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UGANDA 283


The chapter is structured as follows. We fi rst review the structure of Uganda’s agricultural sector as refl ected in the farm typology. We then describe the structure of the Ugandan DCGE model and the data used to calibrate it. The simulation results are then discussed for the baseline growth scenario and various accelerated agricultural growth scenarios. This is followed by an investment cost analysis. We conclude the chapter by summarizing our fi ndings.


Agriculture in Uganda To construct a typology of the farmers making up Uganda’s agricultural sector, we fi rst disaggregate agricultural production across rural and urban areas. Information on crop production was drawn from the 2005/06 Uganda National Household Survey (UNHS3) (Uganda, BOS 2008a), which asked households how much of their agricultural land was devoted to different crops. It also asked households about their cattle assets. The farm typology is based on these cropping patterns and live- stock endowments. The aim of the typology is to group farmers into major catego- ries based on their agricultural activities, which mainly refl ect agroecological, technological, and marketing conditions. These initial conditions will be important in determining the distributional and poverty implications of growth in different agricultural subsectors. According to the survey, 3.61 million rural households reported agricultural


crop incomes in 2005/06. This number is shown in the leftmost box of Figure 10.1, which illustrates the general structure of the farm typology for all rural households engaged in crop production. At this stage we exclude urban agriculturalists and nonfarm households. We fi rst separate out farm households that reported produc- ing high-value export-oriented crops, such as coffee, cotton, tobacco, and tea. In 2005/06, 1.59 million farm households produced these export crops—almost half of all rural farm households in Uganda. Figure 10.1 shows that coffee is the dominant export crop, with 1.13 million households allocating land to coffee production. We then further split farm house- holds according to whether they produced maize. Although matoke is the key staple foodcrop for most farmers, it is less effective than maize as a means of distinguishing farm types. This can be seen in Table 10.1, which presents summary statistics for the various farm types. The table shows that, even though coffee farmers generally have larger than


average farm plots, farmers growing both coffee and maize (farm groups T1–2) tend to have larger plots than coffee farmers without maize (T3–4) (2.69 hectares com- pared to 1.86 hectares). However, coffee farmers typically allocate similar amounts of land to matoke (about 0.8 hectares), regardless of whether they grow maize. This tendency is equally true of other farm types. More importantly, coffee and maize


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