Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria.

ABSTRACT: 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 sola

SUBMITTER: Dhillon MS 

PROVIDER: S-EPMC12367677 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature