Fertilizer profitability for smallholder maize farmers in Tanzania: A spatially-explicit ex ante analysis

PLoS One. 2020 Sep 18;15(9):e0239149. doi: 10.1371/journal.pone.0239149. eCollection 2020.

Abstract

We present an easily calibrated spatial modeling framework for estimating location-specific fertilizer responses, using smallholder maize farming in Tanzania as a case study. By incorporating spatially varying input and output prices, we predict the expected profitability for a location-specific smallholder farmer. A stochastic rainfall component of the model allows us to quantify the uncertainty around expected economic returns. The resulting mapped estimates of expected profitability and uncertainty are good predictors of actual smallholder fertilizer usage in nationally representative household survey data. The integration of agronomic and economic information in our framework makes it a powerful tool for spatially explicit targeting of agricultural technologies and complementary investments, as well as estimating returns to investments at multiple scales.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Crop Production / economics*
  • Crop Production / methods
  • Farms / economics
  • Farms / statistics & numerical data
  • Fertilizers / economics*
  • Forecasting / methods
  • Investments / economics*
  • Models, Economic*
  • Rain
  • Spatial Analysis
  • Stochastic Processes
  • Tanzania
  • Uncertainty
  • Zea mays / growth & development*

Substances

  • Fertilizers

Grants and funding

Support for this study was provided by the Bill & Melinda Gates Foundation, through the Taking Maize Agronomy to Scale in Africa (TAMASA) project (grant no: OPP1113374); from a grant from the U.S. Agency for International Development (USAID) via the Geospatial and Farming Systems Consortium led by the University of California at Davis; and from the CGIAR Research Program MAIZE, led by the International Maize and Wheat Improvement Centre (CIMMYT).