Makerere University Department of Surveying
Faculty of Technology Kampala
This project examines the use of GIS in creating an agro-climatic zone map. The map recognises that the major aspects of climate that affect plant growth are moisture availability and temperature. The agro-climatic zones are therefore specific combinations of moisture availability zones and temperature zones. The project was undertaken following the variability of rainfall and recurrent droughts in the country that affects the lives of millions of people whose livelihood is mainly dependent on agriculture. The project is of much importance as it is aimed at showing areas that are climatologically suitable for particular crops and also as a guide to the work of planners and farmers. The goal of this study is to extrapolate empirical research findings from the map for agric-environmental experimental design and as a framework for assessment of the impact of climate change on Soroti region agriculture. The objectives were achieved using Idris32 software incorporating various methods. A temperature map was created from a derived relationship (regression equation) between temperature and elevation using tabular temperature and elevation data obtained from four weather stations. The temperature zones were created by reclassifyng the temperature map. An evaporation map was created using a published relationship between evaporation and temperature. The moisture availability map was created by getting the ratio of rainfall map to evaporation. Moisture zones were created by reclassifying the moisture availability map. An agro climatic zone map was then created through a combination of the temperature zones with the moisture zones. The agro climatic zone map was classified using the Köppen and Thornthwaite agro climatic classification system. Finally crop growth in each zone was selected through a comparison of the temperature and moisture requirements for each crop based on published FAO data (climatic adaptability of crops) with the prevailing temperature and moisture conditions in each zone. The agro climatic zones were suitable for the growth of cassava, sweet potatoes, rice, sorghum, maize, millet and ground nuts. The results show that zones in the humid and dry-sub humid regions are highly suitable for agricultural production than those in the semi arid regions. The results are recommended for use on a lager scale only if they are accurate and representative of the climatic conditions in the selected region of the country. However, if many alternatives are to be included based on the stated criterion, an agro-ecological zone map should be created.
1.1 Background to the Study
According to NARL (2010), agriculture is the backbone of Uganda’s economy. However, agriculture in Uganda is characterised by low productivity due to low external inputs, nutrient mining, soil erosion, and other losses. Another characteristic is that agriculture almost entirely depends on rainfall. According to Corbett (1996), the unpredictable onset and cessation of rains as a result of climate variability makes it difficult for farmers to plan when to plant crops. There have been instances of frequent crop failures of late (Aribo et al 2010). According to Aribo et al (2010), the occurrence of hydro-climatic events such as droughts and floods increase in many parts of the country (e.g. Soroti District), causing severe socio-economic impacts that include food insecurity, famine, deaths, epidemic diseases, pests and economic losses among others. These impacts seem to spread over large areas and differ in severity, magnitude and duration. The problem has caused public outcry for adequate agro-climatic information for planning and management purposes. Since agriculture is highly location-specific, grouping the available land area in the country into different agro-climatic regions based on certain identifiable characteristics becomes all the more important. This may help the country to engage in more rational planning and optimizing resource use for the present and in preserving them for the future. Thus, according to Balaguru (2010) an agro-climatic zone is a land unit in terms of major climates, suitable for a certain range of crops and cultivars.
The problem of selecting the correct land for the cultivation of certain agriculture crops is a long-standing and mainly empirical issue (Kalogirou, 2002). Therefore many researchers have tried to prepare a standard framework for suitable and optimum agriculture land use. FAO (1976, 1984 and 1985) classifies agricultural potential based on soil and environmental characteristics into five classes including highly suitable, moderately suitable, marginally suitable, currently not suitable and permanently not suitable. The objective of this study is therefore to extrapolate and generate a digital agro-climatic zone map showing areas suitable for various agricultural alternatives in Soroti District using information on environmental condition, altitude, rainfall and other relevant parameters of the case study where the variability of rainfall and recurrent droughts have a great impact on the lives of people whose livelihood is mainly dependent on subsistence agriculture.
1.2 Problem statement and justification
The variability of rainfall in Uganda is highly complex and not well known yet. This variability of rainfall and recurrent droughts in the country affects the lives of millions of people whose livelihood is mainly dependent on subsistence agriculture. Despite the fact that agriculture is the backbone in the country’s economic development as well as the most weather sensitive sector, the communication of seasonal climate predictive information for flexible and improved decision making by the farmers in risk management is minimal ( Corbett, 1996). Various analysts reason that the persistence of the subsistence nature of Uganda agriculture is partly due to the lack of proper understanding of the agro-climatic resources of the country. Proper agro climatic zoning and seasonal climate forecasts are crucial elements in minimising climatic risks (Sue et al, 2010). They can assist both agriculturalists and policy makers particularly in countries like Uganda where climatic risk is very high. The purpose of an agro-climatic zone map is therefore to show the areas that are climatologically suitable for particular crops and to guide the work of planners and farmers. It is therefore necessary for a study as this to be carried out to clearly specify the new methods which incorporate GIS to clearly map out geographical areas that are suitable for various agricultural alternatives and also to mitigate the variability of rainfall and the minimal communication of the predicted seasonal climate in Soroti district.
1.3 Aim and objectives
To create an agro-climatic zone map for Soroti district.
- To assess the climatic suitability of geographical areas for various agricultural alternatives
- To extrapolate empirical research findings from the resulting agro climatic zone map for agricultural experimental design and as a framework for the assessment of the impact of climate change on Soroti region agr
1.4 The study area
The study area is Soroti District found in Eastern Uganda. The district is entirely located in a semi-arid area dominated by Savannah grasslands characterised with thorny Acacia species. The North moist farmlands and North central farm bush lands with sandy soils are the main farming units (Egeru and Majaliwa, 2009). It has a humid and hot climate. It receives an annual rainfall between 1,000mm-1200mm. Most rain is experienced between March-May, reducing to light showers between June-August and again to heavy rains between September-November. The dry season begins in December and lasts up to February. It has an average minimum and maximum temperature of 18oC-30oC respectively.
The methodology for this research work forms the basis of creating an agro-climatic zone map and then selecting areas suitable for various agricultural alternatives.
Figure3.1: Flow chart showing methodology
2.2 Data acquisition
The data for the analysis was:
- A mean annual rainfall map of the study area
- A relief map
- Temperature and altitude data from four weather stations located in the study area
- Relation between elevation and potential evaporation in the study area (From published literature)
The data was collected from two sources. Arc View shape files for both the mean annual rainfall and a digital elevation model of Uganda were obtained from Makerere Institute of Environment and Natural Resources (MUIENR). Temperature and altitude data from four weather stations located in the study area were collected from the Ministry of Water and Environment, Department of Meteorology Uganda (Table 4.1). The equation relating evaporation to elevation (Equation 3.2) for Uganda was obtained from Rijks et al (1970)
2.3 Importing data and format conversions
Arc View shape files (Figure A.1 and Figure B.1) of contours representing relief and the rainfall of Uganda were imported into the software (Idris32). The attribute values files for these shape files were obtained from the database workshop and then assigned to the shape files for the contours and the rainfall map. The attribute files listed the attributes for a set of features since features such as contours and rainfall were to be identified by means of integer numbers.
2.4 Clipping region of interest
The contour vector line layer type was converted to raster. Similarly the rainfall vector polygon was converted to raster. The maps in the process of conversion were referenced from plane to UTM Zone 36 and projected to the WGS84 ellipsoid. The study area or region of interest indicating mean annual rainfall and contours were then clipped from the raster maps. The conversion from vector to raster was adopted since in a raster format the region of interest can be easily clipped out from the map using the maximum and minimum geographical coordinates for the study area.
2.5 Creating a digital elevation model (DEM)
The rasterised Soroti contours were interpolated using intercon. This was achieved through a provision of heights for the four corner points on the rasterised contours. These heights were obtained from the vector file for contours using a cursor inquiry mode. A faceted model was filtered using image processing techniques with a mean filter size of 3 ×3 to produce a smooth DEM for Soroti District.
2.6 Creating a temperature map
In the given study area mean annual temperature is closely related to height or elevation. Hence, by working out a relation between temperature and elevation from the weather data (Table C.1) and using elevation as an independent map variable and temperature as the dependent map variable, the temperature map was created following the procedure given below.
The values files for elevation and temperature based on data from four weather stations located in the study area (Table C.1) were created. Regress was then run to create a regression equation between elevation and temperature. Elevation was given as the independent variable file and temperature was the dependent variable file since in most instances temperature depends on elevation. The regression equation relating temperature to elevation was obtained.
The equation was input into image calculator a tool that works with equations involving the use of maps and or images as variables to create a temperature map.
2.7 Creating an evaporation map
The map of potential evaporation was created in a similar way of creating a temperature map. This was achieved through the use of a regression equation relating potential evaporation to elevation (Rijks et al 1970)
Where E (mm) is potential evaporation and altitude (feet) is the DEM for Soroti District in feet.
2.8 Creating a moisture availability map
A module overly employing a ratio zero option was used to get a ratio of rainfall map (Figure B.2) to evaporation map. A ratio zero option was used in the process as this was required to give values that were unit less. A moisture availability map for Soroti District was created, displayed and examined as presented in section 4.4.
2.9 Reclassification of moisture and temperature
2.9.1 Moisture zones
A re-class command file classifying a moisture availability map into moisture zones was created. The file was based on the minimum and maximum values as they appeared on the moisture availability map legend giving a moisture availability range within which each zone was to fall. The file was then assigned to the moisture availability map to create moisture zones as shown in section 4.5.
2.9.2 Temperature zones
The map of temperature zones was created following similar steps as those which were used in creating a moisture zone map. The zones obtained were then displayed and examined as presented in section 4.2.
2.10 Cross – Classification of temperature zones and moisture zones
2.10.1 Creation of agro climatic zones
A module Crosstab also described as multiple overly which combines two maps and or images to come up with a unique out put in the result was used to create an agro-climatic zone map. The temperature zone map was input as the first map and the moisture zone map was input as the second map.
2.10.2 Selection of areas suitable for the growth of specific crops
From the agro climatic zone map legend, classes representing the desired combinations of zones were determined and used to create a values file for each class. The files were given a new value of one indicating areas suitable for a particular crop and a value of zero indicating areas unsuitable, the values were then assigned to the agro climatic zone map to create areas suitable for the growth of each of Soroti’s food and cash crops. The areas suitable for a particular crop were then displayed and examined as shown in section 4.7.
3.0 Results and discussions
3.1 The temperature map
Equation (3.1) was input into image calculator a tool that works with equations involving the use of maps and or images as variables and the temperature map resulted as shown in Figure 4.1.
Figure 4.1: Temperature map for Soroti District
From the map legend of Figure 4.1, high temperatures were found in a valley and these had temperatures ranging from 27°C to about 28.98°C as seen on the map legend. Low temperatures were found in higher elevations with temperatures ranging from 25°C to 26.67°C respectively. The temperature map therefore agrees with the data that was collected from the Department of Meteorology Entebbe Uganda given in Table C.1. It can therefore be put into consideration that Soroti indeed has an average temperature ranging from minimum 25°C to maximum 30°C. The result obtained is very close to Egeru and Majaliwa (2009) results which states that Soroti has an average minimum and maximum temperature of 18oC-30oC respectively.
3.2 Temperature zones
The temperature map (Figure 4.1) was re-classed using temperature range values given in Table 4.2 and as a result the temperature zones as they appear in Figure 4.2 were created. The temperature zones that were created indicated zones of varying agricultural suitability. These zones are based on specific classes of temperature values for various crops as listed in Table 4.2.
Figure 4.2: A temperature Zone map
The temperature zone map above indicates five zones of which only two zones are visible on the map as per the sequence of colors shown on the map legend. This means that all the rest of the zones are unsuitable for agriculture. For example zone 1, very high, with temperatures ranging form 40°C – 45°C is suitable for desert plants since here the temperatures are very high and unsuitable for agriculture to be carried out. Therefore zones 4 and 5 with very low and low to medium temperatures respectively as indicated on the map legend and in the table are highly and moderately suitable for various agricultural alternatives.
Table 4.1: Temperature range
|Temperature Zone||Temperature Range (°C)||Classification|
|4||27-30||Low to medium|
3.3 Evaporation map
The regression Equation (3.2) relating evaporation to elevation was input into image calculator and as result an evaporation map was created as shown in Figure (4.3)
Figure 4.3: Evaporation map
The map legend has values ranging from 2004.04mm – 2063.74mm. These values, when compared with Rijks et al (1970), results on evaporation in East Africa described by Figure D.1 agrees with those of Soroti District and hence show that Soroti District has an evaporation rate exceeding the amount of rainfall it receives as discussed in section 4.4.
3.4 Moisture availability
The moisture availability map shown in Figure 4.4 is getting the ratio of average annual rainfall map Figure B.2 to annual potential evaporation map Figure 4.3 to give the moisture index or Aridity index values as shown on the map legend.
Figure 4.4: Moisture Availability map for soroti District
The values in the map indicate the balance between rainfall and evaporation. For example, if a cell has a value of 1.0 in the result, this would indicate that there is an exact balance between rainfall and evaporation. Regions where the moisture index is greater than unity are broadly classified as dry since the evaporative demand cannot be met by precipitation (rainfall). Similarly regions with less than unity are broadly classified as wet.
3.5 Moisture zones
The moisture zone map shown in Figure 4.5 was a result of re-classing the moisture availability map (Figure 4.4) using moisture availability range values in Table 4.2. The result indicates zones of varying agricultural suitability based on specific classes of moisture availability for various crops.
Figure 4.5: A moisture zone map
Since the study area is only a small part of Uganda, it is not surprising that some of the zones are not represented in the result that is, hyper- arid which are extremely dry. Therefore only three zones have been presented. These zones are labeled 1, 2 and 3 as indicated in Table 4.2. These zones are classified as very high, high and low respectively as seen on the map, with enough rainfall for crop growth (there is no rain fall variability), a considerable amount of rainfall, little or no rainfall (rainfall variability) respectively as indicated on the map legend.
Table 4.2: Moisture availability range (moisture index range)
|Moisture Availability Zone||Moisture Availability Range||Classification||Classification(Aridity Index)|
|1||0.65-0.98||Very High||Humid( wet)|
3.6 The agro – climatic zones of Soroti District
The agro-climatic zone map Figure 4.6 was a result of a combination of the moisture and temperature zones. The map shows all combinations of moisture availability and temperature zones in Soroti District. The map assesses the climatic suitability of geographical areas for various agricultural alternatives. It is made up of nine agro climatic zones named A1,A2,A3,A4,A5,A6,A7,A8 and A9 in that order as they appear on the map legend.
Figure 4.6: An Agro-Climatic Zone map for Soroti District
The agro-climatic zones in Figure 4.6 are each sub-divided according to mean annual temperature and moisture availability to identify areas suitable for growing each of Soroti’s major food and cash crops. Areas with temperatures and moisture availability between 25°C- 30°C and 0.65-0.95 respectively have high potential for cropping, and are designated zones A2 and A3 found in the Humid climatic regime. These zones account for 1564.34 sq. miles of Soroti land area. Those with temperatures and moisture indices between 25°C – 30°C and 0.5 -0.65 respectively are moderately suitable for crop growth. These zones lie in the dry sub-humid zones and are designated zone A5 and A6. These zones account for 682.92 sq. miles of Soroti’s land area. Areas with temperatures and moisture availability between 25°C -30°C and 0.2 – 0.5 are less suitable for cropping, and are designated zones A8 and A9 found in the semi-arid climatic regime and cover 308.87 sq. miles of Soroti’s land area.
Unsuitable areas have temperatures above 40°C and these are designated as zone A1, A4, and A7 found in the humid, dry sub-humid and semi arid climatic regimes respectively. They account for only 0.465 sq. miles of Soroti’s land area.
Table 4.3: Agro-climatic zone classification
|Agro-Climatic Zone||Temperature (°C )||Moisture Index||classification||Land Area (square miles)||Suitability for Agriculture||category|
|A4||40-45||0.5-0.65||Dry sub-Humid||0.07||Un suitable||Semi- wet|
|A5||27-30||0.5-0.65||Dry sub-Humid||440.81||moderately suitable||Semi- wet|
|A6||25-27||0.5-0.65||Dry sub-Humid||242.11||moderately suitable||Semi- wet|
|A7||40-45||0.2-0.5||Semi- arid||0.37||Un suitable||Semi- dry|
|A8||27-30||0.2-0.5||Semi- arid||223.02||Less suitable||Semi- dry|
|A9||25-27||0.2-0.5||Semi- arid||85.85||Less suitable||Semi- dry|
3.7 Areas suitable for various agricultural alternatives
The attribute values file for each crop based on the climatic adaptability principles described in FAO (1978, 1993a) were assigned to the agro-climatic zone map and as a result areas suitable for the growth of sweet potatoes, rice, cassava, sorghum, maize, millet Ground nuts and beans were obtained in the results.
3.7.1 Sweet potatoes and rice zone
Figure 4.7 shows that sweet potatoes and rice will grows well in humid areas with temperatures ranging from 27°C – 30°C as shown in Table 4.3.
Figure 4.7: Zone A2, suitable for Sweet potatoes and rice
3.7.2 Cassava and Sorghum zone
Figure 4.8 shows that cassava and sorghum grows well in the humid areas with temperatures ranging from 25°C – 27°C as shown in Table 4.3.
Figure 4.8: Zone A3, suitable for cassava and Sorghum growth
3.7.3 Maize and millet zone
Figure 4.9 shows that maize and millet grows well in the dry sub humid areas with temperatures ranging from 27°C – 30°C as shown in Table 4.3.
Figure 4.9: Zone A5, suitable for Maize and Millet growth
3.7.4 Ground nuts and beans zone
Figure 4.10 shows that ground nuts and beans will grow well in the dry sub humid areas with temperatures ranging from 25°C – 27°C as shown in Table 4.3.
Figure 4.10: Zone A6, suitable for ground nuts and bean growth
The zones above were derived from the agro climatic zone map (Fig 4.6). The temperature and moisture availability range that was used for each crop was obtained from the Atlas of Uganda (1968) and FAO (1993a) data on the climatic adaptability of crops as shown in table D.1. To relate the above zones to what appears on ground visual analysis was used. That is, a map of Uganda (Atlas of Uganda, 1968) showing regions in which various crops are grown was compared to the above crop zones.
4.0 Conclusions and recommendations
The aim of the study was to create an agro-climatic zone map for the study area. Nine zones resulted, the first three zones were found in the humid, the second three in the dry sub humid and the last three were in the semi-arid climatic regime as shown in Table 4.3 .The classification of the zones was based on those of WMO-UNEP (1971-2000) and FAO (1993a). The Köppen and Thornthwaite agro climatic classification system were used. From the results thus obtained it can clearly be stated that Soroti has a humid and a hot climate with an annual rainfall and temperatures between 450mm-1800mm and 25°C- 30°C as shown on the map legend of Figure B.2 and Figure 4.1 respectively.
The objective of the study was to assess the suitability of geographical areas for various agriculture alternatives. This was achieved by delineating zones suitable for the growth of sweet potatoes, rice, green gram, cassava, sorghum, cowpea maize, millet, ground nuts, and bean as indicated in Figures 4.7, Figure 4.8, Figure 4.9 and Figure 4.10. From the results it is clear that sweet potatoes, sorghum, and cassava grows well in the humid area with temperatures between 27°C- 30°C and 25°C- 27°C respectively while maize, millet and ground nuts will grow well in the dry sub humid area with temperatures between 27°C- 30°C and 25°C- 27°C respectively.
The empirical research finding (Tables 4.1, Table 4.2 and Table 4.3) derived from the maps can be used by researchers to plan for agricultural activities in the study area and also to carry out further research on climatic change in the region. From the results (Table 4.3), it can clearly be stated that zones which lie in the humid and dry-sub humid regions are highly suitable for agricultural production and these take a larger area of the total Soroti land area while those which lie in the semi -arid regions take a small part of Soroti’s land area and are considered unsuitable for agriculture provided water for irrigation is obtained from the nearby lakes and rivers. While important agricultural factors such as length and intensity of the rainy and dry seasons and annual variation are not accounted for in this model, the results obtained provide a basic tool for national planning purposes. 4.2 Recommendations
The results are recommended for use on a lager scale only if they are accurate and representative of the climatic conditions in the selected region of the country. However if many alternatives are to be included based on the stated criterion, this paper recommends the creation of an agro-ecological zone (a land resource mapping unit), since it is a unique combination of land form, soil and climatic characteristics and/or land cover having a specific range of potentials and constraints for land use (FAO, 1993a).
- Allaby.M. (2004). A dictionary of Ecology, Oxford University press
- Aribo. L,Bazira. E, Masinde .M, Waiswa .M (2010) Validation of weather observation system data in Uganda , Ministry of Water and Environment , Depertment of Meteorology Entebbe
- A tlas of Uganda, (1968) Department of Lands and Surveys, Entebbe, Uganda
- Balaguru. T. (2010) Agro-ecological Regions in India, National Academy of agricultural research
- Belbin, L (1987). The use of non-hierarchical allocation methods for clustering large sets of dat. Australian Journal of Computing 19: 32-41pp;
- Corbett, J.D. (1996) the changing faces of agro ecosystem characterization: Models and spatial data, the basis for robust agro ecosystem characterization. Proceedings of the Third International
- Conference on the Integration of GIS and Environmental Modeling, Santa Fe, New Mexico,
- Egeru, A. and Majaliwa, M.G.J. (2009) landuse/cover change trend in soroti district eastern uganda Institute of Environment and Natural Resources, Makerere University vol. 13(4) 77 – 79
- FAO Guidelines (1983): Land Evaluation for Rainfed Agriculture. Soils Bull. No. 52, FAO, Rome, 237 p.
- Food and Agriculture Organization of the United Nations (FAO), (1976) A framework for land evaluation, Soils bulletin, 32, Rome: FAO.
- Food and Agriculture Organization of the United Nations (FAO), (1984) Guidelines for land evaluation for rainfed agriculture, Soils bulletin, 52, Rome: FAO.
- Food and Agriculture Organization of the United Nations (FAO), (1985) Guidelines for land evaluation for irrigated agriculture, Soils bulletin, 55, Rome: FAO.
- FAO (1993a) Agro-ecological land resources assessment for agricultural development planning., A case study of Kenya, Resource data base and land productivity
- Fitzpatrick, E.A.; Nix, H.A. (1970) The climatic factor in Australian grassland ecology. In: Milton Moore, R. ed. Australian Grasslands. Australian National University Press, Canberra; 3-26.
- Griffiths, J. F. (1985) Climatology, Chapter 2 in Handbook of Applied Meteorology, Ed. by David D. Houghton, John Wiley and Sons, ISBN 0-471-08404-2
- Gonfa, L., (1996). Climate classification of Ethiopia. NMSA, Addis Ababa, Ethiopia
- Gemechu, D, (1977). Aspects of climate and water budget in Ethiopia. Addis Ababa University Press, pp. 77.
- Hutchinson, M.F. (1989a) A new objective method for spatial interpolation of meteorological variables from irregular networks applied to the estimation of monthly mean solar radiation, temperature, precipitation and wind run. CSIRO Division of Water and Land Resources, Technical Memorandum 89/5, Canberra: CSIRO; 10 pp.
- Hutchinson, M.F. (1989b). A new procedure for gridding elevation and stream line data with automatic removal of spurious pits, Journal of Hydrology 106: 211-232;
- Haile, T. and Yarotskaya, L., (1987). Onset and cessation of rains in Ethiopia, NMSA Memo, Addis Ababa, Ethiopia.
- Jatzold. R , Schmidt. H, (1983) Farm Management Handbook of Kenya, Ministry of Agriculture Kenya, Rossdorf
- Kalogirou S, (2002) Experts systems and GIS: an application of land suitability evaluation, Computer, Environment and Urban Systems, 26, P 89-112.
- Kenya soils survey (1982) Exploratory soils Map and Agro- Climatic Zone Map of Kenya, Nairobi
- Köppen, W. ( 1936) Das Geographische System der Klimate. In: Handbuch der Klimatologie I.,
- Gebrüder Borntraeger, Berlin; C: 1-44;
- Kannangara ,R. (1998) An agro-climatic analysis of Sri Lanka. PhD thesis, Australian National University, Canberra;
- NMSA (National Meteorology Service Agency), (1996). Climatic and Agroclimatic Resources of Ethiopia. Vol. 1, No. 1. National Meteorology Service Agency of Ethiopia, Addis Ababa.137 pp.
- Rijks A, Woodhead. T, Dagg. M (1970). Evaporation in East Africa, Bulletin of the international Association of scientific hydrology vol15
- Sue .W, Yohannes G, Eyesus. G, Kindie .T, Girma .M, Abebe. Y, Endalkachew .B. (2010). The use of Agro climatic zones as a basis for tailored seasonal rainfall forecasts for the cropping systems in the central rift valley of Ethiopia University of the Free State Bloemfontein South Africa, Ethiopian Agricultural Research Organisation, Melkassa, Ethiopia, National Meteorological Service Agency, Ethiopia.
- Sombroek W C, Braun H M H and Van der Pouw B J A. (1982) Explanatory soil map and agro-climatic zone map of Kenya. Report E1. National Agricultural Laboratories, Soil Survey Unit, Nairobi, Kenya. 56 pp United Nations Environment Programme (UNEP). (1997). World Atlas of Desertification. Second Edition. Middleton N. and Thomas D. (eds). 182p
- White.D. H , Godfrey .L, Heping. Z, William .W and Jan .S (2010) Agro-climatic classification systems for estimating the global distribution of livestock numbers and commodities Zuo .H, (1996) Agro-climatic analysis for mainland East Asia by a GIS approach. PhD thesis, Australian National University, 264 pp