Climate Impact on Agricultural Labour Productivity (CIALP): presentation and paper

In the CIALP project, we used ML techniques to downscale subnational labour statistics to create high-resolution maps that show the geospatial distribution of agricultural workers for India. We selected India as a case-study because this country is and will be experiencing extreme heat stress as illustrated by recent heat waves. The maps were combined with a spatially explicit  heat stress metric (wet bulb globe temperature, WBGT) and exposure response functions to assess (a) the number of agricultural workers that are affected by heat stress and (b) the related loss in productivity, both under current and future climatic conditions. Preliminary results of the analysis indicate that existing approaches to assess the impact of heat stress do not appropriately account for the location of farmers and the labour force participation rate. As a consequence, our estimation of hours lost due to heat stress in India is much lower, but still substantial, in comparison to the existing literature.

The aim of this follow-up project is to (a) share the research findings at a conference where similar topics are presented, which allows us build up a network of relationships in the machine learning and climate adaptation communities and (b) prepare a scientific journal article that demonstrates the WUR experience in the field of machine learning and climate adaptation and in particular on the topic of the impact of heat stress on agricultural labour productivity.