Use of an enhanced cultivar calibration framework for DSSAT to examine effects of ecotype and time-series data

Abstract

Abstract Process-based crop modelling platforms such as DSSAT are potentially valuable tools for crop breeding programmes, with the capacity to predict genotype-by-environment-by-management interactions. However, their application for breeding is challenged by the need to calibrate large numbers of genotypes within populations. In wheat (Triticum aestivum L.), using pre-existing DSSAT-CERES wheat ecotypes can introduce unrealistic parameter compensation during cultivar calibration. To address this, we developed a two-phase sequential calibration framework. This workflow uses phenotypic clustering to first define representative ecotypes using experiment-specific data before proceeding with cultivar-level parameter estimation. We demonstrate the utility of this framework to integrate direct measurements from proximal and remote sensing data collected on 14 genotypes grown under well-watered, drought, and heat stress field conditions. Incorporating experiment-derived ecotypes reduced compensatory adjustments in cultivar coefficients and improved simulation accuracy compared with default or non-representative ecotypes. Time-series data enhanced calibration, although the effect of different data combinations varied with environmental scenario and trait. Model simulations under stress conditions generally captured drought effects on biomass but underestimated heat stress impacts. This framework provides a systematic and scalable approach for integrating high-throughput phenotyping and process-based crop modelling.

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