Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria
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...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1630087/full |
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| author | Maninder Singh Dhillon Thomas Koellner Sarah Asam Jakob Bogenreuther Stefan Dech Stefan Dech Ursula Gessner Daniel Gruschwitz Sylvia Helena Annuth Tanja Kraus Thomas Rummler Christian Schaefer Sarah Schönbrodt-Stitt Ingolf Steffan-Dewenter Martina Wilde Martina Wilde Tobias Ullmann |
| author_facet | Maninder Singh Dhillon Thomas Koellner Sarah Asam Jakob Bogenreuther Stefan Dech Stefan Dech Ursula Gessner Daniel Gruschwitz Sylvia Helena Annuth Tanja Kraus Thomas Rummler Christian Schaefer Sarah Schönbrodt-Stitt Ingolf Steffan-Dewenter Martina Wilde Martina Wilde Tobias Ullmann |
| author_sort | Maninder Singh Dhillon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d8e1cf1c65a434082d0a30dc98d77dc |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-3d8e1cf1c65a434082d0a30dc98d77dc2025-08-20T02:57:17ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16300871630087Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of BavariaManinder Singh Dhillon0Thomas Koellner1Sarah Asam2Jakob Bogenreuther3Stefan Dech4Stefan Dech5Ursula Gessner6Daniel Gruschwitz7Sylvia Helena Annuth8Tanja Kraus9Thomas Rummler10Christian Schaefer11Sarah Schönbrodt-Stitt12Ingolf Steffan-Dewenter13Martina Wilde14Martina Wilde15Tobias Ullmann16Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Ecological Services, Faculty of Biology, Chemistry and Earth Sciences, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Wessling, GermanyDepartment of Ecological Services, Faculty of Biology, Chemistry and Earth Sciences, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Wessling, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Wessling, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Ecological Services, Faculty of Biology, Chemistry and Earth Sciences, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Wessling, GermanyDepartment of Applied Computer Science, Institute of Geography, University of Augsburg, Augsburg, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Animal Ecology and Tropical Biology, University of Würzburg, Würzburg, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Physical Geography and Soil Science, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyDepartment of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, GermanyUnderstanding 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1630087/fullcrop biomass modelinglandscape diversityclimate variabilityrandom forest regressionsmall woody featuresclimate-resilient agriculture |
| spellingShingle | Maninder Singh Dhillon Thomas Koellner Sarah Asam Jakob Bogenreuther Stefan Dech Stefan Dech Ursula Gessner Daniel Gruschwitz Sylvia Helena Annuth Tanja Kraus Thomas Rummler Christian Schaefer Sarah Schönbrodt-Stitt Ingolf Steffan-Dewenter Martina Wilde Martina Wilde Tobias Ullmann Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria Frontiers in Plant Science crop biomass modeling landscape diversity climate variability random forest regression small woody features climate-resilient agriculture |
| title | Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria |
| title_full | Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria |
| title_fullStr | Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria |
| title_full_unstemmed | Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria |
| title_short | Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria |
| title_sort | landscape structure climate variability and soil quality shape crop biomass patterns in agricultural ecosystems of bavaria |
| topic | crop biomass modeling landscape diversity climate variability random forest regression small woody features climate-resilient agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1630087/full |
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