the use of geographic information systems in agricultural...

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NETWORK METHODS WORKING PAPER

Number2

May 1996

The Use of Geographic Information Systems in

Agricultural Research in Malawi

By

Todd Benson

' . CIMMYT

CIMMYT Maize Programme and Naturai Resources Group

THE USE OF GEOGRAPHIC INFORMATION SYSTEMS IN AGRICULTURAL RESEARCH IN MALAWI

Todd Benson

Maize Commodity Team, Chitedze Agricultural Research Station, and the Rockefeller Foundation, P.O. Box 30721, Lilongwe 3, Malawi

This is a modified version of a paper originally presented on 2 February 1996 at the Second Chitedze Research Seminar, Chitedze Agricultural Research Station, Lilongwe, Malawi

1. Introduction

In a broad sense, Geographic Information Systems or GIS include a wide range of materials -- maps, travel books and guides, regional geographies, and the like. All of these provide the user with information about a particular part of the earth's surface. However, in the past few years, the term GIS has come to be used to designate a particular type of this genre of material: computer-based systems for the storage, manipulation, analysis, and mapping of data that is geographically-referenced. GIS are a special form of computerized database management systems.

By geographically-referenced, we simply mean that the data can be mapped. Most data can be broken down into at least three components. The first and arguably the most important is an attribute component. This is typically in the form of a value or category label. Most of us would agree that this component is the defining characteristic of a datum. However, there are two other components to consider. Most data has a temporal component which describes when the data was collected or to what period of time it refers. Finally, most data also has a geographic position component -- where the data was collected or to what location or locations the data applies.

This last feature of the data is fundamental to its use within a GIS. A GIS couples the attribute component of a datum with its geographic component in a much more powerful fashion than does a simple database system. The geographical position component of the data enables the GIS operator to combine in analysis quite diverse data-sets by linking individual datum from each data-set through their common position in space. The attribute components of each data-set may be conceptually quite d;fferent from each other, e.g. language spoken by the population of an area and maize yield by district. However, since both data-sets have a locational component, one can incorporate both in an analysis, e.g. to determine average maize yield for each language group of a nation. Of course, as the example demonstrates, whether the analysis is of any use is entirely a different question.

Geographic Information Systems should be of particular interest to agricultural researchers simply because most of the phenomena of interest to such scientists has a strong spatial component. Variations in the subjects of interest to us are tied to spatial distributions of a wide range of elements, e.g. fertilizer response in soils, pathogen distributions, rainfall patterns, market access, cropping systems, to name but a few. The interactions between these elements and how that interaction may relate to our topic of interest can readily be captured through the use of GIS.

Soil Fertility Network Methods Working Paper 2: Use of Geographic Information Systems

As an example of how spatial distribution of such elements might interact to influence the scope of our research, we may know that a certain insect pest on cassava can only tolerate sites where rainfall conditions are less than 900 mm annually and minimum temperature are greater than 8° C. If our research interest is to devise locally-specific control measures for this pest, we can use this information within a GIS to generate a map of the country showing where conditions are suitable for the pest, link it with a map of where cassava is grown in the country, and thereby determine where our research efforts should be focused.

It needs to be emphasized that GIS is a research tool. It is not a discipline or a science. As a research method, like all methods, it will be appropriate for use in addressing some questions and inappropriate for others. Several in which this technology can be used in agricultural research are sketched in the last section of this paper.

2. Spatial Data-Sets

A spatial data-set is quite different in many respects from the tabular data-sets with which most of us are familiar. In a spatial data-set some means must be provided to incorporate the spatial component of the data. This is most commonly done through the development of a digital map. 1

Digital maps, or, alternatively, GIS layers, are usually developed through the tracing of existing paper maps with a computer-linked tool called a digitizing tablet or digitizer. The digitizer converts the lines on the paper map into a series of point coordinates which the computer can then interpret in displaying the map or in undertaking transformation procedures with other digital maps. The development of these digital maps is the major investment which must be made in developing the capacity to do GIS analysis within an institution. Without these digital maps, you can do very little with a GIS.

The paper maps which are digitized can be virtually of any sort. Maps of point features, such as weather stations or population centers, can be used in certain analyses. Likewise, digital maps of linear features, such as elevation contours, roads, or rivers, may be of use for some applications. Bounded area maps are usually the most common sort digitized - maps of administrative areas, soil mapping units, or agro-climatic zones, for instance. Maps of continuously variable data frequently are generated by digitizing contour lines or data points from paper maps. An interpolation procedure is then run on these maps to produce the final digital map which presents data values at all points.

Digital maps can also be created through classifying digital satellite images or aerial photos. Land use and land cover maps are quite efficiently created through this process of image interpretation. Digital scanners can als·o be used to develop layers for use in a GIS.

t Several terms are used here to refer to the computer files which contain the data that is used within a GIS: digital maps, GIS layers, or spatial data sets.

The first term highlights the fact that these files are analogous in the way in which the computer displays them to the paper maps with which we all are familiar.

The term 'GIS layer' is useful when one is describing the analytical process used to extract new information from a series of such digital maps. The process is often b98t conceptualized as a layering of one map on top of a second to extract the interactions between the variables contained in each. An alternative term often used in this manner is 'coverage'.

Finally, the third term, 'spatial data set', emphasizes the fact that these layers are data sets, comparable to those contained in any database, but with the added characteristic of each datum also having a spatial reference in addition to its attribute characteristic.

Soil Fertility Network Methods Working Paper 2: Use of Geographic Information Systems 2

In many countries of the world, one is now able to purchase prepared spatial data-sets at national and sub-national scales. While considerable mapping and traditional geographic analysis has taken place in southern Africa, with the exception of South Africa, GIS users here are not as yet able to purchase digital maps for their particular nation or region of interest. Nevertheless, individual researchers and institutions in many of the countries in the sub-region presently are compiling a wide range of spatial data-sets. Once they have prepared these GIS layers, researchers usually will freely share them.

In Malawi, for example, a number of important digital maps which would be of value to agricultural scientists have already been prepared for use at the national scale. These include maps of soils, agro-climate zones, boundaries of Extension Planning Areas and districts, natural regions, elevation, land-use and land cover, and various climatic variables such as rainfall, temperature, and potential evapotranspiration. Several other digital maps are in preparation, most notably a map of census enumeration boundaries and a more detailed soils map. We are fortunate in Malawi to be now at a stage in the development of these resources where we can begin doing wide-ranging spatial analysis on agricultural topics.

3. Representation of Space

Geographic Information Systems enable the spatial element of a datum to be brought into any data analysis. How space and features within a given area are represented within the GIS then becomes important. The range of systems available are typically categorized into two groups based on how each system accomplishes this task: vectorbased or raster-based. 2 These are portrayed in figure la.

3.1. Vector-based A vector-based system is analogous to most paper maps which we use. One finds points, lines, and polygons on the paper map. These same geometric units are represented within the spatial data-sets. Within the computer file, a Cartesian coordinate system is developed to correspond to the area of interest. Point features in the area are represented in the computer file by a single coordinate pair with an associated attribute feature, e.g. "health center", "primary school", "district agricultural extension headquarters", etc. For a linear feature, a vector, a string of coordinate pairs, are used, also with an associated attribute feature, e.g. "Ml highway", "telephone line'', "Mazowe river'', etc. Finally, area features are represented by a string of coordinate pairs of which the last is the same as the first -- the string represents the boundary of the area. These polygons also have an associated attribute feature, e.g. "Farmer Kaunda's farm boundary", "Dowa District boundary", "the area of Zimbabwe receiving more that 750 mm of rainfall annually'', etc.. In sum, each geometric unit in the digital map representing a geographic feature is defined by one or more coordinate pairs plus an attribute component to identify the characteristic of interest about that feature.

Vector-based systems are very efficient in representing features in space. The precise location of a feature is inherent to the system. They provide good visual representations which are similar to the paper maps with which all of us are familiar. Vector-based systems are quite commonly used for applications which are based on homogenous spatial units, such as administrative units, or for data which has a strongly linear

2 Virtually all GIS will allow you to combine in a single image both vector and raster layers. Many of the images in this paper combine both types of layers. However, in undertaking spatial analysis one cannot combine layers from the two different system types in a straight-forward manner. A vector layer would first have to be 'rasterized', or a raster image 'vectori7.ed', if one was to use it in an analysis with layers of the opposite type.


character. For example, utility and transportation companies use vector-based GIS to manage their distribution networks, be they pipelines, overhead wires, or transport routes. Property tax assessors will use such systems to manage their data on land parcels.

Globally, some of the most powerful applications in spatial analysis are done using vector-based systems. However, such systems are less useful when one is dealing with a phenomenon which is quite variable through space. As noted in the introduction, agricultural researchers typically deal with phenomena of this sort. Consequently, vector-based GIS are less significant in agricultural research than one would expect given their global importance. Moreover, they have not been used very extensively in Malawi up to the present. As this paper will focus on applications of GIS to agricultural research in Malawi, vector-based GIS will not feature further in this paper.

3.2. Raster-based

A raster-based system is the second way in which data can be represented within a GIS. Most of the work in GIS analysis on agriculture and natural resource management topics in Malawi has been done using raster-based systems. A raster-based system models space as a fine grid-cell structure made up of homogenous areal units. The area of interest is represented within the computer file as a fine grid-cell structure. As is diagrammed in figure 1, the unique characteristic of any given point in the area of interest is assigned to the grid cell in the computer file corresponding to that location.

Alternatively, one can conceptualize a raster-based GIS layer as a bitmap computer graphics file. Indeed, a raster GIS layer is portrayed on the computer screen as a bitmap. The bitmap image is comprised of individual homogenous pixels. Just as a single color is assigned to each pixel of a bitmap, in a raster layer one data value is assigned to each grid cell representing a particular area in space.

Such a model of space is conceptually quite simple. Much of the strength of such systems lies in this simplicity. As a result, it is relatively easy to learn how to use a raster-based GIS.

The representation of spatial variability is not much of a problem for a raster system. Adjoining cells are, for all practical purposes, independent of each other in representing the data. Whereas within a vector-based system each variation of a type of data through space requires that separate polygons be formed to reflect each variation, with a rasterbased system the area of interest is already efficiently structured in such a fashion. The entire area of interest is already divided into a fine grid of polygons. Whether the dataset is quite homogeneous or highly variable, a raster model of spatial representation will characterize the data-set in a similar fashion whereby each individual grid cell will have a value assigned to it.

Analyses using data in a raster model are easy to conceptualize. If a single digital map is transformed, typically the operation simply involves some sort of mathematical relationship between the attribute of each grid cell and its nearest neighboring cells to produce a new map. If multiple digital maps are analyzed, the operations usually involve a mathematical relationship between the attribute of a grid cell on one digital map to the attribute of the corresponding grid cell of the same location on the other digital maps of the same area to produce a third map portraying the results of that relationship. The use of multiple digital maps in analysis is shown in figure lb.

However, many of the deficiencies of such systems also lie in their simplicity. spatial variation is easily handled in a raster system, remember that such a assumes homogenous areal grid-cell units. The areal extent of the grid-cells

Soil Fertility Network Methods Working Paper 2: Use of Geographic Information Systems

While model

the

4

resolution of the digital map -- determines the amount of detail which such a data model can contain. For example, if in an elevation map each grid cell is one kilometer square, clearly you will not be able to pick out every rise and fall in the land surface. If the grid cell resolution is ten kilometers, for example, the entirety of Bunda mountain, a prominent inselberg just south of Lilongwe, may not be apparent on the elevation map at all, as it is less than ten kilometers square in area. The raster cell corresponding to the area of Bunda mountain would necessarily be assigned a single elevation value, a notion which at the local scale is patently false. This is in contrast to the vector-based systems, in which locational accuracy is an inherent feature.

Additionally, raster-based systems require considerable computer storage space. Every cell in a digital map requires some memory. For a phenomenon which does not cover the entire area of interest, many grid cells will simply have a 'zero' attribute, but those cells still will require computer memory resources.

Finally, raster systems do not do a very good job at handling point and line features. A road may be only 20 meters in width, but if the digital map one is using is based on a one-kilometer resolution, that road is represented as being one kilometer wide. The precision which a vector-based system allows cannot be duplicated in a raster-based system without great demands in computer resources. Related to this, the visual output from raster systems is less satisfactory than might be desired. The grid cells are square, so the area boundaries that appear on the maps resulting from any analysis consist of a series of blocky, right angles rather than smooth lines.

However, in spite of these disadvantages, the advantage of raster-based systems in portraying data with a high degree of spatial variability makes it particularly useful for agricultural researchers who typically examine data with such a character, e.g., rainfall, soil nutrient levels, elevation, pest incidence, market access. Such systems are also relatively easy to learn to use effectively. This being the case, scientists in agricultural research do themselves a disservice if they do not consider how they might use GIS in their own work.

4. Computer Requirements

As an example of what level of computer hardware is required to use a GIS system, IDRISI is the GIS program which was used in the analyses which follow. This program runs on PC computers. The software costs about US $400 at present (1996). While it is continually being upgraded to take advantage of the increased speed and capacity of newer computers, very sophisticated analyses can be undertaken using older DOS versions of IDRISI. These versions will run on the computers available to most agricultural researchers. Ideally, one should have the latest and best computers, but one can use IDRISI ver. 4.01 on a computer with a 386 processor, hard drive and 640K of RAM. The earlier version 3 will run fine on slower computers with a floppy drive, although the storage of results will require a great many diskettes. That said, there are a great many advantages to having as fast a computer as possible to run the program, particularly when using the latest version, IDRISI for Windows. On older computers, having a math co-processor speeds performance, as does increased RAM. Remembering that raster-based spatial data-sets require considerable amounts of storage space, one should also have as large a hard-drive as possible.

A color monitor will allow for easier interpretation of intermediate steps in a GIS analysis. If available, it makes little sense not to use one.

The final output from the analysis can be directed to a printer. As most frequently the scientists will produce at least a few maps from his or her analysis, one should have


available a good printer -- either an ink-jet or laser printer. If possible, the ability to print on larger format paper, such as A3, is an advantage. By allowing you to portray the images at a larger scale, printing on such paper will allow you to show your readers more detail.

However, while there are advantages to large format printers, color printers, which allow for a great deal of flexibility in mapping, cannot be recommended at present. Recognizing that we do our research to inform other people, it is very difficult to cheaply and easily disseminate to a wide audience maps which are printed in color. Produce maps in black and white so that they can be photocopied. You will reach many more people with your results if you do so.

A digitizing tablet is not necessary to begin work in GIS. Tablets are needed to prepare many of the data layers which you will use in your analysis. However, once these layers are prepared, they don't need to be done again. As noted above, for Malawi a sufficient variety of spatial data sets I digital maps has been prepared to allow most agricultural researchers to make use of the technology. Comparable collections of digital maps should be available for scientists to use in the other countries in the region as well without their having to resort to digitizing. A digitizing tablet should be available within the agricultural research system in a country, but certainly not every scientist or research institution needs one.

5. Geographic Scale and GIS

Researchers can use GIS for analysis at any geographic scale. Soil scientists could conceivably utilize GIS for analysis of within-field soil nutrient level variations, while agricultural policy makers could use the same tool at the global scale to analyze the competitive advantage which a nation might have for the production of certain crops. This scale independent aspect of GIS is also one of its analytical strengths. One is not constrained in using GIS by the scale at which one is undertaking the analysis. If you have data which is reliable for the scale at which you are working, you can use GIS to perform an analysis of the data.

However, we often do not have reliable data at the scale we need. Here caution needs to be exercised. For example, you may be doing a geographical analysis at the scale of a watershed. Clearly any data that is collected at the level of the watersheq itself or at even more local scales, such as that of farmers' fields, can reliably be used in your analysis. However, much of the data which you might want to bring to bear on your analysis will not be available at that level of detail. For example, you may wish to include data such as rainfall or soils which originated from a mapping of the entire country (1:1,000,000 scale) or the region (1:250,000). Obviously much local variation in a phenomenon is hidden when one produces maps at these broader scales. Using the data for your area from such maps may lead you into error.

As a rule of thumb, one should never use data which originated from investigations which were carried out at a broader scale than that at which one is operating. This is a guideline. More practically, if working at the watershed level, one should be able to decide whether or not data produced at the 1:250,000 scale would be appropriate -- some data will be, other data will not. Clearly the majority of data originating from the 1:1,000,000 scale would not be appropriate. All data covering your region of interest is not equally valid, even though, given the scale-independent nature of GIS, it all may look equivalent in the form of digital maps within a GIS. One must pay attention to the history of where the data came from and consider how it may or may not be appropriate for your particular purposes.


6. GIS Operations

In the final section of this paper, several operations using GIS on national-scale Malawi data will be demonstrated. The range of operations shown is by no means comprehensive, but should serve to give a flavor of the sorts of analyses which one could undertake using data which is currently available. Among the operations not demonstrated are a wide range of statistical analyses which can be done within IDRISI or a similar program; image manipulation to produce GIS layers which are more useful or comprehensible; distance operations which can be employed to define areas around points, such as towns, or linear features, such as roads; and context analyses which involve transformations of cell values based on the characteristics of neighboring cell values.

Analysis within a GIS is a multi-step process. Most of the operations which are used to build an analysis are quite fundamental and elementary. When they are brought together into a logical, analytical framework, however, quite powerful results emerge. In several of the examples which follow, this framework is explicitly laid out in accompanying diagrams.

6.1. Data-file linking

A very efficient way for portraying considerable amounts of diverse data spatially is to prepare a digital map of the most elementary areal units for the phenomena of interest. One then prepares a data-file linked to the digital map. This data-file contains all of the characteristics of interest for each of the elementary areal units. Most GIS programs will then allow one to extract from the data-file the individual variable of interest to you, link the variable to the digital map containing the individual elementary polygons, and display the spatial distribution of that variable across these polygons.

Figure 2 shows how this works using a digital map of agro-climate zones for Malawi. These zones were delineated as part of the Land Resources Evaluation Project carried out within the Land Resources and Conservation Branch of the Ministry of Agriculture in the late-1980s. Each agro-climate zone has associated with it a number of defining parameters. These include such variables as the average annual rainfall of the zone, the number of dry months annually, and the average temperature during the growing season.

In this example, three maps are presented. The first simply shows the boundaries of each agro-climate zone for Malawi. There are over 150 uniquely identified zones in total for the nation. To deal with each individually in extracting information on a defining. parameter would be very tedious. Fortunately, this agro-climate zone map was developed with a linked data-file. This data-file has a record for each zone. In the record are listed the relevant agro-climatic characteristics of that zone. The GIS allows us to quite quickly replace the unique identifier for each zone in the base map of agro-climate zones with another identifier from the data-file field of the characteristic of interest. The result is a map showing the distribution in Malawi of the specific agro-climatic variable. By way of example, two maps are shown here. The first shows the number of dry months annually for sites in Malawi, and the second the length of growing period.

Both of these maps were produced from the same base map. In the production of the final version of the number of dry mqnths map, some modifications were made to the original boundaries to smooth them. In contrast, note the jagged edges of the areas in the length of growing season map reflecting the raster-based origin of the map portrayed at a three-kilometer resolution. Nevertheless, one should be able to pick out how the boundaries of the areas on both derived maps correspond to boundaries found on the original agro-climate zone base map.


6.2. Reclassification

Reclassification is among the most common operation performed within a GIS in an agricultural research context. Frequently one wants to determine the limits of an area defined by one or several conditions. To do this, one simply reclassifies a digital map or maps which contains data on the conditions of interest into a Boolean map. Boolean, in this case, simply refers to a map which has two categories: zero to indicate where the condition is absent and one to indicate the presence of the condition.

In figure 3, three maps are shown. The shading on the first indicates where in Malawi the elevation is within 400 meters of that of Chitedze Research Station (1150 m). This map was quickly produced by reclassifying an elevation map of Malawi, assigning zero to all grid cells in which the value was either less than 750 or greater than 1550 and assigning one to all other grid cells. In a similar manner, the second map, indicating where in Malawi ferruginous soils are found, was produced by reclassifying a soils map of Malawi. One was assigned to all grid cells which had a classification code which corresponded to a soil classification category that included ferruginous soils. Zero was assigned to cells with all other classification codes.

This process offers the researcher an extremely quick way to investigate the defining values for a given parameter which, for example, might serve to limit the distribution of a phenomenon. This could be done simply through seeing how well the map produced by the GIS corresponds to the mental map which the expert may have on the distribution of the phenomenon. Alternatively, a map of the actual distribution of a phenomenon could be developed and then, using the statistical analysis modules in a GIS, that map could be compared to a series of maps which delineate a range of values for a given parameter which is thought to limit the distribution. For example, if elevation was thought to define the distribution of a pest, one could compare a series of digital maps of varying elevation ranges to determine which of those ranges best fit the distribution map of the pest.

6.3. Overlay

One can take the reclassification done in the previous section a step further by overlaying the two maps to determine where in Malawi one finds ferruginous soils within the elevation range of interest. This is shown in the third map of figure 3 and is simply done by a process of multiplying the grid cell value of one Boolean map to the value in the corresponding grid cell in the other Boolean map. Where a one is the value in both maps, the resultant image will also have a one. If zero appears in either or both, zero is the value for that grid cell in the final map.

Such a process can be extended to incorporate a number of criteria. This is demonstrated in figures 4a and 4b. The interest in this exercise was to determine where, based on climate characteristics, one should expect that the agro-forestry species Tephrosia vogelii would grow well in Malawi. Initially, some climatic parameters were acquired from the International Center for Research in Agro-forestry (ICRAF). These parameters were used to reclassify four digital maps, which were then overlaid. The results certainly did not reflect where in fact you found T. vogelii in Malawi and were clearly not suitable. Modifications were made to the parameters to try to produce a better, more reasonable distribution. These modifications are shown in the subsequent images. The diagram sketches out how the final map was prod'!ced.

The detail of this analysis is presented in part to demonstrate how GIS can be used in a flexible and iterative fashion to refine one's hypothesis. While the climatic parameter values of the final map should not be taken to reflect the actual limits to the growth of T.

Soil Fertility Network Methods Working PapeF 2: Use of Geographic Information Systems 8

vogelii, they certainly are more realistic than the initial values used from the ICRAF data-file.

6.4. Cross-tabulation

In many cases, a Boolean analysis is not sufficient. The distribution of ranges of values for several variables is often of interest. More detail is required than simply the presence or absence of all or some of the variables. Figure 5 shows how a crosstabulation prqcedure can be used to both retain detail and characterize the entire area of interest in tei;ms of these variables.

In this case, a maize breeding ecology map was sought which incorporated two variables: elevation and length of growing season. The elevation map for the country was simplified by reclassifying the values into four categories or elevation zones. Similarly, the length of growing period map, produced by using the linked data-file with the agroclimate zone map, was reclassified into two zones. These two reclassified maps were then brought together in a cross-tabulation procedure.

The resultant map shows for each grid cell what combination of values of the two maps characterizes that point. In this example, there are eight possible combinations, but in reality one does not find short growing seasons at high altitudes nor long growing· seasons at low altitudes, so only six categories emerged from the analysis.

6.5. Mathematical manipulation of cell values

The values in each grid cell can also be transformed mathematically. The map shown in figure 6 was produced by a transformation of several mean monthly temperature data layers. Total heat units are equal to the sum of the mean daily temperature in Celsius minus 10 degrees over the time period of interest. Layers detailing the total heat units for each month were produced by first subtracting ten from each grid cell in each of the four mean temperature layers (Dec., Jan., Feb., and Mar.). The resultant value was multiplied by the number of days in the month. These four layers were then added to each other in an addition overlay procedure. The final map was then produced by simplifying the heat unit map through reclassification of cell values into five categories.

This capability of mathematical transformation allows the researcher to use surrogate data in his or her research with GIS. If there is a close correlation between one variable, for which the researcher has a GIS layer, and the variable of interest, for which none is available, then by using a simple regression equation or the like the researcher can transform the existing layer into one which reflects the distribution of the variable of interest.

6.6. Tabular data extraction

Finally, GIS can also be used to extract spatial data into a tabular format. Such a capacity is particularly of interest for undertaking more sophisticated statistical analysis of a spatial data-set. This will be done in the near future with the trial results from the extensive 1995/96 Area-specific Fertilizer Recommendations Verification Trial. Close to 1900 trials are being implemented as part of this program.

The aim of this trial is to develop appropriate area-specific fertilizer recommendations for maize grown by smallholders in Malawi. In addition to simply determining which fertilizer rates are most appropriate, researchers need to determine where the boundaries lie for each recommendation zone.

One way in which this can be done is to group trials by several broader regfons, the boundaries of which might serve as appropriate boundaries for the recommendation


zones. Statistical investigations can then be done on the trials to determine which grouping of trials by different regionalization criteria does the best job in minimizing trial result variability within the groups, while maximizing it between groups.

In order to group the trials, spatial grouping identifiers must be assigned to each trial. Given the number of trials, this is quite difficult to do manually, i.e., looking at paper maps to determine to which spatial unit or region, of whatever sort, each individual trial belongs. However, this can easily be done within a GIS. In figure 7, maps are presented of the trial sites, EPAs, the Natural Regions of Malawi, and the provisional recommendation zones developed by the trial designers. Using what is called an extract procedure, the trial sites map is overlaid with each of the regional maps. However, rather than a map transformation procedure occurring, the GIS will produce a simple tabular data-file in which will appear the attribute (identifier) code for each of the trial sites in one column and the value of the corresponding grid cell from the regional maps in a second column. iThis data-file can then be imported into the larger tabular file of trial results. Using statistical analysis software, trials can then be grouped by regional identifier and statistical analysis performed to determine which regional framework does the best job in defining fertilizer recommendation areas.

In the first instance, this procedure will permit an evaluation to be made of the provisional fertilizer recommendation zones. More importantly, researchers will then be able to turn to some of the other regionalization frameworks for Malawi, group the sites by those regions, run a statistical analysis, and then determine from a comparison of results whether some of those frameworks may be more appropriate for delineating recommendation zones than the provisional map.

7. Closing Words

Geographic Information Systems offer agricultural researchers a powerful tool to use in capturing the spatial variability in the subject(s) of their research. Focusing on Malawi, from a physical geography standpoint the country is extremely heterogeneous. Consequently agriculture in Malawi, as elsewhere, also displays considerable heterogeneity in many facets. As agricultural scientists we must conduct our research in full recognition of the limits imposed and opportunities provided by this variability. If we do so, the work we do and the technologies and information we provide farmers will be much more sound. GIS provides a means of managing and incorporating these spatial factors into our work.

To use a GIS requires a certain investment in time, learning, and equipment. The best way to learn how to use a GIS is to take the plunge and muddle around with a program on the computer, making use of the tutorials which are supplied with them. Certainly with the raster-based systems at least, the learning curve which one faces is not unreasonable for most of us. The equipment costs to employ GIS are not far beyond what most researchers would expect for their work with word processing and spreadsheet software.

GIS is not a tool only for experts. If one relies on experts to do the spatial analyses which our agricultural research require, for most of us they will never be done. The level of expertise which the experts in GIS possess is not all that hard won. It is within the capacities of most of us to acquire, learn .. and use it effectively.


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SOtlS

J J JI) J]. J

; r'i'fs 2 ' , i 1 i z7s ·2 lli'~-s i 27°S I IS f 11i ts 1·s I ,·"rs 2 z 2

1 i't" I I ·11S1-!i"r, i I i $ i t I I

I ti S ~ i i S I TOPOORAPHV

Figure 1 b: Overlay analysis using raster data files (ibid., p.167).

OVERLAY ANALYSIS

from: Aronoff, Stan. 1989. Geographic lnfonnation Systems: A Management Perspective. Ottawa: WDL Publications.

Boundaries of Agro-climate Zones from Land Resources Evaluation Project ( I :250,000)

Figure 2

Malawi Number of Dry Months Annually

Dry month= less than 50 mm rainfall

D r : : : :I s to 6 months

• 3 to 4 months

• I to 2 months

D lakes

rrom.: LMd RaoUl'CCll Enll....ao. Pro;cc. Malawi (1991)

Length of Growing Period Period during which precipitation exceeds

half of potential evapotranspiration

0 105-120 days

D 120-135

['] 135-150

D 150-165

D 165-180

!JI 180-195

• 195-210

E:3 210-225

E:3 225-240

~ 240-270

ISl 270-300

!SJ 300-330

0 lakes

fnn: l.-'RCIOURCI £, ... ...._Pro,ect . Malnoi(1991)

Areas of Malawi within 400 metres of the elevation of Chitedze ( 1150 m)

f l:J~; l.4•U · 01lt«1M; V.96

Figure 3

Chitedze Agricultural Research Station

Areas of Malawi with Ferruginous soils, the principal soil class at Chitedze

After the classification of Brown & Young, Stobbs.

T l.54MO'I; Mau · a.ttatzCI; vse

Areas of Malawi having both an elevation within 400 metres of that

of Chitedze and ferruginous soils

-Tephrosia vogelii - I

Climatic suitability for agroforestry species in Malawi

Elevation - min. : 1240 m max: 1800 m

Avg. min. temp. of coldest month: 10° C Avg. max. temp.

of wannest month: 30° C

Rainfall - min: 800 mm max: 2000 mm

Climatic parameters taken from: Multi-purpose Tree Species Dal3base.

1995. ).lairobi: ICRAF.

?' B~W.u-C"?'-O'ut4t.19"

Figure 4a

Tepltrosia vogelii - II


The climatic parameters in the 1-.{PTS Database produced a restricted map of the potential distribution of T. vogelii.

This map is a modification of the tirst map . As most of Malawi is excluded when the Database attitudinal range is used, the alteration made here is that altitude is not considered.

Elevation - min.: NI A max: NIA

Avg. min. temp. of coldest month: Avg. max. temp.

, of warmest month:

10° c

30° c


Climatic parameters taken from: Multi-purpose Tree Species Database.

1995. Nairobi: !CRAF.

t BtNCll\,MaaHCT-OIMllu. l ffS

Tepltrosia vogelii - III


The climatic parameters in the MPTS Database produced restricted maps of the potmual distribution ofT. vogelii in .Malawi. even when altitudinal consideratioM were relaxed

This map is a modification of the earlier two maps. Altitudinal considerations remain relaxed However, additionally the 'average maximum temperature of the wannest month1 has been changed from tho Database figure of 30° to 35° C.

Elevation - min.: max:

Avg. min. temp. of coldest month: Avg. max. temp.

of warmest month:

NIA NIA

10°c

35° c


Climatic parameters taken from: Multi-purpose Tree Species Database.

1995. Nairobi: ICRAF.

T. Bcwon,Ma&e<:r-ChMlla, 1995

Tephrosia vogelii - Revised


Elevation - min.:" 650 m max: 2500 m

Avg. min. temp. of coldest month: 2° C Avg. max. t<!mp.

of warmest month: 32° C


Climatic parameters taken frotn: Blantyre · Bunderson. w:r. 1995. Per:mnal communication evaluating th~ suitability for Malawi of the parameter> noted in the :'vfulti-pmpose Tree Species Oatahase oflCRAF.

T. bnllM.tL Ma&.C'T-Cbi&al«.19'.S.

Figure 4b

How 'Tephrosia vogelii - Revised' map was produced

Elevation map

(0 = <650 or >25fHI; I= 650 to 2500)

Boolean elevation

Avg. mm1111um temp. of

coldest month

Avg. maximum temp. of

warmest month

Rec/ass into Boolean regions

(fl= <2; /= >2)

Bon lean min. temp.

(fl= >32; /= <32)

Boolean max. temp

~ j Overlay I

(essentio/~11 a multiplicotio11 r11ce.1·.~ of ull of Ille Bo11/ea11 rs. Jeu1•ing J only in those cells where I is tfle Po/ue on ul/

layers).

Climatic suitabilitv for Tephrosia vogeiii

map

Annua precipitation

mar>

(0 = <7(1(1 or >25fHI: I= 700 to 2500)

I Boolean

precipitation

Maize breeding ecologies Based on elevation and length of growing period

Maiu breeding ecologies are here defined bv two separate variables: the length of growing season and the elevation above sea level. A ~Toss-tabulation proadure is used to assign each point to an ecological zone according to the kngth of growing season and the elevation there.

Length of growing period (lgp) is the length of the period during which precipitation exceeds half of'potential evapotranspiration.

. .\s there are verv few sites with a long gro,\·ing period at low elevations and a short growi ng period at high elevations. single zo nes are assigned to the two e)'.treme elevation ranges.

" .ns 1m:ters ahove sc:a leve l

475 - 900 masl: < 150 days lgp

475 - 900 masl: > 150 days lgp

900 - 1300 masl: < 150 days lgp

900 - 1300 masl; >1 50 days lgp

> 1300 mas!

Lakes

• &Nrj

• II • D F'.'.1 Ld

Figure 5

How 'Maize breeding ecologies' map was produced

Agro,lintate zone rnap

Leneth of Growing Prriod

map

I Rec/ass into :! LGP:ones

I

Elevation map

I Rt!cuiss into

4 elevation ;:;ona

I Reclassification

of LGP map (<150 days

& >150 days)

Elevation zones map

(0-475; 475-900; 900-1300; 1300+)

~' ~ Cro.-s-1abu/a1um /

' / . ·(';

[nitial breeding ecologies

map

Simp/ifica1io"n of calt!gories by reclas~ifying

Final map

Total Seasonal Heat Units December 1 through March 31

Heat unit total = sum (mean daily temperature - IO degrees C) over the length of the period.

1200 heat units (Jeg.C) .___j -1100 - 1400 -1400 - 1600

II 1600 - 1800

II > 1800

f 1'CIMO":t.t-. · 0.UlllZ.

Figure 6

How 'Total Seasonal Heat Units' map was produced

:\lean monthly temperature -

December

'.\1ean mon1hl~· lem perature -

January

Mean 1110111hly temperature -

February

':\lean monthl~., temperature -

'.\farch

Scalai· operation - subtract 10 from each cell

:\lean dailv temp. nunu5 l

\lean daily temp. minus l

\1e:m dailv temp. nlinus l

\lean clailv 'temp. minus I

Scalar operation - multiply each cell by number of days in month

Total heat units in

February

Overlay

Total seasonal heat units(l)ec. I -.\lar. 31)

map

I Reclassification to simplify

into five categories

I Final map

Total heat

Trial Locations Mai7.c Fcrtili7.cr Verification Trial

1995/96 - Malawi

ADD boundaries shown.

Natural Regions of Malawi (ullcr Drown . Young & Stobbs)

Figure 7

Wfill upland sni ls, .includes m.:dium[ f iJ t..:xturcd nmt hl!ht-textured

;::·:::::::: rccommcndalic~n zones

r ~1 la~c shorc nncl lllhcr nlh1\·ial L s111l zones

rnTil valley hottom soi ls or the t.if&l Lower Shirti Vnllcy

~ zinc delide.nt .nrc:n. of. LJ Dcdzn I l l111v1-L1hdz1

F -----

Fertilizer ommendation Zones

1995 Provisional

-------- - - - - - ··------='-----'

Extension Planning Areas Mala\\'i

ADD boundaries shown

Soil Fertility Network for Maize-Based Cropping Systems in Countries of Southern Africa

Soil Fertility Network Coordinator CIMMYT Maize Research Station P.O. Box MP 163 Mount Pleasant Harare, Zimbabwe

Phone: (263) 4 301807 Fax/Phone: (263) 4 301327

The University of Zimbabwe Farm 12.5 km Peg Mazowe Road

E-Mail: [email protected]

the use of geographic information systems in agricultural...

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