonitet, area by area. This presentation would very accurately and directly highlight the key hotspot areas in a useful and objectively quantifiable measure.

5.1.2 Oblast-level Mapping

Oblast-level soil mapping was made available by KAZ-NSIU for all four of their CAC-5 Priority Area oblasts. Key details of this mapping is given in Table 5.2. Table 5.2: Key Characteristics of the KAZ Oblast Soil Mapping. Oblast Date Scale Format Remarks Almaty 2005 1:500.000 ESRI shape

file Includes E part of Zhambul Oblast. Excludes N extremity of Almaty Obl, N of 46º 40’. 143 mapping units (118common). 2668 individual polygons

SKO 1964 1:300.000 JPEG scan

8 legend/general sheets: Sheet 5: Legend; Sheet 6: complexes; Sheet 7: Soil texture (mech.anal). Includes general map, 1:1m. (Present-day) Maktaraal rayon excluded. 12 sheets-2º EW, 1º 20’ NS

Zhambul 1962 1:300.000 JPEG scan 13 sheets-2º EW, 1º 20’ NS Kyzly Orda

1958 1:300.000 JPEG scan 15 sheets-2º EW, 1º 20’ NS; 3 legend sheets

Almaty Oblast soil mapping was provided in ESRI shape file format, having been converted from the original MapInfo file. This material was from the Institute of Soil Science, Almaty, published in 2005,


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but based on older (Soviet) material. The digitized coverage also includes a substantial area in eastern Zhambul Oblast (land east of the meridian 74ºE), but excludes the northern tip of Almaty oblast (land N of 46º 40’). The legend gives a total of 169 mapping units, spread over a total of 2668 soil polygons. However, inspection revealed that 26 mapping units did not occur in this coverage; a further 14 units occurred in only one polygon, and 11 units occurred in only two polygons each. Fields in the soil polygon database include: Name: Soil Mapping Unit name Idcomp: Soil Mapping Unit number: running from 1 to 169 Mekh.sym: Soil Texture Group: 10groups, and various subgroups. Group II, IV, VII, VIII, V are the most common;VI, O, III and IX are less common. The map legend shows Number, Code name, Map unit name, and a detailed description of the modal soil profile for each unit. The description also gives considerable soil analytical data, including % humus, C/N ratios, pH, CEC, carbonate content etc. Much of this analytical data can now be extracted and put into additional numerical fields. MSEC have now added a further field to record the approximate number of individual polygons represented by each mapping unit: 1, 2, 3-5, 6-29, >30. A further 16 layers are given in this coverage, including a Soil Zone theme, and many topo layers. A greatly simplified map legend, also showing relationships between soil type and soil forming factors (climate, parent material, slope etc), and with major land uses, is shown in the accompanying table to Figure 5.2. The equivalent numbers to these major soil types, for the Almaty Oblast listing, are given in the final column. SKO soil mapping (and that for the other 2 oblasts) was produced only as JPEG scans, but these covered together a total of 40 sheets of map detail, and another 10 or so sheets covering the various aspects of map legends. The format of the mapping was very similar to that for Almaty, with most of the soil names and code numbers being identical. The voluminous legend is in three parts: map unit description (105 units for SKO); Soil Texture Descriptions (Mechanical Soil Structure) (62 units for SKO); Complex Units (12 main units, and very many subunits for SKO). SKO legends were translated during the KAZ-EMIMS study in late 2007, and these are given in Annex E, together with the original Russian names, and Russian Code names. A greatly simplified legend, and simplified map for the main populated area of SKO is presented in Figure 5.2 and accompany table, in order to show relationships between the units and factors such as precipitation, elevation and parent material. Also presented, in the second column, are the common ranges in organic matter (humus) content in the surface (A-horizon) of the soil profile, and the total calculated organic carbon content (in terms of tonnes-C/ha) for each major soil type. A section of the SKO soil map, covering the area to the N and E of Shymkent, including Karamurt (Fig 5.1) is shown in Fig 5.3. A full presentation of soil profile descriptions and accompanying analytical data for the Soils of SKO is presented as Annex B to the KAZ-EMIMS Final Report (Nov. 2007). Fig.5.2: Southern Kazakhstan: Major Soil Units, relationships with Soil Factors, Land Use, and Oblast Legends.


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Major Soil Types

Humus% A-hor / tonnes C/ha

Mean (mm) Annual Precipitation

Elevation Range (m)

Soil parent Material & Texture

Dominant Slope ranges

Dominant Land Uses / Cropping

Remarks (& equivalent soil units in Almaty Oblast)

1 Meadow serozemic soils

2% 50tonnes

180 - 300 200 - 300

Stratified alluvium

0 – 2%, some 2-4%

Cotton (Rice)Natural haylands (v.high productivity): river flooding & high WaterTables. Pasture, moderate productivity in higher areas.

Saline & sodic phases common; winter river flooding common; drainage & drainability problems in places. (82)

2 Light serozems

0,8 – 1,5% 15– 30tonnes

250 - 400 300 - 400

Sandy loam; & loessial

2-4% 4-8%

Cotton; Pasture mod-low productivity

Dry wind-erosion prone soils. (71)

3 Normal serozems

1,5 – 2% 25-40tonnes

350 - 600 350 - 700

Loamy; & loessial

2-4%, 4-8%, (8-16%)

Wheat, oilseeds, alfalfa; Cotton, Horticultural; Pasture (mod-high

Water erosion (gulleying) a problem on steeper cultivated land.


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productivity) (65) 4 Grey brown soils

c.2,5% 80tonnes

450 - 700 600 - 1100

Heavy loam; & loessial

2-4%; 4-8%; 8-16%

Wheat, alfalfa, oilseeds; Horticultural Pasture (high productivity)

Major problems with water erosion (gulleying) – no soil cons measures, even on steep cultiv. land (56)

5 Brown soils

4 – 7% 75-115tonnes

600 - 800 900 - 1400

Heavy loam

4-8%; 2-4%; 8-16%; 16-30%

Wheat, alfalfa, Horticultural Pasture (high productivity)

Major problems with water erosion (gulleying) – no soil conservn measures, even on steep cultiv. land(41) / (49)

6 Mountain brown & Mountain grey brown

4 – 8% 80-150tonnes

650 - 950 1000-2800

Various; over hard crystal-line rock

30-60%; 16-30%; >60%

Summer pasture (mod. & high productivity);

Water erosion problems in places. (20) / (25)

7 Mountain meadow steppe, alpine & subalpine

8 – 12% 150 –240tonnes

700 – 1200?

2200-4000

Hard crystalline rock

30-60%; >>60%

Summer pasture (mod. productivity, v. short season)

Limited local pasture use; good in places. (3) / (13)

Bold under land uses denotes irrigated cropping; italics denotes rainfed; normal type denotes both rainfed & irrigated


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Figure 5.3: SKO: Oblast-level Soil Mapping, 1:300.000.

Source: Scan of small portion of 1:300.000 soil map, SKO, covering Shymkent area and most of Sairam Rayon (note rayon boundaries). Karamurt lies in the S part of the E edge of this figure. Positions of some of the Kolkhozes and Sovkhozes in this area are also given here. Legend tables (English and Russian, MS Word format), given in Annex E, and scanned JPEG format, with texture symbols, given in original JPEG scans. Cartographic detail of the original mapping is not particularly great, and once digitised and assigned a logical set of coloured polygonshades (as in, for example Fig 5.2) the mapping for SKO and for the other two oblasts would stand reduction to 1:500.000 and be fully compatible with the existing Almaty Obl mapping at that scale.

5.1.3 National and Multi-Country Mapping

Details of the national soil mapping currently made available b the four NSIU teams are given in Table 5.3. Table 5.3: Key Characteristics of the CAC-5 National Soil Mapping. Country Date Scale Format Remarks KAZ 1976 1:2.500.000 JPEG

scan Cartographically detailed, well-designed coloured legend (stands enlargement to 1:1,5-2m scale); good information on soil texture/parent material; excellent English/Russian legend; Russian soil classification system. Sets standard for all regional work. Sample presented as Figure 5.2.

UZB 1975 c1:2.000.000 ESRI shp

FAO_uzb. 53 mapping units. Fields: Uz (Uzbek code, 1-53); Code_fao; Rus_name; FAO_name; FAO_sum; Area; Fao_nm_sum. Good for soil correlation across different classification systems, but cartographic precision rather low. 534 map polygons.

UZB 1985 1:3.000.000 JPEG scan

41 mapping units; physiographic, then soil genetic groupings


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UZB 1975 c1:2.000.000 JPEG scan

FAO Soils Map. 36 main mapping units, English legend: well organized & useful. Physiographic, then soil genetic groupings. Desert zone (15 units) piedmont, low hills, sierozems (16), mod.mountains, brown soils (4); high mountains (1). Main irrigation mapping units (8) shown in green.

TUK 1989 1:2.500.000 / 1:1m

JPEG scan

45 mapping units. Variable cartographic detail: most areas viewable only at 1:2,5m scale, but some at 1:1m scale. 981 polygons

KYR 1988 1:750.000 ESRI shp

63 soils mapping units. 6 miscellaneous land units (rock, ice etc). Cartographically quite detailed: excellent base for regional land use planning. Legend needs correlating with KAZ and UZB legends. Sample for central section presented as Figure 5.3 in relation to precipitation isohyets (Fig. 5.4).

Figure 5.4: KYR Soil Map (W half of country), Scale 1:750.000 (reduction here to c.1:1.500.000)


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Source: KYR-NSIU; Watershed boundaries (thick black lines) and legend (avl) file: MSEC, 2009. Full details of legend given in Annex E. Of the national soil (and land unit) mapping, the KYR mapping is significantly more detailed than that required for 1:1m coverages, although good legend design can make it readable and interpretable at that scale (Fig.5.4). The national soils coverages of the other countries were much less precise, with print-outs at around 1:2m scale being optimum. The oblast-level mapping of KAZ, although originally produced at 1:300.000 scale, would stand significant reduction once an equivalent well-balanced colour legend were designed, and would probably be fairly compatible with this KYR national mapping. The KYR mapping shows the same format of overall legend as does the KAZ national and oblast mapping, combining physiographic zoning, climatic zoning, lithological mapping, and local geomorphological factors. The pronounced differences in precipitation, themselves being elevation and aspect-related (Fig 5.5), have a close relationship with soil type. Figure 5.5: KYR Mean Annual Precipitation (W half of country), Scale 1:750.000 (reduction here to c.1:1.500.000)


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Source: KYR-NSIU; Watershed boundaries (thick black lines) and legend (avl) file: MSEC, 2009. Mean annual precipitation: 1: <100mm; 2:100-200; 3:200-300; 4:300-400; 5:400-500; 6:500-600; 7:600-700; 8:700-800; 9:800-900; 10:900-1000; 11:1000-1200; 12:1200-1400; 13:1400-1600; 14:1600-1800; 15:>1800mm. 5.2 COMMONALITIES OF LEGENDS AND ATTRIBUTE DATA

All four countries showing commonalities of soil map legends: - they all embrace the Russian soil classification system with its embedded concept of zonality

(zonal, intrazonal, and azonal soils, and close relationships with climatic parameters);


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- they all include map unit divisions on negative soil profile characteristics – e.g. shallow soils, stoniness, salt and gypsum accumulation, sodicity etc (this information is important as a baseline for land degradation work);

- they include physiographic divisions as the first category of map legend organization; - they all include further legend information on soil texture and soil parent material.

However, on trying to combine the four national maps considerable differences were seen along all national borders. Explanation here is that cartographical and cartographical generalization from the more detailed oblast and rayon mapping had been performed differently, UZB maps showing considerable cartographic generalization, while KYR mapping shows the least generalization. Also, for the sandy desert areas, sand sheet and dune land has been treated differently: TUK have attempted to map small areas of mobile dunes, while both KAZ and UZB have taken different approaches. Devising a regional soil map which is sufficiently detailed both categorically and cartographically is important and must form a key element of any future work on land resources and related climate-impact work in the region. Mapping needs to be brought up to the standards of the KYR soil mapping, so that colour print-outs at 1:1m scale, following a uniform high-quality legend, can be made. The good oblast-level mapping also needs to be incorporated into the regional mapping, at least for the more populated areas showing both irrigated and rainfed arable, and higher-productivity pasture lands. The more arid areas are also highly photo-interpretable and it would be proposed to use satellite remote sensing to improve characterization and mapping of these areas, and particularly the sand dune and sand sheet areas. The latter represent an underexploited resource, both for improved pasture management, and more particularly for major extension of highly-efficient centre pivot irrigation, which requires high-infiltration, high bearing-capacity soils for economic operation. Soil correlation and remapping in order to produce a high quality regional soil map at 1:500.000 – 1:1m scale will be quite a major task for the CAC5 area. This is a topic that could be assigned to the European Soil Bureau, who have expressed preliminary interest in such work, and also have some funds for possible co-financing activities. 5.3 LAND QUALITY (BONITET) RATINGS AND FACTORS AFFECTING LAND DEGRADATION

Land quality (bonitet) ratings represent a key concept which is common to all FSU countries, and applies to all scales and levels of generalization, from field, through enterprise, municipality, rayon, to oblast. Details of the bonitet concept and its application is given in Box 5.1. From the fieldwork undertaken during the KAZ-EMIMS study, bonitet ratings were obtained for a compilation made towards the end of the Soviet period covering all 130 available enterprises (Iorganskiy). This included assessment of both irrigated and rainfed arable land. Results of this work are shown in Table 5.4. Table 5.4: Land Quality (Bonitet) Ratings of Irrigated & Rainfed Arable Lands of Enterprises & Rayons of SKO-KAZ Rayon ____Irrigated

Arable____ ____Rainfed Arable____

Remarks

Mean Max Min Mean Max Min Sozak 16 46 14 8 14 3 Major arid area Turkestan 23 25 19 10 15 4 Marginal irrigation:salinity &

WTproblem Ordabas 43 57 28 12 16 6 Baidebek 42 61 36 15 19 9 Marginal rainfed arable land Sairam 42 48 40 24 34 17 Main rainfed arable area Tulkibas 55 62 39 31 39 26 Main rainfed arable area: high

elevn. Tolebi 60 70 55 26 37 13 Main rainfed arable area Kazygurt 64 81 34 18 25 13 Major rainfed arable area Saryagash 33 43 26 11 13 9 ((v.near Tashkent


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conurbation)) Shardara 20 21 20 8 10 7 Major irrigation area: cotton

dominant Maktaraal 19 23 16 (9) (-) (-) Major irrigation area: cotton

dominant SKO: 29 81 14 16 39 3

Source: Dr Anatoli Iorganskiy, based on soil survey work undertaken in SKO in 1970s and ‘80s. Note: i. Max and Min refer to maximum and minimum in list of enterprises for each rayon (e.g. Max of 81 for Kaygurt was achieved by kolkhoz F.Engelsa, whose bonitet average was 81 points), ii. V.low ratings for Maktaraal and Shardara may be due to v.low organic-C and possibly high salinity and gypsum content in relatively new irrigated soils: good management over period 1970-1990 involving crop rotations including leguminous fodder crops and with irrigation with adequate drainage should have increased bonitets to a much higher figure, perhaps 45-50points. Of interest are the low irrigated bonitet figures for Maktaraal and Shardara rayons (both major irrigation areas). However, both have major salinity problems, as well as high WTs (esp.Maktaraal); also a heavy weighting is given to soil organic matter content and other parameters of natural fertility, both of which were very low in the early years of development of these virgin lands for irrigation. Good land management and full technical inputs (most importantly irrigation and drainage, and fertilizer) can compensate for low bonitet rating. However, both of these are in very short supply, and thus the low bonitet is a realistic assessment of current land capability. BOX 5.1: Land Quality (Bonitet) Ratings _________________________________________________________________________________ Bonitet Ratings represent a useful single-parameter system for assessing overall land capability as they relate directly to productivity of vegetation and crops as determined by soil and climate (and irrigation, if applied). Factors of soil water relations (rootable depth, available water-holding capacity, sometimes also infiltration), drainage, salinity and sodicity (alkalinity), and inherent (native) soil fertility and especially soil organic matter content all affect the bonitet assessment. Factors of climate are also included: mean values and reliability of rainfall as it affects soil moisture status during cropping period, temperature, windspeed, sunshine, humidity and hence potential evapotranspiration rates. Importantly, rehabilitation or degradation of the land can also greatly affect bonitet rating. For example, in Belarus, large areas of land had a bonitet of just 35 due to poor drainage (other factors such as salinity, alkalinity in this case were not limiting). After drainage rehabilitation the bonitet assessment increased to 47 points. An increase of 12 points was thus achieved, and these 12 points could be quantified in terms of a direct increase of grain and fodder crop yields. For major crops each bonitet point thus equates to production of a certain yield of a defined crop in an average season. Similarly, there are relationships between kg of fertiliser applied and extra crop yield, again for the average season, and for a specified (low) maximum total of fertiliser to be applied (commonly around 50kg N /ha). As an example, in Belarus for (rainfed) winter rye, each bonitet point equated to to 27kg grain / ha, and each kg / ha of active nutrient in fertiliser equated to 4.1kg / ha increase in rye grain yield. By comparison, in UZBEKISTAN each bonitet point equates to 40kg/ha cotton, or 60kg/ha winter wheat, or 75kg/ha maize grain, or 100kg/ha of lucerne hay in Year 1, and 200kg/ha in Year 2. Bonitet ratings of arable lands in theory were routinely reassessed every 5 years across the USSR. In practise this was achieved in the more productive and more densely populated lands (especially in the westernmost republics) but less systematically undertaken for the less productive or more problematic lands (e.g. those occurring over large expanses of the rainfed areas of the CAC-5). Nevertheless, the concept of bonitet rating is considered extremely useful in giving a single parameter rating to land capability. Also, because of the vast amount of work done in the past following this concept, and more particularly because these assessments have continued to the present (including assessment of soil organic matter which is included as a routine determination for all samples) it would be recommended that this methodology continue and form an important component of the


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SLMIS. Assessments were done on each soil mapping unit (see Figure 5.1), and averages were taken on each enterprise for the irrigated land, and for the rainfed land. Bonitet assessments were presented for both irrigated and rainfed land for each of the Kolkhozes and Sovkhozes of the SKO-KAZ (Iorganskiy et al, 1987) (seeTables 6.1 and 6.4, and Fig 6.9). Weighted averages were also given for each of the rayons within the SKO, again for both irrigated and rainfed lands (Table 5.4). For UZBEKISTAN, (Irrigated) Bonitet Ratings for each rayon are shown graphically for 1990 and 2003 as Figure 9.1, changes of colour denoting bonitet rating changes (in this case considerable falls). In the case of SKO-KAZ, for the irrigated assessment, totalling 285.303ha of land, the highest (irrigated) bonitet was achieved by Kolkhoz F.Engelsa with 81 points (Table 5.4). The highest weighted average for a rayon was obtained by Leninskiy (now Kazygurt) rayon (64 points): the minimum was 16 points (for Sozak rayon) and the irrigated average for the whole of the SKO was 29 points. Implied in these figures is that the best irrigated land is very productive but the average productivity is low, affected by very large areas of low productivity irrigated land. Nevertheless, irrigation (at least) doubles the productivity of the land across the full range of soil / land types in the SKO. For the SKO-KAZ rainfed assessment, totalling 819.285ha, the highest (rainfed) bonitet was achieved by Kolkhoz Kuybysheva with 39 points. The highest weighted average for a rayon was obtained by Tulkibas rayon (31 points): the minimum was 8 points (again for Sozak rayon) and the rainfed average for the whole of the SKO was 16 points. Bonitet assessments have also been made for all individual mapping units on the Agricultural Enterprise (now Selskiy Okrug / Municipality) soil maps originally undertaken at 1:10.000 or 1:25.000 scale over the period 1960-1989. Each individual polygon on the map is labelled with a soil number (e.g. 28r) and adjacent to this is the individual polygon’s bonitet rating (e.g. 20.6) with the area of the polygon ( e.g. 384,7ha) given below. Reassessments of soil and bonitet ratings have continued to the present, with recent maps being available for many municipalities (see Section 6). . Trends on bonitet ratings over time are available at both rayon and oblast levels. Oblast averages, over the period 1980-2006 are illustrated in Table 5.5, showing that overall there has been a 10% fall over this period, with the fall essentially occurring over the period 1990-1999. Table 5.5: UZBEKISTAN: Irrigated Land: Land Quality (Bonitet) ratings, Oblast Averages, 1980–2006

OBLAST 1980-85 1990 1999 2006

Khorezm 76 54 54 53

Namanghan 70 66 59 59

Surkhandarya 70 68 60 56

Tashkent 66 66 59 59

Ferghana 65 66 56 56

Andijan 60 60 60 57

Average:UZB 59 58 55 55

Samarkand 57 67 57 57

Bukhara 56 58 53 50

Navoi 56 59 52 52

Djizak 54 53 50 50

Syrdarya 54 53 49 49

Karakalpaksan 46 44 41 41

Kashkadarya 46 54 51 51 UZBEKISTAN: Irrigated Land: Land Quality (Bonitet) ratings, 1980 – 2006. Figures show an overall decline in Land Quality (Bonitet) ratings since the 1980-85 baseline period but big differences are observed between one oblast to another. Biggest overall declines were seen


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between 1990 and 1999, in the years following the break-up of the former Soviet Union (FSU), with further declines over the period 1999 – 2006 being much less marked. Within the Soviet period (1980-85; and 1990) bonitet ratings actually increased substantially in Samarkand (from 57 to 67), Kashkadarya (46 – 54), Navoi (56 – 59), and Bukhara (56-58) as land drainage and rehabilitation programmes continued to be implemented in these areas. However, large falls in bonitet rating were experienced over this period in Khorezm (76 – 54), and appreciable falls were seen in Namanghan (70 – 66), Surkhandarya (70 – 68) and Karakalpaksan (46 – 44). Overall, weighted ratings for the period were negative. Over the period 1990 – 1999 appreciable declines were seen in nearly all areas except in Khorezm and Andijan where ratings remained stable. But between 1999 and 2006 further appreciable declines were seen only in Surkhandarya (60 -56); Andijan (60-57) and Bukhara (53 – 50) Soil surface infiltration rate is a key indicator of soil quality status, with marked reduction in infiltration rate being one of the main manifestations of land degradation. In most soil types severe reduction in infiltration rate will occur due to soil structure breakdown and resultant blocking of soil pores, and much of this would be associated with reduction in soil organic matter and reduction in the biological processes in the upper soil profile, most notably reduction in soil animal activity. Repeated cultivation with insufficient additional of organic residues is likely to bring about such a condition: conversely, zero tillage, irrigation with soil nutrient additions, and inclusion of deep-rooting forage crops (e.g. alfalfa) in the crop rotation, are all likely to lead to an improvement. Figure 5.6 shows infiltration curves for an irrigated site in Kashkadarya Oblast, UZB, with recent results (for 2003) compared with results for three initial investigations over the period 1980-1983. Of interest here is the massive decrease in terminal rates over this 20+ year period, from around 40mm/hour to around 3mm/hour, with initial rates also declining (from around 75mm/hour to around 40mm/hour). For surface irrigation, these figures should still not be a problem, as 65mm of irrigation water infiltrating over a 3-hour period is quite reasonable. However, the decline in infiltration on these and other soils need continued monitoring, as further declines could well be problematic. Table 5.6 shows declines in soil infiltration rates for deep loessial soils in the main rainfed arable belt in SKO-KAZ, with soil infiltration being measured with a simple sprinkler infiltrometer, designed to simulate high-intensity rainfall or applications by irrigation sprinklers. The ten sites monitored under long-term pasture vegetation recorded a 2-hour infiltration of some 42mm, which is sufficient to absorb the 1-in-20 year storm rainfall event. However, in the six sites monitored that were cultivated but excluded alfalfa, 2-hour infiltration had declined to 18mm, well below the 1-in-5 year storm event. For the 3 sites cultivated to (well established) alfalfa, 2-hour infiltration was some 35mm – i.e. land under alfalfa behaves more like pasture than conventional cultivations.


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Figure 5.6: Decline in Soil Infiltration Rates for Irrigated Soils of Kashkadarya Oblast, UZB

Source: UZB-NSIU Final Report, August, 2009, quoting FAO/TCP/UZB/2901, 2004, and A.Morozov, 1989.

Table 5.6: Decline in Soil Infiltration Rates for Rainfed Loessial Soils of SKO-KAZ Total Sprinkler Infiltration (mm) over 1-hour periods: Pasture

(10sites) Cultivated, incl. alfalfa

(9sites) Alfalfa (3sites)

Cultivated, excl.alfalfa (6sites)

1st hour 23,2 12,9 21,5 8,5 2nd hour 18,3 10,4 13,2 9,0 Total, 2 hours

41,5 23,3 34,7 17,6

Source: EMIMS-SLU study, EMIMS Design Guidelines, November, 2007: fieldwork in rainfed arable areas, SKO-KAZ Soil salinity is a key determinant in land degradation in most irrigated areas within the CAC5, particularly those on flat plains and depressed areas where drainage infrastructure has not been maintained and operated properly. Standard measurement for soil salinity is elect5rical conductivity of a saturation paste extract (ECe), although for rapid soil screening 1:2,5 soil water suspensions are often used. Salinity levels corresponding to yield decreases of 10%, 25% and 50% for commonly grown crops in the CAC5 are shown in Table 5.7. This demonstrates the relative tolerance of cotton, but sensitivity of leguminous crops (Beans, alfalfa) and high-value vegetables and fruits. Figure 5.7 shows the relationship between salinity levels and rice yields in ICARDA’s experimental site in Kyzyl Orda, KAZ. Here the bright red and purple mapped polygons correspond to the highest salinity levels, and have given generally the lowest yields. However, growing rice (a relatively tolerant crop) is one method of land reclamation of saline land, the key factor here being drainage in the adjacent land, in order to remove the saline groundwater from damaging land growing other crops. (The EU-TACIS study, 2005, reported on this, and other related problems, in their Kyzyl Orda study.) Other factors determining bonitet include: sodicity (exchangeable sodium as a percentage of CEC); high magnesium content / inverted calcium/magnesium ratios (a particular problem in low-lying irrigated lands in the Syr Darya Basin); soil organic-C content (all soils); plus a large number of profile


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characteristics assessed in the field (rooting depth; texture; structure; stone and gravel content; slope; etc). Table 5.7: Affect of Soil Salinity on Depression of Crop Yield Crop: Remarks: ECe (dS/m) at 25ºC

for Yield Decrease of: 10% 25% 50% Field Crops: Barley At germination, ECe not to exceed 4–

5dS/m 12,0 16,0 18,0

Cotton 10,0 12,0 16,0 Sugar beet At germination, ECe not to exceed

3dS/m 8,5 11,0 15,0

Wheat At germination, ECe not to exceed 4–5dS/m

7,0 10,0 14,0

Rice Wetland conditions may automatically leach salt

5,0 6,0 8,0

Maize 5,0 6,0 7,0 Beans 1,5 2,0 3,5 Forage Crops: Lucerne (alfalfa) 3,0 5,0 8,0 Clovers 2,0 2,5 4,0 Vegetable Crops: Tomato 4,0 6,5 8,0 Potato 2,5 4,0 6,0 Onion 2,0 3,5 4,0 Fruit Crops: Grape 2,5 4,0 6,5 Apple 2,5 3,0 5,0 Apricot 2,0 2,5 3,5 Strawberry 1,3 1,8 2,5

Sources: University of California Comm. Of Consultants, 1974; FAO Irrigation & Drainage Paper No.29, 1976 Note: ECe: electrical conductivity of saturation paste extract, emulating natural soil water in situ in contact with crop roots.


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Figure 5.7: Rice Yields in relation to Soil Salinity: Kaptagay, Kyzyl Orda, KAZ (ICARDA Experiment Site)

Source: ICARDA, 2009. CACILM Sustainable Land Management Research Project, TA-6357. Jul-Dec08 Report. Note: Numbers in circles are average rice yields for respective fields in centners / ha. Site is no. 26 in Location Map, Figure 1.3.


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6 CACILM USE OF MUNICIPALITIES MAPPING AND LINKED DATABASES

6.1 SOVIET AGRICULTURAL ENTERPRISES AND LINKS TO PRESENT-DAY MUNICIPALITIES

As discussed in Chapter 5, Soviet-era agricultural enterprises, notably the State Farms (Sovkhozes) and Collective Farms (Kolkhozes) were the fundamental unit for land administration, and for agricultural planning and production at the grass-roots level. Detailed (1:10.000) cadastral / field boundary plans were produced for all enterprises, followed by detailed soil and land quality (bonitet) rating surveys which evaluated each individual soil mapping unit in the field, assigning a bonitet (irrigated or rainfed arable) rating to each. According to areas of land under each grouping of bonitet ratings (45-50, 50-55 etc) and the production history of the specific area, production demands were made for the coming year for the various allocated crops or livestock produce, and respective inputs (machinery, fuel, fertilizer, pesticides etc) were provided in order to meet these stipulated targets. Generally, farmers in existing villages were organized into Kolkhozes, while new areas of cultivation and areas of very low population density (e.g. marginal pasture areas) were run under Sovkhozes. In the areas of existing villages, invariably a Kolkhoz centre and main village would be established which would commonly house a central farm office building, a workshop, a school, a pre-school nursery, a community centre, and one or two shops. Smaller satellite villages might also house workers on the same Kolkhoz, in outlying areas. All workers were provided with housing and a village homegarden plot – most commonly these plots being contiguous with the house, and occupying an area of around 0.35ha. Often a Kolkhoz would have outlying pasture areas several kilometers away, commonly surrounded by land of other enterprises. After independence, the policy of most governments has been to privatize the land and the assets of each enterprise. Individual private farmers have been created, each with around 7-8ha of arable land (perhaps one-third of that being irrigated), and perhaps 20-25ha of pasture land. But, because of problems of lack of machinery, credit, technical advise, marketing information and many other factors many farmers have had to continue to work collectively, although they hold title to their own allocated parcels of land. The enterprise centre has thus continued to retain many of its original functions. Table 6.1: Soviet-era Enterprises, Old and New Municipality Names and Codes, and Average Bonitet Ratings


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Sources: Compilation by ADB consultant and Shymkent staff of EMIMS: information on bonitet ratings and enterprise names from Dr Anatoli Iorganskiy. With independence the National Governments have continued using the enterpirse centres as the lowest level of government, but changing the name to Municipality (KAZ: Selskiy Okrug; KYR: Aiyl Okmotu) and also changing most of the given names from Soviet to National. In many cases, however, names have changed yet again in the intervening 18-or-so years. Table 6.1 tracks the names of 34 of the selskiy okrugs (SO) in SKO, and also gives the Government’s numerical code number for each SO and the average bonitet rating of the arable land within the SO (both irrigated and rainfed). 6.2 PROBLEMS OF MUNICIPALITIES BOUNDARIES MAPPING

During the KAZ-EMIMS SKO pilot study the consultants tried to obtain a small sample of the detailed mapping available at SO level, and also an index map of all the SOs in the oblast. Surprisingly, the latter mapping was completely unavailable, apart from a planning map which had just been compiled for Sairam Rayon by GosNPCZem (Figure 6.1), which shows the area of each SO in unique colours. This mapping is notable for several reasons: - SO map polygons are very tortuous, made worse in this case by the fact that Sairam rayon effectively surrounds the city of Shymkent on three sides; - many island polygons exist: small inclusions of areas of one SO occur in another SO (or in an adjacent rayon); - village areas, including homegarden plots and in some cases peri-urban areas, represent further irregular polygons within the SOs (grey areas in Figure 6.1). Figure 6.1: Sairam Rayon, SKO: Planning Map 1:300.000 scale (Individual Selskiy Okrugs shown in unique colours)

Source: GosNPCZem, SKO Oblast Office. Data collected on KAZ-EMIMS study. Scale on screen c:1:300.000; original scale 1:100.000.


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The nature of the island polygons and the village garden areas are further shown in Figures 6.2 and 6.3, also showing that some detail is incomplete in Figure 6.1. The map from which Figures 6.2 and 6.3 were derived was at a scale of 1:20.000, itself probably generalized from 1:10.000 originals, and with some of the sensitive topographical detail removed for security reasons. Figure 6.3 gives areas shown in two rectangles referring to Figure 6.4, the Google earth detailed image for that area, and Figure 5.1, the detailed soil and bonitet rating map. Outside Sairam Rayon a very incomplete picture of SO location was obtained. Much of the information was in the format of Figure 6.5, a location map from Maktaraal Rayon, SKO, showing boundaries of the Sovkhozes in that rayon. However, in KYR municipality data is in better format, and Figure 6.6 shows the Aiyl Okmotu layout for the N part of the country, with municipalities shown in yellow colour. The SLMIS has now just been updated with this mapping in ESRI shape file format. Figure 6.2: Karamurt Selskiy Okrug (Sairam Rayon, SKO) and adjacent areas (detail from original of above map)

Source: GosNPCZem, SKO Oblast Office. Data collected on KAZ-EMIMS study. Scale on screen c. 1:100.000. Fig. 6.3 Karamurt Selskiy Okrug. Scale on screen: c. 1:60.000


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Source: GosNPCZem, SKO Oblast Office. Data collected on KAZ-EMIMS study. Area of outer rectangle is area represented by Google Earth imagery (Figure 6.4 below). Area of inner rectangle is area covered by detailed soil and bonitet rating map, Figure 5.1. Fig 6.4: Karamurt Selskiy Okrug, W-C part, & adjacent area in Akbulak S.O. Google Earth detailed image, c.1:25.000

Google Earth detailed imagery, centred on 42º 19,5’N, 69º 55’E. Note irrigation canals on contour, and cultivation up & down slope. Village homegarden areas clearly visible in Karamurt village, in SE of image. Detailed soil and bonitet rating map presented in Figure 5.1 covers most of this image.


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Figure 6.5: Sketchmap of Maktaraal, SKO, showing location of Soviet-era Sovkhozes

Source: Material obtained during KAZ-EMIMS SLU SKO pilot study, 2007. Figure 6.6 also shows a similar complicated pattern of municipality boundaries to that which prevails in Sairam Rayon, SKO. The figure also shows that much of the land is not within the boundaries of municipalities. Green areas here are lands of the State Forest Territory, white areas are remote and mountainous areas, mostly with unused wilderness land and low-productivity pastures. While 59% of the KYR forest lands are within the State Forest Territory, some 22% is within the Aiyl Okmotu, and 19% within the State Reserve Territory (mostly wilderness areas). Figure 6.6: Boundaries of Municipalities (Aiyl Okmotu) in Central and Northern Kyrgyzstan.


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Source: Map of Forest Location in the Kyrgyz republic, Scale:1:1.500.000. 2009. State Agency of Environmental Protection and Forestry, Bishkek. 6.3 ESTABLISHMENT OF MUNICIPALITIES POINT FILE

Due to problems both of obtaining reliable municipality boundary mapping and also of digitizing very complicated boundaries when these maps were finally made available, it was decided that it would be preferable to represent municipality statistics on a point file basis, at least until the massive exercise of digitization of municipality boundaries was completed. Figure 6.7: Southern SKO: Selskiy Okrug centres (small grey circles). Scale c.1 : 2million.


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Source: KAZ-EMIMS SLU SKO pilot study, 2007. Pink polygon in E of map is Karamurt Selskiy Okrug. Figure 6.8: Selskiy Okrug Land Use representation: Sairam Rayon and adjacent areas

Source: ADB consultant on data collected from various sources during KAZ-EMIMS SKO pilot study, 2007 Positions of all of the municipalities (SO) centres in SKO were obtained from GosNPCZem, and the centres plotted as per Figure 6.7 (small grey circles). Notable here is the clustering of the SO centres in irrigated areas and in the main rainfed arable belt. Full land use (and crop) breakdown for each SO can then be plotted in the same format as was used in Figure 3.5 for the Rayon irrigation areas. Table 6.2: Bonitet Ratings (Irrigated Land and Rainfed Land) of Agricultural Enterprises and Relationship of current names of Municipalities to names of Soviet Agricultural Enterprises Name: Sobonitet.shp, dbf etc (ESRI shape file) [under directory: E:\uk_obl\2\..] Type: Point file Established: June-Oct, 2007 Created by: Staff of EMIMS-SLU, Shymkent, SKO, and ADB consultants Data Sources: Positions of SKO Municipalities’ Centres: staff of GOSNPCzem, Shymkent.

Bonitet ratings of Soviet-era agricultural enterprises: Dr Anatoli Iorganskii. Relationship of municipalities names and enterprise names: Dr Iorganskii, staff of GosNPCzem and various other sources.

Records (current): 175 (for SKO) Representation on map:

Pie chart showing rainfed and irrigated bonitet ratings, centred on municipalities centre: slices of pie proportional to bonitet rating, light green representing rainfed bonitet rating, dark green representing increase of bonitet rating due to irrigation.

Fields (current): Name Unique identification number for municipality (or rayon), e.g. 515853000

Podvid-all Name of municipality (or rayon), e.g. Karatobe SO Uncult Uncultivated land (pasture & unused): (ha - numerical field;

e.g. 14613) Indno Index number (numerical field, e.g. 103)


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OldSOname Sovetskii K RayonNwNm New (current) name for rayon RayonOldNm Former name for rayon Bonirg (Average) bonitet rating of irrigated land (e.g.65) Bonrf (Average) bonitet rating of rainfed land (e.g.28) (shown as

light green in pie chart) Bonirg_rf Difference between irrigated and rainfed ratings (dark green

in pie chart) (e.g.37) Bon100_irg Difference between 100points and irrigated bonitet rating Fields (possible future):

Remarks: Bonitet reassessment surveys are now being undertaken by GosNPCzem. Changes in rainfed and irrigated bonitet ratings over time may again be represented by concentric circles/pies.

6.4 DATA TO BE LINKED TO MUNICIPALITIES POINT FILE:

Figure 6.9: Average Irrigated and Rainfed Bonitet Ratings for Agricultural Lands of Municipalities (SKO-KAZ): Scale: approx. 1:500.000. (Results identified for Karaobe SO, centre right in map detail)

Source: ADB consultant on data collected from various sources (see Table 6.2) during KAZ-EMIMS SKO pilot study, 2007.


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Figure 6.10: Land Use and Cropping breakdown for six Selskiy Okrugs in northern Sairam Rayon, SKO.

SLMIS output, 2009, scale c. 1:100.000. Basemap information (hyrology and topographical contours) is precise only for 1:1m presentation, but SO boundaries (thick black lines) precise for 1:50.000 mapping. Note contrasting proportions of cultivated land (35% - 65%), and contrasting proportions of high-value crops (strong colours: 3% - 22%). Table 6.3: Major Charactersitcs of the Municipalities Land Use/Cropping (Stats06) Database Name: Stats06.shp, dbf etc (ESRI shape file) [under directory: E:\uk_obl\2\..] Type: Point file Established: June-Nov, 2007 Created by: Staff of EMIMS-SLU, Shymkent, SKO, and ADB consultants Data Sources: Positions of SKO Municipalities’ Centres: staff of GOSNPCzem, Shymkent

SKO Oblast Land Use Statistics (at municipalities level) : Statistics Agency, Shymkent

Records (current): 175 (for SKO) Representation on map:

Pie chart showing different crops/land uses for each municipality, centred on municipalities centre: slices of pie proportional to specific land use area; valuable crops shown in strong/ bright colours; low value crops shown in muted/dull colours; unused/sparsely used land shown unshaded.

Fields (current): Name Unique identification number for municipality (or rayon), e.g. 514600000

Podvid-all

Name of municipality (or rayon), e.g. Ordabasy Rayon

Vin Vines (grapes): bright purple representation (ha - numerical field)

B_f Berries & fruits: dark bright blue (ha - numerical field) M_g Melons & gourds: bright pink (ha - numerical field) Veg Vegetables: bright red (ha - numerical field) Pot Potatoes: strong brown (ha - numerical field) Sun Sunflower (oilseed): bright yellow (ha - numerical field) Saf Safflower (oilseed): bright/light green (ha - numerical field) Grn Grain (wheat, barley, rice, maize grain): v.light yellow (ha -


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numerical field) Oth Other crops (alfalfa & other fodder crops): v.light grey (ha -

numerical field) Uncult Uncultivated land (pasture & unused): white (ha - numerical

field) Indno Index number (numerical field) Fields (possible future):

Breakdown of Grain grouping into winter cereals, spring cereals, and rice would be useful (but currently not differentiated by Stats Agency); inclusion of cotton into ‘Other Crops’ and not differentiated as a separate class is a major omission.

Remarks: Municipalities land use/cropping statistics collected by Oblast Statistics Agencies only from 2005; now collected and published annually. Municipalities stats are important as they can reflect severity of problems in land degradation hotspot areas (erosion, salinity, successive drought etc) which may be missed with larger areas (i.e. rayon statistics). Representation of different years can be made with concentric circles (pies) – later years with successively larger circles.

Data that are linked to the Municipalities Point file include the following: - Bonitet ratings (irrigated and rainfed arable) (Table 6.2 and Figure 6.9); - Land Use and Cropping breakdown (Figures 6.10 and 6.8; Table 6.3); - Land Use and Cropping breakdown in relation to other factors: Mean Annual Precipitation; Elevation; Marketing centres and Communication Infrastructure (Figure 6.12); - Data on Soil and Bonitet Assessment and Reassessment Surveys (Table 6.4); - Meteorological Data: Mean Annual Precipitation, Mean Annual Temperature, Mean January Temperature, Mean July Temperature. Figure 6.9 shows Average Irrigated and Rainfed Bonitet Ratings for Agricultural Lands of individual Municipalities within Saryagash Rayon, just across the border from Tashkent, in the SE part of SKO-KAZ. The scale of this presentation is approximately 1:500.000. Results accessed from the database (by the ArcView tool) are shown in the inset for Karaobe SO, which is located in the centre right in the map detail. Bonitet of rainfed land here is 28 (shown as a light green slice in the pie chart); bonitet of irrigated land is 65: the difference between the irrigated and rainfed ratings (65-28=37) is shown as the dark green slice in the pie chart. Given bonitet data over two sets of dates, a double pie representation can be produced (methodology as in Figure 3.5), with the earlier year in the inner pie, the later year in the outer pie. This would be the single most useful parameter reflecting land degradation at a local level, and thus acquisition of all of this data must be a high priority for the project. As can be seen from the incomplete picture presented by this figure, considerable variation exists within the same rayon on the bonitet ratings of the different SOs. Figures 6.10 and 6.8 show the Land Use and Cropping breakdown for the entire SO area, for respectively 6 SOs in the northern part of Sairam rayon and for all the SOs in Sairam and the surrounding areas. The legend shows some commonalities with that used in Figure 3.5, and also some differences. Blank areas are uncultivated land: any cultivated land shows some shading. Rainfed and irrigated land is included together here, and not differentiated. Grey areas are ‘other crops’ including both cotton and fodder crops (notably alfalfa); light yellow indicate cereals; oilseeds, (safflower and sunflower) are denoted by bright green and bright yellow respectively; potatoes (strong brown); vegetables (bright red); melons & gourds (pink); berries & frieuts (blue); and vines (grapes) (violet). The contrasting picture in adjacent SOs is notable, reflecting the different qualities of land, different population densities, and distances to markets. The inclusion of cotton and fodder together within the same class is a problem: it would be preferable if these were differentiated as they are totally different in terms of value, labour requirement, and potential impact on land degradation.


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Land Use and Cropping breakdown in relation to Mean Annual Precipitation isohyets are given in Figure 6.12. Apart from major irrigation areas (Maktaraal, Shardara..) a close relationship can be seen between land use and precipitation, with slope and elevation also having a major impact. Fig. 6.12 SKO: Land Use / Cropping at Municipality Level for 2005, in relation to Mean Annual Precipitation Isohyets

Source: Compilation MSEC 2009 based on data from KAZ-EMIMS study, 2007. Table 6.4: Sairam Rayon, SKO-KAZ: Correlation of Municipalities (Selskiy Okrugs) New Names with Soviet-era Enterprise Names, and Average Land Quality Ratings for Irrigated and Rainfed Lands.


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Sources: Compilation by ADB consultant and Shymkent staff of EMIMS-SLU 6.5 SUMMARY AND CONCLUSIONS: MUNICIPALITIES DATABASES

Present day municipalities (KAZ: Selskiy Okrugs; KYR Ayil Okmotu..) are descended from the Soviet-era agricultural enterprises (mainly Kolkhozes: collective farms; and Sovhozes: state farms; some Leshozes: forestry enterprises). There is invariably excellent topographical and soil mapping for these enterprises, largely undertaken over the period 1960-1990, and excellent detailed statistical information covering crop areas, yields, and production, and inputs used. Mapping is generally at a scale of 1:10.000 for the irrigated areas and for the more intensively-farmed rainfed areas, and at 1:25.000 or 1:50.000 for the other, less intensively cultivated, areas. Original mapping was undertaken by manual methods, and only a few copies of (hand coloured) maps were originally produced. Soil / land institutes and agencies at oblast and national levels may have one copy of these maps, the counterpart office in Moscow another copy, and occasionally the (former) enterprise office may hold a further copy. Obtaining access to this material is difficult, but not impossible. These detailed maps and accompanying reports were rarely published, although in some FSU countries good reductions and generalizations were made of this mapping, and compilations at rayon or more commonly oblast level subsequently published at a more general scale (commonly 1:200.000 or 1:300.000, sometimes 1:500.000). Statistical information collected at the time of these surveys is commonly still held at the centres of the former enterprises, although again this is usually difficult and time-consuming to access. Sections 6.2 - 6.4 give examples of the mapping and data available at municipality level. Topographical (basemap) information at scales more detailed than 1:100.000 is officially secret in most FSU republics. However soil information at detailed scales is not secret, and it is possible (but again usually difficult) to access this detailed municipality-level soils information. Linked to each of the detailed soils mapping units is a Land Quality (Bonitet) rating, a single parameter rating accurately expressing the current quality of the land and hence the state of degradation (or restoration) of the


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land. The Bonitet Rating, by definition, is proportional to the weighted yields of a basket of commonly-grown crops expressed in the other single currency, ‘fodder units’. Bonitet Ratings are assessed independently for irrigated and for rainfed conditions. Bonitet reassessment surveys were undertaken periodically after the original surveys were undertaken, and give a useful measure on changes of soil fertility and soil organic-C content over time. These surveys are continuing, albeit with severe budget constraints in most countries. There is scope for outside agencies (finance, development, scientific etc) to assist in promoting these reassessment programs, and in introduction of new equipment and techniques (GIS, GPS, field data recorders, portable field laboratory equipment etc) as well as making data more accessible to potential users. Changes of name of original agricultural enterprises and subsequent municipalities, and some land changes between enterprises / municipalities has added a further element of complication to the enterprises / municipalities database. Country by country, oblast by oblast, there is an important correlation exercise to be undertaken. During the current phase of the project, this has largely been completed in KYR (467 municipalities) and in SKO-KAZ (170 municipalities), and now needs to be completed for the other areas. In addition, Municipalities have very tortuous boundaries, as their parent enterprises often operated land parcels in different areas, and usually these irregularities have not been simplified and updated. A very complicated pattern exists if one needs to work with polygon files. Because of these difficulties, the current SLMIS has sought establish a municipalities point file, with the points being positioned on the respective municipalities centres. Land use, cropping, bonitet ratings and other parameters can be represented as pie charts (and concentric pies), or as histograms, positioned on these points. Mapping and information at municipality level are important as within a single rayon land degradation might be affecting badly one or two municipalities but sparing the others. Rayon statistics, averaged over a much bigger area, will not adequately reflect the severity of land degradation problems, and this applies, even more so, for oblast-level statistics. This was very clearly seen in Baidebek rayon, SKO-KAZ, where two of the municipalities are very badly affected by gulley erosion (see Section 9) and several more, being marginal rainfed areas, are commonly affected by drought. Any implementation project to focus on land degradation and restoration thus has to specifically target municipality (and not rayon) level.


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7 CACILM USE OF METEOROLOGICAL AND HYDRO- METEOROLOGICAL INFORMATION

7.1 CENTRAL ASIAN METEOROLOGICAL AND HYDROLOGICAL RECORDS

7.1.1 Introduction

Climate and weather data – meteorological records – and the river gauging data – hydrometorological records - are equally important as the land use and soil & land data in the SLMIS. Given reasonable soil fertility and potential over large areas (at least outside the mountainous areas), it is climate which largely dictates the farming systems and cropping patterns which prevail in the different areas. However, extremes of weather – droughts and periods of excess rainfall, periods of unseasonal temperatures, periods of high winds – all have a serious negative impact on the economics of the farming systems, and need to be closely evaluated in any assessment of land degradation and crop productivity trends. Work reported here includes data collected from the NSIUs, from ICARDA, and from the SKO (ADB) and Kyzyl Orda (EU-Tacis) studies. Work emphasizes the variation in weather from year to year, the small but distinct trends of rising temperatures and variability in rainfall, the worries about glacial retreat, and the possibilities for much improved water use efficiencies in agriculture in the CAC-5 Priority Area.

7.1.2 Met Stations, Met Records and their Applicability to Land Use Management

A large number of Met Stations are located in the Priority Area, and many of these have records going back to the 1930s and before. Most of these stations are still recording on a daily basis. Measurements include precipitation and temperature (all stations), relative humidity and windspeed (most stations), sunshine and pan evaporation (some stations). A few stations also now have continuous automatic data recorders. Location of Met Stations – compiled largely from NSIU sources - are shown in Figure 7.1. Derived from these long-term precipitation and temperature data are two maps, obtained from the ICARDA GIS (2009): Mean Annual Precipitation (Figure 7.2) and Annual Growing Periods as limited by Precipitation and by Temperature (Figure 7.3). Further derived from these last two sources is the Agro-Climatic Map of ICARDA (Figure 7.4). Again these maps have been reproduced here just for the Priority Area, and not for the entire CAC-5 area. Although daily figures are available for most of the above stations, decade (10-day) or monthly records are more commonly available and used in the agricultural communities and offices. Table 7.1 shows 10-day averages over the recent 16-year period (1991-2006) for the key agro-met parameters for two of the key Met Stations in the main rainfed farming belt of SKO-KAZ: Tasaryk (at 1122m elevation, 25km ESE of Shymkent) and Ryskulov (at 809m elevation, 40km NE of Shymkent). From this data the Reference Evapotranspiration (ETo) has been calculated by the FAO-CROPWAT program, giving the last columns in these tables. Ryskulov shows much higher ETo figures (maximum of 7,1mm/day) than does Tasaryk (maximum of 4,9mm/day) for the peak of the summer period, due to higher temperatures (July max 25,5ºC cf. 22,0ºC), higher windspeeds (mean 3,2m/sec cf. 0,7m/sec), and lower relative humidities (July means of 40% cf. 50%). Sunshine records were available for neither station, and thus records for Shymkent have had to be used in both cases. Comparisons of the 16-year averages of the two stations in Table.7.1 reveal that, as expected, the lower-elevation station (Ryskulov) has the longer potential growing season (March Decade III to November Decade I, cf. April II to October III with decade averages of 8ºC or more). However, with lower spring and summer rainfall and higher ETo values, summer drought is much more of a factor at Ryskulov than at Tasaryk. Rainfed crop yields are thus likely to be significantly lower at Ryskulov than at Tasaryk and field inspection of farms in that area have verified this. During the EMIMS-SLU SKO Study daily agro-met data was investigated for three stations in the rainfed agricultural belt (Tasaryk, Ryskulov, and also Shymkent) and soil water balance studies and rainfall-runoff simulations were run for this data, given modal values for key soil physical properties


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(soil horizon depths and available water holding capacities, infiltration rates etc). Full details of the methodology using the DLYSLWB9 daily rainfall-runoff model is explained in the EMIMS Guidelines, November, 2007, Annex E. Some of the results for both Tasaryk and Ryskulov are reproduced graphically as Table 7.2 and Figures 7.5 and 7.6, with Tasaryk data presented on the left and Ryskulov data on the right in each of these tables and figures. Figure 7.2: CAC-5 Priority Area: Mean Annual Precipitation Isohyets (ICARDA GIS)

Figure 7.3: CAC-5 Priority Area: Growing Period Limited by Precipitation and by Temperature (ICARDA GIS)

Figure 7.4: CAC-5 Priority Area: Agro-Climatic Zones (ICARDA GIS)


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Figure 7.1: Location of Meteorological Stations within the CAC-5 Priority Area


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Table 7.1: 10-day Agro-Met Data (averages for 1991-2006) for Tasaryk (left) and Ryskulov (right) (SKO-KAZ)

Source: Consultant’s present analysis based on KAZHYDROMET met data obtained during EMIMS-SLU project. The modal deep loess-derived silt loam soil profile predominates over much of the rainfed agricultural belt in SKO, and this soil type shows deep profiles (>200cm) with relatively high AWHCs (15-22%) and moderate soil infiltration rates and only a slight decrease in permeability in the B-horizon over that in the surface (A)-horizon. Perennial grass or alfafa vegetation, with deep rooting, was taken as the vegetation in the model. Work also undertaken on the SKO study (EMIMS-SLU Guidelines, Annex F) showed that soil infiltration was very sensitive to cultivation history, with annual cultivation to seasonal crops reducing infiltration markedly over that prevailing under grass or alfalfa vegetation.


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Table 7.2: Annual Water Balances (mm / year), 1991-2006, for modal loess-derived silt loam soil: Tasaryk (left); Ryskulov (right).

Source: Consultant’s present analysis based on KAZHYDROMET met data obtained during EMIMS-SLU project.


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Fig. 7.5: Growing Season Pentades limited by drought (colour) and by low temperatures (grey shading) : Comparisons of 16 consecutive years of records (1991-2006) from Tasaryk (left) and Ryskulov (right)

:Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) Fig. 7.6: Pentades with available water in topsoil (green colour) in relation to periods of low temperatures (black shading) : Comparisons of 16 consecutive years of records (1991-2006) from Tasaryk (left) and Ryskulov (right)


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:Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) From Table 7.2 the following observations are made: Precipitation: 16-year averages were 817mm and 734mm respectively for the two stations. Year to year variability is high, but both stations showed the same trends for most years. (This is expected, with most of the rain being cyclonic). In only one year -2006 – was the rainfall higher in Ryskulov than in Tasaryk. Years with >25% increase on average precipitation included 1993, 1998, and 1999, applying to both stations, and 2003 (Tasaryk only). Years with >20% decrease on average precipitation included 1995, 1996, 2000, and 2001. The presence of runs of two consecutive years of greater or lesser precipitation is notable. Run-off Coefficient: Overall run-off coefficients varied from 0 to 31% in the different years. However, average run-off coefficient over the 16 years proved to be the same in the two stations, with the figure being low (c.8,2%). The deep soil profile, providing a large potential store for soil water, is the main reason for this low figure. Deep-rooting natural vegetation (and alfalfa) can make use of this water, at least in most years. (This has further implications with respect to CO2 sequestration and storage in the deep soil profile). Significant deep leaching was observed in 1993 and 1998 (wet years), but also in 2002 and 2005. Surface run-off (from periods of high-intensity rainfall) was notable in 1992 and 1993 for both stations. Surface wash erosion would also have been problematic for these periods. Actual Evapotranspiration (ETa): 16-year averages were 736mm and 664mm respectively. Year to year variability is somewhat lower than that for precipitation. High ETa years (>20% more than average) included again 1998 and 1999; low ETa years (>15% decrease on average) included 2000 and 2001. For 2001 the decrease was 22% and 23% respectively for the two stations. Relationship between rainfall, ETa and crop yields: Data is available from Karamurt SO (20NW of Tasaryk) from GosNPCzem (reported on in EMIMS_SLU, Final Report, Nov 2007, Annex B), covering yields for the period 2001-2004 for the major crops grown. Although Karamurt lies between Tasaryk and Ryskulov it is at a lower elevation and appears to be in a local rain shadow area, having a mean annual rainfall of only 517mm. Yields of winter wheat averaged 18,9centners/ha for the three average


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to good years 2002 – 2004, but were only 12,9centners/ha for the dry year 2001 (a decrease of 32%). Yields of perennial grass (as hay) were 32,1 and 21,4cnt/ha respectively (a decrease of 33%). However, even in the three better years, yields of winter wheat at Karamurt are significantly lower than long-term average yields recorded for Sairam rayon which are 24cnt/ha, under mean annual rainfall regimes of around 550-650mm.

7.1.3 Rainfall-Runoff Modeling and Soil Water Balance Studies using Met Data

The status of soil moisture in the deep silt-loam loess-derived soil profile modeled over the 16-year period is given for the two met stations in Figures 7.5 and 7.6. Modeling was undertaken with the DLYSLWB9 model using daily records obtained from KAZHYDROMET. In both figures grey shading denotes periods when crop growth is limited by temperature: dark grey denotes decades (10-day periods) when average temperatures are below zero; light grey denotes those decades when temperatures are between zero and 5ºC. Unshaded decades would be those where crop growth is possible, or at least not severely limited by low temperature. Figure 7.5 denotes the soil moisture status over the whole soil profile, the numbers giving the ratio of actual to potential evapotranspiration (ETa to ETp). The number 1,0 would signify that actual evapotranspiration would match potential evapotranspiration, a situation which would prevail only if less than 50% of the available water stored in the soil profile has been used. Once this 50% limit is reached, low water availability begins to limit actual transpiration, and the crop comes under moisture stress. Yellow colours in Fig.7.5 denote when moisture stress becomes severe, with ETa/ETp ratios being less than 0,55. Orange colours denote very severe moisture stress, ratios of 0,15 to 0,33 prevailing, with red colours denoting extreme stress, ratios falling below 0,15. Inspection of Figure 7.5 shows that the period of extreme drought (i.e. ETa/ETc values of <0,15) is very much shorter in Tasaryk that at Ryskulov, with extreme drought occurring in 5 out of the 16 years at Tasaryk but 15 out of the 16 years at Ryskulov. On average, there were only 7 days of such extreme drought per year at Tasaryk but about 60 days at Ryskulov. Significant moisture stress (ETa/ETo ratios less than 1) occurs on average by 11Jul at Tasaryk but by 8Jun at Ryskulov. Onset of severe moisture stress (yellow colours in Fig.7.5 ) occurs on average by 10Aug at Tasaryk but by 13Jul at Ryskulov. In the driest year (2001) respective dates are 16Jun and 6Jul at Tasaryk, and 21May and 10Jun at Ryskulov. In general, Tasaryk as available water in the soil profile for almost 4 additional weeks in summer than does Ryskulov, and this pattern is both reflected in increased crop yields and also increased NDVI values as viewed in the remote sensing imagery (Chapter 4). Crop yields are related not just to overall water availability in the entire soil profile but also to nutrient availability, for which the moisture status of the surface (A)-horizon is the most critical, particularly for a seasonal crop such as wheat. Figure 7.6 thus shows graphically the periods of water availability in the topsoil, green colours denoting >25mm of available water in this A-horizon, light green denoting 10-25mm, and yellow 1-10mm. Most spring/early summer seasons show several wetting/drying cycles, these promoting nutrient availability, particularly the mineralization of soil N from decomposition of organic matter. For winter cereals, nutrient and topsoil water availability is most critical at the time of ear initiation, and this occurs during early May at lower elevations (c.600m) and mid to late May at higher elevations (e.g. Tasaryk at 1100m). Comparing the respective critical periods for the two stations, from Fig.7.6 it is noted that at Ryskulov six years showed periods of poor topsoil water availability (1991, 1995, 2000, 2001, 2005 and 2006) whereas at Tasaryk only two years of poor topsoil water availability were seen (2000, 2001). Figure 7.6 also shows topsoil water availability for the autumn period, critical for the autumn cultivation period and the sowing of the new winter cereal crops. Autumn water availability would have been a problem in the years 1994, 1999 and 2005 at Ryskulov, but only in 2005 at Tasaryk. Both late spring and autumn periods of low water availability very adversely affect rainfed crop yields in this area and explain why overall crop yields are so low in relation to the reasonably good overall precipitation figures. In western Europe (e.g. France, SE England) natural precipitation of 650mm will


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produce an average of 60-70cnt/ha of winter cereals, as compared to 24cnt/ha under similar precipitation in SKO.

7.1.4 Hydromet Stations and the Hydromet Record

Numerous Hydromet Stations are located throughout the Priority Area, on both major and minor rivers, canals, and outfall drains. As with Met Data, Hydrographic Data is recorded routinely on a daily basis, with major stations also recording on a continuous basis. As part of the SKO Study, detailed Hydromet Data was obtained for 15 years for 3 contrasting stations in order to assess its relevance and importance in an information system embracing land and water information relating to possible long-term degradation of natural resources. Location of these and other stations in SKO are shown at a more detailed scale in Figure 7.7 in relation to other notable features, including Watershed Areas for the three hydromet stations, met stations, and key places where other key data is available. Figure 7.7 Met and Hydromet Stations within SKO-KAZ, and Three Key Watershed Areas (Scale c. 1:1m)

Source: CACILM SLMIS. Key: Symbols from top: KAZ Met stations (red half circle); UZB Met stations (pink half-circle); KAZ Hydromet (River gauging) stations (blue triangles); Catchment boundary (Arys River & tributaries – thick black line); Rivers & streams (various sizes – blue lines); Topographical contours (colours reflecting elevation); Three Key Watershed Areas (yellow polygons). Publication of the daily data from KAZHYDROMET is in the form of Table 7.3, together with an accompanying flow hydrograph (Figure 7.8), both of these applying to the Tasyryk hydromet station on the Sairam River for the year 2005. Two other stations studied, with equivalent data available, include the Pystelee hydromet station on the Kokbulak River (whose hydrograph for 2005 is given in Figure 7.9) and the Novonikolayevska hydromet station on the Zhabaglisu River. Variation in mean monthly flows over the 16-year period is presented in Table 7.4 for the Tasaryk hydromet station. Key hydromet parameters for the three stations and the three contrasting watersheds are given in Table 7.5.


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Figure 7.8: Flow Hydrograph for 2005 for Tasaryk (Sairam River)

Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) Table 7.3: Daily Hydromet Data: Year 2005: Sairam River, Tasaryk Village, SKO, KAZ.

Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) Figure 7.8 and the upper part of Table 7.3 demonstrate that for 2005 river flows measured at Tasaryk on the Sairam River showed a steady rise from early March to end-June, and a subsequent steady fall to mid-September. Flows peaked on 24th June at 46,3m³/sec, with earlier peaks on 13/14th March at 11,6m³/sec and on 6th May at 20,9m³/sec. Minimum baseflows for 2005 (February) were in the order of 3,8m³/sec. Inspection of the met data for the nearby Tasaryk met station show that heavy rain (28mm) was experienced on 13th March and 12mm on 14th March, following 19mm on the previous 2 days. A further 55mm of rain was experienced over the following 5 days, and 28mm fell between 25th-27th. Some 50mm was also experienced over the period 3-5April, 10mm during 12-14April, and a further 22mm on 30April. A further 33mm fell over the period 5-8May, 42mm over 12-13May and 16mm over 18/19May. Rainfall in June was negligible. All of the rain events, as expected, would appear to have had some effect on the hydrograph, although the 3/4April hydrograph peak was very


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much smaller than expected. Snow melt is another and perhaps much bigger factor: Temperatures at Tasaryk met station (1100m elevation) were at zero or below from December decade III until the end of February. Mean temperature then climbed to 8°C in the first decade of March, and climbed successively over the next five months. Much of the flows for March, April and May are thus likely to be snow melt, as the snow accumulation at successively higher elevations started melting. Tasaryk, at 1100m, marks the lowest elevation of a watershed area that rises to a maximum of some 4100m, with large areas between the 2000m and 3000m elevation. Finally, continuing glacier melt would then have made the dominant contribution to flows in June, July and August. The lower part of Table 7.3 gives the long-term average monthly flows for the 1936-2005 period in relation to maximum and minimum recorded flows for each of the months, and the year in which these extreme flows were measured. Of interest is the fact that maximum spring and summer peak flows (April-July) were recorded in the period 1954-1959 while minimum flows (for 11 of the 12 months of the year) were recorded during the first 10 years of recording, and mostly in the period 1936-1940. The absolute maximum flow, 204m³/sec, was recorded in May, 1958, this being some 16 times higher than average May flows. Absolute maximum flows in June and July, some 79m³/sec and 73m³/sec respectively, were only 4 times higher than the respective averages for these months. Absolute minimum flows for the winter period (December-February) were approximately half of the long-term monthly averages, while minimum flows for the normal peak months of June and July, some 6,3m³/sec, were one-third of the long-term monthly averages. Figure 7.9: Flow Hydrograph for 2005 for Pystelee (Kokbulak River)

Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) Figure 7.9 shows the contrasting flows for 2005 for the Kokbulak River at Pystelee (714m elevation). Here a very sharp peak was experienced in mid-March with flows having climbed steadily from 1-6 March (4,9m³/sec) and then very rapidly form 7-14th March, peaking on 14th March at 28,6m³/sec. Thereafter a rapid fall was experienced to 22March, when a rate of 3,7m³/sec was attained. A further small peak of 5,3m³/sec occurred on 7th April, with a steady fall from then until end-April by which time flow rate had declined to 1,9m³/sec. Further steady declines occurred, with rates falling to 0,95m³/sec by end May, 0,59m³/sec by end June, and 0,50m³/sec by end July. Minimum baseflows (October) were in the order of 0,30m³/sec. Inspection of the met data for the nearby Ryskulov met station show that heavy rain (41mm) was experienced on 13th March, following 17mm on the previous 2 days. A further 36mm of rain was experienced over the following 6 days. Appreciable rain (80mm) was also experienced over the period 3-5April, but only minor rain occurred over the period 6-9May (18mm) and 13/14May (25mm). Rainfall in June was negligible. All of the rain events, as expected, would appear to have had some effect on the hydrograph. Again, snow melt is another and perhaps bigger factor: temperatures at the nearby Ryskulov met station (809m elevation) were at zero or below from December decade III until the last decade of February, when they were just above zero. Mean temperature then climbed to 10°C in the first decade of March, and climbed successively over the next five months. Much of the sharp peak flow in March is thus likely to be snow melt, as the snow accumulation in the watershed, situated at 714m-1770m elevation, rapidly melted. Table 7.4 gives the average monthly flows at Tasaryk over the period 1991-2005. A considerable variability is seen from one year to the next in flows for any one month, and also in average yearly


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flows. For the peak flow month of June, the smallest figure was achieved in the Year 2000 (16,5m³/sec) while the largest was achieved in 2003 (36,5m³/sec). But for average yearly figures, the smallest figure was achieved in the Year 2001 (8,01m³/sec) while the largest was achieved in 2002 (14,2m³/sec). However, the largest average yearly figure recorded was in 1969 (15,9m³/sec), while the smallest was in 1957 (5,04m³/sec). For the period 1991-2005 there appeared to be some relationship with precipitation figures (from the Tasaryk met station), but the differences in precipitation explained only some of the differences in average yearly flow rate. Clearly there were other factors (temperature in the current and in the preceding year, for example) that also were having a large effect on current flow rate. However, there is a clear trend for increased flows, the average for the period 1991-2005 being 10,41m³/sec, while the average for the preceding period (1936-1991) was just 8,16m³/sec. The last column in Table 7.4 gives the ‘recent melt factor’ which is a measure of the proportion of the recent flows which can be accounted for by additional glacial melt, over and above the preceding long-term (1936-1991) averages. The overall increase in flow due to this factor is some 29%, but this varies considerably from year to year. Generally, years of higher precipitation (hence higher snowfall on the high elevation areas) corresponded to lower factors, and years of lower precipitation to higher factors, but some years were exceptions: 2002, a high precipitation year, had a high factor (1,54), while 1991 and 1995, low precipitation years, showed rather low factors. Table 7.4: Average Monthly Flows (m³/sec): Years 1991-2005: Sairam River, Tasaryk Village, SKO,KAZ

Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007) Table 7.5 summarises the key hydrometeorological parameters for the three contrasting watersheds for which detailed met and hydromet data has been made available. Watershed 1 (Sairam River) represents a large and diverse watershed at a wide range of elevations (1100m-4200m) including large high-elevation glacier-fed headwaters areas. Watershed 2 (Kokbulak River) is a moderate-sized, moderate-elevation area, spanning only the elevation range 714m-1770m, and being well below the snow-line. Watershed 3 (Zhabaglisu River) is a smaller version of Watershed 1, but in more of a rain-shadow area and with less glacial cover. Both Watershed 1 and Watershed 3 show a pronounced summer peak for maximum flows, watershed 1 showing the peak in late June/early July, whereas Watershed 3 showed a broader but flatter peak running from May till July. In Table7.5 flow rates are expressed both in terms of m³/sec and also in terms of mm/month, averaged over the watershed area, facilitating direct comparisons between the three watersheds. Of surprise is the fact that run-off is so high: at 583mm, 443mm and 435mm respectively. (From rainfall-runoff modeling runoff


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coefficients are only 8%, meaning that there is a considerable difference which is explained only by the very large snow- and ice-melt components.) The above hydromet data is fundamental to any work on water management and specifically irrigation planning and management, and these topics are discussed, in relation to the above data, in the next Chapter.

7.1.5 Glacier Retreat and Snow-Melt Modeling

Modeling of river flows in relation to snow-melt and glacier melt and retreat has been undertaken by the NSIU GIS specialists in Uzbekistan and Kyrgyzstan, as well as staff of KAZHYDROMET in Kazakhstan. It is clear that much primary and derived data has been collected, although availability of much of this data is now problematic. Given more the land focus of the present work, this work has not been so actively pursued under the current phase of the project. However, its importance, particularly for irrigation planning and management, is fundamental, and it would be recommended that this form a key part of the work in any future phase of the project. A further essential input for this work would be mapping of glacier and high-elevation snow covering by remote sensing. Ji (2008 and 2009) has given some examples of this in his reports, but correlation with field mapping and with both met and hydromet data now needs to be made. Table 7.5: Key Hydrometeorological Parameters for Three Contrasting Watersheds (SKO-KAZ) Parameter Watershed 1 Watershed 2 Watershed 3 River Sairam Kokbulak Jabaglisu Station Location Tasaryk Pystelee Novonikolayevka Coordinates N 42° 14’; E 70° 8’ N 42° 40’; E 70° 15’ N 42° 25’; E 70° 33’ Elevation (m) 1100 714 1300 Watershed Area (km²) 468 76 172 Watershed:max. elevation (m)

4193 1770 3971

Watershed:approx.mean elevn(m)

2600 1200 2600

Period of Recording: 1936 – 2005 1947 – 2005 1936 – 2005 Mean Annual Flow (m³/sec) 8,65 1,07 2,33 Minimum Annual Flow (& Year)

5,04 (1957) 0,499 (1988) 0,460 (1965)

Maximum Annual Flow (& Year)

15,9 (1969) 2,31 (1969) 4,50 (1969)

Mean Monthly Flows: (m³/sec) (mm/mo.)

(m³/sec) (mm/mo.)

(m³/sec) (mm/mo.)

January 3,69 21,1 0,839 29,6 0,816 13,1 February 3,54 18,3 1,27 40,4 0,805 17,9 March 3,9 22,3 2,97 104,7 1,16 18,1 April 6,78 37,5 2,97 101,3 2,66 40,1 May 12,8 73,2 1,37 48,3 4,33 67,4 June 20,6 114,1 0,623 21,3 5,17 77,9 July 18,7 107,0 0,393 13,9 4,69 73,0 August 12,1 69,2 0,335 11,8 3,03 47,2 September 7,31 40,5 0,328 11,6 1,82 27,4 October 5,26 30,1 0,36 12,7 1,32 20,6 November 4,63 25,6 0,565 19,3 1,14 17,2 December 4,12 23,6 0,789 27,8 0,962 15,0 Annual flows (mm): 582,5 442,7 434,9

Source: ADB Consultants (2009) based on data of KAZHYDROMET (Shymkent Office) obtained from EMIMS-SLU (Mott MacDonald, for ADB, November, 2007)


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7.1.6 Institutions Responsible and Data Availability

In each country, met and hydromet measurements are the responsibility of the meteorological and hydrometeorological agencies (UZHYDROMET, KAZHYDROMET, etc). Government funding for continuing high-level of activities in some cases is problematic, and KAZHYDROMET in particular has had to obtain a high proportion of its funding from private sources. This has put undue pressure on the organization to make exorbitant charges for provision of historic data, and this has been a major problem area for past projects (notably EMIMS-SLU). A better funding policy would be to charge for continuing services (e.g. met forecasts for news agencies, aviation sector; hydromet data to water utility companies etc) but to make historic records available for a modest fee (the practice now in most countries). However, in the meantime, the SLMIS has had to rely heavily on old Soviet data (already published), and obtain any updated data as and when available. 7.2 CONCLUSIONS ON TRENDS IN THE CENTRAL ASIAN MET AND HYDROMET RECORD

The above data has emphasized the importance of considering the pattern of met and hydromet records over many years, and not just relying on average figures or runs of data from just a few years. Figures 7.10 and 7.11 show these long-term trends for Uzbekistan, respectively for temperature and precipitation. In both cases, the sum of all the met stations have been considered, for all years available from the period 1933-2005 (data taken from the UZB-NSIU Final Report, August, 2009). Overall temperature shows an increase of 0,29°C / 10 years over this 70-year period, which is double the world average. The number of days when temperatures have exceeded 40°C have doubled in the Aral Sea area over this period (which is unsurprising, as large areas of water would have had a moderating effect on temperature, and most of this Aral water has now disappeared) but have still increased by 50% elsewhere. Accelerated melting of glaciers has occurred, with 104km³ of water (20% of their 1957 stocks), having been lost already. These trends seem likely to continue, until much of the ice will have disappeared, after which a new equilibrium would be reached with melt rates stabilizing to 10-15% below former, steady-state, pre-1957 levels, and having a proportional negative affect on overall river flows. Figure 7. 10: Changes in Annual Average Air Tempertures (all met stations) for Uzbekistan, 1933-2005

Source: Uzhydromet 2007, as quoted in UZB-NSIU, Final Report, August 2009.


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Figure 7. 11: Changes in Annual Sum of Precipitation (all met stations) for Uzbekistan, 1933-2005

Source: Uzhydromet 2007, as quoted in UZB-NSIU, Final Report, August 2009. Overall rising temperature trends are also seen in Kazakhstan, with the mean annual temperatures for Arys (some 200km NW of Tashkent) being given in Figure 7.12 for the same period. Of interest here is the fact that temperature variations from year to year are considerable, lowest temperature being around 10°C in 1971 and highest being in both 1937 and the recent period 1999-2001, at 15°C . Also, the long-term period to 1971 would have shown an overall slight fall in temperature, flowed by a very significant rise since that time. However, single stations show the effects of local factors – e.g. commissioning of large areas of irrigation schemes might have a decreasing effect on summer temperatures, with increased evapotranspiration moderating summer heat (the ‘oasis affect’), and this may well be a factor in this case. But more likely is the pattern of sub-zero winter temperatures, which varies very greatly from year to year. Temperature records also need to be investigated for the different months of the year to see what trends are occurring over the long-term. Of critical importance would be any increase for the summer months, which would worsen the pattern of evapotranspiration and cause longer summer drought periods (see Figure 7.5). Conversely, temperature increases for the winter months might be beneficial, as intermittent snow melt periods might lead to greater winter percolation of precipitation into the deep soil profile. Also warmer temperatures in March and April would increase efficient crop growth in these early spring months. If an overall increase of 0,29°C / 10 years were to entail an increase of 0,5°C / 10years during the winter and early spring months and a corresponding rise of only 0,1°C /10years during the summer period the overall increase might be beneficial in terms of a more favourable agro-climate. But this needs to be investigated with the long-term records of all stations for all countries.


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Fig. 7.12: Climate trend (red – average annual temperatures, blue average annual rainfall) at Arys, SKO-KAZ

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500

600 mm

Средниегодовыетемпературы

Суммаосадковсейзонна соктябряпредыд года(мм)

Annual rainfall is calculated between October of previous year and September, because of relevance for vegetation. Source: EMIMS-SLU, Nov.2007. Long-term precipitation trends are somewhat less clear than those for temperature although the sum total of the UZB precipitation shows an overall increase, with a trend-line showing a 10-15% rise over the 70-year period. However, variability from year to year is very great, and, as with the KAZ stations analysed (see figures for Tasaryk and Ryskulov, SKO-KAZ, Table 7.2), dry years tend to occur consecutively (1995 & 1996; 2000 & 2001) as do wet years (2002 & 2003; 1998 & 1999). Both SKO-KAZ stations followed the same patterns of dry and wet years (at least for the 1991-2005 period investigated) as do the UZB stations, although their variability around the norm appeared somewhat lower (c.20%) than that for UZB (c.30%). The presence of a large number of arid-zone stations in UZB may explain this, desert stations having a notoriously high variability in annual precipitation. By contrast, the two SKO-KAZ stations are in the most favourable part of the rainfed arable zone where a lower variability would be expected. As with KAZ, 1969 was the record wet year in UZB, with precipitation 90% above normal. More recently, 1993 was also particularly wet, with precipitation 50% above normal for that year in UZB (and 20% above normal in the rainfed stations of SKO-KAZ). However, the long-term precipitation trend for Arys shows a slight decrease over the 70-year period, of about 5-7%, but as for UZB, variability from year to year of this (arid zone) station is very high. Closer inspection of the precipitation pattern shows a marked rise of precipitation over the period 1927-1958, followed by a steady and continuing fall. Implications of this precipitation (and temperature) pattern on pasture production were investigated in the course of the EMIMS-SLU study (EMIMS Guidelines, Nov. 2007, Annex G). Although long term trends in temperature and precipitation are the two most important met parameters requiring investigation, other met parameters are also important, including windspeed, relative humidity, and most importantly sunshine. There is currently increasing evidence of ‘global dimming’ which is having some effect on mitigating global warming, with high-altitude aerosols and in some areas vapour trails from jet aircraft having a major effect in reducing solar radiation. Good data on this is available from Israel, and also from North America where quantification of (lack of) jet vapour trails was undertaken from the 3 days September 12-15, 2001, during which most aircraft movements were banned.


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8 USE OF WATER MANAGEMENT INFORMATION

8.1 THE AMU DARYA AND SYR DARYA BASINS WATER MANAGEMENT MAP

The Water Management Map (SREDAZGIPROVODKHLOPOK, 1990, some updates, UZGIP, 2008) was illustrated in part in Figures 1.3 and 4.18. In addition to mapped information, this work has essential basic tabular data appended, including average annual river flows at key river gauging stations (generally those above areas of irrigation water consumption). This data shows that the sum of these river flows total 115,5km³ / year for the two river systems together, including 37,12km³ / year for the Syr Darya and 78,4km³ / year for the Amu Darya. This total is just below the total of 123,08km³ / year given elsewhere as the average gross flow totals of the two river systems. In terms of actual flows measured on the main river channels of the two rivers (Fig. 8.1), the average measured annual flows are around 19km³ / year and 58km³ / year respectively, with the Syr Darya varying between 12km³ / year and 30km³ / year and the Amu Darya between 45km³ / year and 75km³ / year. (These measured flows, of course, are net of any irrigation abstractions upstream.) Figure 8.1: Annual Measured Flows at various points on Syr Darya and Amu Darya Rivers, 1932-1999

Source: UZB-NSIU Final Report (August, 2009), quoting earlier sources.


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Fig 8.1 also shows graphically the decline of flows in the lower reaches of both rivers since the early 1930s. For the Amu Darya, the flow at Kerki (below the Karaumkskiy Canal diverting major flows into Turkmenistan) was considerably higher than the combined flows of the Pyandj and Vaksh until the late-1950s, whereas in the 1980-1999 period it has been significantly lower in most years. Flows nearer the Aral Sea outfall dropped markedly from around 1974 as further irrigation schemes came into operation. For the Syr Darya, Bekabad flows (near the outfall) declined markedly over the period 1947-1952 as post-war irrigation schemes came into operation. At Kal flows declined markedly over the period 1971-75, coinciding with the opening of the Toktugal Dam and consequent filling of the Reservoir. Since 1975 flows at Kal have again picked up, once the reservoir had completely filled. The presence of water storage reservoirs, has, to a certain extent, attenuated peak year flows, and flow rates such as those seen in 1969 have not been seen since that peak year. Allocation of water in both river systems is governed by inter-governmental agreement. In a good year, UZB is allocated 63km³, out of which 59km³ will be consumed by irrigation. In a poor year UZB would be allocated 54,2km³, with 49km³ used by irrigation. Breakdown of the various sources making up the 63km³ total is given in Table 8.1. But over half of the water allocated for irrigation returns to the river system as drainage return flows, benefiting downstream users in terms of increased water availability, but causing problems to them – at least, in the low flow later summer period – as water quality of the return flow water is significantly worse than the primary irrigation water. Changes of water quality (average total salt content (g/litre) over the year) for the main channels of the Syr Darya and Amu Darya are given in Figure 8.2, showing the worsening water quality over time and on moving downriver. Table 8.1: Annual Water Allocations for UZB Use (km³) River Main

Channel Tributaries Total Groundwater CDW Total

Syr Darya 10,49 9,2 19,69 1,59 4,21 25,49 Amu Darya 26,92 6,98 33,9 1,00 2,63 37,53 Total 37,41 16,18 53,59 2,59 6,84 63,02

Source: UZB-NSIU Final Report (August, 2009), quoting earlier sources. Figure 8.2: Average Water Quality Measurements at various points on Syr Darya and Amu Darya Rivers, 1932-1999


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Source: UZB-NSIU Final Report (August, 2009), quoting earlier sources. Water quality data is not yet formally included on the SLMIS, and this aspect of the work must be a high priority for the near future. Of interest is the fact that primary irrigation water shows magnesium and sulphate as the major ions, with the more damaging sodium and chloride being relatively low. However, drainage water, reused downstream, is much more saline and often with higher proportions of NaCl. International (FAO, USDA) water quality criteria are given in Annex B, and application of these criteria to a range of water samples of contrasting quality is shown in Box B.1. 8.2 OTHER WATER MANAGEMENT INFORMATION

8.2.1 The CACILM Dams Database

Largely inherited from the Soviet period, there is an enormous hydraulic infrastructure of hydro-electric, flood protection and irrigation dams, reservoirs, water diversion weirs and drainage canals whose continued operation is essential to the success of the economies of each of the five countries. Although major irrigation and drainage canals, and the largest of the reservoirs, are shown on the above water management map, much important infrastructure is missing, this infrastructure being sufficiently important to be shown on mapping at 1:1million scale. The CACILM Dams Database was hence devised to fill this gap and also to extend the coverage to areas outside the Amu Darya and Syr Darya Basins. The major characteristics of the CACILM Dams Database, covering both dams and water diversion weirs, are given in Table 8.2. Output of this coverage on the SLMIS for the central part of the Priority area is shown in Figure 8.3 at a scale of approximately 1:3,5m.


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Figure 8.3: Dams and Water Diversion Weirs in the central part of the Priority Area. Scale c. 1:3,5m.

Source: SLMIS output, showing the following themes (legend bar at right edge of map): Dams, watershed boundaries, rivers & canals, major urban areas, national and oblast boundaries, bare rock outcrop areas, CACILM National Projects. Inset small table shows ID information obtained from point covering Toktugal Dam. Sources of information for the Dams Database include mapping and reporting on dams and hydropower reservoirs for KYR (giving both existing structures and future proposals), the above Amu Darya and Syr Darya Water Management Map, the lower Syr Darya water management map of the Shymkent BVO office, and internet-connected sources obtainable through Google Earth (useful in the case of the major dam projects, both existing and proposed). Positions for each of the above sites were obtained and verified from the Google Earth imagery, with latitude and longitude, and elevation parameters being recorded. Figure 8.4 gives an example map print-out from the GIS at approximately 1:1million scale of the area around Bishkek and the areas of Kazakhstan in the KAZ-KYR border areas, including the SE part of Zhambul Oblast and the SW part of Almaty Oblast. Dam and weir sites are shown with orange triangles; major basin boundaries are shown in this case with a thick red line; rivers and major canals are shown with thin blue lines. The grey lines mark oblast (and republic) boundaries. The line of blue dots marks part of the main road from Bishkek to Almaty, obtained from GPS positioning. The large black rectangle marks the area shown in Figure 4.10 and 4.11 (NDVI Mean and Max for 2008), and the small rectangle in the NE corner of the figure marks the area of coverage of Figure 4.13 (Google Earth imagery at 1:100.000 scale of the Ugulek Village area (site of CACILM-UNDP project). Table 8.2: Major Charactersitcs of CACILM Dams Database Name: Dams.shp, dbf etc (ESRI shape file) [under directory: C:\CACtopo\Topo\..] Type: Point file Established: Jan-May, 2009 Created by: MSEC staff and consultants Sources: Available published maps & reports (major sites); inspection with Google

Earth (minor sites, and cross-checking for all sites). Records (current no.):

327

Representation on Orange triangle, thin black outline, 14pt, angle 360


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map: Fields (current): Name Name of dam or water diversion weir – character field ElevBlw Water elevation below dam (m) – numerical field ElevAbv Water elevation above dam (m) – numerical field EyeAlt Eye altitude (scale) for Google Earth viewing / image photo (m) –

numerical field Status Existing / proposed / under construction / suspended – character

field Datecom Date of commissioning of dam / weir (year) – numerical field Mw MW of installed electrical generation capacity (MW) – numerical

field Lat Latitude: (decimal degrees N) – numerical field Long Longitude: (decimal degrees E) – numerical field Fields (possible future):

i. Hyperlinked GE image / photo at EyeAlt recorded above; ii. Name of river / canal; and mean, max and min flows; iii. Total electrical energy (GWh) generated per average year and per dry year; iv.Further relevant management information (particularly relating to irrigation & drainage)

Remarks: Completed only for major dams and diversion weirs, and for minor sites in several selected areas (e.g. KAZ-KYR border areas; Tashkent area of UZB; etc). Needs to be systematically extended, in any future phase of project.

The current coverage extends to only the largest of the dams and water diversion structures, and only for the central parts of the Priority Area. Using the same methodology, this now needs to be extended to all areas, consistent with mapping at 1:1m scale (as in Figure 8.4). Later, inclusion of smaller (moderate sized) structures needs to be included, with print outs finally consistent with mapping at 1:200.000 scale. The latter would be a key item for inclusion in CACILM National Projects. Figure 8.4 Dams and Water Diversion Weirs in Chui Oblast (KYR) and adjacent areas in Kazakhstan: Scale c.1:1m


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Source: SLMIS. Basemap layers (towns, hydrographic layer, oblast boundaries) are from ECONET GIS; all other layers generated by MSEC. Large rectangle shows area of coverage of MODIS imagery given in Figs 4.6 & 4.7; small rectangle, top right, covers Ugulek CACILM project area, Fig. 4.14. BOX 8.1 : Information on River Basin Cascades of Multi-Purpose Hydropower & Irrigation Dams. Experience from Sri Lanka, Mahaweli River Basin Development: Actual Drawdowns and River Flows for 1995 _________________________________________________________________________________ In the upper part of the diagram (schematic plan) mean annual flows for each tributary river and each section of the main river are represented by widths of the respective sections of the black bars. The appended scale bar shows the average flow (the greatest flow, in the lowermost section of the river - top right, in the diagram - being just under 100m³/sec). The relative positions of the four hydropower dams and the one diversion weir on the river are given by the vertical white bars. The volume of water going through the hydropower turbines at each dam is represented by the thickness of the black bar at that position. The thickness of the grey diagonal bars represents the water lost to overspilling at each site during that year, (overspilling having to occur to prevent overtopping of the dam; capacity of the hydroelectric turbines, or demand for electricity, not being sufficient to take all the water through the hydro turbines during these peak-flow times). The lower part of the diagram gives a schematic cross section of the cascade, including the individual reservoirs and the positions of the respective hydropower stations in relation to their parent dams. In the cross section, the solid black parts of each of the reservoirs are the respective depths of drawdown of the stored water for that particular year (1995). Here it is seen that the upper reservoirs (and particularly Kotmale) are selectively emptied while the lower ones suffer relatively little annual storage drawdown. The lowest reservoir (Rantembe) is used as a daily regulator, enabling an even flow of water into the main irrigation canals (just below Rantembe), and storing the peak water coming though from Randenigala on a daily basis (Randenigala being run to generate daily peak electricity: demand being greatest during the evening and early morning peaks, just 5 hours daily). Major drawdowns in the upper reservoirs mean that these are less scenic and less useful for tourism and water sports, a major consideration in a country where tourism is now the most important foreign-exchange earner. Randenigala is much more useful in this respect, suffering relatively little drawdown in most years. Polgolla weir is designed to divert some of the Mahaweli flow, through a transbasin canal and tunnel, into a northern-flowing river, the Amban Ganga. Its waters are used for both irrigation (System H) and for hydroelectricity. However, less power is generated per cubic metre of water for this diversion than would be the case if the water remained in the Mahaweli cascade – the Mahaweli Authority thus has to balance the specific irrigation requirements for the System H farmers with the country’s requirements for more hydropower (and more irrigation in the downstream System C , E and B irrigation areas). As can be seen from the diagram, the operation of the cascade for hydropower is relatively efficient. Almost 600m of the 700m usable elevation difference is tapped for hydropower, and in 1995 some 95% of the total river flows were usefully tapped for hydropower. However, in dry years, and in years where tropical cyclones hit the area, the picture can be very different. 1995 was a relatively good year. The small storage capacity of Rantembe coupled with the large sediment loan of the Uma Oya (whose watershed areas are used for both intensive vegetable cultivation and degraded tea land) have created a major problem of sediment accumulation in this reservoir. This has thus recently been the topic of a major project, financed by the ADB.


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Source: ADB Consultant’s diagram, prepared for seminar in connection with ADB PPTA for Upper Watershed Management Project, Sri Lanka, 1997.

8.2.2 Information on River Basin Cascades: Mean Flows and Reservoir Drawdowns

It is useful to represent diagrammatically the comparative average and peak river flows for any one year or any one season in relation to the operational management of cascades of dams and reservoirs. This now needs to be done for the large cascades represented by the Syr Darya and Amu Darya basins, with winter flows (for peak hydroelectric generation) and summer flows (for peak irrigation demand) being represented diagrammatically. The appended example (Box 8.1) from the Mahaweli River Basin in Sri Lanka shows a possible format for this sort of diagram. In the upper part of the diagram (schematic plan) mean annual flows for each tributary river and each section of the main river are represented by widths of the respective sections of the black bars. The appended scale bar shows the average flow (the greatest flow, in the lowermost section of the river - top right, in the diagram - being around 100m³/sec). The relative positions of the four hydropower dams and the one diversion weir on the river are given by the vertical white bars. The volume of water going through the hydropower turbines at each dam is represented by the thickness of the black bar at that position. The thickness of the grey diagonal bars represents the water lost to overspilling at each site during that year, (overspilling having to occur to prevent overtopping of the dam; capacity of the hydroelectric turbines, or demand for electricity, not being sufficient to take all the water through the hydro turbines during these peak-flow times). Further representation is needed within the CAC-5 for each of the major irrigation and drainage areas with respect to water diversions for irrigation and return flows of drainage water back into the river system. This representation needs to cover average, maximum and minimum irrigation flows of water, and also water quality parameters for the same periods. 8.3 CONCLUSIONS: WATER MANAGEMENT

The CAC-5 Priority Area is well endowed with water resources but much of these are wasted, evaporating to little economic or ecological benefit in flood plain areas and depressions in the winter and spring periods. Conversely, in late summer and autumn, there are localized severe water deficiencies, particularly in the lower-elevation and drier areas, and with ‘tail-enders’ (irrigation farmers at the end of canals). Basin-wide agreements across national, oblast, and irrigation scheme boundaries have to be drawn up and adhered to, following international norms (e.g. as in the EU


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Water Framework Directive). Within the CAC-5 context, the requirements of the upstream countries for energy and compensatory mechanisms, in order that they can run the hydropower reservoirs more to suit downstream irrigation requirements, also have to be incorporated into these agreements. Data presented here shows the affects of increasing irrigation abstractions over the period 1950-1980 on the two major river systems, resulting in much lower water flows on the lower reaches of the two rivers (and especially the Syr Darya), and worsening overall water quality (again worse on the lower parts of the Syr Darya). At local level deterioration of irrigation and drainage infrastructure and local-level institutions dealing with these have much to blame for increases in soil salinity and seasonal rises in water tables. Very inefficient surface irrigation techniques mean that much water returns to the river system as return flows, and overall rises in ground water tables occur. Later summer return flows are thus invariably highly saline, which is a further problem for downstream farmers. Data on water quality on primary surface and groundwater irrigation sources, and return drainage flows, now have to be incorporated into the SLMIS. Some drip irrigation and some sprinkler irrigation is being used within the Priority Area, reducing irrigation water demand per unit of crop output by a factor of 2 to 3. Although both drip and sprinkler irrigation show some technical problems and constraints, their further extension should be encouraged. Drip is better suited to higher-value perennial horticultural crops, but can also be used with seasonal crops (e.g. tomatoes and peppers). Mobile sprinklers have greater flexibility to be used on a variety of crops, including supplementary irrigation on winter seasonal crops in late April / early May (and perhaps also in October at planting time). Total supplementary applications under this system would average less than 100mm/year, but generate very high marginal returns. The main use of sprinklers, however, would be during the late May-September period for higher value summer crops (notably potatoes and vegetables, perhaps also oilseeds and maize), and the good electricity grid network is a big positive factor here in provision of energy for the necessary water pumping. With climate change leading to greater frequency of extreme weather events, better irrigation management, as well as better land management, can have a major role to play in mitigating any negative consequences. The SLMIS thus must get together all the relevant information to support this, both at a general (multi-country) level with presentation consistent with mapping at 1:1m scale, and at oblast, rayon and project level, with mapping and presentations at 1:200.000 scale and more detailed. Extension of the SLMIS in several areas is required, including on hydrological flows and on water qualities.


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9 CACILM SLMIS APPLICATION: LAND DEGRADATION HOTSPOTS AND BRIGHTSPOTS

9.1 INTRODUCTION: LIVELIHOOD & LAND DEGRADATION BASELINE, AND LAND DEGRADATION TRENDS

One of the main requirements of the SLMIS is to devise a map coverage giving updated information on the current environmental and land degradation hotspot areas, and on those areas where rehabilitation has successfully been undertaken (the ‘brightspots’). Another requirement is to define the current baseline, and then define those areas where trends may be negative (further hotspot zones) or positive (brightspot zones). In all areas there have been problems in undertaking these tasks, but much progress has been also been made in all areas, with the definite conclusion that all of this work should continue. This Chapter reports on progress to date on these tasks. On defining a suitable baseline and undertaking adequate baseline quantification there have been two very major problems: - Difficulties in defining a suitable baseline year: the year of break-up of the FSU and the most recent complete year (2008) might both qualify as a suitable baseline, but neither would present an adequate picture. The FSU break up was followed by a traumatic transition period showing a major downturn between the period 1993-97, continuing to 2000 before significant improvements were experienced. - Weather fluctuations: The Year 2008 has also been a very dry year: 2006 and 2007 were both much more typical. Thus, in defining baseline conditions, figures for three periods need to be considered: 1990/91-end of Soviet period; 1997/99- point of minimum economic activity in the rural sector; 2006-07-most recent typical baseline year. However, ideally trend figures covering every year should finally be obtainable, so that a full picture can be presented – e.g. Figure 3.7 covering livestock numbers (every year, 1985-2005) and Figure 3.4, covering irrigated crop areas (5 years over period 1987-2004). Information is available from official statistics reports which are published annually. These have been studied (KAZ) and have been shown to be relatively reliable. Good sets of data have now been obtained for all parameters from two KAZ oblasts and these have been reported on in Chapter 3. The SLMIS now needs to acquire the equivalent data for all other areas in the CAC-5 Priority Area, in the same (Excel) format. This may involve some payment in order to acquire digital data, but in most cases only hard-copy report tables will be available. The latter will need digitization and checking of completed tables of figures. 9.2 INDICATIONS FROM REMOTE SENSING IMAGERY

Indications of land degradation hotspots from Remote sensing Imagery were discussed in Chapter 4. Briefly, these are summarized as follows: -GIMMS dataset: a major hotspot area has been identified to the S and W of Turkestan (Fig 4.1), associated with the tail-end position of an irrigation scheme (insufficient irrigation water), and insufficient drainage provision made worse by early spring flooding in many years; -MODIS: further detail from NDVI max and mean can pick out low-productivity vegetation areas: shallow soils with rangeland. Care has to be exercised in routine use of MODIS: correlation with LANDSAT ETM+ and with GE detailed imagery, area by area, is essential. -ETM+: much further detail in picking out specific problem areas (e.g. Turkestan, Fig 4.8, Baidebek erosion, Fig 4.12). This imagery, used at around 1:100.000 scale, has proved a good base for routine work, particularly after specific problems have been identified by detailed imagery with GE, and then referred back to ETM for routine and precise mapping (e.g. erosion problem, as in Fig 4.12). 9.3 INDICATIONS FROM RAYON- AND MUNICIPALITIES-LEVEL STATISTICAL DATA

A selection of detailed information was given in Chapter 3 covering rayon-level statistical data and Chapter 6 covering municipalities-level data, and the broad conclusions here are as follows:


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i. good primary data was obtained mainly from Kazakhstan; other NSIUs presented only secondary (former reports) data, although much of this was useful and interesting. For the SLMIS, however, original primary data is required. ii. the KAZ data indicates that major falls in livestock numbers – and particularly goat and sheep numbers – occurred over the period 1993-97, and in most areas goat and sheep numbers are still well below pre-1991 levels. Fall in small livestock numbers generally coincides with improvement in pasture productivity, and this has certainly been confirmed in SKO. Overall, pasture is hence a ‘brightspot’, at least for the period 1997-2007 and at least away from settlement areas, where higher densities have caused degradation (evidence from Remote Sensing) – in these locations we have many localised ‘hotspots’. iii. big falls in overall cropped areas occurred in arable land in both Almaty Oblast and in SKO in the1991-97/99 period. However, many higher-value crops have increased in area, particularly since 1999. iv. for SKO, since 1997 irrigated areas at lower elevation have seen a major increase in cotton and major decreases in both cereals and particularly fodder crops, especially alfalfa. While economically attractive in the short-term, this is of major environmental concern in the longer-term (and hence a hotspot, particularly when reasons for this increase are analysed for this trend: a major rise in seasonal water tables, and in soil salinity). v. for SKO, irrigated yields show a big fall from 1987/90 to 1995/97 for all crops, although for cotton and oilseeds the fall was less severe (c.25%) than it was for other crops (50% or more). The main reason for the fall was a chaotic situation in the reorganization of agriculture, and particularly in the low amount of fertilizer being applied. vi. yields since 1997 have risen considerably, particularly for some higher value crops (oilseeds and potatoes). vii. since 2005 records at municipality level on total land use and cropping have been available in Kazakhstan but unfortunately these do not separate irrigated from rainfed cropping nor cotton from ‘other crops’. However, records do show the extent that cropping patterns differ within a single rayon (see Figures 6.8 and 6.10). viii. these municipality records promise to be very useful in future years, as any hotspot area may represent a high percentage of the land of a single municipality, but a low percentage in a rayon (i.e. rayon statistics may be too coarse to adequately track potential hotspot problems, although these should show up well at municipality level). ix. hotspot municipalities (SOs) within SKO, identified to date include:

- N. Baidebek: 2 SOs with significant proportion of rainfed cropping, particularly cereals: marginal rainfed area, plus increasing erosion problems;

- E. Baidebek: 2 SOs with major gulley erosion problems, again in rather marginal rainfed zone; - Tulkibas: several SOs with high proportion of rainfed cereals and safflower on excessively

steep slopes with major erosion risk; - Turkestan: 3 SOs to W and 4 SOs to S showing major salinity problems, high WTs in spring,

and low irrigation water availability in late summer; - Maktaraal: all SOs require close monitoring for high spring WTs and worsening salinity

problems; Shardara reservoir needs close monitoring in spring for excessively high levels with back-up problems in main drains extending into Maktaraal.

9.4 INDICATIONS FROM RAYON-LEVEL OR IRRIGATION SCHEME-LEVEL STUDIES AND MAPPING

UZB-NSIU have presented excellent mapping covering different parameters of land degradation, and these are shown in the following figures (Figs. 9.1 – 9.3):


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Land Quality Bonitet ratings (Rayon averages) are shown in Figure 9.1 for 1991 and 2003, revealing an overall fall which has affected all areas. Highest ratings were 81 points in 1991, but this had fallen to 69 points by 2003. Rayons with averages of more than 65 points were quite numerous in 1991, but were very few by 2003. The pattern for salinity levels in soils is presented in Figure 9.2 for the years 1990, 2000 and 2007 (Oblast averages). This shows a marked deterioration between 1990 and 2000, after which there has been a slight improvement. Figure 9.1: UZB: Average Land Quality (Bonitet) Ratings of Irrigated Land in each Rayon: 1991 (top), 2003 (below)


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Source: UZB-NSIU, 2009, reporting on data of UZB MAWR, 2008. (Minor cartographic adaptation by MSEC.) Ground water tables are shown in Fig. 9.3 for the same years (1990, 2000, 2007). These again show an equivalent picture, worsening trends over the period 1990-2000, flowed by a slight improvement over the period 2000-2007 in some areas and a further deterioration in others, most notably Sirdarya Obl (UZB) adjacent to the problematic Maktaraal Rayon, SKO-KAZ. Fig.9.2: UZB: % of Irrigated Land in each Oblast affected by Soil Salinity: 1990 (top), 2000 (middle), 2007 (bottom)


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Fig.9.3: UZB: % of Irrigated Land in each Oblast affected by High Water Tables: 1990(top),2000(middle),2007(below)


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Source: UZB-NSIU, 2009, reporting on data of UZB MAWR, 2008 (Minor cartographic adaptation by MSEC).


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Fig 9.4 Guzar and Kamashi Rayons, Kashkadarya Oblast, Current Soil Fertility (Bonitet) Ratings

Key: Dark blue: very low; Purple-Pink: low; Red: moderately low; Yellow: average; Light blue: high. Fig 9.5 Guzar and Kamashi Rayons, Kashkadarya Oblast, Current Soil Salinity Status

Key: Light blue: non-saline; Green: slightly saline; Yellow: moderately saline; Purple pink: highly saline.


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Fig 9.6 Guzar and Kamashi Rayons, Kashkadarya Oblast, Current Ground Water Table Status

Key: Dark blue grey: <1m; Bright blue: 1-2m; Light blue: 2-3m; Very light blue: 3-5m; White: .5m. All diagrams: Brown: Settlement areas (including village homegardens); White/stippled: unused/unirrigated. Sources, Figs 9.4 -9.6: UZB-NSIU Final Report, Aug 2009, with minor adaptations by MSEC. Mapping undertaken in two rayons within Kashkadarya Oblast gave a more detailed pattern for hotspot areas defined respectively on land quality (bonitet) ratings (Fig 9.4); salilnity (Fig 9.5) and ground water table (Fig 9.6). Some relationships between the three parameters were seen (for example, the central southern area, just N of Guzar, showed better land as defined by all three parameters, but in other areas no relationships were seen. Also very good land was adjacent to very poor land: the basis for mapping, at first glance, might be questionable, and to answer this it would be useful to plot sample points and analysis points on all three maps. 9.5 INDICATIONS FROM GOOGLE EARTH OBSERVATIONS

Environmental and specifically Land Degradation Hotspot Areas are very easily recognisable from the Google Earth detailed imagery (1:5.000 – 1:10.000), and the worst of these hotspots can also be delineated with the general imagery (1:50.000 – 1:100.000) and the LANDSAT ETM+ coverage. Such hotspot areas are defined as ‘substantial areas (individually of >>50ha and mostly of >100ha) where soils and / or landsurfaces have been permanently damaged by human activity such that any reclamation to achieve original rural functions and remunerative biological productivity would require expensive restoration measures.’ These environmental hotspot areas are of several contrasting types and are further subdivided as shown in Table 9.1. The details of the specific structure of the database created for the SLMIS (called Erhotspot.shp, dbf etc) are given in Table 9.2. The Environmental Hotspot Overlay has been completed for major problem areas, particularly within the main population belts and including all the main agricultural areas (and particularly rainfed agriculture). Some further areas, outside the main areas of initial emphasis (particularly in Turkmenistan and Tajikistan) now need to be included. Some 553 specific sites have so far been identified and represented as a point data file on the GIS. Positions have been transferred manually


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from the imagery to the GIS by means of coordinates of the cursor. An example of the cartographic output from the GIS, including this overlay, is shown in Figure 9.7. Sites of mass movements (gulley erosion and landslides) are very easily recognised in the detailed imagery. Gulley erosion is particularly prevalent on slopes of over 8%, especially in deep loess soil parent materials which predominate in areas of rainfed agriculture, particularly in the northern foothill slopes of the Tien Shan and Karatau mountains. :Table 9.1: Environmental and Land Degradation Hotspots Main Land Degradation Type

Specific Land Degradation Agent

Code Symbol on GIS Colour on GIS

Gullies 1.1 Small flash Bright violet Landslides 1.2 Large flash Black

Erosion

Surface Wash 1.3 Mine (undifferentiated)

2.0 Large circle Grey infill

Open Mine 2.1 Large circle Black infill Borrow pit 2.2 Large circle Light brown infill Coal Mine 2.3 Large circle Dark brown infill U mining 2.4 Large circle Pink infill Mine Spoil Heap 2.5 Large triangle Black Metalworks 2.6 Large square Magenta infill Mine Works 2.7 Large square Black infill U processing 2.8 Large square Pink infill

Mining and associated activities

Works 2.9 Large square Grey infill Prospecting Prospecting (for

mining) 3.0 Large circle No infill

Rubbish Dump Rubbish Dump 4.0 Unique symbol Sewage Works Sewerage Works 5.0 Unique symbol Sedimentation Sedimentation 6.0 Circle, half infilled

Table 9.2: Major Charactersitcs of CACILM Environmental and Land degradation Hotspots Database Name: Erhotspot.shp, dbf etc (ESRI shape file) [under directory:

C:\CACtopo\Topo\..] Type: Point file Established: Jan-Mar, 2009 Created by: MSEC staff and consultants Sources: Thorough scan of Google Earth imagery; local knowledge, information, and

some field assessment; available published maps & reports with re-check of Google Earth

Records (current): 553 Representation on map:

(see Table 9.1)

Fields (current): ID ID no. (1 to 553) Type Specific Land Degradation Agent (see Table 9.1 for details) Name Location of site (& mine/factory name, type, where relevant) Code Code number for Specific Degradation Agent (see Table 9.1 for

details) Fields (possible future):

i. Area affected (ha); ii. Severity of problem for specific site; iii. Any land restoration measures applicable; iv. Ministry/public agency responsible for monitoring problem v. Surface wash (Surface erosion) hotspots (Code 1.3 in Table 9.1) vi. Salinity and High water table hotspots (Proposed Code 1.4 in Table 9.1)


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Remarks: Major focus so far has been on KAZ, KYR and UZB, particularly in more highly populated areas, with little work undertaken in TUK and TAJ. Needs to be systematically extended, in any future phase of project.

Many of these mass movement features are not active, having been caused by past periods of land misuse. Extensive areas have effectively been abandoned, with re-vegetation seen to a variety of woody perennial and annual species. However, many areas are currently still in use for (marginal) rainfed cultivation, and some of these are very badly affected by both rilling and gulleying, and downstream sedimentation. Again, in most of these areas little or no attempt has been made with soil conservation, even with simple measures such as contour cultivation, and cultivation up and down the slope with heavy machinery would appear to have been the cause of much of this gulleying. Further cases of gulley erosion occur due to cattle tracks, particularly near to village areas abutting steep land areas (see Figure 9.9). Major areas of concentration of landslides are highly localised, and related to difficult geology coinciding with steep slopes. Particularly notable is a terrain belt on the borders of Jalalabad and Naryn Oblasts, Kyrgyzstan, where many massive landslides have occurred, some of these threatening local towns and villages as well as downstream cultivated areas. Surface wash erosion commonly shows up better through variations in vegetation tone on the less-detailed imagery, as the latter tends to be taken at the period of maximum rate of crop growth. Sites of bad land degradation caused by such erosion are illustrated by Figs.9.9 & 9.8. Sites where land degradation has been reversed are also seen in Fig 9.10 (a brightspot ). Figure 9.7 Land Degradation Hotspots in the border of the SKO and Zhambul Oblasts: Scale c. 1:1million

Source: SLMIS material (MSEC, 2009) with MSEC legend; topolayers of ECONET. Fig 9.8: Land Degradation Hotspot: Land and Communities Devastated by Gulley Erosion, SE SKO (KAZ)


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Figure 9.9: Widespread Impact of Surface Wash Erosion NE of Jalalabad (Google Earth): scalebar of 10km


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Figure 9.10 Surface Wash Erosion Hotspot (and Rehabilitated Brightspot): NE of Jalalabad (GE): scalebar of 100m

Other major problem areas include mining and associated industrial areas, some of these being very extensive (thousands of hectares of contiguous devastated land). Adjacent agricultural areas may also be adversely affected, either by wind-blown dust deposition (all adjacent areas) or by contamination or surface or groundwater, used downstream for irrigation and for domestic water (specific areas). Mine areas are represented as point files in the GIS, these being depicted as large circles. Infill areas of the circles reflect the type of mine, and are shown in different colours. Undifferentiated open mines (mainly for heavy metals) are shown in solid black; coal mines in dark brown and uranium mining operations are shown with pink infilling. Further differentiation could be contemplated on status of mine: continuing (or expanding) activity; virtually dormant; or abandoned. Further follow up would be justified on status of land restoration activities: in most countries, approval for mining is now conditional on land being restored for agricultural, livestock or biodiversity conservation uses. Areas of mine spoil are again easily recognised in the detailed Google earth imagery. These are depicted in the GIS as a solid black triangle. Areas of metal and mine works are depicted in the GIS as large square symbols. Undifferentiated works (no specific use visible) are shown as an open square and mine works as a solid square. Metal works have a magenta infill: uranium processing works as a pink infill. Again, many of these sites show toxic wastes, and many of these are within areas of high population density (Shymkent and Kantau being prime examples). Figure 9.7 shows an example (originally at 1:1m scale) combining layers from the ECONET basemap with this Environmental Hotspot point data obtained from a detailed scan of the Google Earth imagery. This area covers the E part of the SKO and the W fringes of Zhambul Oblast and demonstrates graphically the aerial extents of the various environmental problems. The clusters of


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magenta flashes in the SE parts of this coverage are areas devastated by gulley erosion; the black dots in a band to the north of these present areas of open-cast phosphate mining around the town of Zanatas (Zhambul Oblast) (see Figure 9.11 for the detailed Google Earth image covering some of these operations); pink dots in the NW part are areas of uranium mining in Sozak Rayon, SKO; magenta dots in two areas in the W (Karatau Mountains) cover the polymetal (notably zinc and lead) mining and metal processing centred on Kentau. (Thick grey lines in this figure denote oblast boundaries; thick black lines denote watershed boundaries separating the Syr Darya watershed with the small watersheds represented by the local N-flowing streams.) Figure 9.11: Open-pit Phosphate Mining: detail of hotspot centred on 43.50°N, 69.76°E, South of Zanatas

Routine scanning now should be undertaken for the full extent of the priority area, and polygon shape files created on the GIS. Optimum techniques for this process need to be devised: either undertaking this through the Google Earth Professional add-on (costing a few hundred dollars per year) or undertaking this through geo-referenced JPEGs of screenshots of the respective hotpot areas. 9.6 FIELD CHECKING AND RECORDINGS, AND USE OF GPS DEVICES

All remote sensing work needs to be correlated with ground-truth observations, and for this use of GPS devices is now essential. Costs of GPSs have now fallen, and a standard model, such as the Garmin E-trex can now be bought for around $100-150. Such a device will function for about 6-7 hours of continuous operation on two rechargeable AA batteries and is able to store positions for 500 waypoints. With a connecting cable (to UPS or Serial ports) points can be downloaded within a few minutes to a field laptop as an mps file in Mapsource software. Mapsource has topographic map coverage of many countries (and general coverage worldwide), and downloaded points can then be compared with topomap detail. For the CAC-5, only very general topo coverage was available in Mapsource (information consistent with only 1:2,5m-scale mapping). However, Garmin waypoints can be imported into Google Earth (see Figure 9.12b) and also into ArcView. Figure 8.4 gives an example of the latter, where waypoints were taken along the Bishkek-Almaty highway to confirm its position


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(the position has changed from that given in the ECONET coverages). (In Figure 8.4, the road position is represented by a series of light blue dots.) Accuracy / repeatability of recordings is usually within 5-7m, and differential GPS use can bring this down to 2m, which is more than sufficient for anything other than the most detailed cadastral mapping work. Fieldwork is expensive, time consuming, and relatively little is viewable within a short time. Use of all available imagery to back up routine remote sensing imagery (particularly MODIS and LANDSAT-ETM+) is essential, and for this correlation with what can be observed in Google Earth imagery is now an important tool in this work.: Google Earth becomes even more indispensable for work in the CAC-5 countries, as it can serve as a base for detailed topographical mapping. As all CAC-5 countries restrict the availability and use of maps more detailed than 1:100.000, this is a huge advantage. Mapping as detailed as 1:2.500 scale can be compiled in this way (see Fig 4.15 for potential application at homegarden allotment level). Figure 9.12. Use of Garmin GPS (a: left) to record position of gulley in field (in this case, in otherwise good rangeland in the N footslopes of the Karatau Mountains, SKO); and (b) combined with detailed Google Earth imagery (in this case to denote track and pathway in Ala Archa Protected Area).

9.7 OTHER ENVIRONMENTAL HOTSPOTS

Land degradation hotspots so far have focused on erosion and sedimentation, particularly gulleying (affecting most severely the loess soils) and other mass movements (particularly in hilly and mountainous areas). The hotspot database now needs to be extended to sites where salinity, sodicity, and high water tables are problematic, but this would be more usefully shown with a polygon file than with a point file. However, establishing polygon files require precise mapping, which may be difficult to undertake in most areas. As an interim, a point file presentation on this theme may be more appropriate. The hotspot database has already substantial data on land affected by mining activities. Although mining and other industrial activities may be considered as outside the purview of work on rural land


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degradation, it is a very significant factor in the CAC-5 area rural environment for the following reasons:

- very large areas are devastated, running in total to hundreds of thousands of hectares, and in some municipalities more than 10% of the landscape is affected, with the mining directly or indirectly contributing to more than 90% of the gross revenues and employment in those municipalities;

- some of the mined minerals are highly toxic, including mercury, lead, cadmium, and uranium and other associated radioactive minerals;

- given the dry, dusty conditions during the summer period, aeolian deposition of toxic dust materials is a big factor for agricultural users of adjacent land;

- spoil materials from the mines are contaminating nearby water sources, these sources used downstream for irrigation, and, in some cases, for domestic purposes;

- some of the leachate from spoil materials is highly acid, the spoil containing sulphides which are oxidized to sulphuric acid;

- some metal-extracting processes use large amounts of very acid or very toxic chemicals. Current uranium extraction uses 70kg of suphuric acid for every 1kg of uranium produced;

- although soils are generally neutral to slightly alkaline (commonly pH 6,5 to 7,3 in topsoils, 7,3 to 7,9 in subsoils) and have fairly high cation exchange capacities (and thus can fix toxic metals into an unavailable form), horticultural use of more sandy soils and with greater use of acidifying nitrogen fertilizers can show appreciable heavy metal uptake (particularly if irrigation water is acid);

- the mining sector has suffered from very large fluctuations in commodity prices over the last ten years, and consequent mine closures have left many communities as poverty hotspots. Unemployed ex-mining staff have gone into agricultural and livestock activities on surrounding land, thereby causing directly land degradation.

Particular mining hotspot areas have both direct and downstream impacts include many areas in the Fergana Valley (highly toxic metals being mined near to irrigation areas, affecting both UZB and KYR and relations between the two countries); the areas to the S and SE of Tashkent (heavy concentration of various mining activities, also very close to irrigation areas); the Kentau area of SKO-KAZ (lead and zinc mining); the area of phosphate mining in Zhambul Oblast, KAZ (see Fig 9.11). As well as mining, other environmental hotspots include sites of household waste disposal, effluent from factories, chicken farms and animal yards. All of these can have major negative impacts on the rural communities. Environmental ministries are currently taking much more interest in the monitoring of environmental hotspots, and this is acquiring a higher profile in trade and commerce negotiations between CAC-5 countries and the WTO and the EU. KAZ, in particular, has been very active in this area over the last few years.


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10 RECOMMENDATIONS FOR CACILM SLMIS AT MULTI-COUNTRY AND NATIONAL LEVELS

10.1 SYNOPSIS: ACHIEVEMENTS TO DATE

Work on the SLMIS development has progressed for just 12 months, but within this short time there has been considerable progress. Maps and other data on land, water and climate resources have been collected from a wide range of sources; statistics on cropping yields and production have been obtained for key baseline years; formats for presentation of data relating to land degradation and land productivity changes have been developed; and key institutions have been strengthened at national and multicountry level. The MSEC – NSIU - NSEC arrangements under the present project have developed over this period, and generally have been successful, particularly in the following main areas: - key data, which hitherto has been difficult to obtain, has been made available to CACILM for the SLMIS. This has included metadata (essentially information on what data is available), all the key map coverages produced within the scale range 1:750.000 – 1:2,5m, and also a very substantial cross section of all the primary data available (and a major subject of what is reported here in Chapters 2-9); - the CAC-5 multicountry GIS coverage, initially established by the UNEP-WWF-ECONET and having good 1:1m scale basemap information, has now been considerably strengthened with both national and multi-country coverages relating to land degradation and restoration, land use, physical resources, crop production and rural livelihoods, and environmental hotspots, and all countries as well as MSEC staff have contributed to this; - MSEC has benefited greatly from receiving new information from the NSIUs and NSEC staff and on important subjects on which it was not aware (e.g. the interactive computerized climate Atlas of KYR; data on snowmelt and ice-melt modeling of both UZB and KYR; rangeland monitoring of TUK and KAZ; municipalities mapping of KYR and KAZ; bonitet statistics at rayon level for UZB and KAZ, water management mapping and data of UZB; oblast-level and detailed level soils and bonitet mapping of KAZ..) - different countries have all shown keen interest and strong points, and have benefited greatly by seminar presentations and discussions among themselves, as well as with MSEC. Some of the presentations and some of the reports have included outstanding material (much of which has been extracted for this report); - major physical problem areas and controversial issues have been openly discussed, particularly concerning watershed management issues and the operational policies of the large hydroelectric reservoirs, and transboundary pollution issues; - communications between each NSIU, NSEC, and parent national ministries in most cases have been good, and good cooperation has resulted; - a promising start has been made with use of remote sensing imagery and data, and with other new techniques, including delivery of a large amount of updated georeferenced imagery of various effective scales and resolutions, and with on-the-job training of UZB NSIU staff in remote sensing and image processing techniques. All of this work (reported here in Chapter 4) urgently needs further development and further expansion in the near future; - a good interdisciplinary approach to technical presentations and reporting had been developed. However, much work remains to be done, and the following sections describe what would be proposed for any future phase of the project. Sections 10.2 - 10.8 deal with SLMIS development, in most cases continuation of the current work. Table 10.1, summarising these activities, also gives


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section numbers and figure numbers where this work has been described and illustrated earlier in this report. In most cases proposed work can be undertaken under the existing structures set up under the current phase of the project, and with approximately the same level of financial commitment. Section 10.9 covers possible areas of implementation projects for which specific project proposals and areas of coverage would have to be formulated. 10.2 SOCIO-ECONOMIC AND LAND PRODUCTIVITY STATISTICS

The good KAZ statistical data for the three key baseline years (and indeed for all years over the transition period) presents an interesting picture and correlates well with other data sources for the same time and same areas (RS, GE, ECONET, met data, MoA data, GosNPCzem data). This primary data now needs to be obtained from the Statistical Agencies of the other countries for these key years. The data can be added to the ECONET rayon coverage shape file and graphics as per Figures 3.1-3.4 and printed out for a wide range of themes. Statistics on land use and cropping breakdown can best be represented as pie charts, and comparisons can be made across two or more years in the form of concentric pies positioned on the rayon centres (Figure 3.5). Data on irrigated and rainfed bonitet ratings are also available at rayon level, and these need to be obtained for all areas and represented graphically as in Figure 6.9 or Figure 9.1. Statistics are available at municipality level since 2005 in Kazakhstan, and further years need to be obtained to monitor trends in cropped areas and yields, particularly for suspected hotspot and brightspot municipalities (Figure 6.10). Corresponding municipalities figures need to be obtained for other countries, concentrating first of the suspected hotspot areas. This work is important, as hotspots can dominate the area of a specific municipality, and any implementation project formulated to deal with the problem would have to work at municipality level and would have to obtain access to statistical data at the earliest stages of project formulation. 10.3 DEFINITIVE LAND COVER AND LAND USE MAPPING UPDATE AND STANDARDISATION: 1:500.000.

A good start has been made on creating a CACILM multi-country coverage on Land Use and dominant crop groups, but both of these coverages need some further refinement. This includes corrections for over-mapping of arable lands in the Kazakh part of these maps, and also better and more precise definition of unused desert lands in comparison to low-productivity rangelands. Remote sensing, using imagery for a typical-precipitation year, and specifically use of MODIS-250 NDVImax would be most useful in this definition. NDVImax needs to be correlated with actual pasture measurements made in the field during the late March-early June period so that this can be done. Pasture productivity at 500kgDM / ha / year and 2000kgDM / ha / year would separate the three classes. Low productivity pasture would be separated from unused (desert) lands at around 100-200kgDM / ha / year productivity levels. The existing cropping mapping needs further checking against the Statistics Agency crop areas data, and a reorientation of the legend needs to be undertaken, with revised and standardized colour coding. The land use and cropping mapping should be thoroughly rechecked using multi-source imagery (LANDSAT ETM+ as the base, at 1:100.000scale, routinely cross-checked against MODIS-250 NDVImax and NDVImean, and further checked against detailed GE imagery in some key sample sites where this imagery is available). A more precise coverage, finally printable at 1:500.000 scale, should be aimed for. This mapping should follow the main existing CACILM Land Use Classes, but further sub-classes could be added as follows: - irrigated arable: degraded and low intensity use areas (see Fig 4.8 covering irrigation areas near Turkestan, SKO) to be separated from moderate and high-intensity areas; - rainfed arable, degraded and marginal areas to be differentiated; - pasture areas: high, moderate, and low productivity areas (defined by MODIS NDVImax and mean values);


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- peri-urban and village homegarden areas (see Figs 4.15 and 4.16) – these should be mappable on ETM+ imagery at 1:100.000, and, if shown in bright colour, then printable and readable at 1:500.000 scale. Although ECONET basemap layers have been generally fairly accurate and very helpful for the current work at around 1:1m-1:2m scale, an immediate upgrade on checking and correction of rayon boundaries needs to be made. Other, less urgent requirements would include: - georeferencing of existing scanned topo (and other) mapping (so that these could act as a base on which other layers could be superimposed); - correlating the village settlement file (Naselenye_punkty_tochki.shp, dbf..) with the listing of municipality centres, and adding a further field to this database to denote these centres; - for areas with a concentration of arable land (large contiguous blocks with, say, >35% arable) upgrade the basemap layers with 1:500.000- (or even 1:200.000-) scale material. - in particular, upgrade hydrological file for these areas, including canals, drains and smaller rivers. - updating the high-voltage electricity grid coverage; Table 10.1: Proposed Activities Summary: Future SLMIS Development 1 Collection of Baseline Years Statistics: Section 10.2 1.1 - crop areas, yields, production: rayon: Section 3.4 Figures

3.4 – 3.5 1.2 - population, income: rayon Section 3.3 Figures

3.1 – 3.3 1.3 - livestock numbers: rayon Section 3.5 Figure

3.6 1.4 - bonitet ratings, irrigated, rainfed: rayon averages Section 5.3 Figure

9.1, Figure 6.9 1.5 - crop areas, yields, production: municipality Section 6.4 Figures

6.8, 6.10, 6.12 1.6 - bonitet ratings, irrigated, rainfed: municipality

averages Section 6.4 Figure 6.9

2 1:500.000 land use mapping Section 10.3, Section 2.7 2.1 -Landsat ETM+ 1:100k interpretation, including: Section 4.6 Figures

4.8, 4.9 2.2 -MODIS-250 NDVI cross-checking Section 4.5 Figures

4.4 – 4.7 2.3 -GE detailed imagery cross-checking Section 4.7 Figures

4.11, 4.15, 4.16 2.4 -field checking: questionable areas & a selection

of routine areas Section 9.6 Figure 9.12

3 Upgrading ECONET basemap layers Section 10.3, Sections 2.6, 2.7 3.1 - checking and re-digitising rayon boundaries Sections 2.6, 2.7 Figure

2.4 3.2 - georeferencing and adding scanned topomaps Sections 2.6, 2.7 Figure

2.10 3.3 - updating village file: correlating with municipality

centres Sections 6.4, 6.5 Figures 6.1, 4.15, 4.16

3.4 - upgrade topolayers with 1:500.000 material for populated areas

Sections 2.6, 2.7

3.5 - update & reclassify hydrological layers (1:500.000 / 1:200.000)

Sections 2.6, 2.7

3.6 - update high-voltage electricity grid coverage Sections 2.6, 2.7 4 Upgrading and simplifying Ecosis Legend Section 10.3, Section 2.5 Figure


187

2.9 4.1 - adding climate data Figure

5.2 4.2 - adding geology data Figure

5.2 4.3 - adding geomorphology data Figure

5.2 4.4 - adding soil data Figure

5.2 4.5 - aggregating & simplifying vegetation data Figure

5.2 5 Remote Sensing Development Section 10.4, Section 4. 5.1 Pasture monitoring: correlation NDVI*time with

pasture productivity Section 4.8.2

5.2 RS preciptn maps, & correlation w. pasture & rainfed arable productv

Section 4.8.2

5.3 RS and erosion, salinity, and high WT mapping Section 4.8.3 5.4 RS and gross & net measurements of C-

sequestration by vegetation Section 4.8.4

5.5 Downloading and/or purchase of further imagery 5.6 Set up/strengthen CACILM image processing

facilities: UZB/or KYR

6 Soil/Land Unit Mapping & standardization,

1:500.000 Section 10.5

6.1 Obtain oblast maps for populated areas, UZB, TUK, TAJ

6.2 Correlation & recification of legends for all maps 6.3 Digitisation of all oblast mapping 6.4 Soil/land unit photo-interpretation: de-populated /

desert areas

6.5 Field checking: photointerpreted areas, and sample of other areas

6.6 Compilation of revised mapping 6.7 Compilation of revised legend; addn of geol,

geomrph, & climate data

7 Evaluation of Land Suitability and Land Productivity Section 10.6, KAZ-EMIMS Guidelines,

Annex D 7.1 Defining Land Utilisation Types (LUTs) Annex D3 7.2 Evaluation of LUT economics: best land (S1) &

marginal land (S3) Annex D1

7.3 Defining mappable land qualities: climate, soil, erosion, topo..

Annex D3

7.4 Defining LS criteria wr to land qualities for each LUT (for S1, S2, S3)

Annex D3

7.5 Using GIS layers to devise Land Suitability ratings for each LUT

Annex D3

8 Evaluation of Soil Erosion Hotspots Section 10.7, Section 9.5 8.1 Extension of existing gullies/landslide cvrg over

entire project area Figures 9.7, 9.8

8.2 Evaluation of surface wash problems: mapping by GIS techniques

Table 9.1


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9 Evaluation of High WT and Soil Salinity Hotspots Section 10.8, Section 9.4 9.1 Upgrade GIS hydrol.layer on drains: at

least1:200.000

9.2 Obtain/digitize salinity mapping of salinity hotspot municipalities

9.3 Obtain & synthesize reports of HME and equivalent agencies

9.4 Obtain & synthesize reports of drainage & salinity rehabilittn projects

Figure 9.4 – 9.6

9.5 Devise suitable GIS coverages on High WT and Soil Salinity

Note: Right-hand column in table denotes Sections in this report where work activities are described, and figures where SLMIS outputs are displayed. The 1:2,5m CAC-5 vegetation map and the associated ECONET ecosis coverage could be upgraded by addition of climate, geology, geomorphology, and soil information to the legend which should further be revised (particularly in the case of the Ecosis mapping) to make it simpler and clearer. The resultant work would then be of great value for potential land use recommendations and zoning. 10.4 REMOTE SENSING DEVELOPMENT

Remote sensing is particularly important for the CAC-5 as it produces imagery of uniform specification and quality at many points of time and enables multi-country interpretation and mapping to be undertaken of similar uniform quality. Section 4.8 describes most of the proposed activities here. The immediate need is to devise a remote sensing means of obtaining vegetation productivity parameters, most notably for pasture monitoring and mapping, but also for measurements of C-sequestration by vegetation. Much further work needs to be undertaken on RS applications in erosion, salinity and high WT mapping. Most importantly here is to undertake correlation studies across different times and types of RS imagery, and also with ground-truthing. Remote sensing also has a major role in both the Land Use Mapping (Section 10.3) and the Soil Mapping (Section 10.5). The imagery so far obtained is probably sufficient for these tasks, but some further downloading of imagery may well be required during the next phase of the work. In particular, MODIS-250 of 2007 and 2009 would be useful as a comparison with the 2008 imagery. 2007 was an average rainfall year over many areas, whereas 2009 was very wet and 2008 very dry. Further RS training with all the NSIU’s GIS/RS staff would be very useful. Finally, consideration could be given to setting up a central RS processing facility for CACILM by the end of the next phase of work, this facility based either in Bishkek, Almaty or Tashkent, but to service the requirements of the other centres, with activities coordinated by CACILM. 10.5 SOIL AND LAND UNIT MAPPING CORRELATION AND STANDARDIZATION: 1:500.000.

Devising a regional soil map which is sufficiently detailed both categorically and cartographically is important and must form a key element of any future work on land resources and related climate-impact work in the region. Mapping needs to be brought up to the standards of the KYR soil mapping, so that colour print-outs at 1:500.000-1:1m scale, following a uniform high-quality legend, can be made. The good oblast-level mapping also needs to be incorporated into the regional mapping, at least for the more populated areas showing both irrigated and rainfed arable, and higher-productivity pasture lands. The more arid areas are also highly photo-interpretable and it would be proposed to use satellite remote sensing to improve characterization and mapping of these areas, and particularly the sand dune and sand sheet areas. The latter represent an underexploited resource, both for improved pasture management, and more particularly for major extension of highly-efficient centre pivot irrigation, which requires high-infiltration, high bearing-capacity soils for economic operation. Soil correlation and remapping in order to produce a high quality regional soil map at 1:500.000 – 1:1m scale will be quite a major and specialist task for the CAC5 area. This is a topic that could be


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assigned to the European Soil Bureau, who have expressed preliminary interest in such work, and also have some funds for possible co-financing activities. The specific tasks which would be envisaged here would be: - standardization and rectification of map legends for existing maps now held by MSEC: KYR national; KAZ oblast; - obtaining oblast-level soil maps for populated areas of other countries (UZB, TUK, TAJ) and extending standardization and rectification to these areas; - digitization of mapping and marking any problem boundary areas between maps; - undertaking photo-interpretation exercise (mainly by satellite remote sensing) for soils and land units for low-population areas; - undertaking fieldwork in key problem areas and in a selection of all areas; - producing a single regional map with uniform legend, printable within the 1:500.000 – 1:1m scale range. 10.6 EVALUATION OF LAND SUITABILITY AND LAND PRODUCTIVITY

Given much improved soil and land unit mapping, with output at around 1:500.000 scale, the results of this work can then be synthesized into a series of land suitability maps for the major crop types and cropping systems (or ‘Land Utilization Types’ (LUTs) in FAO Framework for Land Evaluation parlance). Areas of land degradation can then be viewed in relation to these suitability coverages. The KAZ-EMIMS Guidelines, Annex D, briefly describes the process of land suitability evaluation, using computerized databases of soil information. The process begins with the definition of the LUTs – the main crop types and cropping systems, about 12 -15 of which would be sufficient to covering the main variation within the CAC-5 Priority Area. An evaluation of the economics of these crop types/cropping systems next needs to be made, taking a 1-ha standard model (Annex D1), with the economics for the best land (S1 suitability rating) and marginal land (just S3 rating) being evaluated. Mappable land qualities and next defined: climate, soil, erosion, topography etc. Land suitability criteria are then defined, in terms of land qualities, for each LUT, for S1, S2 and S3 ratings. GIS layers are then used to devise the land suitability ratings for the different mapping units for each LUT. Of particular interest here is to view the existing land use and cropping maps against the suitability maps, to see what current land uses would be deemed to be unsuitable / unsustainable. Marginal (S3) areas also need to be investigated. 10.7 EVALUATION OF SOIL EROSION HOTSPOTS

Work here is described in Section 9.5. Work undertaken to date has concentrated on KAZ and adjacent areas in UZB and KYR: it now needs to be extended to all areas, following existing methodology. Evaluation of surface wash problems needs to be undertaken by GIS overlay techniques, combining slope mapping (obtained from high-resolution DEMs), with soil type (defined on texture and parent material), together with land use, and land misuse overlays. Most importantly, a fieldwork programme is required here, both to investigate problem areas but also to include check routine sites. Local farmers in the hotspot areas also need to be interviewed in order to obtain the history of the erosion problems. 10.8 EVALUATION OF HIGH WATER TABLE AND SALINITY HOTSPOTS

The first requirement here is to upgrade the surface hydrological layer on the GIS to differentiate natural rivers, irrigation canals and effluent drains for the main areas of irrigation. The scale of data capture here should be at least as detailed as 1:200.000. The dbf file here also needs refining, with further fields added. The positions of outfall of the main drains need highlighting. Municipalities where the salinity and high WT problems are serious next need prioritizing and highlighting on the GIS. Initially a point file would be sufficient here, although polygon files should be eventually constructed. Routine annual reports of HME (KAZ) and sister agencies in the other


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countries should be obtained, and information extracted for the GIS. A similar process should apply for any reports of the drainage and salinity rehabilitation projects which have recently been undertaken. Suitable GIS coverages on high WTs and on soil salinity need to be devised from the synthesis of this work. 10.9 LAND DEGRADATION, AND PREPARATION FOR CLIMATE CHANGE

Global warming and warming-induced climate change is becoming a major concern worldwide, with attention being focused on how the international community can reduce output of greenhouse gases (notably carbon dioxide, methane, and nitrous oxide). Of theses gases, CO2 is by far the largest component, with 6,3 billion tonnes C being put into the atmosphere each year by burning of fossil fuel, and a further 1,6 billion tonnes C coming from land use changes, notably cultivation and deforestation. Methane and nitrous oxide, although respectively 23 times and 296 times more damaging per tonne of gas than CO2, are produced in very much smaller quantities, so their overall affect is much less. Of the 7,9bln tonnes C annually released into the atmosphere, current increase in atmospheric C is put at 3,2 bln tonnes C/year; increase in oceanic C is 2,3 and increase in terrestrial C a further 2,3 bln tonnes C / year. Worldwide, there is now much evidence that atmospheric CO2 has increased steadily, at least since around 1700, and with major acceleration post-1850, 1940, and 1990. Commensurate with this increase has been an overall increase in global temperature (although, at a somewhat smaller rate than expected). However, rate of warming in the critical polar regions has been more than double average global rates, and this is particularly worrying because of polar glacier and ice-sheet melt, and corresponding rise in global sea level. This will directly affect some countries much more than others: Bangladesh, Netherlands, and many small coral islands will be very badly affected; Gondwanaland countries (high plateau) and Central Asian Countries will be little affected by this specific problem. However, overall temperature increase is likely to generally move all the current global climate zones further to the poles, increase evaporation of sea water in most areas, and generally to increase severity and frequency of extreme weather events. However, there are many counterbalancing factors which apply over large areas – for example, the flow of cold, salty, deep current water from the north pole (the ‘Atlantic Conveyor’) has already shown a decrease in flow, leading to an equivalent reduction of the Gulf Stream flow (warm, lower salinity water), which normally brings warmer winter temperatures and much cyclonic rainfall to Western Europe. Ironically, global warming could well mean that Western Europe suffers much cooler temperatures than at present (plus overall sea level rise). Trends in the recent (last 80-years) climate record for the CAC-5 countries are presented in Section7. There is much evidence that average temperatures have increased steadily, with winters getting somewhat less severe, interrupted with significant periods of snowmelt. Met stations near the Aral Sea area have seen a temperature rise of double the regional average. Precipitation has also generally increased over the entire area, albeit with big variations from year to year, although at least one (arid area) station has shown an overall decrease, particularly over the last 40 years.

10.9.1 Carbon sequestration and release from vegetation: conclusions for the CAC5 area

Trends 1982-2006 have shown that, in the populated parts of the CAC-5Priority Area, vegetation productivity (as measured by NDVI, GIMMS dataset, Fig 4.1) has shown a net increase, although in arid areas there has been a net decrease. Also, in certain populated areas, there has been a marked fall in NDVI over this period (e.g. Turkestan, SKO; significant areas of TAJ..). Further, more detailed inspection of clearly degraded areas (e.g. land devastated by gulleying, Figs 9.12, 9.8, 9.7 and 4.12) have also shown an increase in NDVI for these areas, as abandoned land is naturally being re-vegetated: that land may have lost economic value, but not necessarily ecological value. Evidence is also available that during the period of rapid decline in livestock numbers during the years of


191

economic restructuring (especially 1993-1997) pasture lands recovered significantly, with pasture productivity increasing in absolute terms (kgDM/ha) and increasing even more so in terms of kgDM per unit of water transpired. However, current trends (post 2000) may be less positive. Livestock numbers are currently increasing and there are large areas of very bad overgrazing, particularly near population centres. Burning of pasture land over large areas (both accidental and controlled) is still occurring, as viewed in recent RS imagery. Less fertilizer is being used on crop land (particularly on rainfed cropland).Crop rotations – particularly involving deep-rooting and N-fixing leguminous fodder crops – are practiced much less. Land quality (bonitet) ratings are declining (with soil OM being a key determinant of this). Soil salinity and rises in water table are major and worsening problems in many areas. Management of irrigation and drainage is much more difficult with large numbers of small farmers than with kolkhoz and sovkhoz heads with their specialist technical staff. Looking at good and bad land use practices and rates of C-fixation and release: - degraded marginal rainfed arable land reverting to pasture and scrubland: net gain of up to 0,7t C/ha/year for 400mm precipitation areas, up to 3t C/ha year for 800mm areas; - establishing forest shelter belts in similar areas: net gain of 1,2tC/ha at 400mm; 5tC/ha for 800mm. - de-stocking overgrazed areas: net gain of 0,7t C/ha/yr at 400mm, 0,3t C at 250mm. - burning of pasture land: net losses of around 1-2t/ha at 400mm; 5-10t at 800mm.

10.9.2 Carbon sequestration and release from soils: conclusions for the CAC5 area

Calculations made during the KAZ-EMIMS study in SKO-KAZ put the total organic carbon content of the soils of that oblast at around 325m tonnes. The total area of SKO is 11,7m ha, about 5% of the total CAC-5 Priority Area, and SKO is broadly representative of all land types of the entire area. Extrapolating this figure would hence put the total organic-C content of the Priority Area soils at around 6,5bln tonnes, not far short of the total amount of carbon put into the atmosphere annually on a global basis. For the whole of the CAC-5 area, including the large tracts of steppe and some forest land in northern Kazakhstan, the soil organic-C content may be proportionately much larger, as some of the predominant soil types contain much higher proportions of organic-C. (Russian chernozems commonly contain 350-460tonnes C/ha, in comparison to SKO soils with a range of 5 – 130tonnes C/ha, and an overall average of just below 30t/ha). C-fixation by soil is determined by net increases in OM additions by vegetation – and particularly by deep-rooted pasture species roots – and by oxidation of OM by microbial action. Considering good and bad land use practices and C-fixation and release from soils: - cultivation of virgin steppe (400mm annual precipitation): loss of 5 tC/ha/year for first 3-4 years, thereafter 1t/ha/yr for 5 years, falling to 0 if good land use practises are followed, including following crop rotations with adequate proportions of deep rooting fodder crops. Excluding cultivation of virgin steppe land, in general a suite of good land use practises on long-cultivated land would be leading to C-fixation of around 0,5t/ha/year over a long-term basis; a suite of poor land use practices would be leading to C-release of around the same quantity. Difference between good and bad land use management could be quantified at approximately 1tC/ha/year.

10.9.3 Sustainable land management, and carbon sequestration

Good land use practices which would all lead to increased carbon sequestration would include the following: - maintaining adequate crop rotations with deep-rooted leguminous crops; - following minimal cultivation techniques;


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- minimizing water run-off and maximizing soil infiltration from snow melt water and from rainfall; - planting multi-use wind-break protection belts around fields; - applying organic residues (animal manure, domestic compost; dried sewage sludge) to the land; - promoting supplementary irrigation for largely rainfed cropping; - maintaining adequate drainage with low WTs for irrigated land; - feeding or composting of any crop residues (rather than burning). Differences between good and bad land use practices for much of the CAC-5 populated area would be in the order of 1tC/ha /year over large areas (i.e. difference between 0,5t/ha fixation and 0,5t/ha release). For smaller areas, particularly those under irrigation, expected differences would be up to 3t/ha/year. Valuing atmospheric carbon at $200/tonne-C (a tax/credit figure required to seriously deal with the greenhouse gas problem) would mean that the average SKO private farmer could earn an annual tax credit of around $350 for his 7-ha holding. This would assume that each ha of agricultural land is fixing 0,5t organic-C and that this figure is objectively verified (as under the GosNPCZem bonitet reassessment surveys). Phasing in C-taxation and a corresponding system of tax credits would be undertaken over a period of 10-15years in order that national economies can plan for forward investment in C-saving technologies. The $200/tonne-C proposed tax/credit figure is quite modest: motor fuel in Europe is currently taxed at around $1200/tonne-C by the respective national governments. The detailed soil and bonitet reassessment mapping being continued by GosNPCZem (KAZ) and counterpart agencies in the other countries are providing excellent detailed monitoring figures on soil organic-C contents, and this work should be continued and further promoted as a high priority. It could further be developed as a basis for a system of internationally-verifiable carbon taxation and credits designed to help poor farming communities manage land sustainably and in so doing provide long-term economic benefits to their families and increase carbon sequestration from soil and vegetation. 10.10 SYNOPSIS: POSSIBLE MEASURES TO ADDRESS ADVERSE AFFECTS OF CLIMATE CHANGE

Proposals to strengthen preparedness to deal with climate change, and particularly extremes of weather which climate change will bring, also coincide with better land and water use management and with dealing with land degradation problems and processes. These measures include the following: i. Improving overall management of the water resource across the 5 countries: Current management involves running the upstream hydropower reservoirs independently by the two upstream countries (KYR and TAJ) to provide more electricity in winter and early spring. For downstream countries, requiring summer water for irrigation, this untimely water throughput is not only wasted, but it causes flooding and high water tables at the time of land preparation, delaying planting (Chapter 8). During the Soviet period there was provision of winter fuel to upstream communities – TAJ families were annually provided each with 3 tonnes coal – and some form of fuel compensation will again be necessary if the hydropower reservoirs are to be run in the best interests of the downstream irrigation users. These measures alone would provide an extra 3bln cubic metres of irrigation water during the peak summer period. ii. Improving water use efficiencies of irrigation; More than 99% of irrigation water is used through surface irrigation methods which are notoriously inefficient, 30-35% efficiencies being common. Also crop performance is more uneven under surface irrigation than it is under the much more efficient sprinkler and drip methods, where efficiencies of 90-95% are common. Inefficient surface irrigation is more a problem in some areas than others: where return drainage flows are used downstream the inefficiency is not problematic, provided there is enough water at the particular point for the planned irrigation. However, this is not always the case, and in these situations change of irrigation method to sprinkler or drip will spread the available water more efficiently over a bigger area. Such is the case with 1500ha of drip irrigation recently set-up in Tulkibas rayon, SKO, for tomato cultivation, which has been highly successful.


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High-tech centre pivot irrigation on low-elevation sand-sheet soils can also be very efficient, and economically very attractive, particularly for irrigation of winter crops where very high water use efficiencies can be achieved. This is currently big business in Middle East countries, largely using groundwater, but can be extended to suitable areas in the CAC-5. Yields of 9-10t/ha of winter wheat are quite common under this system, with 10-tower centre pivot systems each irrigating 65ha circles. The eventual SLMIS should be precise enough to identify suitable high-infiltration soils (ideally sand sheet areas), in suitable agro-climatic zones (low elevation / relatively mild winters) and with nearby water supplies of reasonable quality. Access to high-voltage mains electricity here would be a further bonus, as electrical operation is cheaper and more efficient than diesel. Supplementary irrigation for largely rainfed winter crops, growing in rainfed arable areas, also shows extremely high water use efficiencies, and again is a very attractive option in the many areas where suitable land, climate and water resources coincide. However, top priority, both for economic and for social reasons, should go to ensuring efficient irrigation water provision to all homegarden areas, which are currently providing most of the high-value horticultural crops. They also represent a very useful social buffer with respect to prevention of poverty and unemployment. iii. Improving drainage and salinity control measures, and local institutions dealing with these; With the break up of the sovkhozes and kolkhozes reorganization of irrigation and drainage at farm level has become a major priority, with a need to organize all farmers into Water User Groups. Much of the infrastructure was not designed or constructed to sufficiently high standards; most of it has been maintained poorly, and some of it is not working at all (e.g. pumps for vertical drainage, the drainage system prevailing in some of Maktaraal rayon, SKO. The biggest need now is to get water User Groups set up and run under a system of full accountability to all members. (see KAZ-EMIMS design Guidelines: Annex H, Appendices III and IV for analysis of prevailing situation in irrigation farming in Maktaraal rayon). iv. Improving use and management of organic wastes; Measures here would include:

- Substituting fuelwood for animal manure, which is still used as a fuel in rural communities; animal manure is best used directly on the land;

- Major planting of shelter belts; particularly in windswept rainfed arable areas of Kazakhstan-use of some of this material as fuelwood;

- Separating compostable waste from urban solid waste, and organizing industrial scale composting operations, the final compost again being returned to the land;

- Full use of digested sewerage sludge for use on the land, particularly for industrial crops and for certain land rehabilitation activities;

- More efficient effluent management from animal fattening yards, slaughter houses and chicken farms, so that high-nutrient materials are again turned into useable composts and land ameliorating materials.

v. Improving conservation measures for both soil and water; Soil conservation hotspots have already been identified largely from GE imagery, particularly gulleying and major landslide features. Some of these are mainly former erosion features (possibly associated with the virgin lands programme of the early 1960s) but large areas are still affected by current rilling and gulleying. Use of heavy cultivation machinery running up and down the slope, cultivation on excessively steep slopes, and no attempts at contour cultivation or any other soil conservation measure are all to blame for the situation. The main rainfed agricultural area in SKO and Zhambyl oblasts in KAZ, and several areas in KYR are all very badly affected by water erosion. Animal trails on sloping lands near villages are also badly affected (Fig 9.12). The government in KAZ is well aware of the problems, and has been considering reorientation of any farm subsidies program towards improved land management measures, rather than crop subsidies (such as the current one on oilseeds). A programme for contour cultivation and re-orientation of field boundaries might be a useful start here, coupled with training and support on contour operations.


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vi. Increasing carbon sequestration from vegetation and soils; A pilot project could usefully be set up to promote practices outlined in Section 10.9 and to undertake the necessary detailed monitoring to track these changes. Ideally this should be run over several municipalities in contrasting agroclimatic zones:

- Municipality with 100% Irrigation area suffering from a virtual cotton monoculture; - Municipality with Irrigation area with more balanced cropping; - Municipality with irrigation, rainfed arable, and with significant pasture land; - Municipality dominated by rainfed arable, in a good arable area; - Municipality dominated by marginal arable land.

viii. Improving advisory and information services available to farmers and rural communities. The many small private farmers encounter many problems not faced by their counterparts in western countries, advisory and information services being a major area of difference. Under the Soviet period, technical departments in each of the farm enterprises received information from oblast and national technical centres, as well as the necessary inputs. Fertilizer, in particular was made available by the state and applied at the (generally modest) recommended amount. Now fertilizer has to be bought from a private company whose charges may be high and whose advise is not necessarily impartial. Support to farmers is far behind what is required and what is provided by both public agencies and private companies in most countries elsewhere. A further major, and related, problem area is access to credit, and (real) interest rates prevailing. Whereas farmers in the west can borrow short-term money at below 10% annual interest, the figure in the CAC-5 is nearer 25%. More seriously, for large secured loans, western farmers have access to credit at around 5%, while CAC-5 equivalents have problems in getting loans at below 20% real annual interest. As farmers acquire land registration documents and can offer better collateral for loans, real rates of interest should fall. A farmers database could further be developed with farm-level data which could be linked to the SLMIS., and accessed by the relevant credit and funding agencies. As part of the SLMIS, the next stage of development needs to include one or more websites, both to provide general information over the whole CAC-5 priority area and also some area-specific recommendations.


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Annexes:

Annex A: NSIU Datasets: shared data between NSIUs and MSEC Annex B: MSEC Multi-Country Datasets Annex C: GEF Project Site Details Annex D: Details of Remote Sensing Imagery and Techniques Annex E: Detailed Soils and Land Quality (Bonitet Reassessment) Mapping at Oblast, Rayon, &

Municipality Levels Annex G: Rayon and Municipalities Statistics Data: Changes in Land Use, Cropping, Production

and Yields, 1987 – 2006 Annex H: Degradation of Land and Water Resources: Photographic Evidence Annex I: Daily Soil Water Balance and Rainfall / Runoff Modeling: a basic for climate change

studies. Annex J: Bibliography


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Annex A: NSIU Datasets: shared data between NSIUs and MSEC


197

Annex B: MSEC Multi-Country Datasets


198

Annex C: GEF Project Site Details


199

Annex D: Details of Remote Sensing Imagery and Techniques


200

Annex E: Detailed Soils and Land Quality (Bonitet Reassessment) Mapping at Oblast, Rayon, & Municipality Levels


201

Soil Map Legend (Условные обозначения): 1:300.000 Soil Map, SKO-KAZ (scanned sheet 5) № Code Description (English) Описание (Русский)

MOUNTAIN SOILS Mountain meadow-steppe alpine soils Горные лугово-степные альпийские

Глгс1 normally developed Нормально развитые 1 Глгс1

пр primitive Примитивные 2 Гст1 Mountain-steppe alpine soils Горно-степные альпийские

3 Глгс2 Mountain meadow-steppe subalpine soils

Горные лугово-степные субальпийские

4 Гст2 Mountain-steppe subalpine soils Горно-степные субальпийские

5 Гтц Mountain dark colored subalpine moor soils

Горные темноцветные субальпийские торфянистые

6 Глг2 Mountain-meadow hydromorphic subalpine soils

Горно-луговые гидроморфные субальпийские

Mountain brown, dark colored soils: Горные коричневые темные Гкч3 with sparse growth of juniper trees сухих арчевых редколесий 7 Гкч2 with dead standing bushes сухих кустарников

8 Гкч1 Mountain brown, light colored soils Горные коричневые светлые

9 Гкч Mountain brown (dark and light colored non-segmented) soils

Горные коричневые (темные и светлые, нерасчлененные)

10 Гкчс Mountain brown steppificated soils Горные коричневые остепненные 11 Гс3 Mountain grey-brown soils Горные серокоричневые

Mountain common sierozems: Горные сероземы обыкновенные Гс2 non-saline Незасоленные 12 Гс2

s gypsiferous Гипсоносные Mountain light sierozems: Горные сероземы светлые

Гс1 non-saline Незасоленные 13 Гс2

s gypsiferous Гипсоносные

SOILS OF INTERMOUNTAIN

VALLEYS, SUBMONTANE AND LOWLAND PLAINS

ПОЧВЫ МЕЖГОРНЫХ ДОЛИН, ПРЕДГОРНЫХ И НИЗМЕННЫХ РАВНИН

14 ЧкчВ Black-brown leached soils Чернокоричневые выщелоченные Brown soils (Коричневые Кч)

15 КчВ Brown leached soils Коричневые выщелоченные 16 КчН Brown normal (“typical”) soils Коричневые нормальные («типичные»)

17 Кч∆ Brown young (on dense rocks) soils (1)

Коричневые малоразвитые (на плотных породах)

Grey-brown soils – С3 (dark sierozem, light chestnut, dark grey of other authors)

Серокоричневые – С3 (темные сероземы, светлокаштановые, темносерые других авторов)

18 С3В Grey-brown leached soils Серокоричневые выщелоченные

19 С3ГВ Grey-brown deep effervescent soils Серокоричневые глубоковскипающие

20 С3Н Grey-brown normal (“typical”) soils Серокоричневые нормальные («типичные)

Grey-brown calcareous soils: Серокоричневые карбонатные С3

к non-irrigated Неполивные 21 С3

к Irrigated Орошаемые 22 С3

ЭР Grey-brown eroded soils Серокоричневые эродированные Grey-brown young soils: Серокоричневые малоразвитиые

С3∆ on dense Paleozoic rocks на плотных палеозойских породах 23

С3∆

on Tertiary clays, conglomerates, sandstones

на третичных глинах, конгломератах, песчаниках

Meadow grey-brown soils – Сл3 Лугово-серокоричневые – Сл3 Meadow grey-brown non-saline soils

(common and calcareous): Лугово-серокоричневые незасоленные (обыкновенные и карбонатные)

24

Сл3 non-irrigated Неполивные


202

Сл3 Irrigated Орошаемые

Common sierozems – Сю2, Сс2 (Сю2 – common south (typical) sierozems, Сс2 – common north (Karatau) sierozems)

Сероземы обыкновенные – Сю2, Сс2

(Сю2 – сероземы обыкновенные южные (типичные), Сс2 – сероземы обыкновенные северные (прикаратауские)

25 Сю2ГВ

Common southern deep effervescent sierozems

Сероземы обыкновенные южные глубоковскипающие

Common normal south sierozems (calcareous non-saline):

Сероземы обыкновенные южные нормальные (карбонатные незасоленные)

Сю2 non-irrigated неполивные 26

Сю2 irrigated орошаемые

27 Сю2ЭР Common eroded south sierozems Сероземы обыкновенные южные

эродированные

28

Сю2∆

Common young south sierozems (on Tertiary conglomerates, sandstones, clays)

Сероземы обыкновенные южные малоразвитые (на третичных конгломератах, песчаниках, глинах)

Common normal north sierozems (calcareous non-saline):

Сероземы обыкновенные северные нормальные (карбонатные незасоленные)

Сс2 non-irrigated неполивные 29

Сс2 irrigated орошаемые

30 Сс2ЭР Common eroded north sierozems Сероземы обыкновенные северные

эродированные

31 Сс2КМ

Common xeromorphous north sierozems

Сероземы обыкновенные северные ксероморфные

32 Сс2S Common gypsiferous north sierozems Сероземы обыкновенные северные

гипсоносные

Common young north sierozems: Сероземы обыкновенные северные

малоразвитиые С3

∆ on dense Paleozoic rocks на плотных палеозойских породах 33

С3∆

on Tertiary, conglomerates, sandstones clays

на третичных конгломератах, песчаниках, глинах

Light sierozems – Сю1, Сс1 (Сю1 –

light south sierozems, Сс1 – light north (Karatau) sierozems)

Сероземы светлые – Сю1, Сс1 (Сю1 – сероземы светлые южные, Сс1–сероземы светлые северные(прикаратауские)

Light normal south sierozems (calcareous non-saline)

Сероземы светлые южные нормальные (карбонатные незасоленные)

Сю1 granular зернистые ___ Сю1

lumpy

комковатые

34

Сю1 irrigated granular зернистые орошаемые

35 Сю1ГСЧ

Light deep saline south sierozems (lumpy)

Сероземы светлые южные глубоко солончаковатые (комковатые)

36 Сю1СЧ

Light saline south sierozems (lumpy) Сероземы светлые южные солончаковатые (комковатые)

37 Сю1ЭР Light south eroded sierozems Сероземы светлые южные эродированные

38 -

Takyr saline sierozems (2) Сероземы такыровидные солончаковатые

Light normal north sierozems (calcareous non-saline):

Сероземы светлые северные нормальные (карбонатные незасоленные)

Сс1 granular зернистые ___ Сс1

lumpy Комковатые 39

Сс1 irrigated granular зернистые орошаемые

40 Сс1ГСЧ

Light deep saline north sierozems Сероземы светлые северные глубоко солончаковатые

41

Light xeromorphous north sierozems: Сероземы светлые северные


203

ксероморфные Сс1

КМ granular Зернистые ___ Сс1

КМ lumpy Комковатые

42 Сс1S Light gypsiferous north sierozems Сероземы светлые северные гипсоносные

43 Сс1

∆ Light young north sierozems (on dense Paleozoic rocks)

Сероземы светлые северные малоразвитые (на плотных палеозойских породах)

Meadow-sierozem soils – Сл and Meadow like sierozem soils – Слт

Лугово-сероземные – Сл и луговато-сероземные – Слт

Meadow-like sierozemic non-saline soils

Луговато-сероземные незасоленные

Слт non-irrigated неполивные

44

Слт irrigated орошаемые 45

СлтS Meadow-like gypsiferous sierozemic soils

Луговато-сероземные гипсоносные

46 СлтЗС Meadow-like saline sierozemic soils Луговато-сероземные засоленные Meadow non-saline sierozemic soils: Лугово-сероземные незасоленные

Сл non-irrigated неполивные 47

Сл Irrigated орошаемые 48

СлГСН Meadow sierozemic deep alkaline soils

Лугово-сероземные глубоко солонцеватые

Meadow sierozemic alkaline soils: Лугово-сероземные солонцеватые СлСН non-irrigated неполивные 49 СлСН irrigated орошаемые

Meadow sierozemic saline soils Лугово-сероземные солончаковатые СлСЧ non-irrigated неполивные 50 СлСЧ irrigated орошаемые

Meadow sierozemic saline soils: Лугово-сероземные солончаковые СлСК non-irrigated неполивные 51 СлСК irrigated орошаемые

Серобурые пустынные – СБ Grey-fulvous non-saline soils: Серобурые незасоленные

СБ on two-segmented loamy-gristly alluviums

на двучленных суглинисто-хрящеватых наносах 52

__ СБ

“light”

«легкие»

Grey-fulvous gypsiferous soils: Серобурые гипсоносные

СБS on two-segmented loamy-gristly alluviums

на двучленных суглинисто-хрящеватых наносах 53

___ СБS

“light”

«легкие»

54

CБСН

Grey-fulvous alkaline soils (On two-segmented loamy-gristly silts)

Серобурые солонцеватые (на двучленных суглинисто-хрящеватых наносах)

Grey-fulvous young soils: Серобурые малоразвитые СБ∆ on dense Paleozoic rocks на платных палеозойских породах

СБ∆S gypsiferous on dense Paleozoic rocks

тоже, но гипсоносные 55

СБ∆ on Tertiary chink rocks на третичных породах чинков Grey-fulvous takyr non-salted soils: Серобурые такыровидные незасоленные

СБт on old alluvial stratified loamy silts на древнеаллювиальных слоистых

суглинистых наносах 56 ___ СБТ

light on old alluvial stratified loamy silts

то же, но «легкие»

57 СБтСЧ

Grey-fulvous takyr saline soils (on old alluvial stratified loamy silts)

Серобурые такыровидные солончаковатые (на древнеаллювиальных слоистых суглинистых наносах)


204

Takyrs– Тк and primitive takyr soils - Ткв

(Такыры – Тк и примитивные такыровые почвы Ткв)

58 ТквСК Primitive takyr brackish soils Примитивные такыровые солончаковые 59 ТкСН Solonetzic takyrs Такыры солонцеватые 60 ТкСК Brackish takyrs Такыры солончаковые

Old-meadow takyr (desertificated) soils - Лгт

Древнелуговые такыровидные (опустыненные) - Лгт

61 Лгт

Old-meadow takyr non-saline soils Древнелуговые такыровидные незасоленные

62 ЛгтСЧ

Old-meadow takyr saline soils Древнелуговые такыровидные солончаковатые

63 ЛгтЗС Old-meadow takyr salted soils Древнелуговые такыровидные засоленные Meadow soils – Лг Луговые – Лг 64 Лг2

ЗС Meadow grey salted soils Луговые серые засоленные 65 Лг1

ЗС Meadow light-grey salted soils Луговые светлосерые засоленные Meadow-swampy soils – Бл Лугово-болотные – Бл 66 БлЗС Meadow-swampy salted soils Лугово-болотные засоленные Swampy soils – Б Болотные – Б 67 Бт Swampy peaty gley soils Болотные торфянисто-глеевые Solonetzs automorphic – Сн Солонцы автоморфные – Сн 68

СнТССК

Sierozemic brackish (crustaceous) takyr solonetzs

Солонцы сероземные такыровидные солончаковые (корковые)

69 СнПСК Brackish (fine) desert solonetzs Солонцы пустынные солончаковые (мелкие)

70 СнТ

СК Desert brackish (crustaceous) takyr solonetzs

Солонцы пустынные такыровидные солончаковые (корковые)

___ Solonetzs semi- hydromorphic – Сн

__ Солонцы полугидроморфные - Сн

71 ____ СнСК

Meadow-sierozemic brackish (crustaceous) solonetzs

Солонцы лугово-сероземные солончаковые (корковые)

72 _____ СнП

СК Meadow-desert brackish (crustaceous) solonetzs

Солонцы лугово-пустынные солончаковые (корковые)

‗‗ Solonetzs hydromorphic - Сн

‗‗ Солонцы гидроморфные - Сн

73 ‗‗‗ СнС

СК Sierozemic-meadow brackish (crustaceous) solonetzs

Солонцы сероземно-луговые солончаковые (корковые)

74 ‗‗‗ СнП

СК Desert-meadow brackish (crustaceous) solonetzs

Солонцы пустынно-луговые солончаковые (корковые)

Saline soils – Ск Солончаки – Ск 75 СкОСТ Residual saline soils Солончаки остаточные 76 СкТ Takyr saline soils Солончаки такыровидные 77 СкЛ Meadow saline soils Солончаки луговые 78 СкОБ Common saline soils Солончаки обыкновенные 79 СкС Impurity saline soils Солончаки соровые 80

Ск Meadow common impurity (nonsegmented) saline soils

Солончаки луговые, обыкновенные, соровые (нерасчлененные)

Inundation forest-meadow (tugai) soils – Аллг

Пойменные лесолуговые (тугайные) – Аллг

81 __ Аллг

Inundation forest-meadow stratified non-saline soils

Пойменные лесолуговые слоистые незасоленные

82 __ АллгЗС

Inundation forest-meadow stratified surface-saline soils

Пойменные лесолуговые слоистые поверхностно засоленные

Inundation meadow soils – Алг Пойменные луговые – Алг 83 Алг Inundation meadow stratified non-

saline soils Пойменные луговые слоистые незасоленные


205

Inundation meadow stratified saline soils

Пойменные луговые слоистые засоленные

__ АлгЗС

non-irrigated

неполивные

84

__ АлгЗС

irrigated

искусственно орошаемые

Inundation meadow-swamping soils – Абл

Пойменные лугово-болотные – Абл

85 __ АблЗС

Inundation meadow-swamping stratified saline soils

Пойменные лугово-болотные слоистые засоленные

Inundation swamping soils – Аб Пойменные болотные – Аб 86 __

АбЗС Inundation peaty swamping and muddy-swamping stratified saline soils

Пойменные болотные торфянистые и иловато-болотные, слоистые засоленные

Sands – П Пески – П 87 2

Пбг С

Sierozemic hilly fixed sands Пески сероземные бугристые закрепленные

88 1

Пбг С

Sierozemic hilly feebly fixed sands Пески сероземные бугристые слабо закрепленные

89 2

Пгбг С

Sierozemic hilly-ridge fixed sands Пески сероземные грядово-бугристые закрепленные

90 2

Пгбг С

Sierozemic hilly-ridge feebly fixed sands

Пески сероземные грядово-бугристые слабо закрепленные

91 0

Пбх С

Sierozemic barchan sands Пески сероземные барханные

92 2

Пр П

Desert flat fixed sands Пески пустынные равнинные закрепленные

93 2

Пбг П

Desert hilly fixed sands Пески пустынные бугристые закрепленные

94 1

Пбг П

Desert hilly feebly fixed sands Пески пустынные бугристые слабо закрепленные

95 1

Пгбг П

Desert hilly-ridge feebly fixed sands Пески пустынные грядово-бугристые слабо закрепленные

96 0

Пбх П

Desert barchan sands Пески пустынные барханные

97 2

Пбг Г

Desert gleyey hilly fixed sands Пески пустынные глееватые бугристые закрепленные

98 1

Пбг Г

Desert gleyey hilly feebly fixed sands Пески пустынные глееватые бугристые слабо закрепленные

Non-soil formations Непочвенные образования 99 Лед Glaciers, eternal snow Ледники, вечные снега 100

Н Crags and slide-rocks of nival zone Скалы и осыпи нивальной зоны

101

КП Entire outcropping of dense edge rocks

Сплошные выходы плотных коренных пород


206

Outcropping of dense edge rocks among soils

Выходы плотных коренных пород среди почв

а Up to 20% До 20% б Up to50% До 50%

102

в More than 50% of total area of map unit

Свыше 50% от общей площади контура

103

Гл Outcropping of tertiary clays Обнажения третичных глин

104

Г Pebbles Галечники

105

Оз. Lakes Озера

(1) Young means undeveloped, raw soils (2) Not readable from the scanned copy

Mechanical structure of soils and underlying rocks (Механический состав почв и подстилающих пород) (Legend information on SKO-KAZ Oblast 1:300.000 Soils Map) (Scanned sheet 7). 1. Clayey silt soils (Глинистые иловатые) 2. Clayey silt soils underlying with stratified predominately clay sediments (Глинистые иловатые, подстилаемые слоистыми отложениями с преобладанием глин) 3. Clayey and loamy soils (Глинистые и суглинистые) 4. Clayey and loamy soils underlying with sand (Глинистые и суглинистые, подстилаемые песком) 5. Clayey and loamy soils underlying with stratified predominately clay sediments (Глинистые и суглинистые, подстилаемые слоистыми отложениями с преобладанием глин) 6. Clayey and loamy soils underlying with stratified predominately sandy and loamy sandy sediments (Глинистые и суглинистые, подстилаемые слоистыми отложениями с преобладанием песков и супесей) 7. Little and medium pebbled clayey and loamy soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Глинистые и суглинистые слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями, преимущественно на глубине 60-120 см.) 8. Silty clay dusty (loess-like) soils Тяжелосуглинистые пылеватые (лессовидные) 9. Silty clay dusty soils underlying with stratified predominately clay sediments (Тяжелосуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием глин) 10. Silty clay dusty soils underlying with stratified predominately loam sediments (Тяжелосуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием суглинков) 11. Silty clay arenaceous soils (Тяжелосуглинистые песчанистые) 12. Silty clay arenaceous soils underlying with uliginous (slimy) clays (Тяжелосуглинистые песчанистые, подстилаемые иловатыми глинами) 13. Silty clay arenaceous soils underlying with sand (Тяжелосуглинистые песчанистые, подстилаемые песком) 14. Silty clay arenaceous soils underlying with stratified predominately loam sediments (Тяжелосуглинистые песчанистые, подстилаемые слоистыми отложениями с преобладанием суглинков) 15. Silty clay arenaceous soils underlying with stratified predominately sandy and loamy sandy sediments (Тяжелосуглинистые песчанистые, подстилаемые слоистыми отложениями с преобладанием песков и супесей) 16. Medium rubbly silty clay arenaceous soils underlying with rubbly sediments predominately at the depth of 30-60cm (Тяжелосуглинистые песчанистые среднещебнистые, подстилаемые щебнистыми отложениями преимущественно на глубине 30-60 см)


207

17. Little and medium rubbly silty clay arenaceous soils underlying with rubbly sediments predominately at the depth of 60-120cm (Тяжелосуглинистые песчанистые, слабо и среднещебнистые, подстилаемые щебнистыми отложениями преимущественно на глубине 60-120 см) 18. Little and medium pebbled clay arenaceous soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Тяжелосуглинистые песчанистые, слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 60-120 см 19. Little and medium pebbled clay arenaceous soils underlying with gypsiferous sandy-pebbly sediments predominantly at the depth of 60-120 cm (Тяжелосуглинистые песчанистые, слабо и средне-галечниковые, подстилаемые гипсоносными песчано-галечниковыми отложениями преимущественно на глубине 60-120 см) 20. Little pebbled clay arenaceous soils underlying with sandy-pebbly sediments predominantly deeper than 120 cm (Тяжелосуглинистые песчанистые, слабо-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно глубже 120 см) 21. Little pebbled clay arenaceous soils underlying with gypsiferous sandy-pebbly sediments predominantly deeper than 120 cm (Тяжелосуглинистые песчанистые, слабо-галечниковые, подстилаемые гипсоносными песчано-галечниковыми отложениями преимущественно глубже 120 см) 22. Semi-loam dusty (loess-like) soils Среднесуглинистые пылеватые (лессовидные) 23. Semi-loam dusty soils underlying with stratified predominately clay sediments (Среднесуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием глин) 24. Semi-loam dusty soils underlying with stratified predominately loam sediments Среднесуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием суглинков 25. Semi-loam dusty soils underlying with stratified predominately sandy and loamy sandy sediments (Среднесуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием песков и супесей) 26. Little pebbled semi-loam dusty soils underlying with gypsiferous sandy-pebbly sediments predominantly deeper than 120 cm (Среднесуглинистые пылеватые слабо-галечниковые, подстилаемые гипсоносными песчано-галечниковыми отложениями преимущественно глубже 120 см) 27. Semi-loam arenaceous soils (Среднесуглинистые песчанистые) 28. Semi-loam arenaceous soils underlying with uliginous clays (Среднесуглинистые песчанистые, подстилаемые иловатыми глинами) 29. Semi-loam arenaceous soils underlying with stratified predominately clay sediments (Среднесуглинистые песчанистые, подстилаемые слоистыми отложениями с преобладанием глин) 30. Medium pebbled semi-loam arenaceous soils underlying with sandy-pebbly sediments predominantly at the depth of 30-60 cm (Среднесуглинистые песчанистые средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 30-60 см) 31. Medium pebbled semi-loam arenaceous soils underlying with sandy-pebbly gypsiferous sediments predominantly at the depth of 30-60 cm (Среднесуглинистые песчанистые средне-галечниковые, подстилаемые песчано-галечниковыми гипсоносными отложениями на глубине 30-60 см) 32. Medium pebbled semi-loam arenaceous soils underlying with sandy-pebbly predominantly gypsiferous sediments at the depth of 30-60 cm (Среднесуглинистые песчанистые средне-галечниковые, подстилаемые песчано-галечниковыми, в основном гипсоносными, отложениями на глубине 30-60 см) 33. Little and medium rubbly semi-loam arenaceous soils underlying with rubbly sediments predominately at the depth of 60-120cm (Среднесуглинистые песчанистые слабо и средне-щебнистые, подстилаемые щебнистыми отложениями преимущественно на глубине 60-120 см) 34. Little and medium pebbled semi-loam arenaceous soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Среднесуглинистые песчанистые слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 60-120 см)


208

35. Little and medium pebbled semi-loam arenaceous soils underlying with gypsiferous sandy-pebbly sediments predominantly at the depth of 60-120 cm (Среднесуглинистые песчанистые слабо и средне-галечниковые, подстилаемые гипсоносными песчано-галечниковыми отложениями преимущественно на глубине 60-120 см) 36. Little pebbled semi-loam arenaceous soils underlying with sandy-pebbly sediments predominantly deeper than 120 cm (Среднесуглинистые песчанистые слабо-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно глубже 120 см) 37. Little pebbled semi-loam arenaceous soils underlying with sandy-pebbly, here and there gypsiferous, sediments predominantly deeper than 120 cm (Среднесуглинистые песчанистые слабо-галечниковые, подстилаемые песчано-галечниковыми, местами гипсоносными отложениями, преимущественно глубже 120 см) 38. Little pebbled semi-loam arenaceous soils underlying with gypsiferous sandy-pebbly sediments predominantly deeper than 120 cm (Среднесуглинистые песчанистые слабо-галечниковые, подстилаемые гипсоносными песчано-галечниковыми отложениями глубже 120 см) 39. Sandy-loam dusty (loess-like) soils (Легкосуглинистые пылеватые (лессовидные) 40. Sandy-loam dusty soils underlying with stratified predominately sandy and loamy sandy sediments (Легкосуглинистые пылеватые, подстилаемые слоистыми отложениями с преобладанием песков и супесей) 41. Sandy-loam arenaceous soils (Легкосуглинистые песчанистые) 42. Sandy-loam arenaceous soils underlying with stratified predominately loam sediments (Легкосуглинистые песчанистые, подстилаемые слоистыми отложениями с преобладанием суглинков) 43. Sandy-loam arenaceous soils underlying with stratified predominately sandy and loamy sandy sediments (Легкосуглинистые песчанистые, подстилаемые слоистыми отложениями с преобладанием песков и супесей) 44. Medium rubbly sandy-loam arenaceous soils underlying with rubbly sediments predominately at the depth of 30-60cm (Легкосуглинистые песчанистые среднещебнистые, подстилаемые щебнистыми отложениями преимущественно на глубине 30-60 см) 45. Medium pebbled sandy-loam arenaceous soils underlying with sandy-pebbly sediments predominantly at the depth of 30-60 cm (Легкосуглинистые песчанистые средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 30-60 см) 46. Little and medium pebbled sandy-loam arenaceous soils underlying with sandy-pebbly gypsiferous sediments predominantly at the depth of 30-60 cm (Легкосуглинистые песчанистые слабо и средне-галечниковые, подстилаемые песчано-галечниковыми гипсоносными отложениями преимущественно на глубине 30-60 см) 47. Little and medium pebbled sandy-loam arenaceous soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Легкосуглинистые песчанистые слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 60-120 см) 48. Little pebbled sandy-loam arenaceous soils underlying with sandy-pebbly sediments predominantly deeper than 120 cm (Легкосуглинистые песчанистые слабо-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно глубже 120 см) 49. Sandy loam soils (Супесчаные) 50. Sandy loam soils underlying with sand (Супесчаные, подстилаемые песком) 51. Medium pebbled sandy loam soils underlying with pebbly sediments predominantly at the depth of 30-60 cm (Супесчаные среднещебнистые, подстилаемые щебнистыми отложениями преимущественно на глубине 30-60 см) 52. Little and medium pebbled sandy loam soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Супесчаные слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 60-120 см) 53. Sandy coherent soils (Песчаные связные) 54. Stratified mostly clay soils (Слоистые с преобладанием глин) 55. Stratified mostly clay and loam soils (Слоистые с преобладанием глин и суглинков) 56. Stratified mostly loam soils (Слоистые с преобладанием суглинков) 57. Little and medium pebbled stratified mostly loam soils underlying with sandy-pebbly sediments predominantly at the depth of 60-120 cm (Слоистые с преобладанием суглинков слабо и средне-галечниковые, подстилаемые песчано-галечниковыми отложениями преимущественно на глубине 60-120 см)


209

58. Stratified mostly sand and loamy sand soils (Слоистые с преобладанием песков и супесей) 59. Heavy pebbled loam soils underlying closely by dense rocks (Суглинистые сильнощебнистые, близко подстилаемые плотными породами) 60. Heavy pebbled loam soils underlying closely by dense rocks, here and there coming to the surface (Суглинистые сильнощебнистые, близко подстилаемые плотными породами, местами выходящими на поверхность) 61. Heterogeneous soils (clay, loam, sandy-loam, sandy), here and there rubbled and with surfaced sandstones (Неоднородные (глинистые, суглинистые, супесчаные и песчаные), местами щебнистые и с выходами песчаников) 62. Rubbled loamy, rarely clay and sandy-loam soils that underlying at different depth with dense rocks, with baring cliffs (for mountain soils) (Суглинистые, реже глинистые и супесчаные, щебнистые, на различной глубине подстилаемые плотными породами, с обнажениями скал (для горных почв) Note: 1. Underlying is up to 2 m of top layer, usually under humus horizon. For homogenous soil-forming bedrocks underlying is not mentioned. (Подстилание указывается в пределах верхней двухметровой толщи, обычно ниже гумусового горизонта. Для однородных почвообразующих пород подстилание не оговаривается). 2. Mechanical compositions of complexes and combinations are indicated on the map only for predominant types of soils. (Механический состав комплексов и сочетаний указан на карте для преобладающих почв). (Translation: CSPC, Almaty, Oct 2007 for KAZ-EMIMS) File: C:\CAC5\Mechanicalsoilstructure[2]Rev.doc


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Annex G: Rayon and Municipalities Statistics Data: Changes in Land Use, Cropping, Production and Yields, 1987 – 2006


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Annex H: Degradation of Land and Water Resources: Photographic Evidence


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Annex I: Daily Soil Water Balance and Rainfall / Runoff

Modeling: a basic for climate change studies.


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Annex J: Bibliography


214

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central asian countries initiative for land management multicountry partnership framework

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