# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 4 Discussion  
### Uncertainties  
In this study, we introduce a comprehensive method for predicting wheat PW in China. This method seamlessly integrates a wheat growth prediction model with an optimized sowing date, enabling the projection of wheat PY changes under future climate scenarios. The core foundations of this methodology are rooted in the EEO theory. In contrast to conventional crop models that often rely on a multitude of type-based parameters calibrated within current climate conditions, our approach takes into account plant optimization behaviors in response to shifting climate dynamics. This enables a more realistic anticipation of plant reactions and mitigates the bias that can arise from utilizing static parameters [24].  
The prediction of wheat PY encompasses three key components: the simulation of wheat growth, the projection of environmental limitations on wheat growth, and the anticipation of the optimized sowing date. The efficacy of these methodologies is substantiated through evaluations with distinct datasets. Employing observed field meteorological indicators and LAI as model inputs, [PERSON] et al. [34] substantiated that wheat yield can be diagnosed by universally applicable GPP estimates derived from the Pmodel. This underscores that the EEO principles observed in natural vegetation can also inform crop modeling within managed environments. An admirable accuracy (R\({}^{2}\) = 0.83) was achieved through comparison with wheat yield observations spanning 584 site-years in China. A prerequisite for the transition from diagnostic crop modeling to predictive modeling entails accounting for the synergistic limitations posed by carbon assimilation and water availability. This approach has effectively quantified the impacts of CO\({}_{2}\) fertilization and facilitated growth predictions in both natural vegetation ([PERSON] et al., 2023) [35] and wheat [11]. Moreover, [PERSON] et al. [48] demonstrated the versatility of the EEO theory by not only guiding vegetation modeling but also predicting human agricultural practices, thereby enhancing the robustness of optimized sowing date predictions. In the present study, we amalgamate the aforementioned methods, introducing a comprehensive approach to predict wheat PY in China. Compared to other crop models featuring static type-based parameters, our method closely approximates the observed PY data from EARTHSTAT under prevailing climate conditions, thereby affirming its robustness.  
Furthermore, it is noteworthy that we opted not to utilize independent calibrations for distinct wheat species, driven by two primary considerations. Firstly, the core processes (such as photosynthesis, environmental constraints, and sowing date determination) were conducted in alignment with the principles of the EEO theory, imparting a universal applicability across wheat species. Secondly, the harvesting index, a pivotal type-based parameter characterizing the proportion of wheat seed weight to total carbon gain, has remained relatively stable since the last century (~50% [60]). Concurrently, due to the current lack of a precise map delineating wheat species distribution, the use of type-based parameters would not significantly enhance prediction accuracy.