Researchers from the Department of Remote Sensing (University of Würzburg); the Department of Ecological Services, BayCEER (University of Bayreuth); the German Remote Sensing Data Center, DLR (Wessling); the Department of Applied Computer Science (University of Augsburg); the Department of Animal Ecology and Tropical Biology (University of Würzburg); and the Department of Physical Geography and Soil Science (University of Würzburg) teamed up to investigate how landscape structure, climate variability, and soil quality shape crop biomass patterns in Bavaria’s agricultural ecosystems.

The paper, “Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria,” has been published in Frontiers in Plant Science by Maninder Singh Dhillon, Thomas Koellner, Sarah Asam, Jakob Bogenreuther, Stefan Dech, Ursula Gessner, Daniel Gruschwitz, Sylvia Helena Annuth, Tanja Kraus, Thomas Rummler, Christian Schaefer, Sarah Schönbrodt-Stitt, Ingolf Steffan-Dewenter, Martina Wilde, and Tobias Ullmann.
DOI: 10.3389/fpls.2025.1630087

Here is the abstract of the paper: Understanding how environmental variability shapes crop biomass is essential for improving yield stability and guiding climate-resilient agriculture. To address this, we compared biomass estimates from a semi-empirical light use efficiency (LUE) model with predictions from a machine learning–remote sensing framework that integrates environmental variables. We applied a combined LUE and random forest (RF) model to estimate the mean biomass of winter wheat and oilseed rape across Bavaria, Germany, from 2001 to 2019. Using a 5 km2 hexagon-based grid, we incorporated landscape metrics (land cover diversity, small woody features), topographic variables (elevation, slope, aspect), soil potential, and seasonal climate predictors (mean and standard deviation of temperature, precipitation, and solar radiation) across the growing season. The RF-based approach improved predictive accuracy over the LUE model alone, particularly for winter wheat. Biomass patterns were shaped by both landscape configuration and climatic conditions. Winter wheat biomass was more influenced by topographic and landscape features, while oilseed rape was more sensitive to solar radiation and soil properties. Moderately diverse landscapes supported higher biomass, whereas an extreme landscape fragmentation or high variability showed lower values. Temperature thresholds, above 21 °C for winter wheat and 12 °C for oilseed rape, were associated with biomass declines, indicating crop-specific sensitivities under Bavarian conditions. This hybrid modeling approach provides a transferable framework to map and understand crop biomass dynamics at scale. The findings offer region-specific insights that can support sustainable agricultural planning in the context of climate change.

Here is the link to the full paper: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1630087/full

This research is part of EO4CAM: https://www.eo4cam.de