Browsing by Person "Uptmoor, Ralf"

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Comprehensive evaluation of the DSSAT‐CSM‐CERES‐Wheat for simulating winter rye against multi‐environment data in Germany
(2024) Shawon, Ashifur Rahman; Memic, Emir; Kottmann, Lorenz; Uptmoor, Ralf; Hackauf, Bernd; Feike, Til
Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye ( Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making.
Impact of calibration strategy and data on wheat simulation with the DSSAT‐Nwheat model
(2025) Shawon, Ashifur Rahman; Attia, Ahmed; Ko, Jonghan; Memic, Emir; Uptmoor, Ralf; Hackauf, Bernd; Feike, Til
Cropping system models (CSMs) are valuable tools for analyzing genotype, environment, and management (G × E × M) interactions in crop production. To apply a CSM in a new region with specific soils, climate, and cultivars, proper calibration and evaluation are required. However, calibration methods vary widely, often depending on modelers' expertise and approach. This study compares three calibration strategies for the DSSAT‐Nwheat model using two datasets: one including yield components (1000‐kernel mass, ears per m 2 , grain number per m 2 ) alongside phenology and grain yield, and another excluding yield components. The datasets cover ∼100 site‐years of winter wheat ( Triticum aestivum ) data from German pre‐registration trials and field experiments. The calibration approaches were (1) stepwise calibration of phenology, biomass, and yield, (2) simultaneous calibration of multiple genetic coefficients, and (3) a hybrid approach combining elements of both. The Time‐Series cultivar coefficient estimator tool was used for implementation. Including yield component data improved model accuracy, reducing root mean square error (RMSE) by up to 10% for key variables such as phenology (3.4–5.5 days). Future wheat yield projections under selected climate scenarios varied by strategy and dataset, ranging from 6376 to 7473 kg ha −1 in fertile, wet soils and 6108 to 6757 kg ha −1 in poorer, dry soils. These results highlight the impact of calibration strategy and dataset choice on model performance. Transparent calibration practices are essential for improving CSM reliability in regional agricultural analysis under diverse environmental conditions.