Crop Yield Estimation

Farmers are highly vulnerable to weather-related crop yield losses, which might increase in the face of climate change. Assessing yields and respective yield losses is an important task to structure indemnity-based and index-based crop insurance solution. Such risk transfer is common in developed countries but scarce in developing countries due to the high premium costs, which are mostly unaffordable for smallholder farmers. To develop new agricultural insurance products a profound knowledge of the temporal and spatial crop yield variability is prerequisite. Currently, actual yield data are often unavailable notably in developing countries. Because of this, other data sources and methods are needed. Tailored for the insurance company Munich Re, GAF AG and PIK will develop new crop modelling applications, which link statistical and process-based crop models with remote sensing data on regional scale for two target regions in Tanzania and Brazil. While process-based models can assess yield impacts across different cropping systems, statistical crop models are especially suitable to differentiate between weather and non-weather-related yield losses. Remote sensing data has a worldwide and constant coverage and provide global time series of optical data with high spatial and temporal resolution. The linkage of these methods and data will contribute to improve crop mapping and yield monitoring accuracy on high spatial resolution.


Mar 01, 2017 until May 31, 2018

Funding Agency

Munich RE


Christoph Gornott