International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
PLoS One. 2020 Sep 18;15(9):e0239149. doi: 10.1371/journal.pone.0239149. eCollection 2020.
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.
我们提出了一个易于校准的空间建模框架,用于估计特定地点的肥料响应,以坦桑尼亚的小农玉米种植为例。通过纳入空间变化的投入和产出价格,我们预测特定地点小农的预期盈利能力。模型中的随机降雨成分使我们能够量化预期经济回报的不确定性。预期盈利能力和不确定性的映射估计结果很好地预测了全国代表性农户调查数据中小农肥料使用的实际情况。我们的框架中农业和经济信息的整合使其成为一种强大的工具,可用于对农业技术和互补投资进行空间明确的定位,以及在多个尺度上估计投资回报。