Improving prediction of crop yield with long-term meteorological forecasts and data assimilation
The prediction of crop yield can be improved by correcting model simulations with measurement data using data assimilation methods. In this project, new data types like captured by stationary and mobile cosmic ray probes, and sun-induced fluorescence will be used for this purpose. The improved quality of weather predictions for the next month and next three months will be evaluated for several study sites in Australia and Germany. It will be evaluated to which degree crop yield prediction is already possible at the beginning of the growing season (with seasonal weather predictions and initial soil moisture content), and to what degree it can be improved later in the growing season (with data assimilation).
Project Duration: 2019 – 2022
Home University: RWTH Aachen University
Partner University: The University of Melbourne
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