Process-based model for estimation of wheat production MATCRO-Wheat

Crop production has been significantly influenced by climate change (Schindeler et al. 2001). In recent years, there is a concern that crop yield might decline due to extreme weather events. Therefore, many crop models have been developed though most of them are statistical or point-scale process-based models. Statistical models are generally not able to represent the energy, water, and carbon cycles in-between climate change and crops mechanistically. On the other hand, though point-scale process-based model has the elaborate representation of crop growth, they rarely have been applied at global scales (Zhang et al .2017). Therefore, this study aims to develop a wheat version of process-based crop model, Minimal Advanced Treatment of Surface Interaction and Runoff (MATSIRO; Takata et a1., 2003) at a global scale, named here as MATCRO-Wheat with introducing a vernalization function. To estimate the wheat yields, MATCRO-Rice model (Masutomi et al. 2016) for rice yield, was modified the physiological parameters and added a vernalization process. At the point scale, parameters related to vernalization were decided to match the modeled biomass evolution and yield to the measured ones. On the other hand, at the global scale, they were adjusted by a function of monthly averaged temperature. Second, the model was validated with observational data at 5 field sites: Memuro, Hokkaido in Japan (Hokkaido Agricultural Research Center), Nanjing in China (Nanjing Agricultural University), Wongan Hills in Australia, Valcarce in Argentina, and Wanningen in Netherland (AgMIP wheat for last three sites; Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations). Finally, MATCRO-Wheat was run at global scale with the spatial resolution of 0.5 degree x 0.5 degree. Simulated yields were validated with the national yields survey according to FAOSTAT(FAO Statistical database). Results at the point-scale showed that wheat yields were generally well represented of field observations. The global simulation indicated that wheat yields were simulated well in not only low latitudes area, but also high latitudes area where the growing season is long. Therefore, these results demonstrated that introducing vernalization improves accurate estimation of wheat yields in high latitude regions.