# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
###### Abstract  
Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Phmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO\({}_{2}\) concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China's main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020-2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security. potential yield (PY) in wheat refers to the maximum yield attainable, determined solely by wheat variety and climatic factors. PY serves as a benchmark for optimizing agricultural management strategies [8]. Numerous studies have utilized wheat PY as a key metric for understanding agriculture's response to climate-related risks [9; 10]. Assessing both the positive and negative impacts of climate variations on wheat growth helps establish yield enhancement goals [11], ultimately facilitating the identification of strategies to increase wheat production [12]. In addition to estimating wheat PY through field experiments conducted under optimal growth conditions, two alternative strategies can be utilized. The first approach is data-driven [11], which involves using historical PY data, such as maximum or 95 th percentile yields, to establish a relationship between PY and corresponding climate indicators. This relationship can then be extrapolated globally or toward future scenarios [13; 14; 15]. Typically, climate and environmental indicators such as air temperature, precipitation, and solar radiation are incorporated into nonlinear fitting algorithms [13]. In recent years, machine learning algorithms capable of identifying complex interactions between crop growth and climate variability have emerged. These models often include numerous trainable parameters, allowing for improved performance over traditional statistical methods, provided a representative training dataset is available [16]. With the right inputs, a data-driven approach can generate a global PY projection, serving as a valuable benchmark for related research [8]. However, this method relies heavily on historical data to establish the climate-PY relationship, which limits its predictive accuracy when applied to scenarios involving unprecedented climate conditions and CO\({}_{2}\) concentrations [17]. Moreover, the scarcity of training data in less-developed regions can weaken the reliability of these predictions [11]. The second strategy for estimating wheat PY involves the use of crop models. This approach simulates key crop growth processes, such as photosynthesis, respiration, and carbon allocation to various plant parts, through numerical representations based on meteorological data and inputs from remote sensing (RS) observations [18; 19].