This project is using climate forecasts of Global Climate Models to predicted crop-yields in near-future in India.
” I have combined government administrative data on crop production at the district level with irrigation data, climate data, soil data, and economic data during the years 1997-2014. I used machine learning methods such as random forests, gradient boosting machine, and neural networks to build a prediction model of crop productivity at the district level in India. Finally, the climate forecasts using Global Climate Models, most suitable for the Indian context are used to predict the crop productivity in the near future at the district level for each crop. The prediction model is expected to assist policy makers and crop insurance companies to provide optimum solutions to farmers through well-targeted or customized programs and products.” （According to the project website：https://researchday.yale.edu/entityform/186）
Increasing temperatures, variability in rainfall, and frequency and intensity of extreme weather events are adding to pressure on agriculture systems all over the world. According to this model, climate change is predicted to result in a 4%-26% loss in net farm income towards the end of the century. Such research provided impetus to formulate national climate adaptation policies in India.