spatial analysis of livelihoods of small holder farmers in striga-infested maize-growing areas of...

8/8/2019 Spatial Analysis of Livelihoods of Small Holder Farmers in Striga-Infested Maize-growing Areas of Eastern and South

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Spatial analysis of livelihoods

of smallholder farmers in

Striga-infested maize-growing areas

of Eastern and Southern Africa

H. Bouwmeester, V.M. Manyong, K.D. Mutabazi, C. Maeda,

G. Omanya, H.D. Mignouna, and M. Bokanga


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Spatial analysis of livelihoodsof smallholder farmers in

Striga-infested maize-growing areasof Eastern and Southern Africa

H. Bouwmeester1

, V.M. Manyong1

, K.D. Mutabazi2

, C. Maeda1

,G. Omanya3, H.D. Mignouna3, and M. Bokanga3

1International Institute of Tropical Agriculture, IITA2Sokoine University of Agriculture

3African Agricultural Technology Foundation, AATF

January 2009


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International Institute of Tropical Agriculture and African Agricultural Technology

Foundation 2009

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Publication layout and design by IITA

Correct citation: Bouwmeester, H., V.M. Manyong, K.D. Mutabazi, C. Maeda, G. Omanya,

H.D. Mignouna, and M. Bokanga. 2009. Spatial analysis of livelihoods of smallholder

farmers in Striga-infested maize growing areas of Eastern and Southern Africa.

International Institute of Tropical Agriculture, Ibadan, Nigeria, and African Agricultural

Technology Foundation (AATF), Nairobi, Kenya. 114 pages.


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Contents

Acknowledgments ..............................................................................................................vi

Executive summary ...........................................................................................................vii

Introduction ........................................................................................................................vii

How to read this report ....................................................................................................... 1

The study area.................................................................................................................... 1

Methodology ....................................................................................................................... 2

Sampling strategy ............................................................................................................... 2

Data collection and pre-analysis management................................................................... 2

Character of data ................................................................................................................ 2

Cleaning of data ................................................................................................................. 3

Plotting the data.................................................................................................................. 4

Background data ................................................................................................................ 4

Relation of households and administrative units ................................................................ 6

Spatial pattern .................................................................................................................... 8

Ordinary Kriging................................................................................................................ 10

Kriging accuracy ............................................................................................................... 11

Results I. Spatial analysis in administrative units ............................................................ 12

Natural capital................................................................................................................... 12

Elevation ........................................................................................................................... 12

Rainfall ............................................................................................................................. 14

Roads ............................................................................................................................... 16

Area of land ..................................................................................................................... 16Share of owned and cultivated land ................................................................................. 16

Physical capital ................................................................................................................. 19

Household productive asset index.................................................................................... 19

Human capital................................................................................................................... 20

Number of yearly extension visits/household ................................................................... 21

Ill-health index .................................................................................................................. 22

Financial capital ................................................................................................................ 23

Composite liquidity index .................................................................................................. 23

Livestock ownership ......................................................................................................... 24

Household income ............................................................................................................ 25

Maize and Striga............................................................................................................... 26

Maize yield........................................................................................................................ 26

Share of land under improved maize varieties ................................................................. 26

Share of land under intercropping .................................................................................... 26

Share of land infested by Striga ....................................................................................... 30

Share of land infested by Striga 10 years ago.................................................................. 31

Livelihood outcomes ......................................................................................................... 31

Body mass index .............................................................................................................. 31


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Country wealth index ........................................................................................................ 32

Regional wealth index ...................................................................................................... 34

Results II. Spatial analysis by interpolation ...................................................................... 35

Striga Infestation............................................................................................................... 35

Maize yield........................................................................................................................ 37

Country wealth index ........................................................................................................ 40

Conclusions and recommendations ................................................................................. 43

References ....................................................................................................................... 45

TablesTable 1. Statistics of the main indicators and variables. . ................................................... 3

Table 2. Coordinates can be written in several formats. ..................................................... 3

Table 3. Points A and B can be written in different formats. ............................................... 4

Table 4. Secondary data les used in the analysis. ............................................................ 6

Table 5. The different administrative units. ......................................................................... 6

Table 6. Distribution of 880 sampled households. .............................................................. 7

FiguresFigure 1. Distribution of surveyed households. .................................................................. 5

Figure 2. Administrative units. ............................................................................................ 8

Figure 3. Distribution of administrative units....................................................................... 9

Figure 4. Clustered distribution of households in Handeni district, Tanzania. ..................... 10

Figure 5. High variability of households at short distance. ............................................... 11

Figure 6. Example of map ................................................................................................ 13Figure 7. Histogram of altitude of all households. ............................................................ 13

Figure 8. Altitude in the area of interest. . ......................................................................... 14

Figure 9. Histogram of mean annual rainfall..................................................................... 15

Figure 10. Annual rainfall in the area of interest. . ............................................................ 15

Figure 11. Location of roads in relation to the surveyed points. ....................................... 17

Figure 12. Histogram of distance from households to the nearest road. ......................... 18

Figure 13. Area of cultivated land owned by the average household in Tanzania. ........... 18

Figure 14. Share of owned, managed land in Uganda. .................................................... 19

Figure 15. Household productive asset index (PAI) for Malawi. ...................................... 20

Figure 16. Number of extension visits/household/year in Tanzania. ................................ 21

Figure 17. Distribution of ill-health index (IHI) for Uganda................................................ 22

Figure 18. Distribution of composite liquidity index (CLI) in Malawi. ................................ 23

Figure 19. Distribution of livestock (Tropical Livestock Units) in Tanzania. ...................... 24

Figure 20. Household income acquired from various enterprises in Malawi. ................... 25

Figure 21. Maize yield (t/ha) in Uganda............................................................................ 27

Figure 22. Share of land under improved maize varieties in Tanzania ............................. 28

Figure 23. Share of land under intercropping in Malawi. .................................................. 29

Figure 24. Striga infestation as a percentage of total land under maize in Malawi. ......... 30


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Figure 25. Striga infestation 10 years ago in Malawi. ....................................................... 32

Figure 26. Distribution of body mass index (BMI) of adult women in Tanzania. ............... 33

Figure 27. Distribution of country wealth index in Tanzania. ............................................ 33

Figure 28. Distribution of regional wealth index in the three countries. ............................ 34

Figure 29. Histogram ofStriga infestation in Malawi. ....................................................... 35

Figure 30. Predicted Striga infestation in Malawi. ............................................................ 36

Figure 31. Striga infestation in Malawi.............................................................................. 36

Figure 32. Condence level of the predicted Striga infestation. ....................................... 37

Figure 33. Histogram of maize yield in Tanzania. ............................................................. 37

Figure 34. Predicted maize yield in Tanzania. .................................................................. 38

Figure 35. Maize yield in Tanzania. .................................................................................. 39

Figure 36. Condence level of the predicted maize yield in Tanzania..............................39

Figure 37. Histogram of country wealth index in Uganda. ................................................ 40

Figure 38. Predicted country wealth index in Uganda. ..................................................... 40

Figure 39. Country wealth index in Uganda. .................................................................... 41

Figure 40. Condence level of the predicted country wealth index in Uganda................. 42

AnnexesAnnex I. Distribution of surveyed points .................................................................. 46

Annex II. Frequency of households per administrative unit ....................................... 50

Annex III. Distribution of administrative units ............................................................. 51

Annex IV. Altitude of surveyed households ................................................................ 55

Annex V. Mean annual rainfall, 19512005 ............................................................... 56

Annex VI. Area of land (acre) ..................................................................................... 60Annex VII. Share of owned cultivated land .................................................................. 63

Annex VIII. Productive asset index ............................................................................... 66

Annex IX. Number of extension visits ......................................................................... 69

Annex X. Ill-health index ............................................................................................ 72

Annex XI. Overall composite liquidity index ................................................................ 75

Annex XII. Tropical livestock units ............................................................................... 78

Annex XIII. Enterprise income per capita (US$/yr) ....................................................... 81

Annex XIV. Overall maize yield (t/ha) ........................................................................... 84

Annex XV. Share of land under hybrid maize .............................................................. 87Annex XVI. Share of land under intercropping .............................................................. 90

Annex XVII. Striga infestation as percentage of total land area ..................................... 93

Annex XVIII. Striga infestation as percentage of total land area 10 years ago ............... 96

Annex XIX. Body mass index ........................................................................................ 99

Annex XX. Country wealth index ................................................................................ 102

Annex XXI. Regional wealth index .............................................................................. 105

Annex XXII. Predicted Striga infestation ...................................................................... 109

Annex XXIII. Predicted maize yield ............................................................................... 112

Annex XXIV. Predicted country health index ................................................................ 114


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Acknowledgments

The authors would like to thank the International Institute of Tropical Agriculture (IITA) for

providing secondary data from the livelihoods project in Malawi, Tanzania, and Uganda

and from the GEO-spatial laboratory at Ibadan. The livelihoods project was funded by

the African Agricultural Technology Foundation (AATF) and this nancial contribution is

acknowledged.

Acronyms and abbreviations

AATF African Agricultural Technology Foundation

ADM Administrative District

BMI Body Mass IndexCLI Composite Liquidity Index

DEM Digital Elevation Model

EPA Economic Planning Unit

GEO Geographic

GIS Geographic Information System

GPS Global Positioning System

IHI Ill-health Index

IITA International Institute of Tropical Agriculture

TLU Tropical Livestock Unit


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Executive summary

This report presents results from a spatial analysis of selected data generated through a

livelihoods project in Striga infested areas of Malawi, Tanzania, and Uganda. In addition

to mapping spatial patterns on livelihood indicators using Global Information Systems

(GIS), the study also compared two interpolation techniques (ordinary Kriging and

averaging) of measured values to surrounding locations. Livelihood indicators considered

and spatially mapped in this report are related to natural capital, human capital, nancial

capital, maize growing Striga infestation and livelihood outcomes. Results show that

many variables and indicators are clearly related to space. This is especially true in

Malawi where many maps show a clear gradient from the poor south to the rich north.

Many other maps in Tanzania and Uganda seem to suggest a similar correlation in space

as nearby administrative units tend to have similar values on indicators. Although the

survey that generated data used for this report was set up according to socioeconomic

criteria and not so much on spatial criteria, the ndings show that any economical study

can prot from spatial analysis. The report also makes recommendations on how toimprove on the collection and recording of geo-referenced data in the farmers elds.

The livelihood project was designed to understand the effects ofStriga on the livelihoods

of the poor. Therefore, the sampled households were always located in areas known to

be heavily infested with Striga. Expansion of areas of interest to areas not heavily infested

to assess the effects on the researched indicators is recommended. This study indicates

the power of GIS in exposing the socioeconomic consequences of a biological threat

(Striga in this case) on smallholder farmers via a set of quantiable indicators. Therefore,

it can be said that databases designed for socioeconomic purposes can be very useful

in spatial analysis. Two methods of interpolation were applied that allow socioeconomic

properties to be predicted for unvisited sites. The results indicate that applying the two

methods generate a spatial correlation in many of the economic indicators.


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Introduction

This report is part of the results of a comprehensive study of livelihoods based on a

baseline study (Manyong et al. 2008a). It describes how Geographical Information

Systems (GIS) can be used in analyzing outcomes and should be read in relation to the

regional and country technical reports of the Livelihood project (Manyong et al. 2008a,

b, c, d). The aim is to demonstrate the strength of GIS in visualizing and analyzinglivelihood surveys (Arbia 1993, Fais et al. 2005, Johansson 2005, Legg et al. 2005). It is a

systematic constellation of spatial maps with critical livelihood indicators across the region

covered in the survey.

GIS offers many benets that make it valuable in any agricultural survey.

GIS allows the visualization of large tables.

GIS can help to identify errors.

By clear and logic presentation of data GIS adds to the presentation of data. GIS

turns tables into attractive maps.

Through GIS, results can be presented in a convincing way.

Spatial analysis allows the conversion from point-values to area-values

Interpolation can be used to identify gaps in surveys.

Interpolation can save costs in surveys by suggesting ways of optimizing sampling

design.

How to read this reportThe second part of this report describes the methodology used and details on the input

data, how they were imported into a GIS, what other geographical data were used in

the analysis, and how the analysis was done. The results of this report are divided into

three sections. The rst set is given in Results I where the survey results are compared

to GEO-physical data and the most important research themes within the administrative

units are mapped. Results II shows a limited selection of these research themes that

are subjected to spatial interpolation. Finally, the last section provides conclusions and

recommendations. The annexes show all resulting maps.

The study areaThis study was conducted in Uganda, Tanzania and Malawi. They are part of the Eastern

Striga belt of Africa, a maize growing area where Striga is a major biological threat to

production. Based on data reported by Agricultural Technology Foundation (AATF 2006),

the three countries account for 46% of the infested area, around 1,120,000 ha of the

Eastern Striga belt and 22% of the total area (2,355,000 ha infested by Striga in Africa

(Manyong 2008a). The fact that the three study countries are Striga hotspots in Eastern

and Southern Africa makes them ideal for this study. More details on the region and on

the surveyed districts in each country are given in the regional report and in the individual

country reports (Manyong et al. 2008a, b, c, and d).


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Methodology

This section describes the targeted area, the sampling strategy, and how the data were

collected. In addition it discusses the quality of the data and the techniques used that led

to the results.

Sampling strategyIn each country, four districts were chosen and in each of these districts a random

sample of 75 households was taken. Through literature and consultation with experts,

these districts were purposively chosen as they rank maize as an important crop and are

regarded as Striga hotspots (Manyong et al. 2008a). The villages within districts were

then listed, based on the high importance of maize and high ranking ofStriga as a major

constraint to maize production. Using an inbuilt sample [%]command in STATA software,

ve villages from each district were randomly selected. Within each sampled village, the

village register from the village government ofce was used to list all the households.

Then, trained enumerators used a random numbers table to select 15 households for

interview and another ve households for replacement if any of the households could not

take part in this survey (Manyong et al. 2008a). The enumerators interviewed members

of the household extensively. The coordinates of the house were recorded and several

measurements relating to farm households were made. A comprehensive description of

the sampling strategy is found in the country reports where the exact procedures followed

may vary slightly (Manyong et al. 2008b, c, and d).

The spatial location of each sampled household in the survey was determined with a

handheld global positioning system (GPS). These units are generally accurate within 100

m (horizontal) in the worse result, and 10 m in the best result. In the region some 900households, 300 in each of the three countries, were geo-positioned.

Data collection and pre-analysis managementDuring country-based methodology workshops, enumerators were trained on how to use

GPS for taking coordinates and measuring areas of elds. A minimum of 5 GPS units

were distributed in each country, at least one unit in each district. The shared use of the

units in the eld was overseen by the district-based extension ofcer and the IITA country

research supervisor. The industrial serial number of each unit was properly recorded

when the unit was handed to a particular user. The coordinates of the households and the

various eld areas were manually entered into the database.

Character of dataAltogether there were 901 questionnaires, all of which had a unique Questionnaire-ID.

The coordinates of all questionnaires were determined using handheld GPS devices and

were added manually on the questionnaires. The questionnaires consisted of a great

quantity of variables or indicators. From this collection the most important were selected

and used for further analyses (Table 1). The map datum used was WGS-84 and the

coordinates are recorded in the degree-decimal format.


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Table 2. Coordinates can be written in several formats.

Item X,Y-coordinates Format

Location X 402621N, 795836W Degreeminutesecond

Location X 40d 26 21 N, 79d 58 36 W Degreeminutesecond

Location X 40.44619N, 79.94886W Degreedecimal

Location X 40.44619, -79.94886 Degreedecimal

Location X 40 26.772, -79 56.931 Degreeminute.decimal

Table 1. Statistics of the main indicators and variables. All indictors are explained and

mapped in Results I.

Mean

Median

Mode

St.Deviation

Skewness

Minimum

Maximum

Count

land owned (acre) 1.8 2.0 1.0 0.9 0.8 1.0 4.0 856share of cultivatedland owned (%)

86.3 100.0 100.0 30.3 2.1 0.0 100.0 856

productive assetindex

14.2 12.0 12.0 10.6 2.5 0.0 102.0 856

extension visits(no./yr)

6.1 0.0 0.0 11.5 3.6 0.0 96.0 845

ill health index 0.0 0.0 0.0 0.1 8.0 0.0 1.0 856

tropical livestockunits (no.)

0.9 0.2 0.0 2.1 6.9 0.0 31.6 856

household income(US$/yr)

77.9 35.2 0.0 131.3 4.5 0.0 1464.3 856

maize yield (t/ha) 1.1 0.8 0.8 1.0 2.2 0.0 7.0 856

share of land underimproved maize (%)

33.0 0.0 0.0 0.5 0.7 0.0 100.0 856

share of land underintercropping (%)

32.2 0.0 0.0 0.5 0.8 0.0 100.0 856

share of land undermaize with Striga (%)

42.1 50.0 0.0 28.6 0.1 0.0 100.0 856

share of land undermaize with Striga 10 years ago (%)

13.3 5.0 0.0 17.8 1.7 0.0 100.0 856

body mass index 21.0 21.9 0.0 7.5 1.1 0.0 56.4 856

country wealth index 0.0 -1.2 7.5 7.9 1.8 -16.2 44.1 856

region wealth index 0.0 -2.1 11.2 9.0 1.5 -11.2 40.2 856

Cleaning of data

As the coordinates were collected by many interviewers with different GPS-units, there

was bound to be some confusion. As the coordinates were manually copied onto the

questionnaires, the method of how a coordinate was written depended on the settings

of each individual GPS unit. Table 2 lists valid and acceptable ways to write geographic

coordinates. The table shows that the same location can be written in several different

ways, resulting in large spatial differences of location X. In Tanzania, for instance, 1

degree corresponds roughly with 110 km of distance.The degree-values are the same

in all systems but what comes after the degree varies. For instance, 26 minutes in thedegreeminute.decimal-format would be written as .44 in the degreedecimal-format. If

interpreted incorrectly, this could cause a spatial difference of 0.18 degree (0.44 0.26)

or roughly 20 km.


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Table 3. Points A and B can be written in different formats.

item x-

degminute

x-

degdecimal

y-

degminute

y-

degdecimal

Location A 3730.000 E 3750.000 E 615.000 S 625.000 S

Location B 3410.000 E 3416667 E 1420.000 S 1433.333 S

Of the 901 questionnaires six had no coordinates recorded and were deleted. Most of

the remaining 895 questionnaires were written in degreeminute.decimal-format, as

this is the default setting of the GPS-units used in this survey. This assumption was

backed because from all 895 Y-coordinates only 15 had decimal values greater than

60000. Statistically this points to the degreeminute-format since the decimal value

cannot exceed values higher than 60000, as one degree is made up out of 60 minutes.

If the survey points were randomly selected, one would expect about 60% of all values

between 0 and 60.000 and 40% between 60.000 and 99.999. Only in degreedecimal-format can the decimal values be anywhere between 0 and 99999, as 1 degree is divided

into 100000 decimals (Table 3).

For the previously described reasons, it was assumed almost all of the coordinates were

written in the degree-format except for 15, which were converted to the same system as

the others. This assumption, however, remains a possible source of error. The plotted

households were compared with the shape-les of the district to explore this source of

error in more detail.

Plotting the dataAs the coordinates of the questionnaires were now in the same format they could

be plotted on a map using GIS software. Of the total 895 questionnaires 14 were

geographically speaking far away from the researched locations. They were veried on

the hard copy of questionnaires to exclude the possibility of typing errors. There appeared

to be no typing errors and it was assumed these outliers were a result of writing errors

and were deleted from the dataset. In Figure 1, the remaining 881 households are

plotted as black dots. This gure shows the regional map and the map of Tanzania as

an example; the full-sized maps can be viewed in Annex I, including the country maps of

Malawi and Uganda.

Background dataA variety of background data was used for the various analyses and the necessary

visualization. Table 4 lists these data, the purpose of use and their source. Items 1

through 6 were used to represent the contours of the different administrative units. Items

7 to 9 were used to represent the roads. Item 10 was used to extract mean rainfall/district

and item 11 to extract the altitude of the households.


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Figure1.Distribution

ofthehouseholdsintheentire

region(left)andinTanzania(right).


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Relation of households and administrative unitsTo allow further analysis the surveyed households had to be linked to shape-les

describing the various administrative units. In principle, three administrative units can be

distinguished: (1) country, (2) district, and (3) ward for Tanzania, parish for Uganda, andeconomic planning units (EPA) for Malawi (Table 5). This paragraph describes the match

between the households location and these three administrative units.

On the country level a simple visual inspection revealed that all households appeared

to spatially match the target countries as illustrated by the shape-le. No editing was

necessary and further analysis on country-level seemed justied.

To validate the households on a district level, the following procedures were undertaken.

One household in Kalulu, Malawi, was deleted because it was not part of the target

districts. Table 6 lists the number of households/district in the three target countries.

Table 4. Secondary data les used in the analysis.

Item Filename Format Purpose Source

1 Adm3 shape-le contours of districts in

Malawi

IITA, Ibadan,

Nigeria

2 districts_2005 shape-le contours of districts in

Tanzania

IITA, Ibadan,

Nigeria

3 uganda_parish_july2006 shape-le contours of parishes and

districts in Uganda

IGAD, Kenya

4 national_boundaries shape-le contours of countries IITA, Ibadan,

Nigeria

5 tz_wards_2005_new shape-le contours of wards in

Tanzania

IITA, Ibadan,

Nigeria

6 Malawi_central shape-le contours of EPA in

Malawi

IITA, Ibadan,

Nigeria

7 uganda_ads_roads shape-le roads in Uganda IITA, Ibadan,

Nigeria

8 Roads shape-le roads in Tanzania IITA, Ibadan,

Nigeria

9 Roald_line shape-le roads in Malawi IITA, Ibadan,

Nigeria

10 Pptnmean5105 Grid-le mean rainfall from 1951

to 2005

IITA, Ibadan,

Nigeria

11 tandem, ugdem,

maldem

Grid-les Altitude in meters IITA, Ibadan,

Nigeria

Table 5. The different administrative units; adm2 is not used as it stands for province.

Item New name

Country adm1District adm3

Ward (Tanzania) adm4

Parish (Uganda) adm4

EPA (Malawi) adm4


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Table 6. Distribution of 880 sampled households.

Country (ADM1) District (ADM3) No. households

Uganda Busia 75

Uganda Namutumba 74

Uganda Pallisa / Budaka 75

Uganda Tororo 72

Tanzania Handeni 74Tanzania Mkinga / Muheza 75

Tanzania Morogoro Rural 70

Tanzania Mvomero 71

Malawi Dedza 70

Malawi Kasungu 75

Malawi Lilongwe 75

Malawi Mchinji 74

The number of households seems evenly distributed over the districts and, after thedataset was cleaned, sufcient points remained to allow further analysis.

To quantify the geographic match between the survey and the shape-les used to portray

the districts, a spatial join between the two les was done. The districts of the three

countries were merged into one shape-le. A frequency test was done to quantify how

many of the household-locations were actually located within the appropriate districts.

The match was quite good, with only seven out of 880 points not being in one of the 12

targeted districts. The mismatch of these seven points can be the result of recording

errors, typing errors, or errors in interpreting the coordinates and they were removed from

the dataset. All in all, this seemed a satisfactory result that provided sufcient reason to

do analyses on a district level.

To validate the household dataset on the 4th administrative level, the following procedure

was undertaken. All 4th administrative units (adm4) were appended into one shape-le.

The resulting shape-le was spatially joined with the households. Of the 873 households

369 did not match with the adm4 of the shapele. Extensive cleaning needed to be

done to improve this match. Some of the major reasons for the mismatch were spelling

mistakes and households that were close to the border of two adjacent adm4s.

In some cases in the shape-le an adm4 was split in two, each with a different name,

while the households used only one name for the adm4. This is illustrated in Figure 2. In

the shapele the adm4 Kaphuka in Malawi was split in two, one called Kaphuka and one

called Mayani. In the households, the adm4 had only the name Kaphuka. In this example,

the adm4 Mayani was renamed Kaphuka.

As mentioned before, there were 369 mismatches where the names of the households

adm4 did not match the name of the shape-les adm4. All these were handled

individually and appropriate action was taken for them to match. Sometimes, the adm4 of

the households was renamed and in others, the adm4 of the shape-le was renamed. To

save the reader a very long list, not all instances are individually described.


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Figure 2. Administrative units might be split in one le but not in another le. The black

dots represent households. One cluster of households is located in the north of Kaphuka

District while another cluster is located just across the border in the adm4 formerly known

as Mayani.

After extensive cleaning, 17 out of the 369 households were left with no clear solution

to the mismatch and were therefore deleted from the household survey. What remained

after this cleaning were 856 households in a total of 66 different adm4. The minimum

amount of households/adm4 was 1 and the maximum, 44. About two-thirds of all adm4

had 10 or more households within their borders (see table in Annex II). Theoretically,

there should not have been adm4 with so few or so many points but the average should

have been about 15. However, this was not the case and could have resulted from

recording errors, copying errors, the removal of points, the renaming during cleaning, or

because the coordinates of the points were in a different format than was assumed.

The results are shown graphically for Malawi in Figure 3 and at full size for all countries

in Annex II.

Spatial patternIn most maps in this report different themes of interest are displayed within an

administrative unit. The map implies that the value of the theme reects the average

value of the administrative unit. Depending on the objective, a variety of sampling designsare proposed that can be used to describe the most probable average (Arbia 1993,


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Figure 3. Distribution of surveyed administrative units in Malawi. Adm1 stands for country,

adm3 for district, and adm4 for EPA in Malawi, for Ward in Tanzania, and Parish in Uganda.

Diggle et al. 2007, Smith et al. 2007). The maps of Annex I reveal that the sampling

pattern of the surveyed households is highly clustered. The reason for the spatial

distribution is the sampling strategy adopted in the Livelihood project. This strategy

aims to sample households in areas with both maize and Striga concentration. Within

these areas individual households were selected through a stratied random sampling

technique (Manyong et al. 2008a).

The clustering caused by this strategy is intensied by the clustering of settlements and

by the clustering of households within the settlements. To illustrate this, Figure 4 shows

32 out of the total 74 households in the district of Handeni, Tanzania.


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All points are located within a 6-km range while some are as close as 40 m apart. The

other 44 households in the district are not shown because they are located about 45 km

to the east and would therefore obscure the gure.

Within the dataset there is a gradation of clustering. In Morogoro Rural and Mvomero in

Tanzania, selected households are spatially conned to a very small proportion of the

district. In some districts such as Busia and Namatumba in Uganda, selected households

are relatively evenly distributed over the district (Annex I). It is therefore expected thatvariability/region will differ signicantly and thus the relation between the value of each

household and the districts average. The same accounts for the smaller administrative

units (adm4), where the sampled households are often clustered in a small area within

an adm4. While there were always at least 70 households in a single district, there might

be only a very limited number of households within an adm4. An example of this is given

in Annex II, where only one household appears to be located in adm4 Sapiri, Uganda. In

this particular instance, the average value for the entire adm4 is identical to the value of

that one household. Although in all other examples the average value is based on more

than one observation, it should be noted that the robustness of the average values varies.

Ordinary KrigingOrdinary Kriging (OK) assumes that the distance or direction between sample points

reects a spatial correlation that can be used to explain variation in the surface. It

assumes a constant but unknown mean and ts a mathematical function to a specied

number of points to determine the output value for all surrounding locations.

z(x,y)

= (xy)

+ e(x,y)

Here Z denotes the realization at location x,y. with as the xed but unknown mean and

e being the variation around this mean (Smith et al. 2007).

Figure 4. Clustered distribution of households in Handeni district, Tanzania.


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Figure 5. High variability of households at short distance of the country wealth index

in Tanzania, with values as low as 8.54 and as high as 7.71 within a distance of 1 km

(standard deviation is 7.9).

Ordinary Kriging is an exact interpolator, meaning that at the location of the householdsthe predicted value is equal to the measured value. This can cause strange looking

maps as there often will be jumps between predicted and measured values, especially in

datasets with a high clustered spatial distribution. Figure 5 illustrates this phenomenon

where a large difference in the wealth index occurs within a short distance (mean is 0 and

standard deviation is 7.9). To limit the importance of individual values the neighborhood

search radius has been increased to a search radius of 50 points with a minimum of 5

points. For the remaining settings the default Arc-GIS settings were used.

Kriging accuracy

Kriging also predicts the accuracy of its prediction by calculating a level of condence

of every location on the map. With each prediction map, a map showing the standard

error map was created using the same Kriging method and parameters that were used to

generate the prediction map. The standard error is the variation of the prediction, thus a

small error corresponds with a small variation of the predicted value and a large error with

a large variation. In other words, the smaller the variation gets, the better the prediction.

The bright yellow colors on the maps (Figures 32, 36, and 40) indicate areas where the

prediction standard error was low or, to state it another way, the level of condence in the

results was high. The dark brown symbolizes areas of low condence. It may not come

as a surprise that all maps clearly show that the level of condence decreases as the

distance from the surveyed households increases.


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Results I. Spatial analysis in administrative units

The survey consists of many quantitative physical, social, and economic data from 856

households divided over three countries. In this section these households are averaged

within a certain administrative unit. This allows the visualization of the data as they

are transformed from point (household) to surface (administrative unit). The reader

should bear in mind that the resulting maps suggest a certain value for a large areathat is sometimes based on the knowledge of only a few households. The rst chosen

administrative unit is the district. Each of the 12 districts has about 70 households (Table

6). The second chosen administrative unit is the adm4. It is based on a much lower

sample size, ranging from 1 to 44 households (Annex II).

The various spatial analyses are focused on socioeconomic indicators developed through

the Livelihood study following the Livelihood framework. A more elaborate explanation

on each indicator is given in the region and country reports (Manyong et al. 2008a, b, c,

and d). To allow the reader to capture the full extent of the indicators, the order in which

the themes are presented is based on these reports. Usually, one theme of research

is represented in four different maps. The rst map is region-based and shows the

entire targeted region. The second map covers the research area in Malawi, the third in

Tanzania, and the fourth in Uganda.

On the maps the average values of the district are shown. These district-values are

overlaid by the average values of the smaller adm4. This shows how the adm4 is related

to the bigger district by having the same legend. The legend is also the same for the

different countries, and this allows comparisons among these countries.

Figure 6 is an example of the procedure followed. It shows classes of the average areaof land owned by households in the Eastern part of Tanzania. The district of Handeni

is represented by the average value of 72 households at 2.65 acres of land/household.

Within Handeni, three adm4s are situated: Chanika with 15 households, Kwedizinga

with 13 households and Vibaoni with 44 households. In Chanika, the average household

owns 2.20 acres (below average for the district where it belongs), in Vibaoni 2.61 acres

(average), and in Kwedzinga 3.08 acres (above average).

Natural capital

Natural capital entails the stock of assets embodied in natural endowments, such aselevation, annual rainfall, infrastructure, and land quantity and quality.

Elevation

To determine the altitude of the households a digital elevation model (DEM) of a 90 m

resolution was used. Figure 7 and Figure 8 show the altitude (m) of all households (Annex

IV at a larger scale). The households in Uganda are positioned between 1.000 and 1300

m in altitude with a majority of the points at around 1100 m. The households in Tanzania

are more or less evenly distributed between 150 and 750 m. The households in Malawi

are situated between 1000 and 1350 m and, as in Uganda, most points are at 1100 m.


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Figure 6. In the district of Handeni three adm4s are included. The average area of land

owned for the whole district is 2.65 acres of land/household. In the adm4 within this

district, an average household owns 2.20 acres in Chanika, 2.61 acres in Vibaoni, and

3.08 acres in Kwedzinga.

Figure 7. Altitude (m) of all households with max = 1348 m and min = 147 m.


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Rainfall

Within the studied area farmers mostly rely on rainfall as their source of water for

agriculture because possibilities of irrigation are often limited. The amount of rainfall/

household is extracted from a 1 km resolution grid displaying the average annual

precipitation in the period 1951 to 2005. Figures 9 and 10 give an idea of the distribution

of rainfall in the area of interest. In Annex V, a map is shown for each targeted country.

In Malawi, the average annual rainfall of the households in that period was between 870

and 1050 mm. In Tanzania the range is a little wider with minimum values of 850 mm and

maximum values of 1120 mm. The eastern part of Uganda appears to be much wetter

with annual minimum values of 1230 mm and maximum values of 1670 mm.

Figure 8. Altitude (m) in the area of interest. The black dots represent the households.

Source: Distributed Active Archive Center (http://edcdaac.usgs.gov).


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Figure 9. Histogram of mean annual rainfall in mm in the period 1951 to 2005 of allhouseholds, with a mean of 1104 mm, a max of 1628 mm and a min of 851mm.

Figure 10. Annual

rainfall in the

area of interest.

The black dots

represent thehouseholds.

Source: Food

and Agriculture

Organization of

the UN (FAO), the

Climatic Research

Unit (CRU), and the

Global Historical

Climatology

Network (GHCN)

(http://www.

worldclim.org).


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Roads

In principle roads are positively correlated with farmers welfare as this type of

infrastructure improves market access (Staal et al. 2000). Getting good and updated

geo-referenced data on road location remains a major problem in this region of Africa. To

save time and costs a standard road shape-le is used. The roads of the three countries

(Table 4) were merged into one shape-le. The attributes of the road-shape-les did

not allow a differentiation in road type. Therefore all roads were regarded as being ofthe same quality. As becomes apparent from Figure 11, there seems to be a strong

spatial correlation with the surveyed points and the location of the roads, where most

of the households are near roads. This could be the result of policies that encouraged

households to settle near roads for ease of access to social and health infrastructure.

Figure 11 shows maps of Tanzania and Malawi.

It can be assumed that many of the indicators are somehow related to the roads, as

infrastructure coincides with market access. To verify this assumption the distance from

each household to the nearest road was determined. This distance between a household

and the nearest road ranges from 0 to 11.5 km. Figure 12 shows a very skewed

distribution with a mean distance of 2.1 km. In Malawi, the average distance is 3.2 km, in

Tanzania, 1.6 km and in Uganda, 1.4 km.

Area of land

The area of land/household represents the total area of land used for agricultural

purposes owned by the household. Figure 13 illustrates the distribution of this land area

in Tanzania, Annex VI shows the entire region of interest. In Tanzania the households in

the district of Handeni seem to own most of the land; the average is raised by the ward

Kwedizinga where households own more than 3 acres of farmland. In Malawi households

in the north own up to 2.5 times more land than in the south. In the south, the districts of

Dedza and Lilongwe score in the lowest category. In Uganda, the average size of land

owned in all four districts is 1.5 to 2.0 acres and no obvious trend becomes apparent as

some parishes are below and some above this average.

Share of owned and cultivated land

The share of owned and cultivated land shows how much of the land that is cultivated

during the reference season is actually owned by the household. This indicator may

explain the shortage of land where ideally a household owns 100% of the land it

cultivates. This share is depicted for Uganda in Figure 14 and for all countries in Annex

VII. In Uganda, only a few parishes in Namutumba own less than 70%. Tanzania seems

not to score well in this category, where farmers generally own less than 80% of their

farmed land. Handeni is noteworthy, as most farmers own more than 90% in this district.

Farmers in Malawi seem to own most of their farmed land; only the EPA Mkanda scores

are very low.


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Figure11.Locationofroadsinrelationtothesurveyedpo

intsinTanzania(left)andMalawi(right).Source:ESRIsChart

oftheworld,

(http://www.maproom.psu.edu/dcw).


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Figure 12. Histogram of distance from households to the nearest road.

Figure 13. Area

of cultivated land

(acres) owned by theaverage household

in Tanzania.


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Figure 14. Share of owned, managed land in Uganda.

Physical capitalPhysical capital refers to man-made assets such as productive assets, housing qualities,

and consumable durables. As opposed to natural capital, physical capital can be inuenced

by the household.

Household productive asset index

A composite productive asset index (PAI) was developed by combining the number and

the working status of all combined productive assets/household. The index therefore

expresses the tools and their quality at hand. Figure 15 shows how this index varieswithin Malawi. Annex VIII shows the resulting maps of the entire region at a bigger scale.


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On a regional level Tanzania has the most even distribution of the PAI with all households

scoring on or above average. Uganda seems to be worse off, with two entire districts

below average. The difference between the south and the north of Malawi seems

prominent where the PAI in the north is generally higher than in the south.

Human capitalHuman capital concerns the people who are both the objects and subjects of

development. Since this study was on smallholder farmers, sources and levels of

accessibility to know-how and human quality were quantied.

Figure 15.

Household

productive

asset index

(PAI) for

Malawi.


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Number of yearly extension visits/household

The number of extension visits is dened as the annual number of visits to a household

by an extension ofcer. Visits are expected to introduce more know-how to farmers and

bring more productivity and market information which might result in higher income.

Figure 16 illustrates the distribution in Tanzania and Annex IX, the distribution for all

countries.

In Uganda, households appear to have little contact with extension ofcers. Only in

the west of Mamutumba do farmers make use of these services where the parish of

Nabitula looks like a hotspot. In Tanzania, only in the district of Mvomero and especially

in the ward of Melela do households frequently consult extension ofcers. In Malawi, the

distribution is irregularly divided and no clear pattern emerges.

Figure 16. Number of extension visits/household/year in Tanzania.


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Ill-health indexHealth as an indicator of human capital was conceptualized through a morbidity composite ill-

health index (IHI). The IHI includes 10 diseases, fever/malaria, dysentery/diarrhea, respiratory

system-related illnesses, measles, typhoid fever, undernutrition, tuberculosis, HIV/AIDS,

accidents causing injury and lifetime diseases/disorders. The more health problems the

interviewees experienced during the reference period of one year preceding the survey, the

higher the value of IHI. An example of the distribution of this index for Uganda is presented in

Figure 17 and for all three countries in Annex X.

On a regional scale, Malawi scores comparatively well with the majority of the EPAs below

average. In Tanzania, only the farmers in the district of Handeni appear to be in a comparatively

healthy condition. In Uganda the IHI looks more or less randomly distributed. Isolated low-

scoring parishes seem to have a big negative inuence on the districts average, for example,

Naboa, Nawansagwa, and Masaba.

Figure 17. Distribution of ill-health index (IHI) for Uganda.


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In Tanzania, despite the occasional ward with a high CLA, all districts score on or below

average. In Malawi access to cash appears to be easier; here only about 25% of the

EPAs score below average. There seem to be large differences, however, while in

Tanzania, the distribution seems to be more even. In Uganda also, the distribution of the

CLA appears to be widely dispersed.

Livestock ownershipFarmers can easily sell their livestock to acquire nancial resources. To measure this

asset a common unit to describe livestock numbers of various species in a single gure

has been developed. To do this, the concept of an Exchange Ratio has been created,

where different species of different average sizes can be compared and described in

relation to a common unit, the Tropical Livestock Unit (TLU). The distribution of this TLU

in Eastern Tanzania is illustrated in Figure 19 and for all three countries in Annex XII.

Figure 19.

Distribution

of livestock

(TropicalLivestock Units)

in Tanzania.


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Farmers in Malawi own comparatively few cattle. In the south of the studied area this

becomes even more apparent than in the north. In Tanzania, only in the district of

Handeni does the TLU rise above 1 although this can be entirely contributed to the ward

of Vibaoni. The TLU in both countries is signicantly lower than in Uganda where about

50% of the farmers in the parishes have TLUs above 1.

Household incomeThe household income is the estimated income the household gets from various

enterprises. Ten enterprises were considered in the computation of the total enterprise

income through simple summation. These were crop production, livestock, business,

salaried employment, casual wage-work, technical work, artisan work/handcrafts, natural

resources, traditional medicine/healing and resource rent out. All enterprise incomes

were converted into US dollars. The differences in this income are shown in Figure 20 for

Malawi and for all three countries in Annex XIII.

Figure 20.

Household

income acquired

from variousenterprises

in Malawi.


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In Tanzania, all districts with the exception of Morogoro have a yearly enterprise income

of more than 50 USD. In Morogoro, income is generally below 50 USD and in the ward of

Mikese, even below 25 USD. The Handeni district, on the other hand, does relatively well.

In Malawi, more than half of the adm4 score below average. The northern part of the study

area seems to be better off than the south. In Uganda only the farmers in the district of

Namutumba have a comparatively high income while Busia had a very low

enterprise income.

Maize and StrigaMaize ranks rst of the major cereal grains in many countries of Eastern and Southern

Africa. It is a very important staple food for the entire population as well as a source of

income. The crop is mainly produced by smallholder farmers on small-scale farms of

less than 3 ha. Striga is a root-parasitic owering plant that causes a considerable loss

in growth and yield of many food and fodder crops. In general, low soil fertility, nitrogen

deciency, well-drained soils, and water stress accentuate the severity ofStriga damage

to the hosts. Striga has a greater impact on human welfare than any other parasitic

angiosperm as its hosts are subsistence crops in marginal agricultural areas (Manyong et

al. 2008a).

Maize yield

The yield of maize, expressed as hectare/ha, uctuates greatly in the study area.

The distribution is illustrated in Figure 21 for Uganda and in Annex XIV for all countries.

On the whole, the average yield of maize is 1.0 to 1.5 t/ha. This is especially true for

Tanzania where only the district of Mkinga/Muheza has a higher yield. In Malawi, the

pattern seems more erratic with EPAs scoring above and below this average. Uganda

seems to have consistently lower production. Only the district of Budaka/Pallisa has

average yields, but this is the result of only two out of six parishes.

Share of land under improved maize varieties

Farmers produce local and improved maize varieties. Improved varieties are expected to

produce higher yields than local varieties. The proportion of land under improved maize

varieties is calculated as the area where improved varieties are grown, divided by the area

where all maize varieties are grown. Figure 22 shows how households in Malawi adapt this

technology, and Annex XV shows this adaption in the studied countries.

From the maps it becomes clear that there are large regional differences in the adoption

of improved maize varieties. In Malawi, it appears to be an accepted and widespread

technique, where more than 40% of farmers make use of these varieties in the western

part of the country. In Tanzania, only the farmers in the district of Mvomero frequently use

improved seeds. In Uganda, the distribution appears dispersed, although the improved

varieties appear to be unpopular in the district of Busia.

Share of land under intercropping

Another technique used to solve the Striga problem is intercropping. Intercropping involves

planting legumes, not susceptible to Striga, in rows alternately with maize. As the legumes

are not a host to Striga, no parasitism is expressed and the Striga seedbank is decreased.


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Figure 21. Maize yield (t/ha) in Uganda.


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Figure 22. Share of land under improved maize varieties in Tanzania.


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The share of land under intercropping is calculated by determining the area of land under

intercropping divided by the total land under maize. The adoption of this technique is

illustrated in Figure 23 for Malawi and in Annex XVI for all three countries.

In Tanzania, the technique is generally disliked except in the district of Morogoro where

more than 25% of cultivated land is farmed using intercropping. This could be related to

the presence ofStriga as it appears to show the same spatial pattern and might conrm

expectations that the parasite is combated with intercropping. In Malawi, intercropping

emerges as very popular in the south of the researched area where more than 75% of

Figure 23. Share of land under intercropping in Malawi.


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farmed land is intercropped. Also in Uganda the method seems popular with all districts

on average and the parishes scoring below or above are more or less equally distributed.

Share of land infested by Striga

The share of land infested by Striga is calculated by determining the area of land under

maize affected by Striga divided by the total land under maize. Figure 24 shows the

distribution ofStriga in Malawi and Annex XVII shows this for the entire region.In Malawi, the situation is rather severe where almost the entire targeted area is facing

Striga on more than 40% of land under maize plots. In the south there are a few EPAs

that seem to escape the threat, but considering the fact that they are surrounded by

more diseased EPAs they too will probably be swiftly infected, provided no appropriate

Figure 24. Striga infestation as a percentage of total land under maize in Malawi.


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action is taken. It is noticeable that Striga is not as common in Tanzania as in Malawi and

Uganda. The problem seems to be concentrated around the district of Morogoro where

up to 60% of farmland under maize is infected. Ten years ago only the ward of Kisemu

within this district had a problem. It might therefore well be that Striga spread from that

ward or a neighboring ward not sampled. In Uganda the situation seems very serious

with more than 60% of land infected in two districts. In the southern district of Busia, two

parishes even have infection values higher than 80%. Nevertheless these farmers do notseem tempted to use combating methods, such as improved varieties and intercropping,

probably because they have not been exposed to these technologies.

Share of land infested by Striga 10 years ago

Farmers were asked to estimate the percentage of their land area under maize that

was infested with Striga 10 years ago. As becomes clear from Figure 25 for Malawi and

from Annex XVIII for all countries, Striga was a relatively limited problem 10 years ago.

Nevertheless, this legend was used to allow comparisons with the Striga situation at this

moment. Striga infestation was already a problem in Malawi 10 years ago, especially in

the central and northern parts of the researched area where values between 20% and

40% are not uncommon. In Tanzania, Striga occupied more than 20% of total farmland

only in the ward of Kisemu. In Uganda, only the eastern part of the area of interest seems

to experience problems with Striga. When compared to the situation at this moment the

spread ofStriga is very serious and has increased strongly in virtually all

administrative units.

Livelihood outcomesDifferent forms of capital ultimately result in a series of outcomes. The exact composition

of these outcomes is discussed in the regional report (Manyong et al. 2008a). For thespatial analysis, the body mass index and the wealth index of the households

were considered.

Body mass index

The body mass index (BMI) is a measure of the nutritional status of adults, expressing

the health effects of body weight relative to height. A BMI score between 22 and 24 is

considered normal. Below 22, an individual is underweight and possibly malnourished.

Above 24, an individual is overweight or obese. Both underweight and overweight

individuals have increased relative risks relative to morbidity and mortality compared tothose of normal weight. The BMI was recorded for the mother or the guardian of each

household. Results for Tanzania are shown in Figure 26 and in Annex XIX for

all countries.

In Tanzania, women are overweight in Handeni but underweight in two of the other

districts. In Mvomero, the district average is normal, despite the fact women in the wards

of Hembeti and Mvomero are underweight. In Malawi, women are generally underweight

as the BMI is at normal levels only in four adm4. This pattern also arises in Uganda

where the women in all districts are clearly underweight, despite the fact that a few adm4

have normal values.


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Figure 25. Striga infestation 10 years ago as a percentage of total land under maize in Malawi.

Country wealth index

A wealth index was computed by aggregating the various asset ownerships and housing

characteristics variables, based on the method of principal components. The asset

variables considered in the analysis were related to main building quality (roong,

wall, oor, toilet, and extra house), consumable durables (iron/wooden bed, iron, sofa,

spongy mattress and watch/wall clock), communication means (television, cell phone,

landline, radio) energy and water source (energy for cooking, energy for lighting and

source of water) and transport means (car, motorbike and bicycle) making the total of 20

variables. The index is mapped for Tanzania in Figure 27 and for all countries in Annex

XX. The index is normalized for each country so comparisons are possible only within

the three countries.


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Figure 26. Distribution of

body mass index (BMI) of

adult women in Tanzania.

Figure 27. Distribution of

country wealth index in

Tanzania.


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In Malawi, the northern part of the studied area is clearly wealthier than the south. In

Tanzania, Morogoro and Mkinga/Muheza appear to be the poorest districts. In Uganda,

wealth also seems rather dispersed with poorer and richer parishes divided over the

country. Unlike in Malawi, the richer part of the studied area appears to be in the south

of Uganda.

Regional wealth indexThe regional wealth index is the same as the wealth index described earlier; the only

difference is that the index is normalized over the entire area of interest. This allows

comparisons between the three researched countries (Figure 28 and Annex XXI).

Tanzania is clearly the wealthiest of the three countries. Even the poor district of

Morogoro scores above average in this respect. Malawi is undoubtedly the poorest,

with the southern part of the country in the worst position. In Uganda, the wealth index

seems to differ over the country, although three out of four districts score above the

regional average.

Figure 28.

Distribution of

regional wealthindex in the three

countries.


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Results II. Spatial analysis by interpolation

In the previous section, a great number of research themes were converted from points

to areas by averaging households over administrative units (Results I). This method

shows the spatial spread of the researched themes but does not say anything about the

distribution and density of points within the administrative units. In this section, the spatial

interpolation technique Ordinary Kriging will be used to assess the spatial distributionofStriga infestation, maize yield and the country wealth index. The technique itself is

described in Methodology.

Striga infestationIn this section Striga infestation will be explored using the data collected in 287

households in Malawi. The histogram (Fig. 29) shows that between 0 and 100% of

farmland is infested with Striga in the targeted area. The distribution appears to be normal

and there are no values that are considered to be outliers.

Figure 30 shows the results of applying the spatial interpolation technique ordinarykriging on 287 households in Malawi. For cosmetic reasons the map extent taken into

consideration is the entire country of Malawi. The households themselves are shown on

the map as black dots. It appears farmers in the Northwestern part of Malawi suffer more

from Striga than those in the Southeast.

Figure 31 shows the Striga infestation based on the values of the same 287 households

averaged over the administrative units. Although the legends are slightly different, the

same pattern emerges as in Figure 30.

In Figure 32, the level of condence clearly decreases as the distance to the households

increases. A darker color means a lower level of condence, or (in other words) a higher

error in prediction. The map also shows that the level of condence is higher in between

households than at the outer edges of household clusters. This suggests the sampling

design is important and a lower prediction error can be reached by optimizing this design.

Annex XXII shows both the prediction and its condence.

Figure 29. Histogram of the percentage of farmland infested with Striga of 287 households

in Malawi (mean = 47.3 and skewness = -0.2).


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Figure 32.

Condence level

of the predicted

Striga infestation.

Maize yieldThis explores maize yield using the data collected in 281 households in Tanzania. Ten

households have unrealistically high maize yields of over 7.0 t/ha and are therefore

omitted from further analysis. The histogram (Fig. 33) shows that the maize yield of the

remaining 271 households in Tanzania ranges from 0 to 7.0 t/ha.

Figure 33. Histogram showing the distribution of maize yield (t/ha) of the 271 households in

Tanzania (mean = 1.1 and skewness = 0.9).


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Figure 34. Predicted maize yield (t/ha) in Tanzania.

Figure 34 shows the predicted maize yield of the target area in Tanzania in t/ha. The

prediction is based on 271 households, shown on the map as black dots (Annex XXIII).

There appears to be a region of high production in the center of the studied area.

Figure 35 shows the maize yield based on the values of the same 271 households

averaged over the administrative units. The pattern that appeared in the predicted map

(Fig. 34) can also be distinguished here. The center area with high production might be

the result of the households in Mvomero that have relatively high yields.

The map (Fig. 36) shows that the level of condence depends on the distance to the

households. As in Figure 32, the prediction error increases with distance from the

households. It also suggests that the prediction error is lower in a location surrounded

by households.

Annex XXIII shows the predicted maize yield and its condence level at a larger scale.


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Figure 35. Maize yield (t/ha)

in Tanzania based on 271

households (black dots).

Figure 36. Condence levelof the predicted maize yield

in Tanzania.


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Country wealth indexIn this section the country wealth index is analyzed using the data collected from 287

households in Uganda. The histogram (Fig. 37) shows the wealth index ranges from

12.0 to 14.0 and is normally distributed.

Figure 38predicts the country wealth index of the Southeastern part of Uganda based

on 287 households, shown on the maps as black dots (Annex XXIV). It seems that the

Southern part of the country (in particular the Southwest) is richer than the Northern part.

Figure 37. Histogram showing the distribution of the country wealth index of 287

households in Uganda (mean = 0.0 and skewness = 0.2).

Figure 38. Predicted

country wealth index inUganda based on 287

households (black dots).


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Figure 40. Condence level of the predicted country wealth index in Uganda.


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To avoid misinterpretation of coordinates and the resulting spatial error, a course on the

proper use of GPS units is strongly recommended for enumerators before they go into the

eld, especially on the subject of spatial projection.

Coordinates should always be recorded using the WGS-84 map datum and in the

degreedecimal-format.

In this survey the coordinates of the households were copied manually by theenumerators from the GPS to the questionnaires. To decrease possible errors, a unique

ID should be designated for each household and the coordinates of this household should

be stored on the GPS. At a later stage, the coordinates can be exported via a cable from

the GPS to a computer. Because of the unique ID, the coordinates can be linked to the

questionnaires. Coordinates in doubt can be validated on the appropriate questionnaires.

Every household owned one cultivated eld or more. The coordinates of these elds

were recorded only in Tanzania and Malawi. The measurement of the area of the elds

was not possible on all the GPS units. Ideally, up-to-date GPS devices should be used

that can record not only the area of separate elds but can also store the shape of theseelds. Via the unique ID, the elds could be easily linked to the households. This would

allow many additional analyses.

The ndings of this report show that the survey was set up more according to

socioeconomic criteria rather than spatial criteria. Nevertheless, any economic study

can prot from spatial analysis. It might even be said that if extra spatial data had been

collected, such as market locations and road networks on the village level, these could

have added value to this socioeconomic study. Therefore, a more multidisciplinary

approach is emphasized in which the sampling design allows spatial analysis.

This study aims to understand the effects that Striga has on the livelihoods of the poor.The sampled households were, however, always located in areas known to be heavily

infested with Striga. It would be interesting to expand the area of interest to locations that

are not heavily infested and see if this has any effect on the researched indicators.

Mapping Striga distribution in the Eastern Striga belt would be worthwhile.


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References

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Annex I. Distribution of the surveyed points


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Annex III. Distribution of administrative units


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8/8/2019 Spatial Analysis of Livelihoods of Small Holder Farmers in Striga-Infested Maize-growing Area

spatial analysis of livelihoods of small holder farmers in striga-infested maize-growing areas of...

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