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Article

Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios

by
Yifei Xu
1,2,
Te Li
3,*,
Min Xu
1,2,*,
Shuanghe Shen
4 and
Ling Tan
5
1
Jiangsu Climate Center, Nanjing 210018, China
2
Jintan National Climate Observatory, Changzhou 213212, China
3
Jiangsu Meteorological Observatory, Nanjing 210018, China
4
School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
5
College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China
*
Authors to whom correspondence should be addressed.
Agriculture 2025, 15(15), 1606; https://doi.org/10.3390/agriculture15151606
Submission received: 24 June 2025 / Revised: 23 July 2025 / Accepted: 24 July 2025 / Published: 25 July 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions.

1. Introduction

Global climate change, characterized by an increasing frequency of extreme weather events, has profoundly affected agriculture. Variability in key climatic factors—such as temperature, precipitation, and solar radiation—has altered crop phenology and the distribution of climate resources. Crop phenology is a critical determinant of yield, as its duration reflects a crop’s adaptability to environmental conditions and directly influences yield variability [1,2,3]. Notably, crops require different climate resources at various growth stages. Strong alignment between climate resources and crop growth stages promotes higher yields, while poor alignment is detrimental [4]. Therefore, assessing future changes in crop phenology and climate resource variability using climate suitability models is essential for optimizing resource use, improving productivity, and ensuring food security. Such research provides a foundation for adapting agricultural practices to changing climatic conditions.
Climate suitability models effectively quantify the sensitivity of crop growth to climate factors and assess changes in potential climate resources [5,6]. By employing fuzzy mathematics and membership functions, these models translate climate resources into specific values for various growth stages [5,7,8]. This approach facilitates the evaluation of variability in climate resources across different crop growth periods and supports regional crop planning.
Despite the recognized importance of phenology and climate suitability changes, accurately capturing future climate suitability variability across crop growth stages remains challenging [1,9,10]. While some uncertainty from future scenarios can be addressed by projected emissions in global climate models (GCMs) [11], inaccuracies in future climate projections persist. Different responses and feedbacks may also emerge under the same forcing due to varying model parameterizations [12,13]. The complex nature of crop growth processes suggests that the characteristics and impacts of future climate change on crop phenology, such as duration and spatial distribution, may vary significantly under different scenarios [14,15]. Nonlinear relationships between crop growth and climatic drivers further complicate the elucidation of the specific influences exerted by individual factors on phenological changes [16,17]. Therefore, it is essential to comprehensively assess future spatiotemporal changes in crop phenology and to identify the underlying drivers under different scenarios.
GCMs provide extensive climate datasets under various scenarios and play a vital role in projecting future climate and agricultural changes. Compared with previous generations, the Coupled Model Intercomparison Project Phase 6 (CMIP6) emphasizes the Earth system’s response to climate forcing and the assessment of future climate variability [18,19]. Although CMIP6 models are valuable for assessing crop phenology shifts and climate projections, inaccuracies in simulating climate factors persist [20,21]. For example, raw CMIP6 models tend to underestimate temperature and precipitation across the Huang-Huai-Hai (3H) Plain and overestimate solar radiation in areas south of 34° N [22]. Multi-model ensemble (MME) and bias-correction methods effectively reduce uncertainties and improve the accuracy of climate projections [23]. Using raw or bias-corrected climate datasets can yield divergent results when assessing crop phenology and climate resource responses to future climate change. Accurate identification of crop phenological timing requires the use of high-quality climate datasets for future scenario analyses. In this study, bias-corrected CMIP6 datasets processed using the equidistant cumulative distribution function (EDCDF) method were used to project climate resources and crop phenology. This approach significantly improves the spatial distribution and accuracy of climate variables [22,24,25].
Integrating crop models with climate datasets under future scenarios is a robust approach for detecting changes in crop phenology and analyzing the impacts of climate change on phenological variability [6,26]. Process-based crop models, such as the Decision Support System for Agrotechnology Transfer (DSSAT), simulate complex interactions among crop varieties, management practices, and climate variables [27]. The DSSAT model has been widely applied to identify future changes in crop phenology [28], yield and planting zoning adjustment [26]. It is capable of simulating crop growth processes on a daily timescale and is applicable across diverse terrains and site-specific conditions [22,29,30]. Furthermore, DSSAT has been applied at both regional and global scales [31,32], providing a comprehensive framework for analyzing spatial and temporal changes in crop phenology.
Advances in computer technology have facilitated the increasing application of machine learning (ML) methods in agriculture, owing to their strengths in extensive data analysis and reduced overfitting when identifying features [33,34]. ML is particularly effective in disentangling complex, nonlinear relationships between climate predictors and response variables [35,36,37,38]. For instance, Feng et al. [38] combined crop modeling with the Random Forest method to identify major factors contributing to yield losses, revealing that increased heat events during the grain-filling period were the principal cause of reduced winter wheat yields. Similarly, Chlingaryan et al. [39] integrated remote sensing with ML techniques to comprehensively assess crop yield prediction and nitrogen status estimation, demonstrating ML’s superior ability to detect nonlinear patterns in long-term datasets. Given these advantages, leveraging ML methods is crucial for exploring future changes in winter wheat phenology, assessing climate suitability, and uncovering the underlying mechanisms driving these changes.
Understanding the influence of future climate scenarios on crop phenology and climate resource suitability is vital for developing adaptation strategies and ensuring regional food security. However, there is a critical gap in comprehensive, region-specific dynamic assessments that integrate advanced climate projections with robust crop modeling and quantitative analysis of climate drivers. To address this gap, this study aims to characterize the spatiotemporal dynamics of crop phenology and climate suitability for winter wheat in the 3H Plain under two climate scenarios in the 21st century. This is achieved by integrating bias-corrected CMIP6 climate projections and the DSSAT CERES-Wheat crop model. Additionally, a machine learning approach was employed to disentangle the roles of various climate factors in phenological changes. The specific objectives of this study were to: (1) identify the spatial patterns of key growth stages—vegetative growth period (VGP) and reproductive growth period (RGP)—of winter wheat by integrating the DSSAT crop model with CMIP6 climate models; (2) determine the climatic factors driving changes in winter wheat phenology using the Random Forest model; (3) develop a climate suitability model encompassing temperature, precipitation, and solar radiation for different growth periods of winter wheat; and (4) investigate the spatial distributions and regional divisions of climate suitability for winter wheat across the 3H Plain under future scenarios.

2. Data and Methods

2.1. Study Area

The 3H Plain (112.3–120.7° E, 31.1–40.3° N), bounded to the east by the Yellow Sea, covers approximately 3.5 × 105 km2 and includes the Hebei, Shandong, Henan, Anhui and Jiangsu provinces (Figure 1). The region experiences a temperate monsoon climate, with more than 70% of the annual precipitation occurring between July and September [40]. The 3H Plain comprises 1.4 × 105 km2 of arable land [41] and is a major grain-producing region in China, contributing over 70% of the nation’s winter wheat yield. Winter wheat is typically sown in October and harvested in June of the following year, with only about 30% of annual precipitation occurring during the wheat growing season [42].

2.2. Data

Reanalysis datasets were chosen as the primary source of observational data, consistent with the recommendations in the official DSSAT guidelines. The reanalysis data, spanning 1902–2020, were obtained from the Climatic Research Unit (CRU TS 4.04) (https://crudata.uea.ac.uk/cru/data/hrg/ accessed on 2 January 2025) and the NOAA-CIRES 20th Century Reanalysis V2c (https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV2c.html accessed on 5 January 2025). These datasets provide monthly temperature, precipitation, and number of wet days, as well as daily solar radiation, serving as the observational foundation for calibrating both the CMIP6 outputs and winter wheat cultivar simulations.
Seven CMIP6 models, covering the period from 1950 to 2100 at a monthly timescale, were utilized as summarized in Table 1. In this study, seven models were selected based on the following criteria: (1) demonstrated good performance in reproducing the spatial distributions of climate factors; (2) did not utilize the same atmospheric or oceanic models; and (3) were developed by different institutions to avoid similar biases from near-duplicate models. Future climate data—including temperature, precipitation, and solar radiation—under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) were calibrated and validated for the selected models using the equidistant cumulative distribution function (EDCDF) method, as previously described [22]. The bias-corrected results closely align with observations, making them suitable for simulating future shifts in winter wheat phenology and changes in climate suitability.

2.3. DSSAT CERES-Wheat Model Simulation

The DSSAT CERES-Wheat model was employed to determine the timing and duration of key phenological phases of winter wheat, specifically the vegetative growth period (VGP, sowing to flowering), reproductive growth period (RGP, flowering to maturity), and whole growth period (WGP, sowing to maturity). Identifying these phases is essential for analyzing crop responses to climate stress during distinct developmental stages. The DSSAT CERES-wheat model has been widely utilized to assess the impacts of climate change on crop phenology and yield, and it is particularly suitable for application in the Chinese region [43,44]. Model inputs included climate variables (maximum and minimum temperature, precipitation, and solar radiation), management information, cultivar coefficients, and soil parameters. Historical climate datasets were obtained from CRU, and future climate datasets were sourced from the World Climate Research Programme (WCRP). Soil parameters and management practices across the 3H Plain were adopted from previous studies [44,45]. Cultivar coefficients for winter wheat were adopted from Xu et al. [22,44] (Table A1). The model performance was evaluated by using observed yields from 5 sites across the 3H Plain (2013–2020), these locations exhibit a range of geographical characteristics, a diversity underscored in the works of Qu et al. [9] and Li et al. [46]. The average Relative Root Mean Square Error (RRMSE) for yield was 10.66% (Figure A1), indicating that the simulated yields closely matched observed yields and demonstrating the reliability and suitability of these coefficients.

2.4. The Random Forest Method

A Random Forest regression model, implemented using the RandomForestRegressor from the Scikit-learn library in Python 3.12, is used to assess the relationship between the selected predictors and the target variable. A total of 100 decision trees are used, with all other hyperparameters maintained at their default values unless otherwise specified. The model is trained on the full dataset without partitioning, as the primary objective is to evaluate feature importance rather than predictive performance. Feature importance scores were calculated based on the mean decrease in impurity (MDI), a standard approach in Random Forests for quantifying the relative contribution of each predictor. To ensure model stability, the random seed (random_state = 42) is fixed and bootstrapping is enabled. The maximum tree depth, minimum samples per split, and other parameters follow the default settings of the Scikit-learn implementation unless otherwise noted.

2.5. The Climate Suitability Model

2.5.1. The Temperature Suitability Model

The temperature suitability of winter wheat is determined by the upper, optimal, and lower temperature thresholds for each growth stage. Optimal temperatures promote rapid growth, whereas temperatures below the lower limit or above the upper limit hinder development [41]. Temperature suitability was calculated as follows:
T t i j = 0 ,     t i j < t l j ( t i j − t l j ) ( t h i − t i j ) C ( t o j − t l j ) ( t h i − t o j ) C 0 ,     t i j ≥ t h j ,   t l j ≤ t i j ≤ t h j
C = t h i − t o i t o i − t l i
where T ( t i j ) is the temperature suitability in the ith growth period of the jth year; t i j is the average temperature for the ith growth stage of the jth year (°C); t l i , t h i , and t o i are the lower limit, upper limit, and optimal temperatures for the ith growth period (°C), respectively. The specific values of Th, Tl and To were taken from previous studies and are shown in Table 2 [5,47].

2.5.2. The Precipitation Suitability Model

Precipitation suitability was categorized as drought, normal, or flood, and was calculated as follows [48,49]:
P p i j = p i j E T C i , p i j < G × E T C i       D r o u g h t   t y p e 1 , G · E T C i ≤ p i j < H · E T C i       N o r m a l   t y p e E T C i p i j , p i j ≥ H × E T C i       F l o o d   t y p e
where P ( p i j ) is the precipitation suitability in the ith growth period of the jth year; p i j and ETC are the accumulated precipitation and crop water requirement in the ith growth stage of the ith year (mm), respectively; G and H are the ratios of precipitation to water requirement for slight drought and slight flood, respectively. According to previous studies, G and H are set to 0.6 and 1.5, respectively, for winter wheat in the 3H Plain [47,50]. The crop water requirement E T c was calculated as:
E T c = K c × E T 0
where K c is the crop coefficient and E T 0 is the reference crop evapotranspiration. Values for Kc during different growth stages were sourced from previous studies [10,47,51] and are listed in Table 2. E T 0 was calculated using the Hargreaves formula:
E T 0 = K × T m a x − T m i n n × ( T m a x + T m i n 2 + T o f f ) × R a ρ λ
R s = ( a s + d s n a N ) R a
R a = 118.08 π d r ω s sin ( φ ) sin ( δ ) + cos ( φ ) cos ( δ ) sin ( ω s )
ω s = cos − 1 [ − tan ( φ ) ] tan ( δ )
d r = 1 + 0.033 cos ( 2 π D J )
δ = 0.409 sin ( 2 π D J − 1.39 )
where K, n and T o f f are the conversion coefficient, index coefficient, and temperature constant, respectively, with localized values referenced from Tang et al. [52]. R a is the extraterrestrial radiation (MJ m−2d−1); λ is the latent heat of vaporization (2.45 MJ kg−1), and ρ is the density of water (103 kg m−3 at 4 °C). R S is the solar shortwave radiation (MJ m−2 d−1), n 1 is the actual solar radiation duration (h), N is the maximum possible solar radiation duration (h), and a s and d s are the regression constants. d r is the relative distance between the sun and Earth, ω s is the solar hour angle (rad), φ is the geographic latitude (rad), δ is the solar declination (rad), J is the day of the year (1–365/366), and D is the total number of days in a year. K c values for different growth periods are provided in Table 2.

2.5.3. The Solar Radiation Suitability Model

Solar radiation suitability for winter wheat was calculated as follows [6,50]:
S s i j = e − [ ( S − S o ) / b ] 2 , S i j < S 0 1 , S i j ≥ S 0
where S ( s i j ) is the solar radiation suitability in the ith growth period of the jth year; s i j is the daily solar radiation hours in the ith growth stage of the jth year (h); S o is the critical solar hour for each growth period (h); and b is a constant determined by climatic conditions; both values for each period are shown in Table 2 [47,53].

2.5.4. The Integrated Climate Suitability Model

Integrated climate suitability for the entire winter wheat growth period was calculated by combining the temperature, precipitation, and solar radiation suitability for each growth stage as follows:
C j = W j × T j × S j 3
P j = ∑ i = 1 m b p i j P p i j       b p i j = E T i j E T s u m j
T j = ∑ i = 1 m b t i j T t i j       b t i j = T i j T s u m j
S j = ∑ i = 1 m b s i j S s i j       b s i j = S 0 i j S 0 s u m j
where C j is the integrated climate suitability, and T j , P j , and S j are the respective suitability for temperature, precipitation, and solar radiation. The variable m represents the number of winter wheat growth periods (in this study, m = 2). The coefficients b p i j , b t i j , and b s i j denote the influence of precipitation, temperature, and solar radiation, respectively. T i j , E T i j , and S 0 i j represent to the active accumulated temperature, E T C and S 0 in the ith growth stage of the jth year, respectively, while T s u m j , E T s u m j , and S 0 s u m j are their respective sums over the entire growth period of the jth year.

3. Results

3.1. Projected Changes in Winter Wheat Phenology

During the baseline period (1995–2014), the VGP of winter wheat was longer in the northern 3H Plain than in the southern region, whereas the RGP was longer in the south (Figure 2a,f,k). The shorter VGP in the south is primarily attributed to higher accumulated thermal conditions resulting from regional climate variability [26]. The longer RGP in the southern region is likely due to earlier flowering, which leads to an earlier onset of the reproductive stage [10,54]. The southern region reaches the optimal temperature for flowering earlier than the north, leading to a shorter VGP. Earlier flowering in the south also extends the RGP, as the reproductive stage occurs under lower temperatures.
Future climate change is expected to substantially alter the phenological stages of winter wheat across the 3H Plain. Under future scenarios, the spatial patterns of VGP and WGP in the mid-21st century remain similar to those observed during the baseline period (Figure 2b,d,l,n). In the central 3H Plain, the VGP and WGP are projected to decrease by 5 and 4 days, respectively, while in the western and eastern regions, the VGP and WGP are expected to increase by 20 and 15 days, respectively. Across most of the 3H Plain, the RGP is projected to be shorter in the mid-21st century than during the baseline under both scenarios (Figure 2g,i). The magnitude of changes in VGP and WGP is expected to be greater in the late 21st century, particularly under the SSP5-8.5 scenario.
The average changes in VGP, WGP, and RGP across the 3H Plain reveal distinct temporal trends (Figure 3). Both VGP and WGP are expected to lengthen in the mid-21st century but shorten in the late 21st century compared to the baseline. In contrast, RGP is projected to decrease slightly in the mid-21st century under both scenarios, but to increase slightly in the late 21st century under SSP5-8.5.

3.2. Future Projections of Temperature Suitability for Winter Wheat

The spatial distributions of temperature suitability for winter wheat at different growth stages in the 3H Plain during the 21st century are shown in Figure 4. During the baseline years (1995–2014), temperature suitability for the VGP and WGP was higher in the southern region and lower in the north (Figure 4a,k). Specifically, temperature suitability during the VGP in the southern region below 36° N exceeded 0.5, while during the WGP it was above 0.6 in the same area. For the RGP, temperature suitability was high across the entire 3H Plain, with values exceeding 0.7 (Figure 4f).
Projections for the mid-21st century indicate that the spatial distribution of temperature suitability at VGP and WGP remains similar under both scenarios (Figure 4b,d,l,n). The centers of high temperature suitability shift southward as emission scenarios increase from moderate to high levels. By the late 21st century, under SSP2-4.5, high temperature suitability at VGP and WGP is concentrated in the 32–35° N zone (Figure 4c,m), whereas under SSP5-8.5, the high-value area extends northward within the southern region (Figure 4e,o). In the mid-21st century, temperature suitability at RGP declines relative to the baseline, with the high-value center shifting southward under SSP5-8.5 (Figure 4g,i). In the late 21st century, under SSP2-4.5, low temperature suitability extends across the region north of 36° N (Figure 4h), while under SSP5-8.5, the high-value center moves northward (Figure 4j). These results are consistent with those reported by Tang and Liu [47].

3.3. Future Projections of Precipitation Suitability for Winter Wheat

Figure 5 presents the spatial patterns of precipitation suitability for winter wheat at different growth periods across the 3H Plain. During the baseline years (1995–2014), precipitation suitability at VGP, RGP, and WGP was high (value > 0.5) in the 32–36° N zone and low (value < 0.5) north of 36° N and south of 32° N (Figure 5a,f,k). This distribution likely reflects water deficiency in the northern 3H Plain and water excess in the southern region.
In the mid-21st century, precipitation resources for winter wheat are projected to improve across the 3H Plain, including areas north of 36° N, under both scenarios (Figure 5b,d,g,i,l,n). Although the spatial patterns during this period are similar to the baseline, the high-value areas expand and shift further south. By the late 21st century, high precipitation suitability at VGP and WGP expands further across the 3H Plain under both SSP2-4.5 and SSP5-8.5, particularly in the southern region (Figure 5c,e). However, precipitation suitability at RGP in the region south of 32° N decreases under both scenarios compared to the baseline (Figure 5g–j). This may be due to a significant increase in precipitation in the southern region during the 21st century, exceeding the requirements of winter wheat [27]. Excess precipitation may promote pest outbreaks and negatively affect flowering and grain filling [22].

3.4. Future Projections of Solar Radiation Suitability for Winter Wheat

The spatial distributions of solar radiation suitability for winter wheat during the baseline years (1995–2014) and throughout the 21st century under climate scenarios are shown in Figure 6. During the baseline period, solar radiation suitability at various growth stages increased from low to high latitudes in the 3H Plain (Figure 6a,f,k), contrasting with the spatial pattern of precipitation suitability. For both the VGP and WGP, suitability exceeded 0.5 north of 39° N and was lower in the southern region (Figure 6a,k). During the RGP, suitability exceeded 0.6 in the region north of 36° N, which was higher than the values observed during other growth stages (Figure 6f).
Under future scenarios, solar radiation suitability at all growth stages maintains a north–south gradient, with higher values in the north, similar to the baseline period. In the mid-21st century, values at VGP and WGP range from 0.3 to 0.5 across most of the 3H Plain under both scenarios (Figure 6b,d,l,n). At RGP, suitability in the region north of 36° N remains above 0.5, athough it is lower than during the baseline period (Figure 6g,i). In the late 21st century, solar radiation suitability at VGP and WGP under SSP2-4.5 shows a slight increase compared with the mid-21st century (Figure 6c,m), and suitability at RGP in the northern 3H Plain also increases slightly (Figure 6h). Under SSP5-8.5, the low-value area at VGP and WGP expands northward (Figure 6e,o), probably attributable to increased precipitation, while the high-value area at RGP increases in the northern 3H Plain compared to the mid-21st century (Figure 6j).

3.5. Spatial Distribution of Integrated Climate Suitability for Winter Wheat Across the 3H Plain

During the baseline years (1995–2014), integrated climate suitability was highest in the central 3H Plain and lowest in the northern and southern regions (Figure 7a,f,k). For the VGP and WGP, integrated suitability values were generally lower than those for the RGP. VGP values remained below 0.56 across the 3H Plain (Figure 7a), while WGP values ranged from 0.32 to 0.65 (Figure 7k). In contrast, RGP suitability increased, with values ranging from 0.42 to 0.78 (Figure 7f).
Under the SSP2-4.5 and SSP5-8.5 scenarios, the future spatial distributions of integrated climate suitability at all growth periods are shown in Figure 7. In the mid-21st century, high-value areas at VGP and WGP are horizontally elongated and shift further south in the central 3H Plain, with slight increases in the southern 32° N zone (Figure 7b,d,l,n). Although the high-value area at RGP expands under both scenarios, the maximum values in the central 3H Plain decrease compared to the baseline (Figure 7g,i). In the late 21st century, high-value areas at VGP and WGP expand between 32–36° N, but integrated suitability in the southern 32° N remains low (Figure 7b,d). Areas with integrated suitability values above 0.52 at RGP become noticeably larger under both scenarios compared to the baseline (Figure 7g–j). However, values in the southern 32° N region generally remain lower than baseline levels.

3.6. Impacts of Changes in Climate Factors on the Winter Wheat Phenology

The impacts of changing climate factors on winter wheat phenology were assessed using the Random Forest model (Figure 8). The analysis indicates that future temperature changes will be the primary driver of VGP and RGP changes in winter wheat across the 3H Plain throughout the 21st century under both scenarios, contributing more than 40%. Both minimum and maximum temperatures are projected to rise during the 21st century [55]. Increased minimum temperatures can alleviate the negative impacts of cold extremes during the VGP, while overall rising temperatures accelerate organic matter accumulation and plant maturity. Notably, the effect of minimum temperature exceeds that of maximum temperature in the mid-21st century, but this relationship reverses in the late 21st century. The influence of precipitation changes on VGP increases in the late 21st century compared to the mid-21st century, likely due to a significant increase in precipitation, as confirmed by previous studies [22]. Increased precipitation improves water availability and reduces drought risk, particularly in the northern 3H Plain, but may also promote pest outbreaks during RGP in the southern region [22,27]. These findings are consistent with the results in Figure 5, where precipitation suitability is projected to improve in the northern 3H Plain and decrease at RGP in the southern 32° N region.
Furthermore, projected solar radiation significantly influences phenological changes, particularly at VGP. The impact of solar radiation on phenology is greater in the mid-21st century than in the late 21st century, which contrasts with the trend observed for precipitation effects.

4. Discussion

In this study, bias-corrected CMIP6 model datasets were employed to investigate future changes in climate variability and phenology for winter wheat. Raw CMIP6 model simulations often inaccurately represent Earth system characteristics, leading to discrepancies in projected climate variability [13,56,57]. For example, inadequate depiction of air–sea interactions and terrain features in uncorrected GCMs results in underestimated precipitation over the 3H Plain [58,59], while inaccurate cloud representation is a critical source of temperature bias [57]. Previous research has demonstrated that bias-corrected CMIP6 datasets improve the accuracy of climate variables and preserve intrinsic characteristics [26]. Specifically, our earlier work demonstrated that bias correction of MME using the EDCDF method effectively captures the spatial distribution of climate factors. This approach reduced the regional average bias for precipitation, temperature, and solar radiation by 5.6%, 18.3%, and 30.7%, respectively [22]. Therefore, the present study utilized bias-corrected CMIP6 MME datasets to project future climate suitability, thereby minimizing uncertainties and improving the reliability of climate resource projections.
The results indicate that, during the baseline period (1995–2014), the duration of the VGP for winter wheat was longer in the northern 3H Plain than in the south, whereas the opposite pattern was observed for the RGP. This difference is primarily due to earlier flowering in the southern region, facilitated by abundant thermal resources, which shortens the VGP. Earlier flowering in the south also causes the RGP to occur under lower temperatures, thereby extending its duration [10,54].
Projections suggest that, in the mid-21st century, the mean durations of the VGP and WGP for winter wheat across the 3H Plain will be prolonged compared with the baseline, particularly in the western and eastern regions under both emission scenarios. However, both the VGP and WGP show a decreasing trend from low-emission to high-emission scenarios, consistent with previous studies [3,26]. Analysis of the contribution of climate factors to VGP changes indicates that rising temperatures are the primary driver. Increased minimum temperatures alleviate cold extremes during the VGP, enhancing autotrophic respiration and promoting photosynthesis [60]. Higher maximum temperatures improve photosynthetic enzyme activity, accelerate organic matter accumulation, and promote plant maturation, ultimately shortening the growth period [61]. Projected temperatures are expected to continue increasing [16,44], and although future warming may shorten the VGP, the overall effect is positive, as indicated by the projected increase in temperature suitability during this stage.
In contrast, rising temperatures exert a relatively negative impact on the RGP. Temperature suitability during the RGP is projected to decrease compared to the baseline, and the area with higher suitability is expected to shift northward. Increased temperatures play a dominant role in RGP changes under both future scenarios in the mid-21st century, contributing more than 50%. The RGP is more sensitive to temperature changes than the VGP [62,63]. Higher temperatures accelerate leaf senescence and shorten the grain-filling period, ultimately reducing yield [64]. Lower latitudes, with abundant thermal resources, may be more affected by increases in maximum temperature than higher latitudes.
Solar radiation also plays a significant role in VGP and RGP changes in winter wheat. Photothermal accumulation is a key determinant of crop phenology, as crops reach specific growth stages once photothermal requirements are met [28]. Our previous study projected a decreasing trend in solar radiation across the 3H Plain [22], consistent with the present finding that solar radiation suitability is expected to decline compared to the baseline. This decline may hinder biomass accumulation and could ultimately prolong the growth period [64]. Notably, the contribution of solar radiation to phenology during the late 21st century is projected to be lower than during the mid-21st century. This shift is probably due to the increasing influence of precipitation on phenological changes in the late 21st century. Increased precipitation during the VGP is expected to meet the water requirements of winter wheat and reduce drought risk, especially in northern regions. Conversely, significant increases in precipitation during the RGP may disrupt pollination and grain filling, delay maturity, and potentially increase pest outbreaks, particularly in the southern 3H Plain [27,44].
Given the varying contributions of climate factors across growth stages and their influence on future suitability, targeted adaptation measures are essential to mitigate the adverse effects of climate change. Developing heat-tolerant varieties or varieties with longer RGP durations is especially important for lower latitudes. Additionally, earlier sowing may mitigate the adverse impacts of high temperatures and excessive precipitation during the RGP, thereby improving climate suitability for winter wheat and enhancing yields.
While this study used the bias-corrected CMIP6 MME datasets to improve projections of climate suitability and phenological shifts [22], the influence of extreme events was not fully addressed. The MME approach may homogenize time series, reducing the representation of extreme values [57]. Moreover, while process-based crop models are effective tools for projecting crop yield, most provide only simplistic descriptions of the effects of extreme events on crops. For example, crop models such as the DSSAT CERES models use linear response functions to parameterize physiological processes resulting from temperature changes, which can lead to underestimation of yield reduction under extreme heat and overestimation of climate suitability [38,65]. The frequency and intensity of extreme events are expected to increase in the future [66]. Given the significant effects of extreme events, future research should explicitly consider the impacts of extreme events and enhance model capabilities to simulate crop responses to temperature and water stresses, particularly under compound extremes.

5. Conclusions

In this study, a climate suitability model and a Random Forest model were developed by integrating bias-corrected CMIP6 datasets with the well-validated DSSAT CERES-Wheat model to investigate the spatial and temporal changes in winter wheat phenology across the 3H Plain under current and future climate scenarios. The main findings are summarized as follows:
(1)
Compared to the baseline years, the average durations of the VGP and WGP across the 3H Plain are projected to be extended in the mid-21st century and shortened in the late 21st century. The RGP is expected to be slightly shorter in the mid-21st century under both scenarios and slightly longer under the SSP5-8.5 scenario in the late 21st century.
(2)
Random Forest analysis identifies temperature as the primary driver of changes in both VGP and RGP throughout the 21st century, with a contribution rate exceeding 40%. Solar radiation plays a significant role in phenological changes, especially in the mid-21st century, while the influence of precipitation surpasses that of solar radiation in the late 21st century due to substantial increases in precipitation.
(3)
Temperature suitability for winter wheat is projected to increase during the VGP and WGP but decline during the RGP under both scenarios throughout the 21st century. Precipitation suitability is expected to improve across the 3H Plain, particularly north of 36° N, but decrease during the RGP south of 32° N compared to the baseline.
(4)
During the 21st century, solar radiation suitability at all growth stages is projected to remain higher in the north and lower in the south, with overall values below baseline levels. Integrated climate suitability during the VGP and WGP is expected to improve across the 3H Plain under both scenarios. For the RGP, integrated suitability is projected to be higher north of 32° N and generally lower south of 32° N compared to the baseline period.
To address the negative impacts of rising temperatures and excessive precipitation during the RGP, breeding programs should prioritize traits related to RGP extension and heat tolerance, and consider earlier sowing as an adaptive strategy. Regional planning should focus on the northern and central zones for intensified wheat production under future climate conditions. These measures can enhance the climate suitability of winter wheat and support yield stability in the 3H Plain.

Author Contributions

Conceptualization, writing of original draft preparation, Y.X.; software, review and editing, T.L.; investigation, M.X.; supervision, S.S. and L.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Youth Foundation of Jiangsu Provincial Meteorological Bureau (grant number KQ202306), the Key Scientific Foundation of Jiangsu Provincial Meteorological Bureau (grant number KZ202302), and the sixth “333 Talents” Cultivation Support Project of Jiangsu Province, and Jiangsu Agriculture Science and Technology Innovation Fund (grant number CX (23) 1002).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are grateful to the World Climate Research Program, for providing the climate data from Coupled Model Comparison Program (CMIP6).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Genetic coefficients for winter wheat used in the DSSAT model for the 3H Plain.
Table A1. Genetic coefficients for winter wheat used in the DSSAT model for the 3H Plain.
Genetic CoefficientsP1VP1DP5G1G2G3PHT
3H Plain36.063.4418.827.428.31.6695
Note: P1V, number of days at an optimal vernalizing temperature required to complete vernalization; P1D, percentage reduction in development rate per photoperiod hour below the threshold, relative to the rate at the threshold; P5, duration of the grain-filling phase; G1, kernel number per unit canopy weight at anthesis; G2, standard kernel size under optimal conditions; G3, standard non-stressed dry weight (total, including grain) of a single tiller at maturity; PHT, interval between successive leaf tip appearances.
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Figure A1. Comparison of simulated and observed yields (a), and (b) RMSE time series across sites from 2013 to 2020.
Figure A1. Comparison of simulated and observed yields (a), and (b) RMSE time series across sites from 2013 to 2020.
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Figure 1. Geographic extent of the Huang-Huai-Hai (3H) Plain study area.
Figure 1. Geographic extent of the Huang-Huai-Hai (3H) Plain study area.
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Figure 2. Spatial distribution of the vegetative growth period (VGP) (a–e), reproductive growth period (RGP) (f–j), and whole growing period (WGP) (k–o) of winter wheat during the baseline period (a,f,k), and differences under future scenarios (b–e,g–j,l–o) across the 3H Plain. The black shade represents the 95% confidence level based on a bootstrap resampling test.
Figure 2. Spatial distribution of the vegetative growth period (VGP) (a–e), reproductive growth period (RGP) (f–j), and whole growing period (WGP) (k–o) of winter wheat during the baseline period (a,f,k), and differences under future scenarios (b–e,g–j,l–o) across the 3H Plain. The black shade represents the 95% confidence level based on a bootstrap resampling test.
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Figure 3. Differences in the duration of the VGP, RGP, and WGP of winter wheat under future scenarios across the 3H Plain, relative to the baseline period.
Figure 3. Differences in the duration of the VGP, RGP, and WGP of winter wheat under future scenarios across the 3H Plain, relative to the baseline period.
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Figure 4. Spatial distribution of temperature suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
Figure 4. Spatial distribution of temperature suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
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Figure 5. Spatial distribution of precipitation suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
Figure 5. Spatial distribution of precipitation suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
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Figure 6. Spatial distribution of solar radiation suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
Figure 6. Spatial distribution of solar radiation suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
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Figure 7. Spatial distribution of integrated climate suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
Figure 7. Spatial distribution of integrated climate suitability for winter wheat during VGP (a–e), RGP (f–j), and WGP (k–o) for the baseline period and future scenarios across the 3H Plain.
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Figure 8. Relative contribution of climate factors to winter wheat phenology for the future scenarios across the 3H Plain, determined by the Random Forest model.
Figure 8. Relative contribution of climate factors to winter wheat phenology for the future scenarios across the 3H Plain, determined by the Random Forest model.
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Table 1. Summary of CMIP6 models used in this study.
Table 1. Summary of CMIP6 models used in this study.
Model NameHorizontal ResolutionsInstitution/Country
BCC-CSM2-MR320 × 160Beijing Climate Center (BCC)/China
MPI-ESM1-2-HR384 × 192Max Planck Institute (MPI) for Meteorology/Germany
MIROC6256 × 128Model for Interdisciplinary Research on Climate (MIROC)/Japan
GISS-E2-1-G144 × 90NASA Goddard Institute for Space Studies (GISS)/USA
IPSL-CM6A-LR144 × 143Institute Pierre-Simon Laplace (IPSL)/France
MRI-ESM2-0320 × 160Meteorological Research Institute (MRI)/Japan
CESM2288 × 192National Center for Atmospheric Research (NCAR)/USA
Table 2. Summary of parameters used to assess temperature, precipitation, and solar radiation suitability in vegetative growth period (VGP) and reproductive period (RGP) of winter wheat.
Table 2. Summary of parameters used to assess temperature, precipitation, and solar radiation suitability in vegetative growth period (VGP) and reproductive period (RGP) of winter wheat.
Growth PeriodThTlToKcS0b
VGP172100.77.664.32
RGP2781619.364.78
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Xu, Y.; Li, T.; Xu, M.; Shen, S.; Tan, L. Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios. Agriculture 2025, 15, 1606. https://doi.org/10.3390/agriculture15151606

AMA Style

Xu Y, Li T, Xu M, Shen S, Tan L. Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios. Agriculture. 2025; 15(15):1606. https://doi.org/10.3390/agriculture15151606

Chicago/Turabian Style

Xu, Yifei, Te Li, Min Xu, Shuanghe Shen, and Ling Tan. 2025. "Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios" Agriculture 15, no. 15: 1606. https://doi.org/10.3390/agriculture15151606

APA Style

Xu, Y., Li, T., Xu, M., Shen, S., & Tan, L. (2025). Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios. Agriculture, 15(15), 1606. https://doi.org/10.3390/agriculture15151606

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