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Article

Construction and Variation Analysis of Comprehensive Climate Indicators for Winter Wheat in Beijing–Tianjin–Hebei Region, China

by
Chang Liu
1,2,†,
Jie Hu
1,†,
Lei Wang
1,
Ming Li
1,
Wenyi Xie
1,
Yining Zhu
2,
Ruijie Che
2,
Lianxi Wang
3,
Jing Hua
1,* and
Jian Wang
1,*
1
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, China
2
School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3
School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2025, 17(20), 9054; https://doi.org/10.3390/su17209054 (registering DOI)
Submission received: 7 September 2025 / Revised: 8 October 2025 / Accepted: 10 October 2025 / Published: 13 October 2025
Browse Figures
Versions Notes

Abstract

Under the global climate change, variations in climatic elements such as temperature, precipitation, and sunshine duration significantly impact the growth, development, and yield formation of winter wheat. A precise understanding of the impact of climate change on winter wheat growth and the scientific use of meteorological resources are crucial for ensuring food security, optimizing agricultural planting structures and agricultural sustainability. This study uses statistical methods and focuses on the Beijing–Tianjin–Hebei region, utilizing data from 25 meteorological stations from 1961 to 2010 and winter wheat yield data from 1978 to 2010. Twelve refined indicators encompassing temperature, precipitation, and sunshine duration were constructed. Path analysis was employed to determine their weights, establishing a comprehensive climate indicator model. Results indicate: Temperature indicators in the region show an upward trend, with accumulated temperature of the whole growth period increasing at a rate of 61.1 °C·d/10a. Precipitation indicators reveal precipitation of the whole growth period rising at 3.9 mm/10a and pre-winter precipitation increasing at 4.2 mm/10a. Sunshine duration exhibits a declining trend, decreasing at 72.7 h/10a during the whole growth period. Comprehensive climate indicators decrease from south to north, with the southwest region exhibiting the highest tendency rate (18.41), while the central and southern regions show greater variability. This study provides scientific basis for optimizing winter wheat planting patterns and rational utilization of climate resources in the Beijing–Tianjin–Hebei region. It recommends prioritizing cultivation in western southern Hebei and improving water conditions in the central and northern areas through irrigation technology to support sustainable crop production.

1. Introduction

Under the global climate change, alterations in climatic elements such as temperature, precipitation, and sunshine duration exert profound impacts on agricultural production—for instance, rising temperatures may shorten crop growth phases and disrupt phenological synchronization, erratic precipitation can increase the risk of drought-induced water stress or waterlogging-related root rot, and reduced sunshine duration may limit photosynthetic efficiency—ultimately affecting the growth, development, and yield formation of grain crops [1,2,3]. As one of the primary grain crops in northern China, winter wheat possesses a relatively long growth cycle and exhibits heightened sensitivity to climatic variations [4,5,6]. The Beijing–Tianjin–Hebei region is a crucial winter wheat production area in China. In recent years, significant climate changes and frequent extreme weather events in this region have posed numerous challenges to winter wheat cultivation and yield stability [7,8,9]. Frequent droughts have damaged crops in the Beijing–Tianjin–Hebei region, where water resource utilization often exceeds internationally recognized safety limits. Late frost occurs frequently during the critical growth period of winter wheat in some areas, with more severe damage in high-latitude regions. Meanwhile, 55% of summer days are heat wave days, and it is predicted that the frequency of blocking heat wave days by the end of the 21st century will be three times that of the end of the 20th century [10,11,12]. Therefore, accurately understanding the impact of climate change on winter wheat growth and rationally utilizing meteorological resources are of great significance for ensuring food security, optimizing agricultural planting structures, and achieving long-term agricultural sustainability [13,14,15]. Integrated climate indicators, as systemic metrics based on the synergistic effects of multiple climate factors, overcome the limitations of single-factor analysis [16,17]. These indicators effectively capture the interactive effects between climate factors and crop yields by approximating and summarizing complex climatic processes. This provides a scientific basis for optimizing planting regions and precision field management by enabling data-driven adjustments to irrigation and fertilization. This, in turn, may contribute to enhancing agriculture’s resilience to complex and extreme climatic conditions [18].
Researchers worldwide have advanced agricultural composite climate indicators through distinct regional case studies, each addressing specific climatic challenges and crop systems. In Europe, Wiréhn et al. [19] systematically evaluated multiple weighting and aggregation methods for agricultural vulnerability in Sweden, revealing that 34 of 36 method combinations yielded significantly different results. This underscores the importance of transparently quantifying methodological uncertainties rather than relying on a single composite index. Moving to Australia, Yao et al. [20] applied machine learning models such as Random Forest and Extreme Gradient Boosting to assess climate impacts on sugarcane. Their findings identified region-specific climatic thresholds—such as reduced yields with rainfall exceeding 1500 mm in the north or more than 70 consecutive dry days in the central region—demonstrating how localized indicators improve explanatory power for yield variation (45–62%). In the Middle East, Mathbout et al. [21] integrated meteorological and agricultural drought indices in Syria, correlating CRU data with observed drought intensification from 1981 to 2021. Their study highlighted severe impacts, including a megadrought (2007–2018) affecting 45% of agricultural land and 32.4% of wheat crops in 2021. In Southern Europe, Massano et al. [22] developed phenology-specific ecoclimatic indicators for Italian viticulture, with multiple regression models explaining 25–50% of yield variability and an adjusted R2 of 0.58 for Franciacorta red wine, emphasizing the value of variety-specific approaches. Collectively, these regionally tailored studies provide critical scientific foundations for assessing agricultural climate risks and formulating adaptive strategies.
China has also developed integrated climate indicators through region-specific studies that address diverse agricultural systems across its major production zones. In eastern China, Xu et al. [23] constructed an Integrated Climate Assessment Index for wheat in Jiangsu Province using machine learning, achieving prediction accuracies of 67.86–100% across northern, central, and southern subregions. Further north on the North China Plain, researchers from Peking University integrated 21 indicators including GDD and SPEI, demonstrating that moisture factors influenced winter wheat quality 2–3 times more than temperature, with warm–dry conditions reducing dough extensibility by 15% [24]. In the arid Loess Plateau of western China, Zhou et al. [25] used 33 years of data from the Longdong Plateau to show a warming rate of 0.75 °C per decade and precipitation decline of −29.17 mm per decade, shortening the growing season and affecting grain weight and panicle formation. In central China, Lu et al. [26] established a comprehensive evaluation system for Anhui Province, identifying the highest climate production potential along the Huai River and Jianghuai region (12,391 kg·hm−2), with drought in northern Huai and waterlogging in southern Jiangnan as key constraints. These studies collectively demonstrate that multi-factor coupled indicators more effectively reveal climate-crop interactions than single-factor approaches, supporting regional adaptation strategies such as variety selection and sowing adjustments.
Although the above-mentioned studies have provided rich theoretical and methodological support for the construction of comprehensive climate indicators for agriculture and the analysis of climate crop response mechanisms, there are still some gaps in research on the Beijing–Tianjin–Hebei region, a core winter wheat production area in the north [27,28,29,30]. Addressing these gaps is critical for formulating targeted and sustainable adaptation strategies to safeguard regional food production.
(a)
Lack of region-specific analysis hindering alignment with Beijing–Tianjin–Hebei’s unique climatic gradient characteristic: Existing studies lack region-specific analysis, which makes it difficult for their conclusions to align with the unique climatic gradient characteristics of the Beijing–Tianjin–Hebei region. Regional studies mostly focus on the national scale or a single province, lacking targeted and detailed analysis of the significant climate gradient from south to north in the Beijing–Tianjin–Hebei region and the synergistic effects of light, water, and temperature factors during the key growth period of winter wheat [31,32].
(b)
Limited single-dimensional integration of climatic factors: Most comprehensive indicator models focus on single factor reinforcement or single stress assessment, and fail to systematically integrate the quantitative weights of the three elements of “temperature–precipitation–sunshine” at different growth stages. Moreover, there is insufficient research on the coupling of the spatiotemporal long-term trends and patterns of long-term climate indicators and yield response [33,34,35].
(c)
Insufficient research on the coupling between the long-term spatiotemporal evolution of climate and yield response: Existing research on North China mainly focuses on the correlation between yield and quality, and the stability and changing trends of comprehensive climate conditions in different sub regions within the Beijing–Tianjin–Hebei region, as well as their practical implications for regional planting strategies, are not yet clear [36,37,38].
Building on this foundation, this study takes the Beijing–Tianjin–Hebei region as its research focus. It utilizes 50 years of long-term meteorological station data and 33 years of winter wheat yield data, refines climatic factors into 12 key indicators, and constructs a comprehensive climatic indicator model. On this basis, the study employs statistical methods to conduct temporal and spatial analyses. This study aims to analyze long-term changes in climate factors and comprehensive indicators in the Beijing–Tianjin–Hebei region, providing a statistical basis for optimizing planting strategies, and guiding the region towards a more sustainable and resilient agricultural future.

2. Data Sources and Processing

The Beijing–Tianjin–Hebei region is located in the northern part of China’s North China Plain, on the western coast of the Bohai Bay. It serves as a pivotal hub connecting Northeast China, Northwest China, and the North China hinterland. This study is based on our previous research and uses meteorological data from 25 meteorological stations (Figure 1, Table 1) in Beijing–Tianjin–Hebei region from 1961 to 2010 [39], including temperature, precipitation, and sunshine hours. The production data are sourced from the statistical bureaus of Beijing, Tianjin and Hebei, covering the period from 1978 to 2010.
Temperature, precipitation, and sunshine data from 25 stations were processed and divided into three winter wheat statistical analysis periods: pre-wintering, wintering, and entire growth period [40]. Based on the maximum planting limit for winter wheat and relevant data from Hebei Province, the sowing date was set as October 1st annually, and the harvest date as June 20th annually [41]. The dates for winter survival and regreening were determined using a five-day moving average method to identify the stable passage through 0 °C [42]. Twelve detailed indicators were calculated using Access database and Excel software: January average temperature, pre-winter accumulated temperature, negative accumulated temperature during overwintering, accumulated temperature throughout the entire growth period, January precipitation, total pre-winter precipitation, precipitation during overwintering, precipitation throughout the entire growth period, average daily sunshine duration in January, pre-winter daily sunshine duration, daily sunshine duration during overwintering, and daily sunshine duration throughout the entire growth period (Table 2).
The meteorological (1961–2010) and yield (1978–2010) data were used strategically: the full 50-year climate record enhances the robustness of long-term trend analysis, while all yield-climate modeling was restricted to the overlapping period (1978–2010) to ensure reliability. The regional-scale averages of climate indicators presented in this study were calculated based on data from the aforementioned 25 meteorological stations.

3. Research Methods

3.1. Yield Data Separation Method

Social factors and technological advancements influence agricultural production methods and returns, leading to a trend of increased crop yields. Yields resulting from such productivity gains are termed trend yield [43]. Variations in yield caused by interannual differences in natural factors (primarily meteorological conditions) are termed meteorological yield [44]. Yield data generally comprise both trend yield and meteorological yield [45,46].
Y = Y w + Y t
where Y denotes actual yield, Yw represents trend yield, and Yt signifies meteorological yield. Yw follows a linear fitting pattern and is a long-term stable variable, whereas Yt exhibits instability and uncertainty. Therefore, Yt can be calculated from Yw.
Yw is fitted using a five-year moving average method [47]. The 5a moving average method treats the winter wheat yield sequence over five consecutive years as a linear function varying over time, reflecting the historical trend of the yield sequence as a whole. The average value simulated by the moving linear regression at each time point represents the trend yield at that point.
y t = ∑ i = 0 p − 1 y ( t + p − 1 2 − i ) p
where t denotes the year, y(t) represents the trend yield in year t, and p is the moving step size, set to 5 here.
The resulting y(t) time series is denoted as Yw. The meteorological yield is defined as the difference between the actual yield Y and the trend yield Yw.

3.2. Weight Determination

This study refined temperature, sunshine duration, and precipitation into 12 indicators. Correlation coefficients between each indicator and yield were calculated. Prior to establishing the standardized path coefficient equations, the significance of each correlation coefficient (between climate indicators and winter wheat yield) was tested using a t-test with a significance level of α = 0.05. In this study, all 12 refined climate indicators were retained for subsequent path analysis and weight calculation, even if individual indicators exhibited non-significant correlations with yield. This retention was based on the consideration that non-significant indicators may still participate in the synergistic regulation of winter wheat growth and yield alongside other climate factors, and excluding them could undermine the integrity of the multi-factor comprehensive climate assessment. Through establishing a system of standardized path coefficient equations, the path coefficients between each indicator and yield were computed. These path coefficients were normalized to serve as weighting coefficients for each indicator.

3.3. Climate Trend Rate

The climate tendency rate reflects the trend of climate indicators. The linear regression coefficient a between the sample y (climate indicator value) and time x (year) is calculated using the least squares method. The changes in each factor can be expressed by a linear equation:
y = a x + b
where a represents the annual change rate of the climate indicator (unit: consistent with the climate indicator, e.g., °C/a, mm/a). The climate tendency rate is defined as 10a, with the unit unified as “per 10 years” (e.g., °C/10a, mm/10a), to characterize the cumulative change amplitude of the climate indicator over a decade.

3.4. Average Relative Variability

In agricultural climate statistics, four commonly used variability indices are absolute variability, relative variability, average absolute variability, and average relative variability [48]. Compared to other indices, average relative variability better reflects: the degree of climate element variation across regions during the same period, and the degree of climate element variation within the same region across different periods. This study employs average relative variability to describe changes in climate indicators.
R = 1 n ∑ i = 1 n | X i − X - | X -
R represents the average relative change rate, n denotes the calculation period (set to 50 in this study), Xi is the climate parameter value for year i, X - X - is the average climate parameter value for 1961–2010, and i = 1, …, n.

3.5. Comprehensive Climate Index

The comprehensive climate index is derived using the summation method based on influence weights.
M = A y 1 + B y 2 + C y 3 + … + L y 12
M represents the comprehensive climate index, A, B,…L denote weight coefficients, and y1, y2,…y12 correspond to individual climate indicators.
Where in the weight coefficients (A, B, …, L) were derived from the standardized path coefficients of the 12 climate indicators (including both yield-significant and non-significant indicators, as detailed in Section 3.2). This inclusion ensures that the comprehensive climate index fully integrates the combined contributions of temperature, precipitation, and sunshine duration—avoiding information loss from unilaterally excluding non-significant indicators—and thereby more accurately reflects the actual climate conditions affecting winter wheat growth.
The comprehensive climate index (M) established in this study quantifies the synergistic effects of temperature, precipitation, and sunshine duration on winter wheat growth, and its agricultural significance lies in directly reflecting the suitability of regional climate conditions for winter wheat yield formation. A higher M value indicates more optimal light, temperature, and water matching, which is conducive to robust seedling establishment in the pre-winter period, safe overwintering, and sufficient dry matter accumulation during the reproductive growth period—ultimately promoting high and stable yields.
The comprehensive climate index was divided into five suitability grades using the equal-interval method, a approach widely adopted in agroclimatic zoning to ensure clear spatial comparability [49,50,51]. The comprehensive climate trend rate and average relative variability are also divided according to the equal interval method for spatial statistical analysis. All spatial analyses were completed by interpolating data from 25 meteorological stations using the inverse distance weighting (IDW) method. Each grade of comprehensive climate index carries distinct agricultural implications:
(a)
Grade 1 (M > 346): Excellent conditions with optimal light, temperature, and water, suitable for high-yield potential with minimal irrigation.
(b)
Grade 2 (263 < M ≤ 346): Good conditions, generally meeting basic growth needs; yield stability is high with supplementary irrigation.
(c)
Grade 3 (180 < M ≤ 263): Moderate conditions, with obvious constraints in one or two factors, requiring targeted management to mitigate yield reduction risks.
(d)
Grade 4 (98 < M ≤ 180): Poor conditions, with severe multi-factor limitations, making large-scale cultivation unsuitable due to high yield risk.
(e)
Grade 5 (M ≤ 98): Extremely unfavorable conditions where key growth thresholds are not met, and cultivation should be avoided.

4. Results Analysis

4.1. Indicator Trends

The 12 processed indicators comprehensively reflect the climate change patterns during the winter wheat growing season in the Beijing–Tianjin–Hebei region.
From 1978 to 2000, the average January temperature was −5.71 °C, increasing at a rate of 0.35 °C·d/10a. As shown in Figure 2, the accumulated temperature during the whole growth period stabilized around 2000 °C·d and exhibited a slow upward trend. After 2000, it fluctuated significantly. Linear regression analysis indicates that the accumulated temperature increased at a rate of 61.1 °C·d/10a. Pre-winter accumulated temperature values hovered around 500 °C·d, exhibiting periodic fluctuations with a trough at the end of each cycle. The overall trend showed a gradual increase at a rate of 9.9 °C·d/10a. The overwinter accumulated temperature remained relatively stable before 1998 but fluctuated significantly afterward. Regression analysis revealed a trend of 21.5 °C·d/10a. All three temperature indicators showed an increasing trend, but none passed the significance test.
Figure 3 shows the variation in precipitation indicators. Precipitation during the whole growth period exhibited significant fluctuations. Regression analysis indicates this indicator increased at a rate of 3.9 mm/10a. Pre-winter precipitation exhibited three peaks in 1978, 2004, and 2008, reaching 75.22 mm, 116.54 mm, and 65.68 mm respectively. During other periods, it remained stable around 25 mm. Overall, pre-winter precipitation increased at a rate of 4.2 mm/10a. The overwinter precipitation exhibits minimal fluctuation and variation. Due to the influence of China’s monsoon climate, the Beijing–Tianjin–Hebei region experiences cold, dry winters with scarce precipitation, resulting in consistently low values for this indicator. Regression analysis indicates overwinter precipitation increases at a rate of 0.8 mm/10a. Concurrently, average January precipitation increases at a rate of 0.01 mm/10a. All three indicators except growing season precipitation passed the significance test at 0.1. Spatially, precipitation indicators are higher in the southeast than in the northwest.
The Beijing–Tianjin–Hebei region enjoys ample sunlight, with the sunshine hours during whole growth period exceeding 1500 h annually. Northern areas receive more sunlight than southern areas. Figure 4 shows a clear downward trend in sunshine hours of the whole growth period, with regression analysis indicating a decrease of −72.7 h/10a. Pre-winter, overwinter, and January sunshine duration decreased at rates of −21.6 h/10a, −16.8 h/10a, and −0.06 h/10a, respectively. Recent smog events likely contributed to reduced solar radiation, potentially explaining the sunshine decline [52].

4.2. Yield Separation

Regression analysis indicates that the overall trend in winter wheat yield per unit area in the Beijing–Tianjin–Hebei region is an annual increase of 9357.8 kg/km2. The trend yield calculated using a five-year moving average, as shown in Figure 5, closely fits the actual yield trend. The separated meteorological yield exhibits periodic fluctuations, confirming the effectiveness of the five-year moving average method in fitting the trend yield. The trend yield increases by 9405.6 kg/km2 annually. The separated meteorological yield exhibited significant fluctuations prior to 1984, subsequently stabilizing with periodic variations. Regression analysis indicates an overall decrease in meteorological yield at a rate of −47.83 kg/km2 per year, suggesting that climate changes are progressively becoming less favorable for winter wheat production.

4.3. Distribution Characteristics of the Comprehensive Climate Index

The composite climate index for the Beijing–Tianjin–Hebei region reflects the combined influence of light, temperature, and water on winter wheat yield. Numerically, Xingtai recorded the highest average composite climate index at 429, while Zhangbei had the lowest at 28. Higher values indicate a more positive impact on winter wheat yield. Figure 6 shows that the distribution of the comprehensive climate index decreases gradually from south to north, with the largest area falling within the 346–429 range. Zhangjiakou exhibits a relatively high value of 194.43 compared to areas at the same latitude. Except for Zhangjiakou, which shows a distinct northward trend, the dividing lines between tiers generally run east–west. This south-to-north decreasing distribution pattern indicates that temperature is the dominant factor in the region’s comprehensive climate index.

4.4. Distribution Characteristics of Comprehensive Climate Propensity

The span of the tendency rate of comprehensive climate indicators in the Beijing–Tianjin–Hebei region, from the minimum to the maximum, is 15.1. Centering on Beijing, the region is divided into four areas: northeast, northwest, southwest, and southeast. The northeast region exhibits the lowest comprehensive climate tendency rate at 9.12. Among its stations, Chengde is the only one with a pre-winter accumulated temperature tendency rate below zero (−14.5 °C·d/10a). Since the pre-winter accumulated temperature index carries the highest weighting in meteorological yield calculations, Chengde consequently has the lowest climate tendency rate at 5.3. The southeastern region recorded a climate predisposition rate of 14.76, with all stations except Tianjin exceeding 10, and Huanghua reaching the highest value of 17.12. The northwestern region’s indicator was similar to the southeast at 16.00, with all stations exceeding a comprehensive climate predisposition rate of 10. The southwestern region exhibited the greatest variation in comprehensive climate indicators, with an average climate predisposition rate of 18.41. Xingtai recorded the highest indicator at 25.30. Single-sample K-S tests revealed that 44% of stations passed the significance level of 0.1. These tests indicate that the 50-year climate indicator trends at these stations are relatively pronounced, generally aligning with the rate of change in climate predisposition rates.
Figure 7 presents the distribution map of the comprehensive climate tendency rate in the Beijing–Tianjin–Hebei region. The map clearly shows that the climate tendency rate gradually decreases from southwest to northeast. In the central region, the tendency rate exhibits an S-shaped curve distribution, increasing from east to west.

4.5. Distribution Characteristics of Average Relative Variability

Figure 8 shows that the average relative variability of the integrated climate indicators in the Beijing–Tianjin–Hebei region exhibits a peak zone in the central–southern area, reaching a maximum of 10.03. The northern region features extensive low-value zones without forming a distinct low-value center, indicating lower variability levels and smaller differences in the northern area, with limited spatial variation between adjacent stations. Similarly, large low-value zones are also present in the southern region. Notably, the central point in Baoding exhibits greater spatial variation relative to the surrounding low-value zones. Calculations reveal an average difference of 8.695 between the central point and surrounding stations, nearly matching the overall variation across the Beijing–Tianjin–Hebei region. A secondary high-value point exists east of Baoding in Tianjin, with an average relative variability of 4.42. The closer the average relative variability is to zero, the more stable its changes over the time series. Compared to the central Beijing–Tianjin–Hebei region, the southern and northern regions exhibit more stable climatic conditions.

5. Discussion

5.1. Discussion of Meteorological Indicators

The temperature indicators in the Beijing–Tianjin–Hebei region exhibit an overall stable upward trend, with the average accumulated temperature during the growing season around 2000 °C·d. This is consistent with the findings of Li et al. [53], who reported an accumulated temperature of 1950–2170 °C·d for semi-winter wheat throughout its entire growth period. Pre-winter accumulated temperature exceeds 500 °C·d, aligning with Gao et al.’s [54] finding that winter wheat requires at least 400 °C·d of pre-winter accumulated temperature. This optimal pre-winter temperature not only supports robust seedling development but also mitigates excessive pre-winter growth. The average January temperature is −5.71 °C. Research indicates that winter wheat can be widely cultivated when the average winter temperature reaches −8 °C or higher [55]. The accumulated temperature during the overwintering period also generally meets winter wheat’s growth requirements. In summary, the temperature environment in the Beijing–Tianjin–Hebei region is generally suitable for winter wheat growth and development. With global warming, temperature index is gradually rising. Combined with the conclusions above, this change will shift the suitable temperature zone northward, making the temperature environment in the central and northern parts of the Beijing–Tianjin–Hebei region increasingly suitable for winter wheat cultivation.
Regarding precipitation indicators, it is generally recognized that winter wheat requires approximately 400–600 mm of water throughout its entire growth period. Pre-winter and overwintering precipitation demand accounts for about 25% of the total growth period requirement, or roughly 100–150 mm [56]. Analysis shows that winter wheat precipitation during the growth period in the Beijing–Tianjin–Hebei region ranges between 100 mm and 250 mm. Pre-winter precipitation peaks at 100 mm, but generally stabilizes around 50 mm. Winter dormancy precipitation consistently remained below 20 mm. Consequently, the overall precipitation conditions in the Beijing–Tianjin–Hebei region cannot support large-scale winter wheat cultivation. In contrast to temperature constraints, improvements in irrigation techniques can partially alleviate water limitations. Sprinkler irrigation holds broad application prospects in northern regions. Compared to traditional irrigation methods, sprinkler irrigation effectively conserves water, enhances water use efficiency, and improves economic benefits [57]. Plastic mulching technology, due to the strong airtightness of the film, significantly reduces soil moisture evaporation after application, stabilizes soil humidity, and maintains long-term moisture retention [58]. Gu et al. [59] found that plastic mulching can increase winter wheat water use efficiency by 14.8% to 38.2%.
All sunshine indicators show a declining trend, with the duration of sunlight during the growing season ranging from approximately 1500 to 2000 h, while pre-winter and overwintering periods both fall below 500 h. No quantitative standards currently exist for sunlight duration. Given the abundant sunshine in northern China, sunlight generally does not constrain the growth of light-loving crops like winter wheat. However, considering the pronounced decline in sunshine hours in the Beijing–Tianjin–Hebei region in recent years, this study still incorporates sunlight as a reference factor in the calculation of comprehensive climate indicators.
Compared to previous studies that often focus on single climate factors or simply weighted indices, this study systematically integrates the synergistic contributions of temperature, precipitation, and sunshine during key winter wheat growth stages (pre-wintering, overwintering, and the entire growth period) through path analysis, constructing a Comprehensive Climate Index (M). This index not only overcomes the limitations of single-factor analysis but also more comprehensively reflects the integrated impact of light-temperature-water matching on yield. Notably, even if individual factors showed no significant direct correlation with yield, they were still included in the model to avoid information loss, ensuring the index better represents the overall regulatory effect of actual climate conditions on winter wheat growth. The systematic application of this methodology in the Beijing–Tianjin–Hebei region represents a novel contribution, offering a refined basis for regional planting decisions.

5.2. Planting Recommendations Based on the Distribution and Variation Patterns of Comprehensive Meteorological Indicators

The division of the Comprehensive Climate Index into five grades (e.g., M > 346 as excellent, M ≤ 98 as extremely poor) essentially constructs a climate suitability risk map. For instance, areas with M ≤ 180 (Grades 4 and 5) represent zones where winter wheat growth is subject to multiple climatic constraints, posing high yield risks and making them unsuitable for large-scale cultivation. Conversely, areas with M > 346 possess the climatic foundation for high and stable yields. This grading system can be directly applied to regional planting risk zoning, providing a quantitative basis for agricultural services such as insurance pricing and disaster early warning.
The comprehensive climate index reflects the cumulative values of meteorological elements as an overall expression of the climate environment [60]. The comprehensive climate index in the Beijing–Tianjin–Hebei region decreases gradually from south to north, with southern Hebei offering a more favorable climate environment for winter wheat cultivation. Calculations of the tendency rate reveal that the climate environment quality in the Taihang Mountains foothills wheat-growing area, located in the southwest of the Beijing–Tianjin–Hebei region, has also been steadily improving. However, the distribution of the average relative variability indicates that the climate environment around Baoding in central southern Hebei has experienced significant fluctuations over the past 50 years. Therefore, in future winter wheat cultivation planning, priority should be given to the western part of southern Hebei. This region exhibits higher comprehensive climate indicators, a favorable climate environment, and a stable upward trend in the future. Second, the eastern part of southern Hebei should be selected. Although this area lacks stable climate conditions, its overall climate environment, as indicated by climate indicators and tendency rates, plays a positive role in winter wheat cultivation. Compared to the western and eastern parts of southern Hebei, the central Baoding area in southern Hebei is less favorable in terms of future trends and stability, and should therefore be considered last.
Compared to major summer crops in the Beijing–Tianjin–Hebei region, such as maize, winter wheat is more sensitive to overwintering climate conditions (e.g., low temperature, drought). Consequently, the weight of factors from the overwintering stage in our index is significantly higher than their average contribution over the entire growth period. This contrasts clearly with climate indices for maize, which tend to emphasize growing season temperature and precipitation. Furthermore, compared to similar indices developed for Jiangsu Province [23] or the North China Plain [24], this study highlights the influence of the distinct north–south climate gradient within the Beijing–Tianjin–Hebei region, resulting in pronounced spatial differentiation of the index, making it more suitable for guiding localized planting adjustments. Future work could extend this model to crops like maize and rice, enabling comparative analysis of climate suitability across multiple crops.

6. Conclusions

This study analyzed long-term climate variability and its impact on winter wheat in the Beijing–Tianjin-Hebei region to support sustainable agricultural development under a changing climate. By using meteorological data from 1961–2010 and yield data from 1978–2010, subdividing temperature, precipitation, sunshine into 12 indicators and applying path analysis, a comprehensive climate indicator model was developed to assess regional changes and yield responses.
The results indicate that temperature has increased significantly, precipitation shows a slight upward trend but remains below the water requirements of winter wheat, while sunshine duration has declined. These shifts, combined, have led to marked spatial differentiation: southern Hebei shows the most favorable climatic conditions, while northern areas remain more constrained. Yield analysis further reveals that technological progress has driven trend yields upward, but meteorological yields have declined, reflecting increasing climate stress on production.
This study provides a scientific basis for optimizing winter wheat planting layout, improving climate resource utilization, and managing climate risks in the Beijing–Tianjin–Hebei region from a novel statistical perspective, thereby contributing to the sustainability and resilience of the regional agricultural system. The developed Comprehensive Climate Index not only supports current decision-making but also offers a foundation for climate adaptation strategies under future scenarios (e.g., RCP4.5/RCP8.5). For instance, continued warming may elevate index values in northern areas, facilitating a northward shift of the cultivation boundary, though persistent precipitation constraints in central–northern zones will still require water-saving irrigation technologies. Future work integrating climate model projections will further enhance the index’s utility in guiding medium- to long-term sustainable agricultural adaptation across northern China’s grain-producing regions under global warming.

Author Contributions

Writing—original draft preparation, C.L.; conceptualization, J.H. (Jie Hu); visualization, L.W. (Lei Wang); supervision, M.L.; methodology, W.X.; project administration, Y.Z.; investigation, R.C. resources, L.W. (Lianxi Wang); writing—review and editing, J.H. (Jing Hua); data curation, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Science and Technology Major Project of China (2025ZD1205901) and the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institute (GYZX250205).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article are available from the authors upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 09054 g001
Figure 1. Study area information: (a) location of the study area in China; (b) station distribution.
Figure 1. Study area information: (a) location of the study area in China; (b) station distribution.
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Figure 2. Variation in temperature indicators.
Figure 2. Variation in temperature indicators.
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Figure 3. Variation in precipitation indicators.
Figure 3. Variation in precipitation indicators.
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Figure 4. Variation in sunshine indicators.
Figure 4. Variation in sunshine indicators.
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Figure 5. Separated winter wheat yield.
Figure 5. Separated winter wheat yield.
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Figure 6. Distribution of comprehensive climate index.
Figure 6. Distribution of comprehensive climate index.
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Figure 7. Distribution of comprehensive climate trend rate.
Figure 7. Distribution of comprehensive climate trend rate.
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Figure 8. Distribution of average relative variability of climate indicators.
Figure 8. Distribution of average relative variability of climate indicators.
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Table 1. Station information.
Table 1. Station information.
StationRegionLongitudeLatitudeAltitude (m)
ZhangbeiHeibei114.741.151393.3
YuxianHeibei114.5739.83909.5
ShijiazhuangHeibei114.4238.0381
XingtaiHeibei116.537.07183
FengningHeibei116.6341.22735.1
WeichangHeibei117.7541.93892.7
ZhangjiakouHeibei114.8840.78772.8
HuailaiHeibei115.540.4570.9
MiyunBeijing116.8740.3871.8
ChengdeHeibei117.9540.98422.3
ZunhuaHeibei117.9540.254.9
QinglongHeibei118.9540.4254.3
QinhuangdaoHeibei119.5239.852.4
BeijingBeijing116.4739.832.3
LangfangHeibei116.3839.128.9
TianjinTianjin117.0739.083.5
TangshanHeibei118.1539.6723.2
LaotingHeibei118.8839.438.5
BaodingHeibei115.5238.8516.8
RaoyangHeibei115.7338.2319
CangzhouHeibei116.8338.3310.8
BotouHeibei116.5538.0813.2
TangguTianjin117.7239.054.8
HuanghuaHeibei117.3538.374.5
NangongHeibei115.3837.3727.4
Table 2. The twelve refined climate indicators for winter wheat.
Table 2. The twelve refined climate indicators for winter wheat.
CategoryIndicator NameUnitCalculation Method/Period
TemperatureJanuary Average Temperature°CMean daily temperature in January
Pre-winter Accumulated Temperature°C·dSum of daily mean temperatures from sowing to winter dormancy
Negative Accumulated Temperature during Overwintering°C·dSum of negative daily mean temperatures during the overwintering period
Accumulated Temperature of the Entire Growth Period°C·dSum of daily mean temperatures from sowing to harvest
PrecipitationJanuary PrecipitationmmTotal precipitation in January
Total Pre-winter PrecipitationmmTotal precipitation from sowing to winter dormancy
Precipitation during OverwinteringmmTotal precipitation during the overwintering period
Precipitation of the Entire Growth PeriodmmTotal precipitation from sowing to harvest
SunshineAverage Daily Sunshine Duration in JanuaryhoursMean daily sunshine duration in January
Pre-winter Daily Sunshine DurationhoursMean daily sunshine duration from sowing to winter dormancy
Daily Sunshine Duration during OverwinteringhoursMean daily sunshine duration during the overwintering period
Daily Sunshine Duration of the Entire Growth PeriodhoursMean daily sunshine duration from sowing to harvest
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Liu, C.; Hu, J.; Wang, L.; Li, M.; Xie, W.; Zhu, Y.; Che, R.; Wang, L.; Hua, J.; Wang, J. Construction and Variation Analysis of Comprehensive Climate Indicators for Winter Wheat in Beijing–Tianjin–Hebei Region, China. Sustainability 2025, 17, 9054. https://doi.org/10.3390/su17209054

AMA Style

Liu C, Hu J, Wang L, Li M, Xie W, Zhu Y, Che R, Wang L, Hua J, Wang J. Construction and Variation Analysis of Comprehensive Climate Indicators for Winter Wheat in Beijing–Tianjin–Hebei Region, China. Sustainability. 2025; 17(20):9054. https://doi.org/10.3390/su17209054

Chicago/Turabian Style

Liu, Chang, Jie Hu, Lei Wang, Ming Li, Wenyi Xie, Yining Zhu, Ruijie Che, Lianxi Wang, Jing Hua, and Jian Wang. 2025. "Construction and Variation Analysis of Comprehensive Climate Indicators for Winter Wheat in Beijing–Tianjin–Hebei Region, China" Sustainability 17, no. 20: 9054. https://doi.org/10.3390/su17209054

APA Style

Liu, C., Hu, J., Wang, L., Li, M., Xie, W., Zhu, Y., Che, R., Wang, L., Hua, J., & Wang, J. (2025). Construction and Variation Analysis of Comprehensive Climate Indicators for Winter Wheat in Beijing–Tianjin–Hebei Region, China. Sustainability, 17(20), 9054. https://doi.org/10.3390/su17209054

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