Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

This study's regional crop simulation covered an area located between the eastern Taihang Mountains and the northern Yellow River (112.5° E–119.5° E, 34.8° N–40.5° N) in the NCP, encompassing the entire plain of Beijing, Tianjin, Hebei, and northwestern Shandong Province (figure 1). This region has a flat topography and an average elevation of 30 m. It has a relatively mild climate, with an annual average temperature of 14 °C and a frost-free period of over 200 d. The area experiences distinct seasonal variations, with warm and rainy summers and cold, dry winters. These meteorological conditions are typical of East Asian monsoon regions and are conducive to cultivating a wide range of crops. NCP covers an area of approximately 1.4 × 10⁵ km², which constitutes 8.3% of China's total agricultural land. Among that, 54% is designated as cropland, with 85% of the cropland being irrigated (Pei et al 2015). The soil in the NCP is characterized as loamy, with a bulk density of 1.3 g cm⁻³ and a pH range of 7.5–7.9 at the 0–20 cm depth. The concentration of SOM and total nitrogen in the top 20 cm of soil is approximately 1.0%–2% and 1.2 g kg⁻¹, respectively. The grain production in the NCP significantly contributes to China's overall agricultural output, yielding approximately 26.2 million tons of wheat and 26.8 million tons of maize (Zhang et al 2018). These figures account for 20.3% of China's total wheat production and 12.2% of the nation's total maize production. The dominant cropping system in the region is double cropping of winter wheat and summer maize, which accounts for 49.5% of the total area under cropping (figure 1). The cropping patterns map is based on the ChinaCP dataset (Qiu et al 2022). The region has always relied on groundwater for irrigation, ensuring high food production.

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This study utilized the APSIM Wheat and Maize model (Keating et al 2003, Holzworth et al 2018), a process-based crop model. The model is constructed based on the Plant Modelling Framework, which is a general model framework for simulating plant phenological and physiological processes in response to environmental conditions and field management practices. The APSIM model was widely applied to crop simulation in the NCP. (Zhang et al 2012, Gaydon et al 2017).

APSIM simulations are generated using crop management, weather, and soil data inputs. Weather data requires daily measurements of precipitation, solar radiation, and maximum and minimum air temperature (at 2.0 m above ground). The weather file named MET requires three parameters, including latitude, TAV (annual average air temperature), and AMP (annual amplitude in mean monthly temperature). Soil profile data parameters include bulk density, organic carbon, soil pH, electrical conductivity, and soil hydraulic parameters (air dry, crop lower limit (LL15), drained upper limit (DUL), and saturated water content (SAT)). Crop management inputs include cultivar, plant density, crop configuration (plant spacing, row spacing), sowing date, fertilization, and irrigation regime. The climate data employed in the dataset includes air maximum temperature, air minimum temperature, precipitation, and radiation grid at a resolution of 0.25° × 0.25°. The soil dataset is also at a resolution of 0.25° × 0.25°. The climate data is from the China meteorological forcing dataset (He et al 2020b), covering the period from 1979 to 2018, while the soil data is sampled in 2010 and obtained from the SoilGrids database (Hengl et al 2017). Saturated hydraulic conduct, Saturation water content (SAT), DUL and lower limit (LL15) is calculated with Saxton or Rawls method: If soil organic carbon stock (SOCS) values are missing for any of the layers, then Saxton method (Saxton et al 1986) is used, if SOCS values are known for all layers of the soil, then Rawls method (Rawls et al 1982) is used.

Table 1 shows the modeling scenarios setting. The first column lists the crop rotation patterns, including the crop rotation sequence and species. Winter wheat & Summer maize refer to model simulates of an annual double-cropping system of winter wheat and summer maize; Winter wheat & Summer maize + Monocrop maize refer to that model simulates of the 2 year crop rotation cycle. In the first year, winter wheat and summer maize are planted in a double-cropping system. In the second year, only maize is planted. Monocrop Winter wheat and Monocrop maize refer to model simulations of annual monoculture wheat and monoculture maize, respectively. The second to fourth columns describe the water supply scenarios, which include rainfed, deficit irrigation, and full irrigation. The rainfed scenario relies on natural precipitation as the water supply (water stress factor of photosynthesis about 0.52 for maize, 0.77 for wheat). The stress factors for maize range from heavy to light from 0 to 1, while the opposite is true for wheat, ranging from 1 to 0. The deficit irrigation scenario supplies water according to the crop water stress factor of photosynthesis about 0.72 for maize and 0.54 for wheat. The full irrigation scenario ensures that the crop is not subjected to water stress with a full irrigation water supply. The Management module of Automatic_irrigation_based_on_water_deficit is used to simulate the full irrigation scenario, while the deficit irrigation scenario is simulated by the Management module of Automatic Irrigation in the APSIM. The Automatic Irrigation module's irrigation amount range was set from 60 to 420 mm with a step of 60 mm. For the full irrigation simulations, irrigation was triggered based on the plant-available water content (PAWC) in the top 0–0.6 m soil layer. Irrigation occurred when PAWC dropped below 80% of capacity, replenishing soil moisture to field capacity. Irrigation was also triggered if more than 5 d elapsed since the previous event, again refilling to field capacity to avoid plant water stress. For the deficit irrigation scenarios, total irrigation amounts followed table 1. Irrigation was triggered at 30% PAWC, applying <60 mm per event, with at least 15 d between events, until the seasonal irrigation limit was reached.

The simulation scenarios were designed by combining 281 weather data and soil grid points, four cropping rotations, and three water supply strategies (auto full irrigation, deficit irrigation, rainfed). The plant density and cultivars were fixed to disentangle the rotation and water supply strategy effects on yield variance from those due to interactions and feedback effects among crop management, soil, and weather conditions. The cultivars' genotype parameters used in this study, as reported by Yan et al (2020), showed exceptional performance. The wheat cultivar used in this study was Kenong199 and the maize hybrid cultivar was Zhengdan958, both of which are commonly grown in NCP. Variety turnover at different years and variety variation in space were not considered in this study.

The sowing in this study was done by automatically selecting the sowing date based on preset conditions rather than a fixed date (table 2). The sowing conditions for wheat and maize were based on the harvest of the previous crop, soil moisture content, and precipitation conditions to determine sowing suitability. Once the conditions met the criteria, sowing would occur. The APSIM model simulates monocrops and rotations in the same way, with both being a succession of soil-centered crops grown over decades years with no soil state initialization in between. The monocrop model has a longer fallow period, which can also be referred to as a rotation of one crop, whereas the wheat-maize double-crop rotation has almost no fallow period.

Python language was used for data analysis and visualization. The Cartopy library was used for map visualization, and the Matplotlib library was used for bar plots. Statistical analysis was performed using the SciPy and statsmodels libraries. The Mann–Kendall test was used to test the time series trend of the grain yield and climate factors data, while Pearson's correlation was used to test the correlation between the grain yield and climate factors. Two sensitivity indices were used to quantify the contribution of climate factors to grain yield (Wallach et al 2006). The main effect (ME) was used to quantify the effect of individual climate factors on grain yield, and the total effect (TE) was used to quantify the interaction effect of climate factors on grain yield. The ME is calculated by the following formula:

$$\begin{equation*}{\text{M}}{{\text{E}}_i} = \frac{{{\text{Var}}\left( {{{\bar Y}_{{F_i}}}} \right)}}{{{\text{Var}}\left( Y \right)}}\end{equation*}$$

$$\begin{equation*}{\text{T}}{{\text{E}}_i} = 1 - \frac{{{\text{Var}}\left( {{{\bar Y}_{{F_{ - i}}}}} \right)}}{{{\text{Var}}\left( Y \right)}}\end{equation*}$$

where ${\bar Y_{{F_i}}}$ represents the mean crop yield across all factors F_i , which include soil type, maximum temperature, minimum temperature, rainfall, global solar radiation, rotation and irrigation strategy. ${\bar Y_{{F_{ - i}}}}$ represents the mean crop yield across all factors except F_i. The ME captures the proportion of crop yield variance explained by each individual factor without considering interactions among them. The value of ME = 1 indicates that the assessed factors fully explain the crop yield variance, while the value less than 1 suggests the presence of residuals, indicating the need for additional factors to explain the variance. The residuals refer to the portion of change in the dependent variable y that is caused by factors other than the effect of the independent variable x. In this study, the residual represents the portion of change in crop yield caused by non-climatic factors. The TE takes into account the interactions of a particular factor with other factors, thus, TE does not include residuals. The mean of ME and TE was calculated across the years 1980–2018.

We calculated the WUE and irrigation water productivity (IWP) with these equations:

$$\begin{equation*}{\text{WUE}} = Y/{\text{ET}}\end{equation*}$$

$$\begin{equation*}{\text{IWP}} = \left( {{Y_{{\text{irrigation}}}} - {Y_{{\text{rainfed}}}}} \right)/I\end{equation*}$$

where $Y$ denotes the yield, ${\text{ET}}$ denotes the evapotranspiration, ${Y_{{\text{rainfed}}}}$denotes the yield under rainfed, ${Y_{{\text{irrigation}}}}$ denotes the yield under irrigation.

The study results indicate that various irrigation practices and crop rotations significantly impact grain yield in the study area (figure 2). On average, the yield of different crop rotation patterns was 8105.5 kg ha⁻¹ under rainfed conditions, 10 707.3 kg ha⁻¹ under irrigated conditions, and 13 088.8 kg ha⁻¹ under fully irrigated conditions. Among the crop rotations, Winter-Wheat & Summer-Maize > Winter-Wheat & Summer Maize + Monocrop-Maize > Monocrop-Maize > Monocrop-Wheat had the highest to lowest yields under the same irrigation conditions. The yield difference analysis revealed that irrigation could significantly enhance crop yields in the NCP. Compared to the rainfed, yields under deficit irrigation were augmented by 28.7%, with the highest increase of 46.6% observed for Monocrop-Wheat. The least yield increase, however, was noted at 8.8% for the combination of Winter-Wheat & Summer Maize + Monocrop-Maize. Yields under full irrigation were, on average, 66.8% higher than those under rainfed conditions. Monocrop-Wheat reported the highest yield increase of 106.1%, whereas Monocrop-Maize observed the smallest increase at 37.7%. These results unequivocally suggest that diverse crop rotations respond differently to various irrigation strategies.

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Additionally, the irrigation strategy and crop rotation influenced field water consumption, WUE, and IWP (table 3). The maximum average water consumption was 581 mm for 2 year maturity, while the minimum was 277 mm for monocrop wheat. WUE increased with increasing irrigation, with the highest value of 2.7 kg m⁻³ for 2 year triple cropping and the lowest value of 2.2 kg m⁻³ for monocrop wheat. The highest WUE for monocrop maize was 3.3 kg m⁻³, while the remaining three rotations had a similar WUE of approximately 1.9 kg m⁻³. The irrigation strategy also significantly affected WUE, with deficit irrigation being higher than irrigation water efficiency under full irrigation conditions for lower monocrop maize, but lower than irrigation water efficiency under full irrigation for monocrop wheat and 2 year triple crop. However, there was no change in irrigation efficiency under two maturity per year.

The Mann–Kendall analysis revealed a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat over the past 40 years (figure 3). Conversely, there has been a significant trend of decreasing yields for fully irrigated Monocrop-Maize. In contrast, the grain yields of the double-cropped Winter-Wheat & Summer Maize (WW&SM) system and the triple-cropped Winter Wheat & Summer Maize + Monocrop-Maize (WW&SM + M) system have shown no significant trends under current climate change conditions. The rate of increase in grain yield for Monocrop-Wheat was 449.0 kg ha⁻¹ per 10 years, while the rate of decrease for Monocrop-Maize was 313.4 kg ha⁻¹ per 10 years. These results suggest that Monocrop-Maize production is struggling to adapt to current climate change trends, whereas Monocrop-Wheat is benefiting from these trends. In contrast, the multi-cropped systems demonstrated resilience to climate change.

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Crop rotation significantly impacted the relationship between yield and radiation and temperature (figure 4). The mean correlation coefficient between yield and radiation was −0.28 for Monocrop-Wheat and 0.22 for Monocrop-Maize, indicating opposing functions of the two crops. The multi-crop rotations were similar to Monocrop-Maize in showing a positive correlation between yield and radiation, with mean correlation coefficients of 0.13 for the WW&SM and 0.10 for the WW&SM + M, and maize system, respectively. The order of correlation strength was Monocrop-Maize > WW&SM + M > WW&SM. The yield of Monocrop-Maize had a negative correlation with the maximum temperature, with a mean maximum correlation coefficient of 0.19. In contrast, the yield of Monocrop-Wheat was positively correlated with the minimum temperature, with a maximum correlation coefficient of 0.28. The yields of the other crop rotations were less correlated with the maximum temperature. As shown in supplementary figure 3, both minimum and maximum temperatures are trending upwards, while radiation is trending downwards. Based on these trends, it can be inferred that monoculture maize yield will be negatively impacted, while monoculture wheat yield will increase, and the other rotations will be less affected or insensitive. The water supply strategy modifies the relationship between yield and precipitation for the different crop rotations, as well as the relationship between yield and maximum temperature for certain rotations. Irrigation reduces the negative correlation between yield and maximum temperature for Monocrop-Maize and enhances the positive correlation for the other three rotation patterns. Wise use of irrigation will increase the resilience of food production to climate change.

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Sensitivity analysis is a technique used to determine how changes in input variables can affect the output of a system. In the context of climate change impact on yield, sensitivity analysis can identify the most important environmental factors, such as temperature, precipitation, and soil properties, for crop yield. By understanding the most influential environmental factors, decision-makers can develop strategies to mitigate the effects of climate change and increase crop yields. In this study, sensitivity analysis showed that the maximum impact of precipitation on the yield of monocrop wheat was 49.7% and 46.6% under rainfed and deficit irrigation, respectively (figure 5). Although the effect of precipitation on yield was significantly reduced to 10.2% when sufficient irrigation was applied, the interaction with the other three climatic factors still had the greatest impact on yield (figure 6). The impact of precipitation on yield was weaker for Monocrop-Maize, WW&SM + M, and WW&SM, at 22.1%, 17.5%, and 19.2%, respectively. The average effect of climatic factors on yield under water stress was 65.8%, and the order of the strength of the climatic factors on the yield of different rotations was Monocrop-Maize > Monocrop-Wheat > WW&SM + M > WW&SM, indicating that wheat was less affected by climate than maize, and multi-crop systems were less affected by climate than single-crop systems. The average effect of climatic factors on yield was reduced to 44.1% under full irrigation, indicating that irrigation reduced the sensitivity of grain production to climate by 33.0%.

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The varying growing seasons and sowing dates of different crops result in differing climate environments for different crop rotation methods (Ojeda et al 2021). This study shows that different rotations have varying climatic conditions within the study area. Specifically, monocrop wheat exhibits lower levels of precipitation, radiation, and both maximum and minimum temperatures compared to the annual averages. In contrast, monocrop maize has higher levels of precipitation and lower levels of radiation, as well as significantly higher maximum and minimum temperatures. The 2 year triple cropping pattern has intermediate levels of precipitation and radiation, as well as higher maximum and minimum temperatures compared to the annual averages. Finally, the traditional double cropping pattern has the highest utilization rate of precipitation and radiation, as well as temperatures like the annual averages. It is worth noting that the monocrop wheat model indicates that the growing season for wheat is during a period of poor light, temperature, and water conditions, which greatly limit the growth of other crops. On the other hand, the monocrop maize model indicates that the growing season for maize is at a time when light, temperature, and water conditions are favorable, and these conditions are conducive to the growth of a wide range of crops. The traditional double cropping pattern maximizes the utilization of precipitation and radiation, which can optimize grain yields (Xu et al 2021), However, this system may also have the greatest water demand and risk of groundwater depletion (Sun et al 2011). The 2 year triple cropping pattern has intermediate levels of both precipitation and radiation.

The optimal sowing date and density are key agronomic factors influencing wheat yield. Sowing date affects wheat developmental stages like tillering, which in turn impacts final yield. Earlier sowing often increases tillering due to favorable temperatures and adequate moisture during early growth stages (Rasmussen and Thorup-Kristensen 2016). More tillers allow for increased spike number and kernel count per unit area, both drivers of higher grain yield (Tilley et al 2019). However, late-sown wheat experiences reduced tillering from cooler fall temperatures, constraining yield (Yin et al 2020). Sowing density also affects tillering by changing light interception and the availability of nutrients and moisture per plant. Higher densities reduce light penetration into the canopy inside, increasing competition between plants and lowering tiller production. But sparse stands with excess resources cannot capture adequate light. Optimal stand density maximizes fertile tiller number for a given environment and sowing date (Gao et al 2021). Therefore, the ideal wheat sowing density for a specific sowing date balances individual plant tillering capacity with sufficient population for maximum light interception and yield. Later sowing dates require higher seeding rates to compensate for reduced tillering ability (Ren et al 2019). But excessively high densities exacerbate light competition without improving fertile tiller survival. Integrating sowing date, stand density, and tillering responses is necessary to optimize wheat yields.

The study results demonstrate that irrigation practices and crop rotations have significant impacts on yield in the study area (figure 2). On average, yields increased with increased irrigation, with the highest yields observed under fully irrigated conditions. Among the different crop rotation patterns, Winter-Wheat & Summer-Maize > Winter-Wheat & Summer Maize + Maize > Monocrop-Maize > Monocrop-Wheat had the highest to lowest yields under the same irrigation conditions. However, it is worth noting that the use of irrigation can also have negative environmental impacts, such as water resource depletion and soil erosion. Decision-makers may need to weigh the potential benefits of increased yields against these negative impacts when deciding on irrigation practices. Additionally, the irrigation strategy and crop rotation influenced field water consumption, with the maximum average water consumption observed for double cropping and the minimum for monocrop wheat. This may also be a factor for farmers to consider when deciding on rotation practices (Yan et al 2020).

The study results show that WUE increases with increasing irrigation, with the highest value observed for 2 year triple cropping and the lowest value for monocrop wheat (table 3). However, the irrigation strategy significantly affected WUE, with deficit irrigation being more efficient than full irrigation for monocrop maize but less efficient than full irrigation for monocrop wheat and 2 year triple crop. This suggests that there may be trade-offs between yield and WUE depending on the irrigation strategy and crop rotation used. Zhao et al (2020) also showed, through meta-analysis, that crop rotation increases crop yields by an average of 20% compared to continuous monoculture. Yu et al (2020) reported that appropriate irrigation practices can improve wheat water-use efficiency by 6.6%, with the best results occurring in areas with less than 200 mm of precipitation and loamy or sandy soil, using border and furrow irrigation methods. These findings suggest that farmers can increase grain yields by carefully considering irrigation practices and crop rotations.

The Mann–Kendall analysis in this study shows a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat over the past 40 years, with a rate of increase in grain yield of 449.0 kg ha⁻¹ per 10 years. This may be due to the crop benefiting from the current climate change trends. On the other hand, there has been a significant trend of decreasing yields for fully irrigated Monocrop-Maize, with a rate of decrease of 313.4 kg ha⁻¹ per 10 years. These results suggest that Monocrop-Maize may be struggling to adapt to current climate change trends, while Monocrop-Wheat is benefiting from these trends. Xiao et al (2021) simulated results similarly indicated that future climate change would have negative impact on maize yield across all cropping systems but have positive impact on wheat yield under most climate change scenarios. However, it is worth noting that these findings are based on fully irrigated conditions, which are not applicable to all cropping conditions. Additionally, the grain yields of the double-cropped Winter-Wheat & Summer Maize (WW&SM) system and the triple-cropped Winter Wheat & Summer Maize + Maize (WW&SM + M) system have shown no significant trends under current climate change conditions. This suggests that these multi-cropped systems may be more resilient to climate change compared to the monocrop systems. Maintaining crop diversity is important for maintaining ecosystem functions related to food production and for supporting crop yields, particularly in the context of ongoing land-use change (Dainese et al 2019). These findings highlight the importance of considering both irrigation practices and crop rotations when analyzing the impacts of climate change on crop yields. While Monocrop-Wheat may be benefiting from current trends, other crops and cropping systems may be more vulnerable or resilient to these trends.

The study results indicate that crop rotation significantly impacts the relationship between yield and radiation and temperature (figure 4). Monocrop-Wheat had a mean correlation coefficient of −0.28 between yield and radiation, while Monocrop-Maize had a coefficient of 0.22, indicating opposing functions in terms of their relationship with radiation. The multi-crop rotations, including the Winter-Wheat & Summer Maize (WW&SM) and the Winter Wheat & Summer Maize + Maize (WW&SM + M) systems, had a positive correlation between yield and radiation, similar to Monocrop-Maize. In terms of correlation strength, the order was Monocrop-Maize > WW&SM + M > WW&SM. Regarding temperature, Monocrop-Maize had a negative correlation with the maximum temperature, with a mean maximum correlation coefficient of 0.19. In contrast, Monocrop-Wheat had a positive correlation with the minimum temperature, with a maximum correlation coefficient of 0.28. The yields of the other crop rotations were less correlated with the maximum temperature (Bao et al 2022). Supplementary figure 3 shows that both minimum and maximum temperatures are trending upwards, while radiation is trending downwards. Based on these trends, it can be inferred that the yield of monoculture maize will be negatively impacted, while the yield of monoculture wheat may be positively impacted. The other crop rotations may be less affected or insensitive to these trends. Observational data analysis reveals that maize yield has increased over the past 30 years, while radiation levels have decreased (Deng et al 2020). This is likely due to improved water and fertilizer management and advances in cultivars (Ren et al 2021). It is worth noting that the water supply strategy can modify the relationship between yield and precipitation for the different crop rotations, as well as the relationship between yield and maximum temperature for certain rotations. Irrigation not only fulfills crop water demand but also mitigates crop heat stress (Tack et al 2017). Irrigation can reduce the negative correlation between yield and maximum temperature for Monocrop-Maize and enhance the positive correlation for the other three rotation patterns. Li et al (2020) found that 16% of the increased yield from irrigation is due to the cooling effect. These results suggest that judicious irrigation use may bolster the resilience of food production to climate change by alleviating the adverse effects of rising temperatures on some crops.

Sensitivity analysis is a useful tool for understanding how changes in certain input variables can affect the output of a system, such as crop yield. In this study, sensitivity analysis was used to identify which environmental factors, including temperature, precipitation, and soil properties, had the greatest impact on crop yield. The results showed that the maximum impact of precipitation on the yield of monocrop wheat was 49.7% and 46.6% under rainfed and deficit irrigation, respectively (figure 5). Although sufficient irrigation reduced the effect of precipitation on yield to 10.2%, the interaction with the other three climatic factors still had the greatest impact on yield. Conversely, the impact of precipitation on yield was weaker for Monocrop-Maize, Winter-Wheat & Summer Maize + Maize (WW&SM + M), and Winter-Wheat & Summer Maize (WW&SM), at 22.1%, 17.5%, and 19.2%, respectively. This suggests that maize may be more sensitive to changes in precipitation than wheat, and that multi-crop systems may be less sensitive to changes in precipitation than single-crop systems. Although there has not been a notable trend in precipitation in recent decades (supplementary figure 3), research suggests that precipitation is likely to fluctuate more significantly in the future as a result of climate change (Yan et al 2022). Under water stress, the average effect of climatic factors on yield was 65.8%, with Monocrop-Maize having the strongest impact, followed by Monocrop-Wheat, WW&SM + M, and WW&SM. This suggests that wheat is less affected by climate than maize, and multi-crop systems are less affected by climate than single-crop systems. Utilizing crop rotations with diverse species takes advantage of the unique attributes of each crop. For instance, winter wheat can benefit from warmer winters to bolster biomass accumulation (He et al 2020a), counterbalancing maize yield declines under hotter temperatures.

Additionally, wheat employs the C₃ photosynthetic pathway, whereas maize utilizes the C₄ pathway, resulting in distinct responses to climatic factors. It has been proposed that winter wheat, as a C₃ plant, would likely see greater benefits in a future scenario involving an increase in atmospheric CO₂ levels, while the C₄ crop maize would likely see fewer benefits (Leakey et al 2009, Loladze 2014). C₄ plants, such as maize, exhibit accelerated growth rates and elevated optimal temperatures compared to C₃ plants. Maize photosynthesis peaks at approximately 25 °C–35 °C (Wang et al 2018), while wheat reaches its zenith between 15 °C and 20 °C (Wang et al 2017). Theoretically, maize may benefit more from increased temperatures than wheat. Nevertheless, considering the wheat-growing environment in the NCP, the average wheat season temperature is a mere 8.3 °C, lower than the optimal growth temperature. Consequently, higher future temperatures should prove more favorable for wheat cultivation. The results of the present study show similar findings. Seasonal maximum temperatures for maize may exceed 35 °C, inducing heat stress and potentially reducing yields (Sánchez et al 2014). C₄ plants generally possess greater water-use efficiency than C₃ plants, rendering them more resilient to drought conditions. In areas where water scarcity is anticipated to intensify due to climate change, C₄ crops may outperform C₃ crops. However, precipitation is expected to increase in North China in the future. C₃ plants exhibit a more pronounced stimulation of photosynthesis in response to elevated CO₂ levels compared to C₄ plants, suggesting that increasing atmospheric CO₂ may benefit wheat more than maize. Synthesizing the above factors, it is likely that C₃ plants will gain more benefits from future climate change trends.

Irrigation can mitigate the impact of climate on crop yield. Full irrigation reduced the average effect of climatic factors on yield to 44.1%, indicating a 33.0% reduction in the sensitivity of grain production to climate. Zaveri and Lobell (2019) reported that irrigation has played a significant role in the growth of wheat as a major crop in India and is expected to become even more important as a way to adapt to climate change. Those results showed national wheat yields in the 2000s to be 13% higher than the yields projected without irrigation expansion since 1970. Furthermore, irrigated wheat exhibited approximately 25% less sensitivity to heat relative to purely rainfed conditions. These findings indicate irrigation could serve as an effective strategy to improve crop yields under climate change. However, this will significantly increase the need for irrigation from the point of view of water use (Tijjani et al 2022).