Prof. Dr. Carl-Philipp Federolf

Grain yields and their variability impact agronomic management decisions, yet accurate yield maps of multiple seasons in a comparable format are often unavailable at farm level. While crop growth models could estimate site-specific yields, they lack sufficient high-resolution input data. This study hypothesized that combining a growth model with satellite imagery-derived vegetation information could generate yield estimations and yield maps. The model was calibrated using field average yield data from 2017–2023 across relevant global cereal growing areas. The model achieved an r² of 0.771 for training data and 0.443 for test data, demonstrating its potential for accurate yield mapping.