Ritvik Sahajpal , Inbal Becker-Reshef, Sylvain Coutu

Accurately determining crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and when aggregated at regional and national scales, crop yield estimates are critical in ensuring food security. Over the last few decades, crop cuts have been widely used to estimate field-scale crop yields. Crop cuts, while cost prohibitive, are the most reliable way to estimate yields at field level. We present a novel machine-learning based method to optimize the number and location of fields selected for performing crop cuts to drive down costs while maintaining the capacity to accurately predict crop yields at field-scale. This method is applied to crop cut data collected through a partnership between NASA Harvest and Swiss Re (Public-Private Partnership) in Ukraine in 2018 and 2019 for multiple crops (including Winter Wheat, Maize and Soybeans). We demonstrate the utility of specific bands and extracted features in improving model performance and show that our machine-learning model can explain nearly 70% of the variation in yields while saving up to 20% of the costs incurred in obtaining these crop cuts. We explore the trade-off between the number of crop cuts performed and model performance and demonstrate that our method has generalizability across agro-ecological zones.