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Spatial analysis of sustainable livelihood enterprises of Uganda cotton production:

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Author Info
You, Liangzhi
Chamberlin, Jordan

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Abstract

"Because the conditions for agricultural development vary considerably across space, we need to develop methods that allow us to take such variability into account when evaluating development strategies for particular crops or farming systems. This paper addresses spatially varying characteristics in an evaluation of the potential economic benefits of three cotton development strategies for Uganda: area expansion, productivity improvement, and domestic consumption increase. We begin with a historical review of cotton production in Uganda. We then described the major challenges and opportunities for Ugandan cotton production, including farm-level production constraints. Household-level production data from the 2000 Uganda National Household Survey (UNHS) are used to estimate the current spatial distribution of cotton production (called the cotton production area, or CPA), based on the association of household cotton production with ranges in mapped variables (altitude, length of growing period, and population density), district cotton production statistics and expert knowledge of local production patterns. Cotton development domains (CDDs) are then defined by agroclimatic suitability, market/ginnery access, and inclusion in the CPA. We use the UNHS data to evaluate the importance of cotton as a livelihood enterprise and its role in rural livelihood strategies. Key ecosystems and protected areas are considered in conjunction with the CDDs in defining feasible areas for expansion of production. Finally, the Dynamic Research Evaluation for Management (DREAM) model is used to estimate benefits that accrue from the three development strategies considered." Authors' Abstract

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Paper provided by International Food Policy Research Institute (IFPRI) in its series EPTD discussion papers with number 121.

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Date of creation: 2004
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Handle: RePEc:fpr:eptddp:121

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Keywords: DREAM; Spatial analysis (Statistics);

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