ZhouZiyao

This study investigates the potential of the WOFOST crop growth simulation model for yield forecasting and crop condition assessment for several key agricultural crops across large extents of Russia over a fifteen-year analysis period (2005–2020) using soil and climatic datasets. Specific emphasis was placed on adapting information from the 1:2,500,000-scale Soil Map (V.M. Fridland edition) to ensure the model's correct performance. Within the study, model parameters for regional hybrids of barley, sunflower, and maize were obtained for several hundred districts within the Russian agricultural belt, including characteristics of phenology, photosynthesis, respiration, and assimilates partitioning. Parameterization was performed using loss functions based on the Brent, Nelder-Mead, and Differential Evolution algorithms to minimize phenology and yield estimation errors. To enable parameterization and independent accuracy assessment, the initial dataset was divided into a training set and a control set in an 80:20 ratio. The regionally parameterized models provided a good match between the error histograms for the training and control sets, with a near-zero systematic bias, while the mean relative absolute deviation of yield estimates ranged from 20% to 26% depending on the crop. Furthermore, it was demonstrated that the model estimates are not only close to the actual yield values for the studied period but also that the model forecast is capable of capturing multi-year trends in the yield dynamics of the studied crops. Finally, the obtained model crop hybrids were used to assess crop state in terms of the deviation of simulated yield from the yield of an optimal season, with the generation of deviation cartogram maps. The results obtained in this study can be used for decision support system of the agricultural sector, including yield forecasting and state assessment of the studied key crops over large areas using simulation modeling, including forecasts made under various climatic scenarios.