2024/01/01 by Mercy Appiah, Gennady Bracho‐Mujica, Simon Fiil Svane +3 · 1 voice
Agricultural and Biological Sciences · #Climate change impacts on agriculture #Crop Yield and Soil Fertility #Wheat and Barley Genetics and Pathology
paper · doi:10.1093/insilicoplants/diae010
openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality (HQ) experimental data. Despite being demanded frequently, such data are still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of HQ experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data, the prediction accuracy of Agricultural Production Systems SIMulator significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the HQ data. Process descriptions regarding, for example, early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.