The substantial uncertainty in crop modeling undermines confidence in the results of crop models, so quantifying and understanding the sources of this uncertainty is a critical first step towards reducing it. Parameter uncertainty is a major source of overall uncertainty in crop models and a systematic analysis of the uncertainty in genotype-specific parameter (GSP) estimates is critical across different model structure (MS) and data available for estimation (DE) scenarios. This study was conducted to quantify the effects of MS and DE on the uncertainty in GSP estimates for wheat crop models. Different MS were created by combining each of the wheat crop models (CERES, CROPSM, and NWHEAT) with maximum evapotranspiration (ETmax) models (Priestley–Taylor and Standardized Short Crop Reference Evapotranspiration) and soil evaporation (SE) models (Ritchie and Suleiman Ritchie), all within the Decision Support System for Agrotechnology Transfer-Cropping Systems Model (DSSAT-CSM). Similarly, different DE consisted of various subsets (the whole dataset and 6 holdouts) of a winter wheat dataset from Oklahoma, United States. Overall, MS, DE, and MS:DE together explained 70% or greater proportion of variability in more than three-quarters of all the estimated parameters across all DSSAT-CSM Wheat models. However, if we compare each factor individually, MS explained 25% or greater proportion of variability in nearly two-thirds of all the estimated parameters across all DSSAT-CSM Wheat models. Each DSSAT-CSM Wheat model showed a unique pattern for the uncertainty in GSP estimates. Overall, the uncertainty in GSP estimates due to MS and DE scenarios exceeded prior expectations for most parameters across models. Our results suggest that care must be taken during GSP estimation when using different MS (especially based on same crop model but different ETmax and SE methods) and DE (especially under limited data availability). This is particularly important in the context of an ensemble modeling approach (when ensemble members include the same crop model with different ETmax and SE methods), which has been increasingly adopted for climate impact assessments.
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