MSU Agricultural, Food, and Resource Economics
The need to feed a rapidly growing population on declining per capita arable land and dwindling research and development (R&D) resources is a common reality in many developing countries and has increased pressure for governments and donors to fund priority R&D activities that promise the greatest welfare benefits. In addition, many R&D institutions are struggling to identify and allocate scarce resources among competing research agenda and target regions. This debate is crucial in Kenya, especially because agriculture is the major source of food, income and livelihood for the majority of the population, and it has been performing poorly lately.
The objectives of this study are: (1) to provide a comprehensive review of production and technology of Kenya’s most important staple crop, maize; (2) to evaluate the differential impacts of maize technologies diffusion on farm profits and income distribution for different households and regions; and (3) to help policy makers and research managers make informed decisions on investments in Kenyan maize R&D. To achieve these objectives, this study uses a GIS-referenced farm- and village-level survey data collected in 1999 from 426 farmers in 30 population clusters. This and other secondary data are used to construct multi-market models that simulate differential impacts of maize technologies on farm profits and income for various households and regions. Gini coefficients are calculated to gauge income distribution effects of those technologies. Simulating impacts of technological change through input and product markets reveals great insight into the distributional implications of alternative technology diffusion patterns.
The results of the simulations indicate several things. First, without technological change, Kenya will suffer a large deficit in maize output, necessitating greater maize import to meet consumer demand or suffer unsustainable increases in maize prices in future. Second, maize technologies that have been developed for high potential regions will continue to have more profound aggregate impacts on maize production, leading to reduction in import demand (if maize prices are controlled) or reduction in maize prices (if maize prices are flexible).
Third, diffusion of maize technologies in the high potential regions has substantially greater positive impacts on aggregate real income and farm profits, with or without accompanying diffusion in marginal regions. However, technology diffusion in the marginal regions has better income distribution effects than when technology diffusion occurs only in the high potential regions, or in both regions. Lastly, the way in which the maize market clears has important ramifications for both the magnitude and distribution of gains and losses from various technology adoption scenarios. In general, aggregate incomes are greater when maize prices are controlled than when the prices are flexible. A notable exception is in urban households, whose welfare improves when maize prices are flexible but remains unchanged when maize prices are fixed and unchanged.