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Article

Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles

by
Shen Tan
1,2,†,
Shengchao Qiao
3,4,†,
Han Wang
4 and
Sheng Chang
5,*
1
State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China
2
Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
3
School of Ecology, Hainan University, Haikou 570228, China
4
Department of Earth System Science, Tsinghua University, Beijing 100084, China
5
Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100101, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2024, 14(11), 2058; https://doi.org/10.3390/agriculture14112058
Submission received: 24 September 2024 / Revised: 9 November 2024 / Accepted: 13 November 2024 / Published: 15 November 2024
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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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 Pmodel, 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 CO2 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.

1. Introduction

Sufficient and sustainable food supplementation is a crucial prerequisite for societal stability and progress. In the context of a warming climate and rising atmospheric CO2 levels, ensuring food security for a growing global population poses new challenges for the agricultural sector and its related industries [1,2,3]. Improving the accuracy of future crop growth predictions by deepening our understanding of how crops respond to climatic trends and fluctuations is key to strengthening resilience against climate change [4]. Wheat, as a vital staple crop, contributes approximately 20% of the global caloric intake [5]. Notably, only 30% of the world’s wheat farmlands have access to sufficient irrigation systems, rendering wheat more vulnerable to future conditions of increased aridity and temperature compared to cash crops. This vulnerability highlights wheat’s importance in research focused on agriculture’s response to climate change [6,7]. The concept of 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 95th 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 CO2 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]. 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].

2. Material and Methods

2.1. Data

This study encompasses the entirety of China, where the wheat is cultivated in most provinces. The representation of the wheat-planted area proportion is adopted from the EARTHSTAT database (accessible at: http://www.earthstat.org/, last accessed: 12 November 2024). This particular dataset is an amalgamation of diverse data sources, encompassing local agricultural surveys and RS images [38]. Throughout the future projection, we assume a consistent wheat cultivation area, i.e., utilizing an unchanging map depicting the proportion of wheat-planted areas. Notably, the water supplement strategies for irrigated and rainfed wheat diverge; precipitation imposes growth restrictions on nonirrigated wheat, whereas the impact of this constraint can be mitigated through irrigation practices. We obtained the map of the two irrigation schemes from the MIRCA2000 dataset, which integrates observations from a variety of data sources [6]. The spatial distribution of China’s wheat-planted area proportion and the two irrigation schemes is visually depicted in Figure 1.
The future projection hinges on climate data extracted from forthcoming scenarios. The prediction of wheat PY necessitates the inclusion of six climate indicators: air temperature (Ta), vapor pressure deficit (VPD), ambient CO2 concentration (Ca), precipitation (Pre), surface pressure (Press), and downwelling shortwave radiation (SW). The annual Ca values corresponding to the two selected pathways were provided by Riahi et al. [39]. We sourced meteorological metrics from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP-3b). This dataset underwent bias correction through comparison with the corrected ERA5 observational data [40]. For this study, two plausible SSPs are considered [41]: the SSP126 pathway characterized by low radiative forcing (2.6 W/m2 in 2100) and the SSP370 pathway representing a medium to high radiative forcing (7.0 W/m2 in 2100). To drive the potential yield prediction, the average outputs of five models are employed: GFDL-ESM4, MPI-ESM1-2-HR, MRI-ESM2-0, IPSLCM6A-LR and UKESM1-0-LL.
To evaluate the resulting PY from this study, we also collect wheat PY prediction from other typical crop models, including CLMcrop [42], EpicBoku [43], EpicIIASA [44], EpicTAMU [45], Gepic [46], and ORCHIDEE-crop [47].

2.2. Predicting Wheat PY Based on the EEO Theory

There are two major tasks in predicting the wheat PY under future climate scenarios: predicting the wheat growth and the sowing date (Figure 2). To predict the wheat growth process in future scenarios, the assimilated carbon from photosynthesis process provides the material basis, which is estimated by the Pmodel. The wheat growth is constrained by the available resources from the local environment. This constraint performs as a limited peak LAI, which can be described by an energy–water synergistic limitation [35]. Secondly, the changing climate affects the phenology of wheat growth, i.e., a warmer climate allows an earlier sowing date. An optimization of the sowing data enhances the synchronization of crop growth with local seasonal cycling. We can find the sowing date according to the maximum yield that can be achieved [48].

2.2.1. Simulating Wheat Growth

The wheat growth is driven by the gross primary production (GPP) estimated by the Pmodel. Based on the EEO theory, the Pmodel considers the plants that can optimize the stomatal behavior to balance the carbon gain and water loss, i.e., a marginal water use efficiency [49]. A least cost of maintaining the carbon assimilation capacity was also taken into the optimizing criterion [29]. The GPP can be estimated by light use efficiency from
G P P = P A R × f A P A R × L U E
where PAR represents the photosynthetically active radiation, which takes up a proportion of downwelling shortwave radiation; fAPAR represents the fraction of absorbed photosynthetically active radiation, which can be estimated from LAI by the Beer–Lambert law; LUE represents the light use efficiency calculated from Ta, VPD, press, and Ca. A detailed description of how this LUE is estimated in the Pmodel can be found in the existing literatures [31,33,50].

2.2.2. Environmental Constraint on Peak LAI

To predict the wheat growth under future scenarios, the temporal variation of LAI follows the strategy suggested by the global dynamic global vegetation model: LPJmL4 [51]. The LAI at time step t ( L A I t ) can be written as a function of peak LAI ( L A I m a x ) and a factor of phenological development ranges from 0 to 1 ( f t ,   r e f ):
L A I t = f t ,   r e f ×   L A I m a x
Traditional static L A I m a x lookup tables ignore the response of this metrics to the climate change [52]. As suggested by Qiao et al. [11] and Zhu et al. [35], this peak greenness is synergistically limited by the absorbed carbon, or, say, available energy, and by water supplement:
L A I m a x = min ⁡ ( L A I c , L A I w )
L A I c represents the peak LAI under the condition that the wheat growth is not limited by the water availability, i.e., no soil moisture stress is employed during the GPP estimation. Part of the total GPP during the growing season ( f L A I ) would be apportioned to the leaves:
∑ G P P = f L A I × L A I c × L M A
where leaf mass per area (LMA) represents the thickness of the leaves. Wang et al. [53] suggested a universal optimizing scheme to predict LMA. By integrating Equation (1) to (4), the L A I c can be solved by an iteration.
Within a rainfed wheat system, the wheat growth is limited by the water availability. The total evapotranspiration (ET) throughout the growing season follows a part of the precipitation:
∑ E T = f E T × P r e
The proportion of ET to total precipitation ( f E T ) can be estimated by a Budyko function [54]:
E T / P r e = P E T / P r e [ 1 + P E T / P r e n ] 1 / n
where PET is the potential evapotranspiration and n is the shape metrics of the Budyko curve that can be estimated by the leaf area [55]. The ET can be estimated based on the Penman–Monteith (PM) equation. A key metric within the PM equation, the canopy conductance ( G s ), can be estimated by a universal ET model suggested by Tan et al. [33] to avoid employing type-based parameters:
G s = 1.6 · G P P / ( c a − c i )
where 1.6 represents the difference in diffusion capacity of CO2 and H2O molecules, and c i is the intrinsic CO2 concentration, which can be estimated by the Pmodel [33]. During the ET calculation, a denser canopy, i.e., a higher LAI value, means that a higher proportion of radiative energy is intercepted by the canopy [56]. By integrating Equation (5) to (6), the L A I w can be solved by an iteration.

2.2.3. Predicting the Optimized Sowing Date

The prediction of optimized sowing dates is based on the theory that an adequately managed agricultural system will optimize the crop calendar to maximize PY. We use two thresholds to constrain sowing dates. First, winter wheat cannot survive extreme cold; therefore, we set a threshold of −10 °C for the mean temperature of the coldest month to determine whether winter wheat or spring wheat should be planted [57]. Second, heavy monsoon precipitation can hinder wheat germination, so a monthly precipitation exceeding 150 mm is considered unsuitable for winter wheat sowing [58]. We assume that winter wheat can be sown at any time, and we simulate PY (using the methods described in Section 2.2.1 and Section 2.2.2) for all possible sowing dates to identify the optimal date that maximizes PY. The effectiveness of this approach has been validated through a global-scale evaluation [48].

2.3. Experiment Designation

This study comprises four distinct experiments (Figure 1). Firstly, we evaluate our model based on the PY estimation from EARTHSTAT and other crop models. Secondly, we conduct simulations to examine the impacts of climate change on wheat growth. Throughout the model configuration process, we omit the potential variation in wheat phenology, opting to use a fixed sowing date derived from the year 2000 CE. Subsequently, our focus shifts to predicting alterations in the sowing date through an approach centered on optimizing wheat yield. This involves forecasting shifts in the sowing date that are conducive to attaining optimal yield outcomes. Lastly, this study endeavors to synthesize the climate’s influence on wheat growth with the dynamically adjusted sowing date. The PYs are predicted based on wheat planting area proportion and the predicted PY of each grid. The temporal variation trend is then calculated by applying a linear fit for each major wheat-producing province. Within the scope of these simulations, we operate on a weekly temporal resolution. This choice is underpinned by the recognition that plants necessitate a period to adapt and acclimate to their surroundings.

3. Results

3.1. Model Evaluation

The PY predictions from this study align with the global patterns reported by EARTHSTAT (Figure 3). Wheat in the Northeast Plain and North China Plain shows higher PY due to abundant energy and water resources, while the arid climate and limited irrigation infrastructure in the Mongolian Plateau result in lower PY. In contrast, typical crop models fail to capture these regional variations in PY. The statistical metrics, MAE (mean absolute difference) and RMSE (root mean squared error), highlight the superior performance of our model: MAE = 1.58 ton/ha and RMSE = 1.44 ton/ha, both of which are notably better than those of other models. Furthermore, our model predicts a PY frequency distribution that is closer to the EARTHSTAT benchmark, with more regions exhibiting PY levels between 4 and 6 tons per hectare, compared to the less than 4 tons per hectare predicted by other crop models (Figure 4). We attribute this superior performance to the accurate prediction of wheat traits based on EEO theory, rather than relying on static parameters. The subsequent analysis is conducted based on our model’s predictions.

3.2. The Wheat Growth Responses to the Climate Change

Considering the two focal SSPs, the responses of wheat PY to climate change manifest two distinct stages, demarcated by the year 2060 (Figure 5). Under SSP126, wheat cultivation in the primary production zones of central China is anticipated to experience favorable effects stemming from climate change, resulting in an upswing in PY. This trajectory underscores the dominance of CO2 fertilization benefits, outweighing the adverse impacts arising from rising temperatures. If robust measures are implemented to control greenhouse gas emissions, the escalating trend in ambient CO2 concentration (Ca) is projected to peak around 2060, subsequently dampening the fertilization effect. Consequently, the increase in wheat PY within substantial regions is projected to diminish. For the majority of production areas, the downward trend in PY remains statistically insignificant (p > 0.05). Under SSP370, characterized by a 0.6 °C elevation in temperatures by 2060, the temperature sensitivity (Ta-induced effect) is more pronounced, exerting a formidable negative influence on wheat growth. This effect offsets the benefits of CO2 fertilization. While a majority of the northern production regions exhibit negligible trends, the escalation in Ca persists beyond 2060, fostering a significant nationwide enhancement in wheat PY.

3.3. Trend of the Optimized Sowing Date

The fluctuations in the optimized sowing date similarly exhibit a two-stage pattern across the two designated SSPs, as depicted in Figure 6. Overall, a warmer climate context empowers agricultural practices to select sowing dates from a broader spectrum, thereby augmenting the likelihood of aligning wheat’s phenology curve with the natural seasonal cycle. This synchronization enhances wheat growth prospects [36,59]. Within the context of SSP126, owing to the advantages conferred by warming conditions, a significant trend emerges, with most of China’s production regions experiencing an advancement in the optimal sowing date. Notably, the northwest regions manifest a particularly robust advance due to the more pronounced warming trend. However, with the temperature escalation being regulated around 2060, the optimized sowing date ceases to exhibit statistically significant trends thereafter (p > 0.05). Under SSP370, characterized by a comparatively less constrained greenhouse gas emissions control and, hence, a more intense warming trend, the advance in the optimal sowing date is even more pronounced. This results in an approximate advancement of around ten days across the majority of the country.

3.4. Trend of the Wheat PY in China

The response of wheat PW in China to climate change diverges under the two characteristic SSPs, as displayed in Figure 7. The observed outcomes underscore a two-stage trajectory in wheat PY, with a peak projected around 2060. Notably, the incorporation of sowing date optimization aligns with this overarching trend while additionally bolstering PY (5.7%, Figure 7). Substantial increases in PY are anticipated before 2060 within key wheat-producing provinces such as Shandong, Henan, and Jiangsu (Figure 8a and Table 1). Subsequently, a nationwide PY trend of statistically insignificant magnitude is anticipated (Figure 8b). Within the context of SSP370, the majority of regions, with the exception of select nonmain production zones in southwestern China, display insignificant trends. Over this timeframe, sowing date optimization is projected to yield an additional PY of 3.7%. Subsequently, from 2061 to 2100, a substantial PY enhancement is foreseen due to the concurrent benefits stemming from both optimized sowing dates and elevated CO2 concentrations.

4. Discussion

4.1. 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, Qiao 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 (R2 = 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 CO2 fertilization and facilitated growth predictions in both natural vegetation (Zhu et al., 2023) [35] and wheat [11]. Moreover, Qiao 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.

4.2. Implications

In accordance with our findings, an increase in both atmospheric CO2 concentration and precipitation contributes to an augmentation of wheat PY. Conversely, a warmer climate poses challenges for wheat growth while concurrently providing an opportunity to select an earlier sowing date, which enhances PY. These observations underscore two principal strategies aimed at boosting wheat PY or reducing the yield gap between actual yield and PY through enhanced resource utilization. Firstly, ensuring an adequate water supply emerges as a pivotal step to augment wheat PY in rainfed agricultural areas and to mitigate the yield gap in irrigated regions. In the future, there is a notable likelihood of heightened radiation [61] and warmer climatic conditions [62], resulting in elevated VPD and consequently increasing ET. This trend necessitates an increased water allocation to counterbalance the growing demand [24]. Adequate water supplementation becomes indispensable, especially within irrigation areas, while simultaneously enhancing farmlands’ water conservation capabilities [22] and improving irrigation efficiency, leading to enhanced water use efficiency [63]. Secondly, optimizing the sowing date emerges as a valuable strategy, as it fosters better synchronization of crop growth with radiative cycles, thereby augmenting wheat PY. Furthermore, enhancing light interception efficiency through the optimization of vertical leaf profiles via genetic engineering has also demonstrated effectiveness in bolstering wheat PY [64].

5. Conclusions

In this study, we harmonized three methods founded on the principles of the EEO theory: the simulation of wheat growth, the projection of environmental limitations, and the optimization of the sowing date. These were amalgamated to predict variations in China’s wheat PY across two representative SSPs. From our findings, several conclusions can be drawn: 1. A consistent rising atmospheric CO2 concentration universally benefits the enhancement of PY, while elevating temperatures negatively impact PY in the majority of regions if we neglect the optimization of future sowing date. The evolution of PY exhibits a discernible two-stage pattern under both SSPs, with a turning point converging around the year 2060. 2. Warmer environmental conditions afford agricultural practitioners the opportunity to opt for an earlier sowing date, thus facilitating a more synchronized crop growth cycle with the availability of resources. Notably, a substantial advancement of the sowing date is projected if farmers intend to optimize yield outcomes. 3. Through the simultaneous integration of wheat growth and optimized sowing date predictions, a notable increase in wheat PY is anticipated from 2021 to 2060 under SSP126, followed by an insignificant trend. In contrast, under SSP370, no substantial variation is foreseen prior to 2060, with a significant increase projected thereafter. As our model does not include type-specific parameters and provides a more reliable response of the wheat planting system to climate change, the wheat PY predictions from this study can be made with greater confidence. A reasonable prediction of the resulting data demonstrates this success. Future PY predictions that encompass a more comprehensive range, such as global-scale predictions, hold great promise for implementation.

Author Contributions

Conceptualization, S.Q., H.W. and S.T.; methodology, S.Q., H.W. and S.T.; software, S.Q.; validation, S.T.; formal analysis, S.Q. and S.T.; investigation, S.C. and H.W.; resources, S.C. and H.W.; data curation, S.C. and H.W.; writing—original draft preparation, S.Q. and S.T.; writing—review and editing, S.C. and H.W.; visualization, S.Q. and S.T.; supervision, H.W. and S.C.; project administration, H.W. and S.C.; funding acquisition, H.W. and S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China under grants 42271406 and 72140005, and in part by the National Natural Science Foundation of China (42001356 and 32301392).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All input data used in this study can be collected with open access. The codes for PY generation in this study are available on reasonable request.

Acknowledgments

We acknowledge the contribution from reviewers.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of cultivated wheat area proportion in China. The black points represent the wheat farmland in this grid that is mainly irrigated.
Figure 1. Map of cultivated wheat area proportion in China. The black points represent the wheat farmland in this grid that is mainly irrigated.
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Figure 2. Workflow for predicting wheat PY based on the EEO theory. In the method block, three major methods are in bold and will be introduced in the following sections. The results of all experiments will be demonstrated in the corresponding sections.
Figure 2. Workflow for predicting wheat PY based on the EEO theory. In the method block, three major methods are in bold and will be introduced in the following sections. The results of all experiments will be demonstrated in the corresponding sections.
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Figure 3. Comparison of wheat PY prediction maps from various sources against EARTHSTAT (a). In addition to the typical crop models shown in panel (b), other models include (c) CLMcrop, (d) EpicBoku, (e) EpicIIASA, (f) EpicTAMU, (g) Gepic, and (h) ORCHIDEE-crop. MAE and RMSE are calculated after masking nonwheat grids. The units for both MAE and RMSE are tons per hectare.
Figure 3. Comparison of wheat PY prediction maps from various sources against EARTHSTAT (a). In addition to the typical crop models shown in panel (b), other models include (c) CLMcrop, (d) EpicBoku, (e) EpicIIASA, (f) EpicTAMU, (g) Gepic, and (h) ORCHIDEE-crop. MAE and RMSE are calculated after masking nonwheat grids. The units for both MAE and RMSE are tons per hectare.
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Figure 4. PY frequency comparison of EARTHSTAT, this study, and ensembled results of other crop models. The shade of model ensemble represents the 95% confidence interval of different model results.
Figure 4. PY frequency comparison of EARTHSTAT, this study, and ensembled results of other crop models. The shade of model ensemble represents the 95% confidence interval of different model results.
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Figure 5. Wheat PY prediction considering the response to the climate change (with static sowing date). (a,b) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points.
Figure 5. Wheat PY prediction considering the response to the climate change (with static sowing date). (a,b) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points.
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Figure 6. Prediction of optimized wheat sowing date. (a,b) represent the sowing date variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the sowing date variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points. A positive value in this figure represents an advance of the sowing date.
Figure 6. Prediction of optimized wheat sowing date. (a,b) represent the sowing date variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the sowing date variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points. A positive value in this figure represents an advance of the sowing date.
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Figure 7. Prediction for relative variation of total wheat PY in China. We use the average PY during 2011 to 2020 as the benchmark. Dashed line represents the annual wheat PY, thick line represents the PY trend after smoothing. Two simulation configurations, with and without the optimizing sowing date, are represented by two colors.
Figure 7. Prediction for relative variation of total wheat PY in China. We use the average PY during 2011 to 2020 as the benchmark. Dashed line represents the annual wheat PY, thick line represents the PY trend after smoothing. Two simulation configurations, with and without the optimizing sowing date, are represented by two colors.
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Figure 8. Prediction for wheat PY in China. (a,b) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points.
Figure 8. Prediction for wheat PY in China. (a,b) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 126, respectively; (c,d) represent the PY variation from 2021 to 2060 and 2061 to 2100 under SSP 370, respectively. The grid with significant variation trend is labeled with the black points.
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Table 1. Annual variation of wheat PY in main production provinces. Only seven production provinces are listed. The rank of the table represents the wheat production. We use the average PY during 2011 to 2020 as the benchmark.
Table 1. Annual variation of wheat PY in main production provinces. Only seven production provinces are listed. The rank of the table represents the wheat production. We use the average PY during 2011 to 2020 as the benchmark.
ProvinceSSP126SSP370
2021–20602061–21002021–20602061–2100
Henan0.72 ± 0.06% ***−0.02 ± 0.10%0.07 ± 0.12%0.74 ± 0.19% **
Shandong0.45 ± 0.15% **−0.03 ± 0.15%0.16 ± 0.15%0.30 ± 0.18% *
Hebei0.43 ± 0.18% *0.04 ± 0.24%0.20 ± 0.18%0.25 ± 0.12% *
Jiangsu0.17 ± 0.02% ***0.01 ± 0.02%0.04 ± 0.03%0.22 ± 0.04% **
Anhui0.20 ± 0.03% ***0.00 ± 0.04%0.03 ± 0.04%0.23 ± 0.05% **
Sichuan0.09 ± 0.02% **−0.03 ± 0.04%0.00 ± 0.02%0.06 ± 0.03% *
Shannxi0.13 ± 0.002% **−0.02 ± 0.03%0.04 ± 0.03%0.11 ± 0.03% *
Note: Significance: “***” is p < 0.001, “**” is p < 0.01, “*” is p < 0.05.
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Tan, S.; Qiao, S.; Wang, H.; Chang, S. Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles. Agriculture 2024, 14, 2058. https://doi.org/10.3390/agriculture14112058

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Tan S, Qiao S, Wang H, Chang S. Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles. Agriculture. 2024; 14(11):2058. https://doi.org/10.3390/agriculture14112058

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Tan, Shen, Shengchao Qiao, Han Wang, and Sheng Chang. 2024. "Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles" Agriculture 14, no. 11: 2058. https://doi.org/10.3390/agriculture14112058

APA Style

Tan, S., Qiao, S., Wang, H., & Chang, S. (2024). Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles. Agriculture, 14(11), 2058. https://doi.org/10.3390/agriculture14112058

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