TY - JOUR A1 - Groh, Jannis A1 - Diamantopoulos, Efstathios A1 - Duan, Xiaohong A1 - Ewert, Frank A1 - Heinlein, Florian A1 - Herbst, Michael A1 - Holbak, Maja A1 - Kamali, Bahareh A1 - Kersebaum, Kurt-Christian A1 - Kuhnert, Matthias A1 - Nendel, Claas A1 - Priesack, Eckart A1 - Steidl, Jörg A1 - Sommer, Michael A1 - Pütz, Thomas A1 - Vanderborght, Jan A1 - Vereecken, Harry A1 - Wallor, Evelyn A1 - Weber, Tobias K. D. A1 - Wegehenkel, Martin A1 - Weihermüller, Lutz A1 - Gerke, Horst H. T1 - Same soil, different climate: Crop model intercomparison on translocated lysimeters JF - Vadose Zone Journal N2 - Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-5395 SN - 1539-1663 VL - 21 IS - 4 PB - Wiley ER - TY - JOUR A1 - Dogar, Salar Saeed A1 - Brogi, Cosimo A1 - O'Leary, Dave A1 - Hernández-Ochoa, Ixchel M. A1 - Donat, Marco A1 - Vereecken, Harry A1 - Huisman, Johan Alexander T1 - Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production JF - SOIL N2 - Abstract Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures aboveground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI–NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and aboveground variability. The final EMI–NDVI-derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modeling and soil surveying. KW - - Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-10152 SN - 2199-398X VL - 11 IS - 2 SP - 655 EP - 679 PB - Copernicus Publications CY - Göttingen, Germany ER -