Institut für Kulturpflanzenwissenschaften

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Browsing Institut für Kulturpflanzenwissenschaften by Journal "Agronomy journal"

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    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; Shawon, Ashifur Rahman; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany; Attia, Ahmed; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany; Ko, Jonghan; Department of Applied Plant Science, Chonnam National University, Gwangju, Republic of Korea; Memic, Emir; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Uptmoor, Ralf; Department of Agronomy, University of Rostock, Rostock, Germany; Hackauf, Bernd; Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Groß Lüsewitz, Germany; Feike, Til; Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI)–Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany
    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.
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    Spatial model selection and design evaluation in the Ethiopian sorghum breeding program
    (2023) Tadese, Diriba; Piepho, Hans‐Peter
    In plant breeding field experiments, proper statistical design and analysis improve precision of genotype comparisons. The focus of this study was to compare the precision of different spatial techniques in estimating genotypic effects using sorghum [Sorghum bicolor (L.) Moench] breeding data from Ethiopia and to investigate alternative design strategies maintaining overall field layout of the current trials while modifying the blocking (replicate, rows, and columns) structures compared to the current practice. The current trials comprise both partially replicated and fully replicated row–column designs where the field layout has short rows and long columns. For model comparison, six partially replicated row–column trials and 10 fully replicated row–column trials of sorghum were used. Relative efficiency calculations for the designs indicate that in most of the trials, alpha designs with block sizes of five, six, 10 and 15, and the alternative row–column designs were more efficient than the current design practice. Moreover, overall model comparison showed that augmenting the baseline model by a two‐dimensional nonlinear spatial model plus nugget improves the precision, while the randomization‐based plus two‐dimensional linear variance model and the randomization based plus a two‐dimensional nonlinear spatial model are also good candidate models. If row and column coordinates are available for all plots, the post‐blocking approach used here can be used in any breeding program and crop to explore alternative design options.