# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
###### Abstract
During the modeling process, type-specific parameters corresponding to different crop types or varieties are calibrated to account for their unique responses to the prevailing climate conditions [20]. Compared to the data-driven approach, crop models generally demonstrate superior predictive performance when forecasting crop responses under changing climate conditions [16; 17; 21]. However, two main challenges introduce uncertainties when extrapolating a well-calibrated crop model to new spatial or temporal contexts, leading to inevitable discrepancies in PY predictions [22]. First, the model's sensitivity to changing climate conditions depends on the representativeness of the training dataset. The use of different datasets for model calibration leads to divergent responses of critical crop traits to climate variations [23; 24; 25]. Second, plants adjust their biochemical and structural traits to optimize resource use in response to local environmental conditions [26]. Yet, few existing crop models incorporate a plant optimization scheme to guide the modeling process [27; 28]). The recently emphasized eco-evolutionary optimality (EEO) theory offers new perspectives for predicting plant traits under evolving climatic conditions, presenting an innovative and efficient approach for crop modeling and PY prediction [26]. This theory suggests that plants continuously adjust their traits to optimize the use of natural resources [29]. Based on specific optimality criteria, the balance between the benefits and costs of various plant processes can be quantitatively characterized and solved numerically. This approach enables an optimized response to changing climate conditions, standing in contrast to traditional methods that rely on static parameters. Building on the EEO theory, a universally applicable photosynthesis model for all C3 plants, known as the Pmodel, has been developed. This model forms the foundation for the next generation of land surface models [30] and crop modeling efforts [31]. The Pmodel demonstrates that the microscopic enzymatic reactions within plant organs follow macroscopic optimization criteria. This optimal macroscopic behavior is further supported by the synchronized adaptation of leaf mitochondrial respiration and photosynthetic capacity [32]. As a result, canopy conductance and transpiration can be predicted based on the universal optimization tendencies of leaf stomata [24; 33]. By leveraging the Pmodel alongside observations of leaf area index (LAI), it becomes feasible to diagnose wheat yield [34]. To transition from a diagnostic crop model to a predictive one, the limiting effects of local environmental factors on marginal growth--specifically the peak LAI during the growing season--can be quantified by the synergistic constraints of available energy and water supply [35]. This enables predictions of wheat growth processes and potential yield under future scenarios [11]. However, current PY predictions do not account for variations and optimizations in crop phenology, particularly overlooking the response of sowing dates to climate change. Enhancing the synchronization of crop growth with resource availability could increase photosynthetic rates during key growth stages, leading to higher wheat PY [36]. Therefore, accurately predicting PY by integrating optimized growth processes and sowing date projections becomes critically important. Given the lack of an optimality-based crop growth basis and adjustable sowing schemes in existing wheat potential yield prediction attempts, this study integrates forecasts of crop growth with corresponding adjustments to sowing dates, drawing on the principles of the EEO theory. Through this combined approach, we project the trajectory of PY in China under various shared socioeconomic pathways (SSPs). The findings provide critical support for policy development aimed at mitigating the vulnerabilities of the agricultural system to climate change. Furthermore, the results offer a guiding framework for implementing strategies to enhance yield outcomes [37].