Climate Impact on Agricultural Labour Productivity
The aim of this project is to develop, test and implement a methodology to better understand and quantify the risks of climate change on agricultural labour productivity. The proposed methodology is comparable to the state-of-the art approaches that assess the risk of climate change on crop yield, which involves overlaying ML-based high-resolution crop-type and yield maps with spatial information on climate hotspots and extreme weather events. In this project, we will use machine learning approaches in combination with a large number of geospatial predictors (e.g. distance to cropland, climate and accessibility) to downscale subnational labour statistics to create high-resolution maps that show the geospatial distribution of agricultural workers for a selected region (e.g. South-East Asia). These maps will be combined with heat metric (wet bulb globe temperature, WBGT) maps and exposure response functions based on daily historical temperature data to assess (a) the number of agricultural workers that are affected by heat stress and (b) the related loss in labour productivity, both under current and future climatic conditions.