Estimating the impact of climate change on crop yields in Malawi: a semiparametric neural network approach

BSE Spatial Data Module Term Paper

In the context of increasing food insecurity and climate risks, accurate crop yield forecasts are critical to mitigate the effects of climate change. This study conducts an analysis of maize yield in Malawi under three different future climate scenarios. Implementing a semi-parametric neural network and comparing the results to other more commonly used machine learning techniques, our results demonstrate the complexity of predicting crop yields, where models tend to overfit to the historical data. SNN’s provide a promising alternative, but must be carefully tuned and utilise bootstrap aggregation as well as other methods in order to produce accurate and reliable results