Sunday, 22 January 2017
4E (Washington State Convention Center )
In rural Sub-Saharan Africa, data about precipitation, temperature, and soil types currently is available only at a poor spatial scale. This poses a major problem when analyzing rural livelihoods that rely predominantly on agriculture, an endeavor that closely depends on favorable climatic conditions. In recent years, institutions like the World Bank have released datasets that sample rural households about their agricultural practices, including in Tanzania. Using the Tanzania National Panel Survey (TNPS), I can determine agricultural yields, planting decisions, and household characteristics for over 2,000 agricultural households in Tanzania. Using the TNPS and the incorporated precipitation, temperature, and soil types data from WorldClim and the National Oceanic and Atmospheric Administration (NOAA), I can analyze if a farmer’s planting decisions are based upon the current year’s weather conditions or a longer term trend, and if those decisions are related to household demographic characteristics. For future work, I intend to run the same regression models but with finer spatial resolution climate data to determine if there are important differences in planting decisions that might guide intervention differently. The finer resolution data will also allow me to attribute more of the agricultural responses to climate rather than household characteristic variation. Since it is easier to respond to household characteristics than climate ones, it is important to separate the impacts of household and climate characteristics.
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