Using reanalysis in crop monitoring and forecasting systems. (January 2019) - Toreti, A.; Maiorano, A.; De Sanctis, G.; Webber, H.; Ruane, A.C.; Fumagalli, D.; Ceglar, A.; Niemeyer, S.; Zampieri, M.
Abstract: Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980–2010 under potential and water-limited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time. Highlights: Crop yield monitoring and forecasting systems driven by reanalysis show similar performance to the ones driven by weather observations. Simulated wheat and maize yields are well correlated with FAO yields. Significant biases in simulated wheat and maize yields still remain.