A fundamental challenge of agricultural development in sub-Saharan Africa (SSA) is that technologies which prove successful at a small scale, in limited locations, and with few farmers, often fail to scale to encompass the preponderance of poor farmers. This study focuses on the economics of deploying technologies and recommendations that are then scaled beyond their initial targeted groups. The dissertation is composed of three essays. In the first essay, we address the stylized fact that experimental crop responses are typically higher than observational crop responses obtained in farmers’ fields. This is arguably a canonical example of a failure to scale from experimental plots. To close these crop response gaps—necessary goal assuming general constant long-term trends in maize output/fertilizer price ratios—, we propose that fertilizer recommendations be based on a Bayesian combination of experimental and observational crop response estimates. We use Bayesian econometric methods to combine estimates from experimental and observational evidence. In the second essay, we build on the first to determine the likelihood that farmers will adopt new varietal technologies. We modify the differentiated product demand models used in the industrial organization literature to the economics of hybrid maize varietal adoption in Malawi. By focusing on the characteristics space of maize varieties, our approach can help in ex-ante evaluation of the scaling-up potential of new crop varieties. The final essay calibrates inter-district food flows in Malawi thereby providing statistics for improving the targeting of national and regional food policies and technology commercialization strategies. We develop a food sector model for Malawi and use it to analyze the impacts of varying transport costs on food traded among districts within the country.
University of Minnesota Ph.D. dissertation. June 2019. Major: Applied Economics. Advisors: Terrance Hurley, Philip Pardey. 1 computer file (PDF); x, 159 pages.
Economics of Scaling Agricultural Research Recommendations to Up-Scale Adoption and Impact.
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