Participant Discussion Group


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Kajjo Darious, Environment and natural Resource Management-Makerere University.

Dr. Shuaib Lwasa, Urban and Regional Planning; Environment and Resource Assessment; Department of Geography, Makerere University (Mentor)



Kampala region is experiencing a high rate of urbanisation with population increasing from    1,189,142 in 2002 (UBOS, 2002) and estimated at 1,602,554 people in 2010. This has led to decreasing agricultural land, food production and impacted on agricultural production and biodiversity an effect that has left urban livelihoods food insecure in the region. The project explored land use/cover trends of Kampala focusing on subsistence agricultural land, effects on food production and future status of agricultural land. Land use data was got through classification methods using a satellite image while secondary data was collected from various government bodies. Analysis was done using GIS, SPSS and Microsoft office (excel) computer programmes. Results showed a 12% increase in Built-up land and a 3% decrease in agricultural land use between 1994-2004. Agricultural land projected for further years still showed a declining trend and was estimated to cease out by year 2030. This negative trend negatively impacted on production where Banana crop production decreased by 3.8%, Beans 12.5% and Cassava 5.7%.  Food security analysis showed that currently (2010), the region requires 97,902 metric tons of food to be grown on 20,525ha. The current available land for agriculture was estimated at 4,989ha. The region was in a deficit of 15,536ha which put it in a state of being food insecure in terms of availability of land for agriculture.

The main Research questions include the following;
  • What is the land use/cover, trend and main drivers behind deteriorating Agricultural land in Kampala region.
  • How does urbanisation affect food production and Agricultural biodiversity
  • What is the future population, land Use Change and its implications on food production and food security.


    The following are the scientific and academic contributions of the project;

    • Introduction of new data acquisition technologies like the GPS mapping, remote sensing in capturing land use/ land cover of Kampala region.  
    • It portrays the power of GIS and remote sensing in evaluation of the region’s land use change.
    • Acquisition of in prediction and change detection computation skills.
    • Presentation of results in easy and understandable cartographic formats (maps). Results are geographically portrait which gives an insight to the extent of the problem other than percentage tables.


      Social impacts of the project

      • Bridging the gap between Scientists and demographers by understanding effects of population growth on non renewable natural resources and consequential impact on agricultural biodiversity.
      • Provision of knowledge and capacity to measure extent/rate of the problem to know the most pressing social and environmental problem facing cities and upcoming urban areas.
      • The results are to give an insight of the problem at stake affecting agricultural biodiversity and food production.
      • The project also points to the danger facing humanity due to deteriorating food production.
      • It is an early warning call to the possible hunger outbreak as agricultural land goes into extinction and the resulting effects like hunger, high food prices and ill health as a result of malnutrition.
      • It is a call to policy makers to regulate land use so as to preserve land for agriculture through land use zoning and legalizing urban agriculture.


        Among activities done was Data acquisition.  Land use/cover data was acquired for 2004 from a quick bird satellite image and 1995 secondary Agricultural statistics got through literature review.

        Remote sensing (2004) – ENVI EX  remote sensing computer programme was used to classify land use on a georeferenced quick-bird satellite image of Kampala region. Using unsupervised classification, eight color classes were got from the classified image and identified in ENVI to represent their true ground representation. The classes were exported into ArcGIS 9.3.1 for further analysis. 


        Ground verification -This was done with help of a GPS to correct classified parcels that seemed not a true representation of what was on ground. For example shadows of major buildings, cultivated land in wetlands, ponds, sewage lagoons and water bodies were classified with a red color due to their dark appearance on image. It was important to differentiate one from another by carrying out ground verification exercise (see photoes below).

        Photo 1: Take from the Field verification exercise in Rubaga Division; the Background shows a reclaimed area that previously used to be used for growing crops. 






        Photo 2: Taken from a Field verification exercise also. The back ground shows a reducing size of a Banana garden. note the cut stems and a few still standing! 

        Object based classification techniques using object properties like shape, texture was used to speed up this process specifically in differentiating sewer lagoons, fish ponds, buildings. Classes in the GIS were directly edited with corrections from field verification.

        ArcGIS spatial analyst-Reclassification option was used to classify land use/cover classes for 2004 into 3(three) functional categories that represented 2004 land use and land cover i.e. “Agricultural,” “Built-up” and “Other.” was (Agricultural land use comprised all land under crops, cultivation stage while Built-up use comprised land under settlement, Industrial, Institutional, roads and “Other” comprised bare grounds, excavated surface, swamps, play fields and open recreation grounds).


        Literature Review- Agricultural output, consumption patterns and population statistics was got from reviewing reports from Uganda Bureau of Statistics and reports from Agricultural surveys previously done by the planning department of the ministry of Agriculture of the republic of Uganda. Additional regional and international literature was got from UN and FAO reports on agriculture in Uganda.  Data analysis – The acreage of agricultural land for 2004 was calculated using GIS and exported into Microsoft Excel. Change detection and rates of change where calculated in Microsoft Excel using formula; r = (Pn/Po)1/t – 1            

        Where;  r = rate, Pn = current land use, Po= previous land use and t = time. Future state of Agricultural land was projected up to the year 2034 using a model developed in SPSS taking Time (years) as the independent and Land (ha) as the dependent variable. Food security was measured in terms of availability of land for agriculture. It was determined by projecting population using formula (Pn=Po(1+r/100)t. where; Pn = Projected population, Po=base year population, r=growth rate and t=time in years. Multiplying the proportion of average adults of this population by the per capita consumption and dividing the product by average production per hactare to get the required land to grow food. The difference between the available land at that year and the division was the main determinant of food security. Where; + meant food insecurity and – answer meant being food insecure. 


        1. All data has been acquired
        2. Analysis has been done
        3. Results got and write-up done
        4. Submission done

          Research Results and Products

          It was established that Kampala region lies on latitude 00o19’N and longitude 32o35’E in the central region of Uganda (East Africa) along the north shores of Lake Victoria and covers a total land area of 176Km2 (approximately 17,600ha). The region is confined into one administrative unit called a District. The district is subdivided into five divisions; Central division that houses the CBD, Lubaga, Makindye, Kawempe and Nakawa divisions. The region has a multiplicity of tribes as a result of rural-urban migrations due to its commercial and industrial superiority over other regions in the country. This has resulted into increased population growth.

          Map 1: Location of Kampala Region in Uganda


          Land use/land cover

          According to the Kampala urban study (1994), Kampala’s land use was classified as 45.5% Built-up, 49% Agricultural and 5.5% other.  However from land use classification of 2004, Table 1 shows that the region’s Built-up land had raisen to 53% thus a 7.5% positive change, “other” to 10.5% (5% positive change). However Agricultural land had a decline from 49% (1994) to 36.5% (2004) which so a -12.5% decrease.

          Table 1: Land use change between 1994 and 2004

          Land use/cover

          1994 (%)

          2004 (%)

          % Change

          Rate  of change




















          The main driver to deteriorating Agricultural land was established as being the increasing built-up use that has come as a result of increased population. The trend for Kampala’s population has been increasing at a rate of 3.8 since the census of 1990 and 2002. Increase in population led to conversion of more agricultural land into built-up use. This led a 7.5% increase in Built-up land.


          Map 2: Land use/cover of Kampala in 2004


          “Other” could have been a result of increase excavation of surface to put up shelter, expand commercial as well as industrial space, establishment of recreational grounds and increased vacant land due to abandonment of agricultural practices and speculation tendencies. This left more land under no specific use and more vulnerable to change.


          Food Crops, Consumption Pattern and Production

          It was found out that 45% of household expenditure is spent on food (UBOS, 2002). Basing on household expenditure on food and beverages, Table 2 shows Kampala’s food and beverage consumption pattern by percentage of household.


          Table 2: Food and Beverage consumption pattern in Kampala by percentage of Household










          Sweet Potatoes

          Ground Nuts






          Edible oils











          Carry Powder







          Passion Fruit




          Maize (flour)




          Source: Uganda food balance sheet; Ministry of finance and Economic Planning 


          From the above table, four commonly and most locally grown food crops were chosen for further analysis. These were Bananas (Musa acuminate), Beans (Phaseolus vulgaris), Sweet potatoes (ipomea batatas,  and Cassava (Manihot esculenta). It was also found out that one or more of these crops were being grown in every agricultural household in Kampala region. This could be because these food crops do not require any further processing and so Households find them convenient to grow. Due to reduced agricultural land, households have adopted mixed cultivation on small pieces of land so as to maximize the land.  

          In terms of production, Figure 1 shows that there was a general decline in land (ha) under cultivation of these four main food crops. The figure further shows that much as Bananas were mostly consumed by Kampala households, it was not the most highly grown. This could be due to the fact that bananas take longer to grow and take too much space than beans. Households preferred to put more land under beans which matures faster.


          Figure 1: Land under Crop cultivation in Kampala 1994/2004


          Source: Analysis from secondary statistics.


          Production in metric tons per hactare was computed for five year data points basing on the available statistics. From table 3, production and land area per crop for each crop for a five year period is shown. The table shows that production for crops like Bananas, Beans and Cassava was declining by  2008.


          Table 3: Production in metric tons per hactare


          2004 (ton/ha)

          2005 (ton/ha)

          2006 (ton/ha)

          2007 (ton/ha)

          2008 (ton/ha)







          Sweet potatoes



















          Food security and projections

          Food security was measured in terms of required amount of food to feed the growing population at different years and availability of agricultural land to grow this food. Using results from table 1 and linear regression analysis, an agricultural land model was developed to project available land at different years; y=8924.2-246t where, y = agricultural land in hectare and t = time (years). Average production for all crops in metric tons per hactare between 2004-2008 was determined to be 4.77ton/ha. Average Per capita consumption for an adult as per Paul. W, (1991) for Bananas (220kg), Sweet potatoes (82kg), Beans (19.2kg) and Cassava (128kg) was determined to be 112.3kg (0.1123 metric tons). The Population for different years was projected basing on the 3.8 Kampala growth rate (UBOS, 2002) and only 54.4% of the total population (average adults) at each year was included in the calculation.

          In table 3, the following formulas were used to determine amount of food required for average adult population for each year, required agricultural land and Food security status in terms of availability of land to grow this food.


          F = PC

          A1 = PC/Mh

           Fs = A0 – (PC/Mh) or A0 – A1

          Where; F = food required for that population at that year, P = Population of that year, C= per capita consumption, A1 = required agricultural, Mh = production in metric tons per hactare,      Fs =Food security, A0 = Available Agricultural land, land to grow food for that year (see results in table 4 and figure 2). +ve positive Fs result would mean food security but  the the negative –ve  results meant food insecurity.

          Table 4: Projected production, land and food security status in terms of agricultural land availability.


          Population (P)

          Per capita Consumption(Metric tons) C)

          Food for Population  (metric tons)F)

          Average Output ton/ha (Mh)

          Required agric land to grow food (ha)A1)


          y=8864.2-240.2t) Ao)

          Food Security Deficit (Fs)

























































           Results from table 4 show that by 1994, Kampala region was already food insecure in terms of availability of land for agriculture with a land deficit of 2682ha. From 1994 onwards as urbanisation increased through increasing population, the agricultural land deficit gap kept on widening as a result of reducing available land as shown in figure 3.  Currently the region is food insecure by a deficit of 15536ha to grow food for the current population almost tripling the available 4989ha.  Other conditions remaining constant, figure 3 also shows that available agricultural land in Kampala region will cease out by the year 2030.

          Figure 3: Required, Available and Deficit gap of agricultural land for years between 1994-2034



          Built-up land increased at a rate of 12% between 1994-2004 which increase led to a decline in agricultural land by 3%. It is important to note that as time increases the world population is increasing so this increase will continue impacting negatively on agricultural land. Table 4 shows a continuing decline in agricultural land from 8624ha, 1994 to 4989ha, 2010 thus making a 42% decline and 85% decline by year 2034. This decline also saw a 4% decrease in food production from 2004-2008. For example between 2004-2008 there was a decline of 3.8%, for Bananas, 12.5% and 5.7% for Beans and cassava respectively.


          Likewise national production for crops like Bananas has been decreasing from 3.5% in 2006, 2% and 1.5% in 2007 and 2008 respectively (UBOS, 2009). The average daily calorie intake of Ugandans per person stands at 2,360 kcal. Out of this, 94 % comes from vegetal products (Abele, 2006). Uganda being an agro economy, vegetal products constitute a bigger percentage on household diet. The reduction in land to grow vegetal products is likely to cause ill-health and malnutrition among households that depend on land as source of livelihood.


          Lillian, N. (2003)  pointed out that many poor people in Kampala lack land ownership rights and  instead gain access to land in poor areas like wetlands, road and railway reservations and waste disposal sites while Others utilize their backyards or encroach on undeveloped land left to fallow by landowners. It is important to note that these spaces are too small to produce significant amount of food. It is further noted that city authorities and utility service providers keep on tampering these areas by slashing crops to give way for the services. This all undermines food production in the region.  

          Urbanisation has also increased Land fragmentation in Kampala region. Sardik, N. (2002) points out that land fragmentation affects food production and is a direct result of rapid population growth in many poor countries. He further points out that very small landholdings which are too small to provide a tolerable livelihood turn into part-time farms, with some household members staying at home to tend crops while others migrate in search of wage employment.  Sirte. L (2008) further stressed that increased food production and consumption is undermined by rapid population growth, unequal land distribution, shrinking landholdings and widespread land degradation. The reduction in man power and reduced fertility of the land automatically reduces acreage of land that would be put to growing crops.

          In some cases the poor have sold out land to prospective developers and moved to cheaper places outside Kampala. The new landlords have changed the use to built-up by putting up commercial shops, residential and institution. This has led to the decline in available land for agricultural.  Sardik, N. (2002) also argues that on small land holdings there is over use of the land through use of agrochemicals which eventually degrades the soil. In this fertility of the soil is reduced and so productivity of the land. The land holder will opt to put it to other use which in most cases would not be agricultural use. This continues to reduce agricultural land, food production and thus impacting negatively on agricultural biodiversity in the region.

          It is still doubtable whether developing countries (Uganda inclusive) will have Urban-Rural migrations to have a situation where Agricultural land would increase at decreasing Built-land land. The fact that rural-urban migrations and natural growth are leading to high rates of urbanisation, agricultural biodiversity and food production are still decreasing up to a point of zero if no measures are put in place to conserve land for agriculture.  

          To improve food production and acreage of agricultural land, Agnes, M and Magaret, K (1994) recommended use of agronomic methods such as irrigation, fertilizers, pesticides and high yielding crop varieties. This would be impractical in a wider part of Kampala region which depends on a convention water system. Also some parts of Kampala lack access to the lake which would provide enough water for this activity. More so, too much use of pesticides would result into pollution of water sources and wetland habitats through rain wash. This would inventuary lead to loss of wetland and underground biodiversity

          In conclusion, Kampala region like other developing regions facing high rates of urbanisation require a policy that incorporates population growth, per capita consumption and natural resource utilization. This would always check on how much natural resource is available to sustain the population. In this way, measures combating population growth and resource utilization would be determined. This will reduce the risks ahead of human life in terms of land and food availability

          It is important to note that the project measured food security  basing on assumption that people are growing food within Kampala and did not consider food and other consumed crop types that come from outside Kampala region.   


          Also the decline in crop production could have been partly due to crop pests. Abele 2006, noted a general decline in production of Bananas and Cassava which could have been due to the outbreak of Banana Xanthomonas Wilt and Cassava Mosaic. These diseases have had a negative impact on banana production in the past few decades.


          Also only 54% of the total population was included in the calculations. Reliable statistic on consumption per capital was only available for the average adults (18+ years).  


          Abele, S., Twine, E. and C. Legg (2006). Food security in the major cassava growing regions of  Uganda. C3P Food Security Briefs No.1. Ibadan, Nigeria (IITA).

           Agnes, M.S., Magaret, K.M. (1994). Uganda food balance sheet, Ministry of Finance and Economic Planning.

           Lillian, N. K., Augustus, N., David, M., Juliet Kiguli  (2003).  Access to Land for Urban Agriculture in Kampala

           Oldeman, L.R., Hakkeling, R.T.A., Sombroek, W.G., 1999. World map of the status of human-induced soil degradation: Global assessment of soil degradation. International Soil Reference and

           Information Centre (ISRIC), Wageningen, The Netherlands.

          Peter, W., Yeko, M., Chales, O. and Paul, Wagubi (1991). Uganda Food Security and Exports;

           Statistical document. Ministry of Health, Nutrition divivsion.

           Sadik, N (2002) Population growth and the food crisis.

           Sirte, Libyan Arab Jamahiriya (2008). Water for Agriculture and Energy in Africa: the Challenges of Climate Change. Uganda National Investment Brief

           UBOS (2002). Uganda Population and Housing Census; Bureau of statistics.

          UBOS (2009). Uganda Bureau of Statistics; Statistical Abstract.


          Personal reflection

          The project has introduced me to new and scientific methods of analysing spatial data, depicting changes over time and measuring quantity of risk ahead. Most of the methods used in this project where learnt in class while some I read about them but I had never applied them at such a level.  The project has increased my capacity to analyze more and more!

          From participating in the project, I have gained technical skills using geographical technologies. Remote sensing sofware like ENVI EX, ArcGIS and spatial analyst operations were new to me. In as much as some training was provided on how to use it, I explored more of this software on my own something that was not so easy but more beneficial in the end.

          Also that one requires more than one geographical tool/software for biodiversity and conservation analyses. The project showed that the GPS captures data but does not analyze it while ENVI is good at remote sensing and ArcGIS good at spatial analysis. Technique gained here is that “at what stage do i transfer into another tool environment, what conversions do I need to do and so on.” It is crucial to understand the GRID, RASTER, VECTOR formats and when each is required.

          It has come to my understanding that data is everywhere but not available anywhere. In executing this project I came across a lot of secondary data and institutions. However, availability of what I wanted specifically was a problem as much of it was in fragments. Some was for years out of my scope, some in different units while some raw and other semi processed.  I have learnt that this causes a change in the planned methodology. The type data and stage at which data is got will determine the appropriate methodology and formulas to use.

          I can not leave out the social side of it. I got exposed to new fellow academicians, great mentors and biodiversity conservation experts. The timely electronic communication, discussions gave a good feeling of family as being part of the MYCOE/SERVIR Biodiversity Initiative in Africa.


          I would like to thank our mentors and trainers for the guidance provided in accomplishing this project, different organizations; the AAG, RCMRD, SERVIR-AFRICA, ESRI, ITT among others for the leadership, training and facilitation offered in this project.  Lastly, special thanks go to Dr.Patricia Solis and the team for the coordination work, well done!

          Kajjo Darious,
          Jun 26, 2010, 3:52 AM
          Kajjo Darious,
          Jun 26, 2010, 4:20 AM
          Kajjo Darious,
          Jun 26, 2010, 4:03 AM
          Kajjo Darious,
          Jun 26, 2010, 3:51 AM