# **Sustainability with Changing Climate and Extremes**

Edited by Xiaodong Yan, Jia Yang and Shaofei Jin Printed Edition of the Special Issue Published in *Sustainability*

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### **Sustainability with Changing Climate and Extremes**

## **Sustainability with Changing Climate and Extremes**

Editors

**Xiaodong Yan Jia Yang Shaofei Jin**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Xiaodong Yan Beijing Normal University China

Jia Yang Oklahoma State University USA

Shaofei Jin Minjiang University China

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

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### **Contents**

#### **Zheng Wang, Yang Pan, Junxia Gu, Yu Zhang and Jianrong Wang**

#### *Editorial* **Sustainability with Changing Climate and Extremes**

**Shaofei Jin 1, Jia Yang 2 and Xiaodong Yan 3,\***

Climate change and extreme events are receiving increasingly more attention in the global sustainable development sphere. Identifying the impacts of climate change and extreme events is not only important in terms of natural processes, such as heat waves and earthquakes, but also in terms of societal processes and the societal consequences of natural disasters. Over the past two years (2021 and 2022), extreme events have occurred across the globe. The February 2021 North American cold wave led to widespread power outages for millions of people in Texas, USA. Another remarkable extreme event, the recent Coronavirus disease in 2019 (COVID-19), is shaping the entire environmental and societal sustainability situation worldwide. In 2022, persistent heatwaves have been affecting Europe, causing evacuations and heat-related deaths with a maximum temperature of 47.0 ◦C reported in Pinhão, Portugal, on 14 July. The flood in Pakistan, the deadliest one in worldwide since 2017, has killed more than 1300 people since 14 June 2022. With the intensity and magnitude of climate change and extreme events being unknown, neither the changes themselves nor the corresponding impacts are clear under the current circumstances. Therefore, we organized this Special Issue under the theme of sustainability with respect to the changing climate and extremes.

We will briefly discuss the contributions of the 34 published papers in this Special Issue in the following four sections.

#### **1. Natural Disasters in Agriculture**

As the most widely cultivated fruit genus, Wang Shuangshuang et al. (Appendix A, Contributor 1) evaluated changes in the quality and yields of citrus under the shared socioeconomic pathways scenario. In their study, first, they proposed statistical models of the relationship between daily meteorological observation variables and the yields and quality of citrus. In short, with respect to predicting the quality of citrus, the monthly mean diurnal temperature range in July was identified as the optimal variable; for predicting yields, a group of variables in October and September was identified as comprising the best predictors. Then, they analyzed the future changes in the quality and yields of citrus in the period from 2021–2060 using the ensemble mean of nineteen Coupled Model Intercomparison Project 6 (CMIP6) models. Finally, they found that the quality of citrus will increase in 3 provinces, while the yield will increase in all 11 provinces. The results can support the design of citrus plantations in the future; further, the statistical models can be coupled with the ecological process model for predicting the yield and quality of citrus fruits.

Potatoes play a significant role in global food security and human diets. As the leading potato producer, China accounts for 22% of the world's potato yield [1]. In China, the potato plant has been the fourth most important crop after rice, wheat, and maize, and is facing the negative impacts of the changing climate. Yang Li-Tao et al. (Contributor 2) analyzed the climatic production potential of potatoes from 1961 in Inner Mongolia,

**Citation:** Jin, S.; Yang, J.; Yan, X. Sustainability with Changing Climate and Extremes. *Sustainability* **2022**, *14*, 11830. https://doi.org/10.3390/ su141911830

Received: 13 September 2022 Accepted: 15 September 2022 Published: 20 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

where they occupied the largest plantation area of potatoes in China. They obtained the annual average climate production potential for potatoes in Inner Mongolia, which was 19,318 kg/hm−2. Over the past 61 years, the climatic production potential exhibited an insignificant linear decreasing trend under the changed climate. The main finding of their study lies in their identification of precipitation as the main impact factor for the productive potential for potatoes in Inner Mongolia.

Maize (*Zea mays* L.) is one of the most important crops in the global and national economies and is the third highest yielding form of crop production in China. In China, the Huanghuaihai plain is the largest summer maize-producing area with a wheat–maize cropping system. The ongoing process of global warming increases the risk of high-temperature injury to maize. In addition, previous studies have shown that the inter- and mixedcropping of maize varieties with different genotypes is one way to effectively alleviate high-temperature injuries during the flowering period. In this study, Li Shuyan et al. (Contributor 3) investigated the response of five main genotypes of maize to high temperatures and different crop models during the flowering period. The main findings revealed that inter- and mixed cropping effectively reduced the impact of high temperatures during flowering, demonstrating that even the genotypic varieties can make significant differences in the yields.

In addition, the diurnal temperature range (DTR) is also an important meteorological variable affecting maize yields. Xie Wenqiang et al. (Contributor 4) evaluated the ability of 26 CMIP6 models to simulate DTR from 1961–2014 and projected the DTR under different shared socioeconomic pathway (SSP) scenarios from 2015 to 2050. The main findings of their study included: (1) CMIP6 models can generally reproduce spatial distribution, especially in the maize cultivation areas; (2) The DTR remains stable from 2015–2050 under SSP245, while a significant decreasing trend is found under SSP585. This study implied the efficiency of the application of DTR in the accurate prediction of maize yield prediction.

The dry-hot wind is a type of severe agricultural wind disaster with high a temperature and low humidity. Dry-hot winds can reduce wheat production by up to 30%. In China, Shandong Province has the greatest agricultural growth value among all of the provinces and is also the most affected by dry-hot winds. Wang Nan et al. (Contributor 5) assessed the spatial distribution of dry-hot wind disasters in Shandong Province. The main finding in their study demonstrated that the more developed areas in the east of Shandong province show high disaster prevention and mitigation capabilities, whereas these same aspects are weak in the west, where the economy is still behind eastern Shandong province. Their study implies the roles of economical input in preventing natural disasters.

Further, Jiang Meiyi et al. (Contributor 6) evaluated the effect of drought on the production of peanuts in Shandong province, which accounts for 16% of the total peanut cultivation area and 20% of the total peanut production in China. Drought has been considered the most severe natural disaster with respect to agricultural production. Therefore, identifying the high drought risk regions will be a benefit to the yields and quality of peanuts in Shandong. As the main finding of their study, they found that the drought risk was concentrated on the Jiaodong Peninsula, covering 20.7% of the province. Their study can support the acquisition of data for developing and carrying out peanut drought prevention, relief policies, and related decisions.

Further, in another study (Contributor 7), Pan Mingxi et al. investigated the effects of snow cover on the spring soil moisture content, which will impact the occurrence of spring drought in high-latitude areas. To investigate the role of winter snow cover with respect to soil moisture, the authors chose the main agricultural areas of Northeast China—the Songnen Plain and the Sanjiang Plain—to address these issues. Their main finding was that they found that snow cover has a strong effect on soil moisture conservation in more arid areas. This work can support the scientific basis for the early warning of spring drought, the development of more efficient irrigation schemes, and crop yield prediction based on the snow cover in Northeast China.

To predict droughts and floods in agriculture in the future, in their study (Contributor 8), Li Jiannan et al. constructed a vulnerability model based on "sensitivity–exposure–adaptability" and "vulnerability-risk, source-risk receptor" drought and flood risk models and established multi-index prediction systems. They predict the vulnerability and risk of droughts and floods in China's agroecosystem under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios from 2020 to 2050 using a combination of AHP and the entropy-weighting method. They found that the levels drought and flood vulnerability intensify, and the drought or flood vulnerability area expands to southern China from SSP1-2.6 to SSP5-8.5.

#### **2. Urban/Rural Ecosystem, Tourism, and Ecosystem Service**

In an urban ecosystem, rainfall plays a crucial role in human mobility and urban management and planning. After four decades of urbanization, China is still facing the rapid developmental period of urbanization in the future. One urgent need involves understanding the impact of rainfall on residents' mobility in the city. Taxi GPS data support a large amount of spatiotemporal information about human activities and mobility in urban areas. Guo Peng et al. (Contributor 9) combined geospatial complex networks with multiscale geospatial analysis to extend the empirical research on human travel patterns using Taxi GPS data to analyze the impact of rainfall on human mobility. They proved that taxi GPS data are highly informative and exploitable in the field of human mobility analysis. The main findings based on their results were the following: (1) rainfall has a reducing effect on trip flow, trip distance, and trip duration on both weekdays and weekends; (2) rainfall has a significant effect on the network indicators; and (3) more mobile communities were detected on weekends than on weekdays, while the number of communities on weekdays and weekends did not change significantly because of rainfall.

Further, climate-related risks threaten urban safety, infrastructure stability, and socioeconomic sustainability. China is facing various climate hazards due to its diverse geomorphic conditions. In this study (Contributor 10), Sun Shao et al. propose a comprehensive analysis of the spatial pattern of major climate hazards in China from 1991 to 2020. The climate hazard types include rainstorms, droughts, heatwaves, cold waves, typhoons, and snowstorms. They found that cities are hotspots affected by intensified climate hazards in a warmer climate and they proposed an urgent need to incorporate a climate adaptation strategy into future city construction to improve social resilience and mitigate climate impacts in the rapid urbanization process in China. For the climate risks of the three major urban agglomerations of Eastern China (Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River Delta), in this study (Contributor 11), Chou Jieming et al. constructed one vulnerability degree using the Gray model (GM (1,1)), and calculated the drought, heat wave, and flood hazards under different emission scenarios based on the CMIP6 model. They found that the Beijing–Tianjin–Hebei urban agglomeration has a good level of urban resilience, the Yangtze River Delta's urban agglomeration has slightly higher overall risk, and the Pearl River Delta's urban agglomeration has the highest relative risk overall.

Rural regions are sensitive to climate disasters. China has achieved its goal of eliminating absolute poverty in China. However, meteorological disasters can promote rural populations' return to poverty. In this study (Contributor 12), Li Aiwei et al. analyzed the spatiotemporal characteristics of rainstorms and droughts and their socioeconomic impacts on China's contiguous poverty-stricken areas from 1984 to 2019. The main findings are as follows: (1) rainstorms showed a significant increasing trend of 0.075 days/decade, while there is no significant trend for drought days; (2) the average annual loss rate due to disasters in the poverty-stricken areas reached 1.6%, which is 0.6% higher than the national level. Their results suggest that to obtain the realization of the United Nations Sustainable Development Goals, it is necessary to improve the capacity for meteorological disaster prevention and reduction in China's contiguous poverty-stricken areas.

The tourism markets are deeply impacted by the air pollution in China due to the necessity of pursuing fresh air in the tourism transformation process. Finding one air index to reflect the air freshness of destinations can support referable data for the tourists. Yang Xiaoyan et al. (Contributor 13) propose a new comfort index for climate tourism: the fresh air–natural microclimate comfort index (FAI-NMCI). This index connects the fresh air index with the natural microclimate comfort index of scenic spots from transdisciplinary and multidisciplinary perspectives. Under contemporary China's high-quality tourism development background, this could provide accurate information not only for the demand and supply sides of the tourism market but could also become an important and comprehensive index for related governmental management.

Further, to explore the influence of changes in climate comfort on Arctic tourism, in this study (Contributor 14), Huang Yutao et al. analyzed the spatial-temporal changes in Arctic summer climate comfort zones from 1979 to 2019 using the ERA5-HEAT (Human thErmAl comforT) dataset from the European Center for Medium-range Weather Forecasts (ECMWF). Their results suggest that global warming increases the Arctic summer climate comfort level and may provide favorable conditions for the further development of regional tourism resources.

In the ecosystem service section, the annual terrestrial gross primary productivity (GPP) was taken as the representative ecological indicator of the ecosystem. In this study (Contributor 15), Zhang Chi et al. analyzed the GPP changes using three earth system models (ESMs) from CMIP6 under 1.5 and 2 ◦C global warming targets in the Shared Socioeconomic Pathway 4.5 W m−2 (SSP245) scenario. In their main findings, they found that Under 1.5 ◦C of global warming, GPP in four climate zones (temperate continental, temperate monsoonal, subtropical–tropical monsoonal, and high-cold Tibetan Plateau) increased significantly with a minimum growth of 12.3% and the increase was greater under 2 ◦C of global warming that at 1.5 ◦C. Their results implied that global warming poses no ecological risk in China from the perspective of ecosystem productivity.

The China–US trade conflict can affect the achievement of climate change goals. In this study (contributor 16), Chou Jieming et al. assessed the impact of the trade conflict on China's climate policy by combining the model from the Global Trade Analysis Project (GTAP) and the input–output analysis method. Their results showed that changes in trade will challenge China's balancing of climate and trade exigencies, implying that China–US cooperation based on energy and technology will help China cope with climate change after the trade conflict. In this Special Issue, another study by (contributor 17) Feng Qiang et al. contributed the response mechanisms of the ecosystem service's trade-offs with respect to land use changes along precipitation gradients in the Loess Plateau of China.

In one study (contributor 18), Deng Xiaofang et al. investigated human resource allocation with respect to adaptation to climate change in a state-owned forest farm in China using the questionnaire method. They found that the human resource professional and industrial structure changes in the context of climate change are the main limiting factors for the key state-owned forest farms of China. In their paper, they suggested that increasing the investment in education on climate change and the income of employees is one way to promote the adaptation to climate change in China.

Extreme events threaten human health. In one paper (contributor 19), Li Junrong et al. used big data to explore the dynamic changes regarding population exposure during a heat wave incident in Zhuhai based on real-time mobile phone data and meteorological data. The main findings are as follows: (1) the overall population exposure shows a trend of first decreasing and then increasing; (2) a high degree of population exposure is concentrated in areas such as primary and secondary schools, colleges and universities, office buildings, and residential areas; and (3) the population exposure changes in the last two days of the heat wave cycle are mainly affected by the combined influence of population factors and climate factors based on the studied heatwave events from 6 to 12 September 2021. This study has a certain practical guiding significance with respect to advancing high-temperature warning and high-temperature disaster risk prevention methods.

In addition, another study (Contributor 20) assessed China's future heatwave population exposure under 1.5 ◦C and 2.0 ◦C warming scenarios with respect to climate change adaptation using models from CMIP6. The main findings of their study are as follows: (1) an additional 20.15% increase in the number of annual heatwave days would occur with an additional temperature increase of 0.5 ◦C to 2.0 ◦C, an over 1.5 ◦C increase in temperature by the mid-century; (2) from three influencing elements, namely, climate, population, and interaction (e.g., as urbanization affects population redistribution), climate explained more than 70% of the variance in different warming scenarios. Their study can support helpful insights for developing mitigation strategies for climate change.

Tropical marine fisheries provide high-quality protein for human diets and play significant roles in food security. Tuna fisheries are one of the four most highly valued fisheries worldwide and have contributed 5.2 million tons of the total capture amount. The tuna fishery in the entire western and central Pacific Ocean (WCPO) supports major industrial tuna fisheries and contributes almost 30% of the total global tuna supply. However, the changing climate is threatening tropical tuna fisheries via the increasing sea surface temperature. In this study (Contributor 21), Zhou Weifeng et al. explored the impact of climate change on tuna fishery resources by investigating the temporal and spatial characteristics of the thermocline in the main yellowfin tuna purse seine fishing grounds in the western and central Pacific Ocean during La Niña and El Niño years from 2008 to 2017. The main findings of their study include: (1) in La Niña years, the catch per unit effort (CPUE) moved westward where the high-value zone of the upper boundary contracted westward to 145◦ E, while in the El Niño years, this boundary moved eastward to 165◦ E; (2) changes in the thermocline caused by abnormal climate events significantly affected the CPUE. This study can provide additional thermocline distribution information and serve as a reference for tuna production in this area.

#### **3. Extreme Climate Indices**

Extreme climate events are more frequent under ongoing climate change. Central China, a key area for the transition between China's east–west and north–south borders and a hub for land and water transportation, is facing a complex weather system. In this study (Contributor 22), Li Yan et al. analyzed the temporal and spatial dynamics of climate events from 1988 to 2017 using nine indices: six extreme temperature indices and three extreme precipitation indices. The six extreme temperature indices are icy days (ID), frost days (FD), the duration of warm periods (WSDI), the duration of cold periods (CSDI), the lowest Tmin (TNn), and the highest Tmax (TXx). The three extreme precipitation indices are very wet days (R95), consecutive dry days (CDD), and consecutive wet days (CWD). The main findings are as follows: (1) the Jiangxi region was at greater risk of extreme climate events in central China, and (2) the drought events in central China around 2025 will be more significant. Following their results, disaster prevention and mitigation projects can be suggested to be prepared in advance for the policy-makers.

In another study (Contributor 23), Yan Weixiong et al. evaluated the distribution of extreme temperature seasonality trends in mainland China by using the following indices: the number of hot days (HD) and frost days (FD), as well as the frequency of warm days (TX90p), cold days (TX10p), warm nights (TN90p), and cold nights (TN10p). They highlighted that extreme temperatures have increased over mainland China from 1979–2020 with obvious seasonal variations, and the increase in the minimum temperature is more distinct than in the maximum temperature.

Heatwave events (HWEs) have strong negative impacts on human health, ecosystems, and sustainable social development. In one study (Contributor 24), Gao Zhibo and Yan Xiaodong analyzed the characteristics of HWEs over the Yangtze River Basin (YRB) in eastern China during the historical period and projected the changes in HWEs over the YRB in the future using a gridded observation dataset and a high-resolution regional climate model. Their main findings include: (1) Short-lived (≥3 days and <6 days) HWEs are projected to increase rapidly under SSP585, but to rise slowly overall under SSP245, and (2) the increase in HWEs over the YRB region is likely to be associated with the enhancement of the western-Pacific subtropical high (WPSH) and South-Asian high (SAH). This study

can support solid references for disaster prevention and mitigation in the future for these developed regions of China.

In another study (contributor 25), Li Kaiwen et al. defined compound drought and heatwave events (CDHEs) using the monthly scale standard precipitation index and evaluated the spatial and temporal variations in CDHEs in China from 1961 to 2018. The main findings of their study are as follows: (1) the mean frequency of CDHEs takes on a nonsignificant decreasing trend, and the mean magnitude of CDHEs takes on a non-significant increasing trend in China; (2) the significant trends in the annual frequency and annual magnitude of CDHEs are attributed to maximum temperature and precipitation changes.

The surface albedo of pure fresh snow is generally between 60% and 95%. When black carbon (BC) aerosols deposit into the surface snow through dry and wet deposition, the albedo of the snow surface will significantly reduce and thereby increase the absorption of solar radiation on the snow surface. Therefore, understanding the BC concentration on the surface of snow will play an important role with respect to studying climate change. In this study (contributor 26), Zheng Yanjiao et al. simulated the black carbon concentration on the surface snow of northeast China using an asymptotic radiative transfer model. The main findings of their study include: (1) human activities played an important role in snow black carbon pollution, and (2) the snow surface albedo will decrease by 16.448% due to the BC pollution of snow in northeast China. These results suggest that the increase in radiative forcing caused by black carbon via snow reflectivity cannot be ignored.

As one part of the earth system and one of the most sensitive regions of climate warming, cold regions are areas with low temperatures and with the presence of ice and snow for at least part of the year. In their study (Contributor 27), Wang Yumeng et al. studied the spatial distribution and variations of cold regions in China from 1961 to 2019. They found that the area of China's cold regions decreased by 49.32 × 104 km2 in the period from 1991–2019 compared with that in the period of 1961–1990. In addition, in another paper (Contributor 28), Li Hao et al. studied the distribution and assessment of snowdisaster risk zoning in Heilongjiang Province. As the main findings of their study, they found that economically developed regions had strong disaster prevention and mitigation capabilities. This implied that the economic input for preventing snow disasters is required in Heilongjiang Province. For another cold region in China, in this Special Issue, Sun Shao et al. (Contributor 29) and Jia Yiru et al. (Contributor 30) evaluated and mapped the meteorological hazards and the locomotion of slope geohazards in the Qinghai-Tibet Plateau (QTP), respectively, in response to climate change.

Extreme cooling (EC) events are also a major challenge to socioeconomic sustainability and human health. In this study (Contributor 31), Song Shuaifeng and Yan Xiaodong analyzed the temporal and spatial distributions of EC events in China using the relative threshold and the relationship between EC events and the Arctic Oscillation (AO) index during the period of 1961–2017. The main findings of their study are as follows: (1) the frequency of EC events in China decreased by 0.730 d from 1961 to 2017, and (2) EC events are significantly negatively correlated with the AO index, which can explain approximately 21% of the EC event variance. Their study can help to improve the prediction and simulation of EC events in China.

To overcome the overestimation for the light precipitation and underestimation of heavy precipitation due to low model resolution, Luo Neng and Yan Guo (Contributor 32) studied the impact of model resolution on simulating the precipitation extremes over China from 1995–2014 with six extreme indices based on five models from CMIP6. All these models include low- and high-resolution versions. Six extreme indices were employed: simple daily intensity index (SDII), wet days (WD), total precipitation (PRCPTOT), extreme precipitation amount (R95p), heavy precipitation days (R20mm), and consecutive dry days (CDD). The main findings of their study are as follows: (1) models with a high resolution demonstrated better performance in reproducing the pattern of climatological precipitation extremes over China, (2) decreased biases of precipitation exist in all high-resolution models over D1, and (3) Improvement could be attributed to fewer weak precipitation

events (0–10 mm/day) in high-resolution models in comparison with their counterparts with low resolutions. Their solid work proved that models with improved resolution show an obvious advantage with respect to the simulation of precipitation extremes, thereby increasing confidence in the simulation of precipitation extremes.

#### **4. Newly Created Dataset for Climate Change**

The forest ecosystem is one of the most concentrated systems under the climate change background. Identifying the forest type plays a crucial role in supporting information for forest managers, conservationists, and forest ecologists. In this study (Contributor 33), Xu Chen et al., generated a forest type distribution using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index data. Their work will improve the depth of research in biodiversity preservation, forest management, and ecological and forestry research.

Modern meteorological methods with high-resolution and high-quality precipitation data are urgently required in the monitoring of mountain flood geological disasters as well as hydrological monitoring and prediction. In this study (Contributor 34), Wang Zheng et al. tested the newly created 0.01◦ multi-source fusion precipitation product developed by the National Meteorological Information Center. Their study proved that the 0.01◦ multi-source fusion precipitation product has better spatial continuity, a more detailed description of precipitation's spatial distribution and a more accurate reflection of extreme precipitation values, and can provide precipitation data support for refined meteorological services, major activity support, and disaster prevention and reduction.

**Author Contributions:** Conceptualization, S.J. and X.Y.; writing—original draft preparation, S.J., J.Y. and X.Y.; writing—review and editing, S.J., J.Y. and X.Y.; supervision, X.Y.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Central Government Guides Local Projects (Grant NO. 2020L3024), and funded by Fujian Province Forestry Science and Technology Research Project (Grant NO. 2022FKJ02).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank all authors for their excellent papers in this Special Issue: *Sustainability with Changing Climate and Extremes*. We also thank the reviewers for their valuable recommendations leading to the improvement of all manuscripts before publication. We are also grateful to all members of the *Sustainability* Editorial Office for providing us with this opportunity and for continuous support in managing and organizing this Special Issue.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. List of Contributions**

#### **Reference**

1. Jansky, S.H.; Jin, L.P.; Xie, K.Y.; Xie, C.H.; Spooner, D.M. Potato production and breeding in China. *Potato Res.* **2009**, *52*, 57–65. [CrossRef]

#### *Article* **Effects of Future Climate Change on Citrus Quality and Yield in China**

**Shuangshuang Wang, Wenqiang Xie and Xiaodong Yan \***

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China<EMAIL_ADDRESS>(S.W<EMAIL_ADDRESS>(W.X.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-13511027765

**Abstract:** As the world's most widely cultivated fruit, citrus in China is increasingly suffering from ongoing climate change, which affects the sustainability of agricultural systems and social economy. In this study, we linked climate factors to citrus quality and yield and established projection models to elucidate the impact of future climate change. Then, we used the ensemble mean of 19 Coupled Model Intercomparison Project 6 (CMIP6) models to project the 2021–2040 and 2041–2060 climate changes relative to the historical baseline 1995–2014 period under different shared socioeconomic pathways scenarios (SSP2-4.5, SSP5-8.5). The results show that the monthly mean diurnal temperature range in July had the greatest influence on quality, and monthly mean temperature in October, monthly mean relative humidity in October, monthly mean minimum temperature in November and monthly mean maximum temperature in September had the greatest influence on yield at the growth and ripening stages. Moreover, the quality and yield of citrus present different characteristics in terms of change in cultivation areas in the future. The quality of Sichuan, Zhejiang and Fujian Provinces in China will become significantly better, however, Hubei, Guangdong and Guangxi Provinces it will become worse. Surprisingly, yield will increase in all plantations due to future suitable climate conditions for citrus growth and ripening.

**Keywords:** citrus; climate change; quality; yield; future projection

**Citation:** Wang, S.; Xie, W.; Yan, X. Effects of Future Climate Change on Citrus Quality and Yield in China. *Sustainability* **2022**, *14*, 9366. https:// doi.org/10.3390/su14159366

Academic Editor: Teodor Rusu

Received: 11 June 2022 Accepted: 26 July 2022 Published: 30 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Climate change brings great challenges to natural resources and affects the sustainable development of human society [1]. It is now widely recognized as the greatest global threat of the 21st century [2]. Among many aspects of impacts, agriculture is the most sensitive sector [3,4]. In this changing environment, the production of many crops is affected, which is related to world food security and global stability [5–10]. As a result, researchers in agriculture have made it a priority to understand the relationship between crops and climate variables [11–13] and to predict crop yield and quality under climate change scenarios [14]. By correctly recognizing the contribution of climate change to crops and adopting effective adaptation measures to agriculture, human beings can make better use of improving production and resistance to adverse effects, maximizing the increase in output, reducing losses, and realizing potential benefits [15–17].

Citrus is the world's largest cultivated fruit crop, with an annual output of approximately 158 million tons, accounting for approximately 18% of the total fruit output [18,19]. As one of the most important crop types, citrus is an important source of income for farmers and is favoured by consumers for nutritional value [20,21], playing a significant role in people's livelihood, not to mention citrus juice. China has the largest population and is the largest citrus producer in the world, with an annual output of 44 million tons, accounting for almost 28% of global citrus production [18]. Thus, the production of citrus in China plays a vital role in the world citrus pattern and needs to be duly considered. However, China is increasingly suffering from ongoing climate change, and no part of the Earth is

immune to this vulnerability, which could have a major effect on citrus [22,23]. Admittedly, the direction and degree of climate change in terms of influencing citrus varies locally due to regional differences in natural responses and anthropogenic factors.

The growth, development, flowering and fruiting of citrus are sensitive to climate conditions, especially in their yield and chemical quality [24–27]. Previous studies revealed that climate change in China affects the yield of citrus. Under the background of global warming, the citrus yield may be affected by climate risk in subtropical regions of China [28]. Soil erosion in citrus orchards caused by increasingly frequent and intense extreme precipitation is the main cause of productivity decline [29]. Additionally, the climate suitability of citrus affects the growth and final yield of citrus due to the change of temperature and precipitation suitability [30]. However, studies on the effect of citrus quality are rare. With advances in agricultural technology [31,32], climate change may have a greater impact on quality than yield. The contents of Vitamin C, naringin and hesperidin decreased significantly, while chlorogenic acid and caffeic acid increased during frozen temperatures [33]. The highest content of peel was observed from October to March and the Vitamin C content decreased during the ripening process [34]. Additionally, essential oils vary in content in different months [35]. Therefore, the development of this study is necessary to reveal the impact of future climate change on citrus quality, which is a factor that is considered to be as important as yield.

To project future citrus production, Tubiello [36] used two different global circulation model (GCM) scenarios to simulate climate change effects on US citrus production and the result showed that simulated fruit production benefited greatly from the projected climate change, as yields will increase by 20–50%. In this study, we combined empirical regression models based on climate factors and citrus quality or yield with datasets in different CMIP6 (Coupled Model Intercomparison Project 6) models in response to different shared socioeconomic pathways (SSPs) of future climate changes (SSP2-4.5, SSP5-8.5) on citrus. The aim of this study was to investigate the changes in citrus quality and yield in China in the near future (2021–2040) and medium future (2041–2060) relative to the historical baseline period (1995–2014) [37,38]. These results may provide useful information for perennial horticultural crops to meet the challenge of climate change and can be generalized to other parts of the world.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The study areas include 79 meteorological stations in 11 major cultivation provinces in China, which are Sichuan, Chongqing, Hubei, Yunnan, Guizhou, Guangxi, Hunan, Jiangxi, Guangdong, Fujian and Zhejiang as shown in Figure 1 [39]. Typically, citrus production in these 11 provinces account for 98% of the total citrus production (45.85 million tons), and the cultivation areas account for 98% of the total citrus cultivation areas (2.62 million hectares) across China based on the National Bureau of Statistics of China (NBSC) [40].

#### *2.2. Data Sources*

#### 2.2.1. Statistical Data

Citrus quality refers to the exterior quality and internal quality. The most important quality, in addition to fruit size, shape, colour and other economic values based on appearance, is chemical composition, mainly related to the sweet, sour or bitter taste of fruit, as well as the of active ingredient contents. The chemical composition of citrus fruit includes the following two categories in addition to water: the first is insoluble in water, such as cellulose, hemicellulose, and propectin, and the other includes water soluble substances, called total soluble solid (TSS). TSS is one of the main factors determining fruit quality [41–43], which reflects the flavor and can directly determine the commercial value of fruit. In this study, we searched relevant published articles containing citrus TSS data from the China National Knowledge Infrastructure (CNKI) and finally selected 125 available TSS data. TSS data combined with information from the China Meteorological

Station (CMS) are listed in the Table S1. Therefore, the climate of citrus cultivation areas was characterized by the local CMS from the China Meteorological Administration. From the NBSC data, the citrus production and planting area of each province were obtained to calculate the yield.

**Figure 1.** 79 meteorological stations in citrus cultivation areas.

#### 2.2.2. Meteorological Observation Data

CMS (V3.0) contains the Chinese benchmark and general weather stations, including the main information of 2474 sites and the basic meteorological observation data since January 1961. We extracted meteorological data from 11 provinces in major citrus producing areas, including mean temperature, maximum temperature, minimum temperature, relative humidity and other variables on a daily scale. Finally, 79 sites were collected from papers and the data were downloaded from China Meteorological Administration as observational data and used to construct citrus quality and yield regression models.

#### 2.2.3. CMIP6 Data

The 19 CMIP6 global climate models were used to simulate climate change in the near future (2021–2040) and medium future (2041–2060) relative to the historical base period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The relevant information of 19 CMIP6 global climate models is shown in Table 1. Among them, EC-Earth3 and EC-Earth3-Veg models have the highest spatial resolution (0.7◦ × 0.7◦), and the CanESM5 model has the lowest spatial resolution (2.8◦ × 2.8◦). The relative humidity, maximum temperature, minimum temperature and mean temperature variables of the CMIP6 model were extracted (there was no simulation of relative humidity variables in the future period in the BCC-CSM2-MR model; therefore, the relative humidity in the future period was replaced by the results of 18 model sets).

#### *2.3. Methods*

Citrus is an important fruit crop with high economic value and in this study, it was assumed that when citrus lacks water, artificial irrigation is provided; therefore, the impact of precipitation was not considered. The distribution of climate factors such as the mean temperature, minimum temperature, maximum temperature and relative humidity in different growing areas is significantly different, which often results in different effects of climate change on citrus fruit quality and yield [25,44]. Diurnal temperature range (DTR) is the difference between daily maximum temperature and daily minimum temperature, which can reflect the change characteristics of the interaction and provides comprehensive information between the two [45,46]. Changes in DTR can convey climate change information, which will have an impact on human health, the circulation of the ecosystem, the

growth of animals and plants, and the use of renewable energy [47]. The DTR described in this paper is the maximum temperature minus the minimum temperature in 24 h. By using a correlation analysis, regression models were established for the relationship between citrus fruit quality and yield and the key climate factors during fruit growth; furthermore, the crop models of citrus fruit quality and yield were obtained. Lobell [48] considered that all process models contain some degree of experience or statistical rules, and all statistical models also contain some hypothesis of crop processes and mechanisms [49–51]. The change in climate elements has a nonnegligible impact on the growth and development of crops. Therefore, when crop quality and yield are only determined by climate factors, the response characteristics of crops to climate elements should be understood. Statistical models can be used to predict crop responses to climate change [52,53]. By incorporating CMIP6 climate data into crop models, changes in citrus fruit quality and yield were obtained for different future periods. See the supplementary materials for the flow chart.

**Table 1.** 19 CMIP6 models used in the study.

#### 2.3.1. Meta-Analysis

A meta-analysis is a method that can be used to conduct a quantitative and comprehensive analysis of research results [54]. In this study, the quality data of fruit was finally determined by summarizing the research results in the relevant published literature and conducting repeated screening and averaging of the sample data. The operation process includes the following four elements: (1) keywords: TSS of citrus, fruit origin and picking year; (2) unity: recording the data of different varieties of fruit, delimiting the research area and the research benchmark period so that the data of different producing areas and different varieties of fruit have uniformity; (3) match external information: according to the origin of fruit, the relevant information from China meteorological Station was matched, and the corresponding climate data were extracted; (4) obtain results: data of TSS of citrus in different years, different producing areas and different climate conditions. The scientific database used in this study was obtained from China National Knowledge Network (CNKI), with a focus on papers published on climate change, and the exclusion of the effects of extreme climate events such as drought, flood and frost and human activities such as technological progress on fruit quality data.

#### 2.3.2. Correlation Analysis

Correlation analysis is a method used to evaluate the relationship between two variables and the correlation coefficient indicates the strength of the relationship between variables [55]. In this study, Pearson's correlation coefficient calculation method was used to calculate the correlation degree between the two variables, i.e., citrus quality and yield with climate factors, for which the formula utilized is as follows:

$$R = \frac{\sum\_{i=1}^{n} \left(\boldsymbol{x}\_{i} - \overline{\boldsymbol{x}}\right) \left(\boldsymbol{y}\_{i} - \overline{\boldsymbol{y}}\right)}{\sqrt{\sum\_{i=1}^{n} \left(\boldsymbol{x}\_{i} - \overline{\boldsymbol{x}}\right)^{2}} \sqrt{\sum\_{i=1}^{n} \left(\boldsymbol{y}\_{i} - \overline{\boldsymbol{y}}\right)^{2}}}$$

where *R* represents the correlation coefficient, n is the number of samples, *xi* and *yi* are the values of the ith sample in the sequence of two variables, and *x* and *y* are the average values of the sequence of two variables.

#### 2.3.3. Least Square Estimation

In this study, a unary linear regression equation was used to fit the relationship between citrus quality and climate factors, and the trend of quality change was defined as the slope of least square estimation, for which the formula is [55] as follows:

$$\mathfrak{Y}\_{i} = k\mathbf{x}\_{i} + b\_{i}\mathbf{i} = \mathbf{1}\_{i}\mathbf{2}\_{i}\dots\mathbf{2}\_{i}\mathbf{n}$$

$$k = \frac{\frac{\sum\_{i=1}^{n} x\_{i}y\_{i} - \frac{1}{n}\left(\sum\_{i=1}^{n} x\_{i}\right)\left(\sum\_{i=1}^{n} y\_{i}\right)}{\sum\_{i=1}^{n} \mathbf{x}\_{i}^{2} - \frac{1}{n}\left(\sum\_{i=1}^{n} x\_{i}\right)}}$$

$$b = \overline{y} - k\overline{\mathbf{x}}$$

$$\overline{y} = \frac{1}{n}\sum\_{i=1}^{n} y\_{i}$$

$$\overline{\mathbf{x}} = \frac{1}{n}\sum\_{i=1}^{n} \mathbf{x}\_{i}$$

where *yi* represents citrus quality with sample size *n*, *xi* corresponds to climate factor, *k* represents the regression coefficient term, and *b* represents the regression constant term.

#### 2.3.4. Bilinear Interpolation and Multimodel Ensemble

To avoid the uncertainties that may occur in a single model simulation, we selected 19 models from the CMIP6 GCMs. Using the bilinear interpolation method, we interpolated the models with different resolutions on a unified 1◦ × 1◦ grid and obtained the average result of the multimodel ensemble [56], for which the formula is as follows:

$$Ensemblc = \frac{\sum\_{i=1}^{19} M\_i}{19}$$

where *Ensemble* represents the average value of the multimodel result, and *Mi* represents the ith model value. Then, by using the method of bilinear interpolation, the average model data of multiple model sets were uniformly interpolated to the China meteorological stations outlined in the Supplementary Materials Table S1 to obtain the local climate information of the meteorological stations simulated by the model [57].

#### **3. Results**

#### *3.1. Changes of Quality in the Climate Change Factors*

#### 3.1.1. Quality in Relation to Climate Variables

The critical period for the growth and ripening of citrus fruits is from July to December. During this period, the change in DTR is the main climatic factor affecting the quality content of fruits [58,59]. Admittedly, a higher day temperature is preferred for the accumulation of sugar and degradation of organic acids in the fruit-ripening process, and a lower night temperature favours the same under suitable fruit growth conditions [60,61]. DTR can comprehensively reflect the information of maximum and minimum temperature, which has a considerable influence on fruit quality. To quantitatively compare the time period with the maximum correlation with TSS, the maximum temperature and minimum temperature observed at meteorological stations in different months were used to calculate the mean values of DTR and conduct a correlation analysis with TSS, as shown in Figure 2. The results show that DTR is positively correlated with TSS in citrus fruit growth in all periods and passes the significance test at 0.05. The correlation between the mean value of the DTR in July and TSS is highest (R = 0.44), and the correlation coefficient of the mean values of the DTR in the periods from July to August, July to September, July to October, July to November and July to December remains above 0.3, which is higher than that in other periods.

Based on the correlation analysis in Figure 2, the maximum significant positive correlation between the mean for the DTR value in July and the TSS content was obtained to select the climate factors that most affected the change in TSS content in the key period. A linear regression equation was obtained, and the quality prediction model was established as follows:

#### **Q = 0.35** × *X***1+9.021 (***R***1 = 0.442,** *P***1 < 0.001)**

where **Q** indicates the TSS content (%); *X***1** indicates the mean DTR value (◦C) in July; *R***1** is the correlation coefficient; and *P***1** is the significance test index.

The citrus quality prediction model indicates that if the DTR becomes larger, the TSS content also increases, and the citrus quality is good; otherwise, it becomes worse under the background of future climate change.

3.1.2. Changes in the Future DTR

The CMIP6 multimodel ensemble data were used to estimate future DTR changes in July in 11 provinces in China where citrus cultivation areas are located, as shown in Figure 3. Compared with the 1995–2014 historical baseline period, the DTR in July of citrus producing areas in China presents an overall spatial distribution of increase-decreaseincrease from west to east in the 2021–2040 future climate state under the two scenarios, SSP2-4.5 and SSP5-8.5. Sichuan Province in the west and Zhejiang and Fujian Provinces in the east show signs of increasing DTRs; however, the DTRs of Hubei, Hunan, Guangxi and other central regions show decreasing trends. Similarly, in the 2041–2060 mid-future period, the DTR shows almost the same spatial distribution, and the range of change is larger than that in the 2021–2040 period. The DTR in the northern part of Sichuan Province increases above 0.4 ◦C under the SSP5-8.5 scenario. The central regions, such as Guizhou, Hunan and Jiangxi Provinces, also show signs of increasing DTR trends, but in addition, the other regions show signs of decreasing DTR changes within 0.2 ◦C under the SSP2-4.5 scenario.

**Figure 3.** Under the two scenarios, SSP2-4.5 and SSP5-8.5, the DTR in July changes in the near future (2021–2040) and mid future (2041–2060) compared with the historical period (1995–2014).

#### 3.1.3. Changes in the future TSS

The change in DTR in July in the whole region of citrus producing areas obtained in Figure 3 can be entered into the quality prediction model to calculate the change in TSS content. Moreover, the spatial grid DTR data calculated by the CMIP6 multimodel were interpolated to the local meteorological stations, and the spatial distribution map of TSS content in actual citrus-producing areas is depicted in Figure 4. Consistent with the spatial distribution in Figure 3, the change in TSS content in citrus also presents a spatial distribution characteristic of increase-decrease-increase from west to east in the 2021–2040 future climate state under the SSP2-4.5 and SSP5-8.5 scenarios. This phenomenon indicates that the quality of citrus has a tendency to become better-worse-better in these corresponding regions. In the 2041–2060 mid-future climate state, the TSS content of citrus shows a ribbon-like distribution of increasing TSS content in the western, central and eastern regions and decreasing TSS content in the southern and northern regions under the SSP2-4.5 and SSP5-8.5 scenarios. It is suggested that the quality of citrus will change in the same trend. Sichuan, Zhejiang and Fujian Provinces in terms of citrus quality present good trends for the entirety of the future situation. Citrus quality in Guizhou, Hunan, Jiangxi, northern Guangdong and northern Guangxi Provinces will change in the near future to worse and in the mid-future to good. Citrus produced in Hubei Province is affected by climate change and shows quality deterioration under different scenarios and in different future climate states.

**Figure 4.** Under the two scenarios, SSP2-4.5 and SSP5-8.5, the TSS content of citrus at the meteorological site scale of Chinese citrus-producing areas changes in the near future (2021–2040) and in the mid future (2041–2060) compared with the historical period (1995–2014). The dots indicate citrus TSS content variations. Blue indicates decreasing change, and red indicates increasing change.

#### *3.2. Changes of Yield in the Climate Change Factors* 3.2.1. Yield in Relation to Climate Variables

Ahmad [62] confirmed that citrus fruit growth requires certain temperature conditions, and the yield is particularly sensitive to temperature. Citrus fruit grows and ripens from July to December each year, and the climate factors during this period have great effects on the yield. Consequently, the correlation between climate factors and yield in the above months was examined, and the correlation coefficient diagram was obtained, as shown in Figure 5. As seen from the figure, mean temperature in October, relative humidity in October, minimum temperature in November and maximum temperature in September have the highest correlation with yield; as a result, the particular climatic conditions were selected as independent variables to establish a multiple linear regression model and obtain the prediction of the yield model as follows:

$$\text{Y} = \mathbf{0.007} \times \mathbf{X}\_2 + \mathbf{0.108} \times \mathbf{X}\_3 - \mathbf{0.025} \times \mathbf{X}\_4 - \mathbf{0.03X\_5} + \mathbf{2.482} \text{ (R}\_2 = \mathbf{0.437}, \text{P}\_2 < \mathbf{0.001})$$

where **Y** indicates the yield (10 t/ha); *X***2** indicates the mean temperature (◦C) in October; *X***3** indicates the mean minimum temperature (◦C) in November; *X***4** indicates the mean maximum temperature (◦C) in September; *X***5** indicates the mean relative humidity (%) in October; *R***2** is the correlation coefficient; and *P***2** is the significance test index. The citrus yield prediction model indicates that mean temperature and minimum temperature have a positive contribution to yield, while maximum temperature and relative humidity have a negative contribution under the synergistic effect of various climate factors.

**Figure 5.** Correlation coefficient diagram between the monthly mean maximum temperature, minimum temperature, relative humidity and mean temperature for each month from July to December and yield. The abscissa represents each month, and the ordinate represents the monthly mean climate variables mentioned above. The correlation coefficients in the figure all passed the significance test at 0.05, and those that did not pass are displayed as blanks.

#### 3.2.2. Changes in the Future Climate Factors

The CMIP6 multimodel ensemble data were used to predict the future changes in four key climate factors affecting citrus yield under different scenarios and different periods in the future as shown in Figure 6 (Figure 6a–d). The mean temperature in October shows an overall increase, as shown in Figure 6a. There is little difference between the simulation results of the two different scenarios, and the mean temperature range is between 0.5 ◦C and 1.5 ◦C in 2021–2040. Undoubtedly, the mean temperature range of the SSP5-8.5 scenario is significantly higher than that of SSP2-4.5 in 2041–2060. The range of mean temperature shows a phenomenon of increasing with latitude moving northwards in the citrus-growing areas. Figure 6b show that the mean minimum temperature in November presents signs of increasing in all regions, and the greatest warming trend is observed for northwestern Sichuan. In addition, the increasing range of most regions is approximately 0.5 ◦C higher under the SSP5-8.5 scenario than under the SSP2-4.5 scenario in 2021–2040, and the spatial distribution of the warming gradient is consistent with that in 2041–2060. In Figure 6c, the warming effect of the mean maximum temperature in September is approximately the same as that of the mean temperature in October in Figure 6a, except that the greatest warming effect shifts from the northwestern to the central and eastern parts of Sichuan. The mean relative humidity in October increases in the western region and decreases in the central and eastern regions, as shown in Figure 6d. The spatial pattern is approximately similar under the two scenarios during different future periods, while the decreasing trend is almost 0.5% higher in 2041–2060.

**Figure 6.** *Cont*.

**Figure 6.** Under the two scenarios, SSP2-4.5 and SSP5-8.5, the various climate factors change in the near future (2021–2040) and mid future (2041–2060) compared with the historical period (1995–2014). (**a**) Mean temperature (◦C) in October. (**b**) Mean minimum temperature (◦C) in November. (**c**) Mean maximum temperature (◦C) in September. (**d**) Mean relative humidity (%) in October.

#### 3.2.3. Changes in the Future Yield

According to Figure 6, we can see key climate factor changes to some extent simulated by CMIP6 models in the future period under two different scenarios, which were entered into the citrus yield prediction model. Compared with the historical period, the change in citrus yield at the meteorological site scale in the future is shown in Figure 7. Citrus yield shows an increasing trend under different conditions, and the yield-increasing effect is more obvious in 2041–2060 than in 2021–2040, almost doubling the change. The citrus yields of Hubei, Hunan, northern Guangxi and northern Guangdong Provinces increase with an increase of 1–2 t/ha under the SSP2-4.5 scenario; however, almost all citrus-producing areas show an increase of 1–2 t/ha with little difference in yield increase among regions under the SSP5-8.5 scenario in 2021–2040. In regard to the 2041–2060 period, the yields in southeastern Sichuan, Hubei and central Hunan Provinces increase by more than 2 t/ha, and those of Zhejiang and Fujian Provinces almost double, while those of southern Hunan, northern Guangxi and northern Guangdong Provinces have almost no difference compared with the 2021–2040 period under the SSP2-4.5 scenario. A yield increase of above 2 t/ha is observed in most citrus-producing areas except those in Fujian, Yunnan and southern Sichuan Provinces and is double that from 2021–2040 under the SSP5-8.5 scenario.

**Figure 7.** Under the two scenarios, SSP2-4.5 and SSP5-8.5, the yield of citrus at the meteorological site scale of Chinese citrus-producing areas changes in the near future (2021–2040) and in the mid future (2041–2060) compared with the historical period (1995–2014). The orange dots indicate yield changes whose sizes show the degree of change.

#### *3.3. Changes of Quality-Yield in the Climate Change Factors*

Quality-Yield (Q-Y) is defined as the total amount of TSS content contained in the yield, which reflects comprehensive information between the quality and yield changes in the future in t/ha. The change in Q-Y is shown in Figure 8 combined with the forecast for TSS content in Figure 4 and the phenomenon of all conditions increasing yield in Figure 6, which reflects the general change in citrus effective composition TSS content in yield. Although the quality of citrus in some producing areas is worsening, affected by climate change, Q-Y still shows an increasing trend with the increase in the citrus yield, which indicates that the amount of TSS content will improve in the future under the two different scenarios. The spatial distribution of the Q-Y increase is approximately 0.1–0.2 t/ha despite the obvious

quality reduction in the central region and almost the same in all planting regions except in the Zhejiang and Fujian Provinces under the SSP2-4.5 and SSP5-8.5 scenarios in 2021–2040. Because of the effect of yield increase, Q-Y almost doubles compared with 2021–2040 under the SSP2-4.5 scenario in 2041–2060. Although the quality obviously declines in Hubei Province, the Q-Y increases by approximately 0.2 t/ha. The increasing effect of Q-Y is weak in Zhejiang and Fujian Provinces and some other places at approximately 0.1–0.2 t/ha. The decline in quality in the central and southern regions does not affect the Q-Y increase by more than 0.2 t/ha under the SSP5-8.5 scenario in 2041–2060.

**Figure 8.** Under the two scenarios, SSP2-4.5 and SSP5-8.5, the quality-yield of citrus at the meteorological site scale of Chinese citrus-producing areas will change in the near future (2021–2040) and in the mid future (2041–2060) compared with the historical period (1995–2014). The orange dots indicate quality-yield changes, whose sizes show the degree of change. The red circles on the outer layer of the dots indicate the increase in TSS content, and the blue circles indicate the decrease in TSS content shown in Figure 4.

#### **4. Discussion**

#### *4.1. Impact Mechanisms of Empirical Models for Predicting Citrus Fruit Quality and Yield*

Many studies revealed the impact of climate change on the yield of crops, indicating that changes in climate factors such as temperature and precipitation will increase or decrease the yield [9,63,64]. However, fruits have not been given the same attention, and it is necessary to study the impact of climate change on fruits and what will happen to fruits in the future, especially in relation to their yield and quality. The climate factors used in this study are also relevant to temperature and precipitation factors, such as maximum temperature, minimum temperature, DTR, mean temperature and relative humidity, on a daily scale, as shown in Figures 2 and 5. Maximum temperature is beneficial for the accumulation of active substances in the fruit-ripening process, and minimum temperature favours the same under suitable fruit growth conditions [59,61]. DTR can comprehensively reflect the information of maximum and minimum temperature, which has a considerable influence on fruit quality. Mean temperature and relative humidity have been proven to be very important in the growth of citrus and have certain effects on phenology and yield [65–67]. The above climate factors in the key growth periods calculated from meteorological station data in citrus producing areas have a strong correlation with quality and yield. It has been proven that local climate change has a direct impact on citrus production, in contrast to large-scale warming conditions. This method was also applied to study climate-crop yield relationships [68]. In this study, statistical models were used to establish the relationship between climate factors and citrus quality and yield, which have certain reference values because some researchers used statistical models to predict crop yield [69,70]. Mechanism models are so complex that few suitable models can be used to predict the quality and yield changes of citrus in the future, which means that it was difficult to select a model in our study. However, the comprehensive use of mechanism models and statistical models will be of great significance for the prediction of the quality and yield of citrus and even for fruit once the mechanism model is developed and perfected [71,72].

#### *4.2. Sensitive Areas Affected by Climate Factors in the Future*

The key period of citrus fruit growth and maturation is from July to December, as shown in Figures 2 and 5, and the climate change in citrus-producing areas in this period has a direct influence on the change in citrus fruit quality and yield. This conclusion shows that the climate factors in the study area have the same trend of change under the two scenarios, but the intensity of change is greater under the SSP5-8.5 scenario, which is also in line with the simulation setting of future emission scenarios. In addition, the prediction of different future time periods presents different spatial distributions. In the 2021–2040 period, the DTR in July is projected to increase in Sichuan, Zhejiang and Fujian Provinces and decrease in most studied areas; the mean temperature in October, maximum temperature in September and minimum temperature in November seem to increase in all areas; and the relative humidity in October is projected to increase in Sichuan and Yunnan Provinces and decrease in other places. In the 2041–2060 period, the DTR in July is projected to decrease in only some parts of Hubei, Guangdong, Guangxi and Yunnan Provinces and increase in other areas; the mean temperature in October, maximum temperature in September and minimum temperature in November will increase in all areas; and the relative humidity in October is projected to decrease largely in the study areas.

#### *4.3. Some Measures May Improve Citrus Quality and Yield*

According to Figure 2, the DTR will decrease in most cultivation regions except for Sichuan, Zhejiang and Fujian Provinces, which indicates that the quality of citrus is projected to worsen. Some artificial adaptation measures may be taken to prevent negative situations. The DTR can be obtained by subtracting the daily maximum temperature from the daily minimum temperature, and both will increase significantly under the background of global warming; therefore, the reason for the decrease in the DTR is that the warming effect has a more significant enhancement effect on the daily minimum temperature. Suggested coping strategies include a lower night temperature and providing enough day warming conditions. On the other hand, changing cultivation regions is a contributing factor. Admittedly, there is a decreasing degree of DTR of between 0.1 ◦C and 0.2 ◦C, which has seldom effected TSS content as shown in Figure 3. Quality decline does not represent a serious concern based on the results under climate warming conditions. Sichuan Province may have the best natural DTR conditions for the accumulation of active substances in the future. Under the joint action of various climate factors, the changing climate is beneficial for citrus fruit growth and ripening, and the yield of citrus is projected to increase in all producing regions, not to mention the improvement of agricultural technology. Based on our hypothesis, offering irrigation is critical.

#### *4.4. Limitations of this Study*

(1) Without the support of specific citrus quality and yield data at the grid scale, the research areas of this study were limited to all provinces, and the locations of meteorological stations were used to represent the local climate conditions, which included certain errors. (2) Due to the inability of the author and the research group to undertake relevant experiments, the research data on TSS contents of citrus in this study were obtained from

other published papers, and the consistency of the data were not guaranteed. (3) In this study, the ensemble mean of 19 CMIP6 models was adopted to reduce the uncertainty of single-model simulations of climate change impacts. However, the GCM climate models also have some system errors in terms of the observations, which lead to the uncertainty in future changes of projected climate variables. (4) This study did not consider the effect of artificial technological progress on the results.

#### **5. Conclusions**

The key climate factors from July to December of citrus fruit growth and maturation have a good relationship with citrus quality and yield. The monthly mean DTR in July has the greatest influence on quality, and monthly mean temperature in October, monthly mean relative humidity in October, monthly mean minimum temperature in November and monthly mean maximum temperature in September have the greatest influence on yield. Moreover, the monthly mean DTR in July is projected to increase in Sichuan, Zhejiang and Fujian Provinces and decrease in other regions; the monthly mean temperature in October, monthly mean minimum temperature in November and monthly mean maximum temperature in September are projected to increase in all studied areas; and the monthly mean relative humidity in October is projected to increase in small regions of Sichuan and Yunnan Provinces and decrease in other places. Thus, the quality and yield of citrus presented different characteristics of change in cultivation areas when using the established prediction model for the 2021–2040 and 2041–2060 future periods relative to the 1995–2014 baseline period. The quality of western cultivation areas in Sichuan Province and eastern cultivation areas in Zhejiang and Fujian Provinces in China will become significantly better; however, that of Hubei, Guangdong and Guangxi Provinces will worsen. Surprisingly, yield will increase in all plantations due to future suitable climate conditions for citrus growth and ripening.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/su14159366/s1, Figure S1: Flow chart; Table S1: TSS data used in this study [73–85].

**Author Contributions:** X.Y. and S.W. conceived of the idea and method, W.X. and S.W. participated in the prepared materials, and S.W. wrote the paper with input from all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key R&D Program of China, grant number 2019YFA0606904.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All relevant data can be found within the paper and its supporting materials. CMIP6 models data are available at link https://esgf-node.llnl.gov/search/cmip6 (accessed on 10 June 2022).

**Acknowledgments:** We would like to thank the authors who did the experiments to get the citrus data in the supplementary material, which enabled our work to proceed smoothly. We acknowledge the China Meteorological Administration for meteorological data and information.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *Article* **Effects of Climate Change on the Climatic Production Potential of Potatoes in Inner Mongolia, China**

**Li-Tao Yang 1,2,3,4, Jun-Fang Zhao 2,\*, Xiang-Ping Jiang 5, Sheng Wang 6, Lin-Hui Li 7 and Hong-Fei Xie 2**

**Abstract:** Understanding the impacts of regional climate change on crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the climatic production potential of potato over the past 61 years in Inner Mongolia was simulated based on long-term observed data and the step-by-step correction method. The results show that the annual average potential for potato climatic production in Inner Mongolia is 19,318 kg·hm−2, fluctuating between the highest value (25,623 kg·hm−2) and the lowest value (15,354 kg·hm−2). Over the past 61 years, the climatic production potential exhibited an insignificant decreasing trend, with large interannual fluctuation, especially since 2000. The high-value areas of the climatic production potential were mainly located in the central and southern regions. The climatic production potential of potato in most areas showed a decreasing trend. The influence of radiation changes on the potato climatic production potential was not obvious in most areas. The effects of temperature changes on the climatic production potential of potato were mostly negative, and were most obvious in the central and western regions and in the southeastern region. The change in precipitation in most parts of western Inner Mongolia, Hohhot, Chifeng and eastern Xingan League had a positive effect on the climatic production potential of potato. However, the change in precipitation in southern Ulanchabu, eastern Chifeng, Hulunbuir and western and eastern regions had a negative effect on the climatic production potential of potato. The main limiting factor for the climatic production potential of potato in Inner Mongolia is precipitation. Our findings have important implications for local potato production to cope with ongoing climate change in China.

**Keywords:** climate change; potato climatic productivity potential; Inner Mongolia; effect

#### **1. Introduction**

Under global warming, extreme weather occurs frequently, and meteorological disasters bring more and more risks to agricultural production. Food is an important commodity related to the national economy and people's livelihood. It is the basis of economic development, social stability and national independence. Ensuring national food security is always the top priority in governing a country. The Intergovernmental Panel on Climate Change (IPCC) officially released on 28 February the contribution of Working Group II to the Sixth Assessment Report (Ar6) on climate change in 2022, highlighting impacts, adaptation and vulnerability. The report further points out the severe food security situation faced at home and abroad against the background of global warming. Food security has once

**Citation:** Yang, L.-T.; Zhao, J.-F.; Jiang, X.-P.; Wang, S.; Li, L.-H.; Xie, H.-F. Effects of Climate Change on the Climatic Production Potential of Potatoes in Inner Mongolia, China. *Sustainability* **2022**, *14*, 7836. https:// doi.org/10.3390/su14137836

Academic Editors: Xiaodong Yan, Jia Yang and Shaofei Jin

Received: 10 April 2022 Accepted: 24 June 2022 Published: 27 June 2022

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again received extensive attention all over the world.  Potato is one of the main climatic characteristic crops in Inner Mongolia, China. Therefore, it is urgent to acquire a more in-depth understanding of how climate change affects potato production for agricultural disaster prevention and mitigation and the healthy development of the potato industry in Inner Mongolia, China.

The climatic production potential of crops refers to the crop yields when light, heat and water resources are optimally matched under ideal conditions [1], and it is one of the important criteria for evaluating agroclimatic resources [2]. The responses of the climatic production potential of crops to climate change differ significantly among regions and crops [3–5]. At present, the commonly used models for calculating the crop production potential include the Miami model [6,7], the Thornthwaite Memory model [8,9], the AEZ model by FAO [10,11] and the step-by-step revision model [12–14]. Among these models, the step-by-step correction model is a statistical model for calculating the climatic production potential of different crops during the growing season. It can effectively reflect the matching status of climatic resources such as light, heat, and water. Its physical meaning and the causal relationships are clear. The step-by-step correction model is one of the most extensively used research methods for analyzing the food production potential [15,16].

The impacts of climate change on the climatic production potential of crops over the past few decades in China have attracted serious concern [14,17–21]. Based on the potential attenuation method, Wang et al. [14] studied the spatial variation in climatic production potential of maize, rice and soybean in the Songnen Plain, and analyzed the utilization efficiency of natural resources. Lu et al. [17] evaluated the evolution of climatic production potential in Anhui Province in the past 50 years. Wang studied and analyzed the climatic production potential of one-season rice in Anhui Province [18]. Wang et al. [19] studied the climatic production potential of winter wheat in northern China and the influence of separated water. Duan et al. [20] estimated the climatic production potential of potato in Jixi County, Ningxia, and analyzed its stability. Lai simulated the climatic production potential of five crops in Ningxia, including soybean, corn, rice, wheat and potato [21]. However, few studies have been conducted to quantitatively assess the long-term impacts of regional climate change on the climatic production potential of potato in Inner Mongolia based on the step-by-step correction model. A better understanding of how potato responds to regional climate change is essential for mitigating the negative effects of potato production to local climate change.

The objectives of the present study were to (1) determine the main parameters in a climatic production potential model for potato based on long-term historical data in Inner Mongolia and (2) explore the advantages and disadvantages of impacts of changes in main meteorological factors (light, temperature and water) on potato production in Inner Mongolia. These findings are significant for substantially improving our understanding of the impacts of regional climate change on agriculture in China.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Inner Mongolia Autonomous Region is one of 13 key grain-producing provinces (regions) in China. As of 2017, the region has 9.271 million hectares of cultivated land, accounting for 7.8% of the region's area, and the per capita cultivated land area is 3.7 times that of the whole country. The effective irrigation area is 3.175 million hectares. The soil in this area shows obvious meridional differentiation. Black soil, dark brown soil, chernozem, chestnut soil, brown calcium soil, brown desert soil and gray desert soil are distributed from east to west [22]. In some areas, cinnamon soil, calcareous soil, meadow soil and aeolian sandy soil are distributed. The content of soil organic matter (9.75~0.56%) [22] and clay particles (12.41~3.65%) [23] decreases gradually from east to west, while the soil pH value (7.39~8.90) increases gradually from east to west [23]. The soil is mostly

neutral and alkaline [23,24]. Inner Mongolia covers a vast territory, with large east–west and north–south spans, complex landforms (e.g., plateaus, mountains, hills and plains) and special geographical locations, which form complex and diverse climate conditions dominated by a temperate continental monsoon climate, with varied temperature, rainfall and heat waves. In the same season, the cold climate is more suitable for the growth of potatoes, which prefer cold and cool conditions.

#### *2.2. Data*

The data are from the climate center of the Inner Mongolia Autonomous Region. The data used consist of the daily surface meteorological observation data, including sunshine hours, average temperature, precipitation, relative humidity, average wind speed and evapotranspiration. These meteorological data were obtained from 119 meteorological stations in the Inner Mongolia Autonomous Region from 1961 to 2021 (Figure 1). The longterm field observation datasets of potato were collected from 11 agricultural meteorological data stations of the Inner Mongolia Autonomous Region from 1981 to 2020 (Figure 1). The administrative boundaries of Inner Mongolia and the cities (alliances) that are involved in this article are based on the standard map with the approval number of Mongolia S (2019) 33, from the Map Institute of the Inner Mongolia Autonomous Region.

**Figure 1.** Distribution of 119 meteorological stations (green triangles) and 11 agrometeorological observation stations (red dots) in the Inner Mongolia Autonomous Region of China.

#### *2.3. Research Methods*

2.3.1. Calculation of the Climatic Production Potential

The step-by-step correction method was used, starting from crop photosynthesis, according to the process of crop energy conversion and yield formation, and gradually estimating the agricultural climatic production potential. It is a model that was developed

from the step-by-step revision of the climatic factor function based on the photosynthetic production potential. The calculation formula is as follows [25]:

$$\mathcal{Y}\_{\rm CFP} = \mathcal{Y}\_{\rm PPP} \times f(T\_{i\bar{j}}) \times f(R\_{i\bar{j}}) = \mathcal{Y}\_{\rm TPP} \times f(R\_{i\bar{j}}) \tag{1}$$

where *Y*CPP, *Y*PPP and *Y*TPP are the climatic production potential, photosynthetic production potential and light–temperature production potential (kg·hm−2), respectively; *f*(*Tij*) is the effective temperature coefficient of the *i*th growth period in the *j*th year; and *f*(*Rij*) is the effective water coefficient of the *i*th growth period in the *j*th year.

#### (1) Photosynthetic production potential of potato

The photosynthetic production potential of potato refers to the potato yields that are uniquely determined by the local solar radiation under the assumption that the most suitable temperature, water, soil fertility, crop population and agricultural technical measures are provided. It is the theoretical upper limit of potato yield. The photosynthetic production potential model of potato adopts the state of energy utilization and loss during yield formation, takes the total solar radiation value of potato in each growth period as the basic data, and corrects it. The calculation formula is as follows [18,19]:

$$\mathcal{Y}\_{\text{PPP}} = \mathbb{C}f(Q) \tag{2}$$

$$f(Q) = \Omega \varepsilon \rho (1 - \mathfrak{a}) (1 - \beta)(1 - \rho)(1 - \gamma)(1 - \omega)(1 - \eta)^{-1}(1 - \xi) \text{s} \boldsymbol{q}^{-1} f(L) \sum Q\_i \tag{3}$$

where *C* is the unit conversion factor, which is 10,000; ∑ *Qi* is the total radiation during the growing season (MJ·m−2); *i* represents the different growth periods; Ω is the ratio of the photosynthetic ability of crops to fix CO2, which is 0.6; *ε* is the ratio of photosynthetic radiation to total radiation, which is taken as 0.47; *ϕ* is the quantum efficiency of photosynthesis, which is taken as 0.224; *α* is the crop population reflectivity, which is taken as 0.17; *β* is the leakage rate of solar radiation by the crop population, which is taken as 0.1; *ρ* is the ineffective absorption rate of non-photosynthetic organs, which is 0.1; *γ* is the light saturation limitation rate, which is 0.01; *ω* is the respiration loss rate, which is 0.3; *η* is the moisture content of mature grains, which is 0.14; *ξ* is the proportion of crop inorganic ash content, which is taken as 0.08; *s* is the crop economic coefficient, which is taken as 0.75; *q* is the heat required to form one unit of dry matter (MJ·kg−1), which is taken as 18; and *f*(*L*) is the corrected value of the dynamic change in leaf area, which is taken as 0.556.

#### (2) Light–temperature production potential of potato

Based on the photosynthetic production potential of potato, by considering the effect of temperature on plant photosynthesis and correcting the temperature coefficient, the light–temperature production potential of potato, determined by the two factors of light and temperature, can be obtained. The calculation formula is as follows [18]:

$$\mathcal{Y}\_{\rm TPP} = \mathcal{Y}\_{\rm PPP} f(T\_{ij}) \tag{4}$$

$$f(T\_{ij}) = \begin{cases} \frac{\left(T\_{ij} - T\_1\right) \times \left(T\_2 - T\_{ij}\right)^b}{\left(T\_0 - T\_1\right) \times \left(T\_2 - T\_0\right)^b} & T\_1 < T < T\_2\\ 0 & T \le T\_1 \text{ or } T \ge T\_2 \end{cases} \tag{5}$$

$$b = \frac{T\_2 - T\_0}{T\_0 - T\_1} \tag{6}$$

where *f*(*Tij*) is the temperature correction function of the *i*th growth period in the *j*th year; *Tij* is the average temperature of the *i*th growth period in the *j*th year (◦C); and *T*1, *T*2 and *T*0 are the lower and the upper limits of temperature and the optimum temperature of crop growth in each growth period (◦C), respectively (Table 1) [25–27].

**Table 1.** Three cardinal temperature points in each potato growth period (◦C).

#### (3) climatic production potential of potato

The climatic production potential is the upper limit of production that can be achieved under the combined influence of radiation, temperature and precipitation. After the light– temperature production potential is corrected by the moisture correction function, the climatic production potential can be obtained, and its expression is as follows [18]:

$$\mathcal{Y}\_{\rm CPP} = \mathcal{Y}\_{\rm TPP} f(R\_{ij}) \tag{7}$$

$$f(R\_{ij}) = \begin{cases} R\_{ij} / R\_{i0} & R\_{ij} < R\_{i0} \\ 1 & R\_{i0} \ge R\_{ij} \end{cases} \tag{8}$$

where *f*(*Rij*) is the water correction function of the *i*th growth period of the *j*th year, *Rij* is the precipitation during the *i*th growth period of the *j*th year (mm) and *Ri*0 is the physiological water demand during the *i*th growth period (mm). The water requirement of potatoes in the entire growth period is approximately 372.5 mm; the physiological water requirements in the sowing–seedling, seedling–branching, branching–inflorescence, inflorescence– flowering and flowering–returnable stages are 50 mm, 40 mm, 30 mm, 52.5 mm and 200 mm, respectively.

#### 2.3.2. Impacts of Climate Change on the Climatic Production Potential of Potato

The climatic production potential of potato reflects the comprehensive impact of various meteorological factors on potato production. The photosynthetic production potential directly corresponds to the impact of light, the light–temperature production potential corresponds to the comprehensive impact of light and heat, and the climatic production potential corresponds to the comprehensive impact of the three factors of light, heat and water. The effects of light, heat, water and the comprehensive climatic conditions on the potato production potential are expressed as *Y*r, *Y*t, *Y*p and *Y*c (kg·hm−2·a−1), respectively. The calculation method is as follows [25,28]:

$$\mathbf{Y\_r} = (a\_\mathbf{r}/\mathbf{Y\_1}) \times \mathbf{Y\_3} \tag{9}$$

$$\mathcal{Y}\_{\mathbf{t}} = (a\_{\mathbf{t}}/\mathcal{Y}\_{\mathbf{2}} - a\_{\mathbf{r}}/\mathcal{Y}\_{\mathbf{1}}) \times \mathcal{Y}\_{\mathbf{3}} \tag{10}$$

$$\mathcal{Y}\_{\mathbf{t}} = (a\_{\mathbf{t}}/\mathcal{Y}\_{\mathbf{2}} - a\_{\mathbf{r}}/\mathcal{Y}\_{\mathbf{1}}) \times \mathcal{Y}\_{\mathbf{3}} \tag{11}$$

$$\mathcal{Y}\_{\mathbb{C}} = a\_{\mathbb{C}} \tag{12}$$

where *a*r, *a*t and *a*c are the propensity rates of photosynthesis, light–temperature and the climatic production potential with time (year) (kg·hm−2·a−1), respectively; *Y*1, *Y*2 and *Y*3 are the multiyear averages of photosynthesis, light–temperature and the climatic production potential (kg·hm−2), respectively.

#### **3. Results**

#### *3.1. Annual and Interdecadal Variations in the Production Potential of Potato*

It can be seen from the change in the abnormal percentage of potato climatic production potential in Inner Mongolia (Figure 2) that before 2000, the abnormal percentage of potato climatic production potential in Inner Mongolia was mainly positive, and after

2000, it was mainly negative. Over the past 61 years, the climatic production potential of potato during the growth period in most of the central and western parts of Inner Mongolia and the southeast parts has shown a decreasing trend, with a decreasing range of 10~164 kg·hm−2·a−1; the decrease is in the areas that are above 40 kg·hm−2·a−1 and are mainly located in most of Bayannaoer city, the southwest and northern parts of Ordos city, the southern part of Baotou city, the central part of Hohhot city, the southwest and eastern parts of Ulanqab city, west of Xilin Gol League, east of Chifeng city, most of Tongliao city, south of Xing'an League and west and southeast of Hulunbuir city. The central part of Hulunbuir city, north of Xing'an League, northeast of Xilingol League, west of Chifeng city, north parts of Baotou city, the south part of Ordos city, the northwest part of Bayannaoer city and southeast of Alxa League experienced increasing trends, with increases of 10~40 kg·hm−2·a−1. The increase areas greater than 20 kg·hm−2·a−1 were mainly located in the central and northern parts of Hulunbuir city, the north part of Xing'an League, the western part of Chifeng city and the northeastern part of Xilingol League. The change trends of the climatic production potential of potato during the growth period were not obvious in other areas (Figure 3). On the whole, the climatic production potential of potato during the growth period of Inner Mongolia mainly exhibits a decreasing trend, and the increases in temperature, radiation and precipitation during the growth period are not conducive to improving the climatic production potential.

**Figure 2.** The anomaly (low–frequency filtering) changes in the potato potential productivity in Inner Mongolia, China, from 1961 to 2021.

Figure 4 shows the interannual and interdecadal variations in the potato production potential at all levels in Inner Mongolia. Figure 4a shows that over the past 61 years, the interannual fluctuations in the production potential of potatoes at all levels in Inner Mongolia have been relatively large, and the overall trend has decreased significantly (*p* < 0.005). The potential and climatic production potentials decreased at rates of 574 kg·hm−2·(10 a)−1, 1048 kg·hm−2·(10 a)−1 and 465 kg·hm−2·(10 a)−1. The photosynthetic production potential and light–temperature production potential were relatively close, with averages of 42,417 kg·hm−2 and 36,244 kg·hm−2 in the whole area, respectively, and the average climatic production potential was 19,318 kg·hm−2, which is significantly lower than the production potential of the first two levels. The highest value of the photosynthetic production potential occurred in 1965 (45,952 kg·hm−2). The highest value of the light– temperature production potential occurred in 1977 (41,001 kg·hm−2). The highest value of the climatic production potential occurred in 1979 (25,623 kg·hm−2). The lowest values of photosynthetic production potential, light–temperature production potential and climatic production potential occurred in 2021 (34,045 kg·hm−2), 2021 (29,553 kg·hm−2) and 1965 (15,354 kg·hm−2), respectively.

**Figure 3.** Change trends of climatic production potential of potato in Inner Mongolia, China (kg·hm−2·a−1).

**Figure 4.** The annual (**a**) and decadal (**b**) changes in the potential productivity of potato of each grade in Inner Mongolia, China, from 1961 to 2021 (The red column in Figure (**b**) is brown because it overlaps with the green column.).

Judging from the interdecadal changes shown in Figure 4b, the changing trends of the production potentials at all levels are not consistent. Among them, the characteristics of the interdecadal variations in photosynthesis and the light–temperature production potential are consistent, being the largest in the 1970s and smallest in the 2010s, while the climatic production potential were the largest in the 1970s and smallest in the 2000s. The climatic production potential exhibited greater fluctuations than the previous two levels, and its variation trend had a significant positive correlation with the interdecadal variations in precipitation (*p* < 0.01), which indicated that the main limiting factor for the potato climatic production potential in Inner Mongolia was precipitation.

#### *3.2. Characteristics of the Spatial Distribution of the Potato Production Potential*

The climatic production potential of potato in Inner Mongolia decreased from the central and southern parts to the periphery, and the climatic production potential in the central and western regions had an obvious geographical distribution. The central and eastern part of Ulanqab city, the southern part of Xilin Gol League, the western part of Chifeng city and the central part of Hulunbuir city were more consistent with the dominant potato-producing areas. Among them, the highest value of the potato climatic production potential was in the Zhenglan Banner of Xilin Gol League, with the value of 27,641 kg·hm−2. The second-highest value (17,601~22,600 kg·hm−2) was located the eastern part of Ordos city and most of Baotou city, most of Hohhot, northern Ulanqab, central and northern Xilin Gol League and most of its eastern areas. The lowest value was located in Ejina Banner of Alxa League, being only 2594 kg·hm−2 (Figure 5). High temperatures and low rainfall amounts are the probable reasons for the lowest potentials of potato climatic production.

**Figure 5.** The spatial distribution of the climatic production potential of potatoes in Inner Mongolia, China (kg·hm−2).

The annual meteorological yields and climatic production potentials of potato over the past 57 years in Wuchuan County of Hohhot city were relatively consistent, with a very significant positive correlation (0.5131 at 0.1% significance). These potato climatic production potentials are based on the results of the step-by-step revision correction method, and can better reflect the changing trends of the potato meteorological yields (Figure 6). The average annual potato yield in Wuchuan County was about 2163 kg·hm−2, with 5.4% of the maximum climatic production potential and 14.2% of the minimum climatic production potential. The average application level of the potato climatic production potential in Wuchuan County was only 7.6%.

**Figure 6.** The relationship between the climatic production potentials and meteorological yields of potato in Wuchuan, Inner Mongolia, China, from 1961 to 2020.

#### *3.3. Influence of Changes in the Main Meteorological Elements on the Potato Climate Production Potential*

3.3.1. The Effect of Radiation Changes on the Potato Climatic Production Potential

Over the past 58 years, the total solar radiation in Inner Mongolia has fluctuated and decreased at a rate of 39.3 MJ·m−2 (10 a)−1 (*p* > 0.05) [29]; this decreasing trend was obvious in some areas (*p* < 0.05). Over the past 61 years, the number of sunshine hours during the potato growth period in Inner Mongolia fluctuated at a rate of 18.1 h·(10 a)−1 (*p* < 0.001) (Figure 7). Potato is a light-loving crop, and reductions in radiation and light are detrimental to its growth. Figure 8 shows that the influences of radiation changes on the climatic production potential of potato during the growth period in most of Inner Mongolia had negative effects, indicating that radiation reductions were not conducive to improving the climatic production potential of potato during the growth period. The areas experiencing negative effects are mainly distributed in the central and northeastern parts of Ordos city, southern part of Baotou city, most of Hohhot city, central and eastern parts of Ulanqab city, central and southern parts of Xilingol League, southeastern part of Chifeng city, most of Tongliao city, the southeastern part of Xing'an League, and the southeastern part of Hulunbuir city. Ulanqab city, the Chayouhou Banner in Lanchabu city and the Arong Banner in Hulunbuir city experienced the greatest impacts. The areas experiencing positive effects were relatively rare and were mainly distributed in the southwest and east parts of Xilingol League, the south part of Chifeng city and the central and eastern, central and northern Xing'an League and northwestern Hulunbuir city. However, the above-mentioned radiation changes in most of the regions had little effects on the climatic production potential of potato during the growth period, and the values were mostly between −20 MJ·m−2·a−1 and 20 MJ·m−2·a−1.

**Figure 7.** Changes in sunshine hours during the potato growing period in Inner Mongolia, China (h).

**Figure 8.** Impacts of radiation changes on the climatic production potential of potato in Inner Mongolia, China (kg·hm−2·a−1).

#### 3.3.2. Effects of Temperature Changes on the Potato Climatic Production Potential

Over the past 61 years, the average temperatures during the potato growing season in Inner Mongolia have exhibited a significant upward trend as a whole (*p* < 0.001), with a climatic trend rate of 0.3 ◦C·(10 a)−1. The temperatures increased significantly in most areas (94% of meteorological stations (*p* < 0.01) (Figure 9). Potato is a crop that prefers cool and cool conditions, and increased temperatures are not conducive to the growth and development of potato. Figure 10 shows that the impacts of the temperature changes on the potato climatic production potential were mostly negative, indicating that the temperature increases during the potato growth period in Inner Mongolia were not conducive to improving the climatic production potential. Negative effects were seen in the central and western parts of Inner Mongolia and in the southeast, while the negative effects were most obvious in the Northwest Territories, which were −102~−20 kg·hm−2·a−1. The greatest negative effects were found in Bayannaoer city, the north and southwest parts of Ordos city, the southwest part of Baotou city, and the central and western parts of Hohhot city. The positive effects were concentrated only in the central and northern parts of Hulunbuir city and the northwestern part of Xing'an League, with an impact range of 20~40 kg·hm−2·a−1; the influence range in the central and northern parts of Hulunbuir city was greater than 30 kg·hm−2·a−1.

**Figure 9.** Variations in average temperature during the potato growth period in Inner Mongolia, China (◦C).

**Figure 10.** Impacts of temperature changes on the climatic production potential of potato in Inner Mongolia, China (kg·hm−2·a−1).

#### 3.3.3. The Impact of Precipitation Changes on the Climatic Production Potential

Over the past 61 years, the precipitation levels during the potato growing season in Inner Mongolia has decreased at a rate of 0.7 mm·(10a)−1 (*p* > 0.1), and the change trend was not significant in most areas (Figure 11). Figure 12 showed that most of western Inner Mongolia, Hohhot, western Chifeng and eastern Xing'an League experienced positive effects on the climatic production potential of potato during the growth period of the precipitation changes, with an impact range of 20~40 kg·hm−2·a−1, in which the positive effects in the central and eastern parts of Ordos city and most of Baotou city were large and were greater than 30 kg·hm−2·a−1. The central and eastern regions experienced mostly negative effects, with an impact range of −102~ −20 kg·hm−2·a−1; the southeast part of Chifeng city, the southwest part of Tongliao city and the northwest part of Hulunbuir city experienced greater negative impacts, which were less than −50 kg·hm−2·a−1.It is worth mentioning that the change in precipitation in most areas of Wulanchabu city, which is the main potato-producing area in Inner Mongolia, had a negative impact on potato climatic production potential.

**Figure 11.** Variations in annual precipitation during the potato growth period in Inner Mongolia, China (mm).

**Figure 12.** Impact of precipitation changes on the climatic production potential of potato in Inner Mongolia, China (kg·hm−2·a−1).

#### **4. Discussion**

Light, temperature and water are important drivers of climatic production potential [30,31]. Analyzing the impact of climate factors on potato climatic production potential will help people take better measures to adapt to climate change, so as to ensure the highquality development of the potato industry. In general, the impact of climate change on potato climatic production potential in Inner Mongolia has both advantages and disadvantages, but the disadvantages outweigh the advantages. In particular, in Wulanchabu city, the main potato-producing area in the Inner Mongolia Autonomous Region, the impact of climate change on potato climatic production potential is relatively adverse. Compared with the average value of potato climatic production potential in Shanxi Province (13,428 kg·hm−2), the average value of potato climatic production potential in Inner Mongolia (19,318 kg·hm−2) in this study was higher. Although the average precipitation during the growing period in Shanxi Province is slightly higher than that in Inner Mongolia, the high average temperature leads to a lower climatic production potential of potato in Shanxi Province than that in Inner Mongolia. The impact of light, temperature and water changes on potato climatic production potential in Shanxi Province is also more negative; particularly, the impact of radiation and temperature changes in most areas is negative, similar to the impact of climate change on the potato climatic production potential in Inner Mongolia. The impact of precipitation change on the potato climatic production potential in Shanxi Province is positive in the north and negative in the south [28,32]. However, we found that the distribution of impacts of precipitation change on potato production potential in Inner Mongolia was more complex, spanning the main southern agricultural areas from southwest to northeast as follows: positive effect—negative effect—positive effect—negative effect—positive effect—negative effect. The average application level of the potato climatic production potential in Wuchuan County was only 7.6%, lower than the application level of the potato climatic production potential in Jixi County of Ningxia [20]. This shows that the potato yield in Wuchuan County is bound to have room for improvement. We need to pursue advantages, avoid disadvantages and make effective use of climate factors.

In order to adapt to the current climate change trend and its adverse impacts, it is necessary to vigorously promote water-saving irrigation technology and biofilm technology in agricultural areas to improve water use efficiency. In particular, most of the central and western parts of Inner Mongolia are mainly rain-fed agriculture. In Ordos, Baotou, Hohhot and other central and western regions, it is necessary to adjust the sowing date of potatoes so that the yield can occur in the critical period to avoid high temperatures and obtain high yield. The potato production in the eastern agricultural area needs to improve field management in order to improve the utilization rate of light energy and obtain high yield.

In this study, the step-by-step correction method was adopted, and the physical meaning of the model was clear. However, there are still shortcomings that need to be resolved. Because of the incompleteness and discontinuity of the data, we used the data of the average growth period of potato from agrometeorological observation stations for many years. In general, studying only the main variety of local potato, i.e., the mid-late maturing variety, without considering the early maturing variety of potato will lead to differences in climatic resources during the growing period of potato, resulting in certain differences in the calculation results of potato climatic production potential. In Inner Mongolia, water condition was the main limiting factor of potato climatic production potential. This is consistent with the previous study on various crops which found that the same precipitation was far greater than the crop water demand, or even impacted the potato climatic production potential in case of flood disaster, so there were inevitably some omissions in the calculation results [33]. The parameter determination in the step-by-step correction method was empirical, and only reflected the overall variety characteristics of current crops. With the continuous development of breeding and cultivation techniques, the parameters of better varieties might be different in the future, which is likely to lead to changes in the calculation results of potato climatic production potential [8,33]. As the precipitation in local agricultural areas was relatively low [20,34,35], the adverse impact

from calculating the water correction coefficient was not considered in this study. In addition, the climatic production potential of potato was the maximum yield under ideal conditions. The actual yield of potato is affected by many factors, such as climate, soil, social economy and so on. Therefore, in the future, multiple methods can be used for comprehensive analysis to conduct in-depth comparative research on climatic production potential and actual yield, so as to explore the differences and responses between the two [35–37].

#### **5. Conclusions**

We investigated the effects of climate change on the climatic production potential of potatoes since 1961 in Inner Mongolia using the step-by-step correction method. From 1961 to 2021, the average climatic production potential of potato in Inner Mongolia decreased from the central south to the surrounding areas. Under the direct influence of local climatic factors, the average climatic production potential of potato showed a downward trend year by year, especially after 2000. The changes in three climatic factors, light, temperature and water, had different effects on the climatic production potential of potato. The impact of radiation change on the climatic production potential of potato was mainly negative, especially in the main potato-producing area of Wulanchabu. Temperature change had both advantages and disadvantages. In the cold climate region of Northeast China, the temperature change had a favorable impact on the climatic production potential of potato. Other areas were mainly adversely affected, and most areas of Ulanqab city were adversely affected by temperature changes. The change in rainfall had a favorable impact on the climatic production potential of potato west of Hohhot and in the southern and central area of Xilin Gol League. However, the change in rainfall had a negative impact on the climatic production potential of potato in most other areas.

**Author Contributions:** J.-F.Z. and L.-T.Y. contributed to the study conception and design. Material preparation, data collection, and analysis were performed by L.-T.Y., X.-P.J., S.W., L.-H.L. and H.-F.X. The first draft of the manuscript was written by L.-T.Y., and all authors commented on subsequent versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Natural Science Foundation of Inner Mongolia, (2021MS04018) and the Climate Change Project of China Meteorological Administration (CCSF201931).

**Data Availability Statement:** Meteorological observation data (including surface meteorological observation data and agricultural meteorological observation data) are from the Bureau of Inner Mongolia Autonomous Region. The relevant map data comes from the map Research Institute of Inner Mongolia Autonomous Region.

**Acknowledgments:** We thank the reviewers for their help in improving this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

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### *Article* **Inter- and Mixed Cropping of Different Varieties Improves High-Temperature Tolerance during Flowering of Summer Maize**

**Shuyan Li 1, Junfang Zhao 2,\*, Junling Li 1,3, Ruixin Shao 4, Hongping Li 4, Wensong Fang 1,3, Liting Hu 1,3 and Tianxue Liu 4,\***

**Abstract:** Global warming increases the risk of high-temperature injury to maize. Inter- and mixedcropping of maize varieties with different genotypes is one way to effectively alleviate the hightemperature injury during the flowering period. However, the mitigation effect of different varieties and intercropping modes on high-temperature injury is still unclear. Based on previous years of field production, Denghai 605, which is more sensitive to high temperatures during the flowering period, was determined as the main test variety, and Zhengdan 958, Dedan 5, Weike 702, and Xianyu 335, which have great genotypic differences, were used as auxiliary varieties. The main test varieties and auxiliary varieties were intercropped and mixed cropped, respectively. Plant height, ear height, leaf area index, population light transmittance, ear characteristics, and yield were measured, and the land equivalent ratio (LER) was calculated. The plant height of Denghai 605 intercropped with Zhengdan 958 and Dedan 5 and mixed with Weike 702 and Xianyu 335 decreased significantly. The population light transmittance of the bottom or middle layer in Denghai 605 increased significantly when intercropped with other varieties. The grain number per ear increased significantly under inter- and mixed cropping with Zhengdan 958 and Weike 702. Except under intercropping with Dedan 5, the yield of Denghai 605 increased significantly, by 8.8–28.0%, under inter- and mixed cropping. Under intercropping with Zhengdan 958 and inter- and mixed cropping with Weike 702 and Xianyu 335, respectively, the group land equivalent ratio was greater than 1.1, indicating that under the combination of these varieties, inter- and mixed cropping effectively reduced the impact of high temperatures during flowering.

**Keywords:** summer maize; inter- and mixed cropping; high temperature; flowering period; yield

#### **1. Introduction**

Maize (*Zea mays* L.) is one of the most important crops in the global and national economy [1,2]. The Huanghuaihai plain is the largest summer-maize-producing area in China with a wheat–maize cropping system. The growing season of summer maize is mainly in the period from June to September, which has the highest temperature in the year and two-thirds of the annual precipitation. In the context of climate change, continuous high-temperature weather occurs frequently [3–5], which increases the risk of heat damage to maize [6,7]. The flowering period of summer maize is when it is most sensitive to high temperatures. High temperatures affect male and female ear development, pollen vigor, and grain development [8–10]. Maize pollen is more susceptible to high-temperature

**Citation:** Li, S.; Zhao, J.; Li, J.; Shao, R.; Li, H.; Fang, W.; Hu, L.; Liu, T. Inter- and Mixed Cropping of Different Varieties Improves High-Temperature Tolerance during Flowering of Summer Maize. *Sustainability* **2022**, *14*, 6993. https://doi.org/10.3390/ su14126993

Academic Editors: Xiaodong Yan, Jia Yang and Shaofei Jin

Received: 29 April 2022 Accepted: 1 June 2022 Published: 8 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

stress than female ears [11]. Pollen vigor declines quickly when the temperature exceeds 32–35 ◦C [12,13]. High-temperature stress can cause abnormal pollen development or abortion, resulting in a decrease in the number of pollen grains. In addition, it can cause abnormal division of pollen mother cells, deformation, and shrinkage leading to deformed pollen grains [14–16], which in turn affects the percentage seed set, yield, and quality of maize [9,10]. Therefore, it is of great significance to study effective methods to protect maize from the harmful high temperatures during the flowering period.

Single crop varieties are more vulnerable to diseases due to their narrow genetic basis, which is particularly prominent in maize. In addition to the influence of the external environment, there are obvious genotypic differences in the tolerance of maize to high temperatures and heat injury during the flowering period [17,18]. According to the principle of ecological complementation and biodiversity, the temperature–stress resistance of a maize population can be effectively improved by intercropping or mixed cropping of different maize varieties [19,20].

Inter- and mixed cropping among different ecotypes and genotypes of maize or intercropping with other crops could improve the utilization rate of natural resources and increase the yield stability of the crop composite population [21]. The inter- and mixed cropping of maize varieties optimizes the population spatial structure and maintains a higher chlorophyll content and photosynthetic rate [22], which is more conducive to gas exchange and promotes photosynthesis [23,24], increased nutrient absorption and dry matter accumulation [25,26], enhanced crop stress resistance [27,28], and improved yield and quality of maize [29,30]. In the intercropping of multiple varieties of maize, the anthesis and pollination time of the population system are relatively prolonged. When the maize encounters adverse weather events such as continuous rain and high temperatures during the flowering period, the fertilization rate of male flowers in the compound population composed of multiple maize varieties is higher than that in the single variety planting mode, which can effectively improve the bald tip and grain shortage caused by poor pollination, so as to achieve the purpose of increasing and stabilizing yield [31–33].

An important strategy to reduce high-temperature stress is to exploit the difference in heat resistance among maize varieties using an inter- and mixed cropping system. However, few studies have considered crucial aspects of this approach, such as maize variety matching, intercropping methods, and mixed cropping ratios. High-temperature and heat damage occurred continuously during the flowering period of maize in Henan from 2013 to 2016. It was found that the yield of Denghai 605 and Xianyu 335 decreased seriously under the high temperatures, while Zhengdan 958, Weike 702, and Dedan 5 had strong heat resistance and stable yield performance. According to the previous results, the high-temperature resistance field experiment of multi-variety inter- and mixed cropping was performed in 2017. In this study, Denghai 605, which has a large planting area and is sensitive to high temperatures, was selected as the main test variety to analyze the effects of inter- and mixed cropping of Denghai 605 with Zhengdan 958, Weike 702, Dedan 5, and Xianyu 335 on the prevention and control of high-temperature and heat damage during the flowering period.

#### **2. Materials and Methods**

#### *2.1. Study Location*

Our study was located in Shizhuang (114.03◦ E, 34.15◦ N), Chencao Township, Xuchang City, Henan Province, which belongs to the north warm temperate monsoon climate zone with abundant heat resources and abundant rainfall. The total area of the experimental field was 0.7 ha, which was mechanized sowing. The heat tolerance evaluation experiment of maize was carried out from 2013 to 2016, and the high-temperature resistance experiment of multi-variety inter- and mixed cropping was performed in 2017. The soil of the experimental field was a fluvo-aquic soil. The soil chemical properties at 0–20 cm depth were as follows: 23.54 g kg−1 soil organic matter, 43.77 mg kg−1 available nitrogen, 202.04 mg kg−1 Olsen

potassium, and 11.07 mg kg−1 Olsen phosphorus. The meteorological variables recorded during the summer-maize-growing season at the study site are shown in Figure 1.

**Figure 1.** Meteorological variables during the summer-maize-growing season at the study site. Temperature (**A**), rainfall (**B**), and sunshine hours (**C**) at Xuchang, Henan, China from June to September in 2017.

#### *2.2. Experimental Design*

'Denghai 605' (Shandong Denghai Seed Industry Co., Ltd.; Laizhou, Shandong, China) was selected as the main test variety, and 'Zhengdan 958' (Henan Academy of Agricultural Sciences Institute of Grain Crops; Zhengzhou, Henan, China), 'Dedan 5' (Beijing Denong Seed Industry Co., Ltd.; Beijing, China), 'Weike 702' (Zhengzhou Weike Crop Breeding Technology Co., Ltd.; Zhengzhou, Henan, China), and 'Xianyu 335' (Tieling Pioneer Seed Research Co., Ltd.; Tieling, Liaoning, China) were chosen as the auxiliary varieties.

Denghai 605 was inter- and mixed cropped with the four auxiliary varieties. A monoculture of each variety was established as the control group. The experimental design and processing codes are summarized in Table 1.

**Table 1.** Intercropping and mixed cropping treatments of the maize varieties.

"||" represents the intercropping of two varieties, and "×" represents the mixed cropping of two varieties. The notation also indicates the variety combination used in the experiment. For example, intercropping of Denghai 605 and Zhengdan 958 is indicated as "605958", "Denghai 605" in the combination is indicated by the form "(605)958", and "Zhengdan 958" is indicated by "605||(958)".

It has been proven that in a relay intercropping system, narrow–wide row planting improves the light environment and seed yields of intercrop species [34]. Thus, the narrow– wide row planting pattern with a wide row of 70 cm and a narrow row of 50 cm was

used in this experiment. The mechanical precision seeding method was applied to fertilize simultaneously. The seeder sowed two rows concurrently. The five varieties were sown on 11 June. The intercropping sowing design was illustrated in Figure 2. For mixed cropping, varieties were sown according to a 1:1 ratio of the number of seeds. Seedlings were thinned at the three-leaf stage, weeds were chemically controlled, and a commercial compound fertilizer (N-P2O5-K2O: 29-5-6, 750 kg ha−2; Luxi,<PHONE_NUMBER>001, Liaocheng, Shandong, China) was applied between rows at the jointing stage. Irrigation was applied in accordance with the soil moisture content to ensure that the entire growth period was free from drought stress. Other management measures were identical to those of local farmer practices.

**Figure 2.** Schematic diagram of the intercropping sowing design. Note: and indicate different varieties.

#### *2.3. Measurement of Parameters and Methods*

#### 2.3.1. Main Reproductive Assessment Period

The timing of the tasseling, silking, flowering, and pollination stages of maize plants growing in the monoculture area was recorded, which for each stage was expressed as the number of days after sowing.

#### 2.3.2. Population Density Determination

Eleven consecutive plants in a row were selected to measure the spacing of ten plants, and eleven consecutive rows (five wide and five narrow rows each) were selected to measure the spacing of ten rows. The measurements were used to calculate the average plant spacing (from three repetitions). Based on these data, the population density of the intercropping mode was 61,215 plant ha−2 and that of the mixed cropping mode was 64,815 plant ha−2.

#### 2.3.3. Main Agricultural Characters Determination

During the tasseling period of maize, five plants of relatively uniform growth were selected for each variety in each treatment area, and the plant height and ear height were measured. The leaf area index was calculated with a length–width coefficient method. The leaf length and maximum leaf width were measured in situ with a ruler. During the tasseling period of the maize, three representative sites were randomly selected in the middle of a narrow row for each treatment. The light transmittance of the middle and

bottom layers in the canopy of the maize population was measured with an LAI-2000 plant canopy analyzer (LI-COR, Inc; Lincoln, NE, USA).

#### 2.3.4. Ear Traits and Yield Determination

In the monoculture of the test variety, ten consecutive ears were selected from plants growing in a middle row of uniform growth. For the mixed cropping mode, in a relatively uniform middle row, ten ears were selected from consecutive plants of each of the two combination varieties (note that ears of the same variety were not collected from a row once ten ears had been sampled). For the intercropping treatment, ten consecutive ears were selected from one row (a non-side row) of each variety. Three replicates were collected; thus, a total of 30 ears of each variety in each treatment was sampled. Each variety was sampled 3 times for the determination of ear traits and yield, and 10 consecutive ears were measured for each replicate, for a total of 30 ears. Among them, 3 ears were selected for each repetition (9 ears were selected in total), and ear length, ear thickness, bald length, and grain number per ear were measured. The 100-grain weight and yield were determined by threshing and mixing once we were finished measuring every ten ears.

The ear length and bald length were measured with a ruler, the ear thickness was measured with a vernier caliper at the thickest portion in the middle of the ear, the 100-grain weight was measured with an electronic balance (sensitivity 0.01 g), and grain moisture content was measured using a grain moisture meter.

The population yield was calculated according to the number of grains per ear, 100-grain weight, and population density of each variety in the inter- and mixed cropping combination. The formulas used were as follows:

$$\mathbf{Y}\_{\mathbf{i}} = (\mathbf{Y}\_{\mathbf{S1}} + \mathbf{Y}\_{\mathbf{S2}}) / 2 \tag{1}$$

$$\mathbf{Y\_{S1}} = \mathbf{W\_{S1}} \times \mathbf{N\_{S1}} \times \mathbf{D\_{S}} \tag{2}$$

$$\mathbf{Y\_{S2}} = \mathbf{W\_{S2}} \times \mathbf{N\_{S2}} \times \mathbf{D\_{S}} \tag{3}$$

where Yi is the population yield of the varieties under inter- and mixed cropping mode per unit area, YS1 and YS2 are the yield of two varieties in the inter-and mixed cropping mode, respectively, WS1 and WS2 are the grain weight of the two varieties equivalent to 14% moisture, NS1 and NS2 are the number of grains per ear of the two varieties, respectively, and DS is the population density.

#### 2.3.5. Calculation of the Land Equivalent Ratio

The land equivalent ratio is the ratio of the income of two or more mixed crops (varieties) to the income of each crop in the same farmland. The land equivalent ratio (LER) was calculated using the following formula:

$$\text{LER} = \frac{\text{Y}\_{\text{i}}}{\text{Y}\_{\text{ii}}} \tag{4}$$

$$\mathbf{Y}\_{\rm ii} = (\mathbf{Y}\_{\rm c1} + \mathbf{Y}\_{\rm c2}) / 2 \tag{5}$$

where Yii is the average yield of the two varieties and Yc1 and Yc2 are the yields of the two varieties grown in the monoculture method, respectively.

#### *2.4. Determination of Maize Flowering Period and High-Temperature Stress Threshold*

The date of the onset of flowering for each variety was recorded from plants growing in the monoculture. Flowering of the entire ear of summer-sown maize usually lasts 7–10 d; therefore, the onset of tasseling is the date for the start of flowering, and the date of the end of flowering is 10 d after tasseling. The maximum temperature ≥ 35 ◦C was used as the critical threshold of high-temperature stress during the flowering period [17,35].

#### *2.5. Statistical Analysis*

Data processing and graphing were performed using Microsoft Excel 2016 and Graph-Pad Prism 8. All data were expressed as the mean ± standard deviation (SD), and n refers to the number of samples in each group. Statistical analyses were performed using SPSS 17.0 (SPSS, IBM Corp., Armonk, NY, USA). Analysis of variance (ANOVA) was performed to determine the significance of differences between treatments. Means for different treatments were compared using the Bonferroni test at the significance level α = 0.05.

#### **3. Results**

#### *3.1. Differences in the Onset of Flowering and the Occurrence of High Temperatures*

As shown in Figure 3, Dedan 5 was the first variety to start flowering (54 d after sowing) and Xianyu 335 was the last variety to flower (57 d after sowing). Two main periods of high temperatures were recorded in 2017. The first period was from 3 to 6 August, in which the highest temperature exceeded 35 ◦C for 4 consecutive days. The second period was from 9 to 11 August, in which the highest temperature exceeded 35 ◦C for 3 consecutive days. The first high-temperature period coincided with the beginning of tasseling of the varieties. The second high-temperature period occurred 2–6 d after tasseling and had a greater impact on flowering and pollination.

**Figure 3.** Flowering period (**A**) and meteorological conditions (**B**) of each variety.

#### *3.2. Plant Height and Ear Height*

As shown in Figure 4, aside from the increase in plant height of Denghai 605 intercropped with Xianyu 335, the plant height of Denghai 605 showed different degrees of decline in other inter- and mixed cropping modes. Among these treatments, intercropping with Zhengdan 958 and Dedan 5 and mixed cropping with Weike 702 and Xianyu 335 resulted in a significant decrease in the plant height of Denghai 605. The plant heights of Zhengdan 958, Dedan 5, and Weike 702 decreased under inter- and mixed cropping with Denghai 605, and the plant heights of the mixed cropping modes all decreased significantly. Given that Dedan 5, Weike 702, and Zhengdan 958 have genetically similar parents, the trends for changes in plant height under inter- and mixed cropping with Denghai 605 were generally similar. However, the plant height of Xianyu 335 increased significantly under inter- and mixed cropping with Denghai 605.

**Figure 4.** Plant height of summer maize varieties under different inter- and mixed cropping modes. Values represent the mean ± SD, n = 5 per group. \* *p* < 0.05.