Global Cropland Yield Monitoring with Gaussian Processes

Agriculture monitoring, and in particular food security, requires near real-time information on crop growing conditions for early detection of possible production deficits. In this work, we propose the use of Gaussian processes (GPs). together with in-situ, EO and ERA-Interim climate reanalysis data for crop yield forecasting. Country-level agricultural survey data from FAOSTAT are used for quantitative assessment. The study is conducted in the framework of the ASAP (Anomaly hot Spots of Agricultural Production) early warning decision support system of the European Commission, which aims at providing timely information about possible crop production anomalies worldwide. After grouping countries with similar growing season periods, we We show that GP models allow predicting the yield of the different planted crops within such groups with coefficient of determination R2 ranging from 0.5 to 0.95. For each country and crop, a better fit that the mean of the data is obtained in all cases, and the errors obtained are comparable to the ones obtained for the group. The proposed modelling framework can be potentially adopted in ASAP operations to forecast national level crop yield and production.