A Robust Profitability Assessment Tool for Targeting Agricultural Investments in Developing Countries: Modeling Spatial Heterogeneity and Uncertainty

Tuesday, 16 December 2014
Julianne D Quinn, Zhaoyu Zeng, Christine A Shoemaker and Joshua Woodard, Cornell University, Ithaca, NY, United States
In sub-Saharan Africa, where the majority of the population earns their living from agriculture, government expenditures in many countries are being re-directed to the sector to increase productivity and decrease poverty. However, many of these investments are seeing low returns because they are poorly targeted. A geographic tool that accounts for spatial heterogeneity and temporal variability in the factors of production would allow governments and donors to optimize their investments by directing them to farmers for whom they are most profitable.

One application for which this is particularly relevant is fertilizer recommendations. It is well-known that soil fertility in much of sub-Saharan Africa is declining due to insufficient nutrient inputs to replenish those lost through harvest. Since fertilizer application rates in sub-Saharan Africa are several times smaller than in other developing countries, it is often assumed that African farmers are under-applying fertilizer. However, this assumption ignores the risk farmers face in choosing whether or how much fertilizer to apply. Simply calculating the benefit/cost ratio of applying a given level of fertilizer in a particular year over a large, aggregated region (as is often done) overlooks the variability in yield response seen at different sites within the region, and at the same site from year to year.

Using Ethiopia as an example, we are developing a 1 km resolution fertilizer distribution tool that provides pre-season fertilizer recommendations throughout the agricultural regions of the country, conditional on seasonal climate forecasts. By accounting for spatial heterogeneity in soil, climate, market and travel conditions, as well as uncertainty in climate and output prices at the time a farmer must purchase fertilizer, this stochastic optimization tool gives better recommendations to governments, fertilizer companies, and aid organizations looking to optimize the welfare benefits achieved by their investments.