Solar Dimming Decreased Maize Yield Potential on the North China Plain

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2020

Solar dimming decreased maize yield potential on the North China Plain

Qingfeng Meng

Baohua Liu

Haishun Yang

Xinping Chen

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This Article is brought to you for free and open access by the Agronomy and Horticulture Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Agronomy & Horticulture -- Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Received: 22 April 2020 | Revised: 16 June 2020 | Accepted: 29 June 2020 DOI: 10.1002/fes3.235

ORIGINAL RESEARCH

Solar dimming decreased maize yield potential on the North China Plain

Qingfeng Meng1 | Baohua Liu1 | Haishun Yang2 | Xinping Chen1,3

1China Agricultural University, Beijing, China Abstract 2Department of Agronomy and Solar dimming has been increasing in rapidly developing regions (China and India) Horticulture, University of Nebraska- and threatening food security. Although previous studies have summarized the ef- Lincoln, Lincoln, NE, USA fects of climate change-associated increases in temperature on agriculture, few have 3College of Resources and Environment, examined the effects due to solar dimming. Here, we analyzed the effects of solar Southwest University, Chongqing, China dimming on maize on the North China Plain (NCP). It is reported that solar dimming Correspondence intensified and maize yield potential decreased since the 1960s. The total decrease Xinping Chen, China Agricultural University, Beijing 100193, China. in solar radiation for the whole maize growing season of this period was 17%, and Email: [email protected] solar dimming explained 87% of the decrease in yield potential. Meanwhile, solar dimming was closely related to the level of anthropogenic fine particulate matter Funding information 3 National Key Research and Development such as PM2.5. The PM2.5 concentration in the NCP averaged 56 μg/m in 2014 and Program of China, Grant/Aw ard Number: 2015, which was approximately three times greater than the global mean. Our results 2018YFD0200601 and 2016YFD0300300; 3 suggested that a 10 µg/m increase of PM2.5 concentration in this region was together National Maize Production System in 2 China, Grant/Award Number: CARS-02-24; with a 55 MJ/m decrease in solar radiation. Solar dimming threatened food security Fundamental Research in the NCP and probably in other areas of the world and has profound implications Funds for the Central Universities. for ongoing and future efforts such as Clean Air Action and other measures.

KEYWORDS climate change, food security, maize yield, solar dimming

1 | INTRODUCTION (Wild, 2012). Solar dimming associated with the increases in air pollution and aerosol emissions was evident around the In addition to greatly affecting climate, solar radiation is the globe from the 1950s to the 1980s. Since then until 2000, ultimate energy source for crop production at the Earth's sur- however, global trends of radiation were more neutral with face (Monteith, 1977; Wild et al., 2005). Solar dimming or brightening in Europe, the United States (USA), and China brightening, which is commonly assessed as decreases or and dimming in India. The latest updates on changes in solar increases in decadal-level incident solar radiation, will sub- radiation since 2000 no longer reveal any globally coher- stantially change the net radiation arriving at crop vegetation ent trends (Wild, 2012). Since 2000, brightening sustains canopies and thereby affect crop photosynthesis and ulti- in Europe and the USA, renewed dimming associated with mately crop yield (Wild et al., 2005). tremendous increases in emissions is evident in China, and Large temporal and spatial variations in solar radia- dimming continues unabated in India. In China, the solar tion change have been observed worldwide since 1950 dimming from the 1960s to the 1980s, which had an average

Qingfeng Meng and Baohua Liu contributed equally to this work.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Food and Energy Security published by John Wiley & Sons Ltd. and the Association of Applied Biologists.

wileyonlinelibrary.com/journal/fes3 | 1 of 8 Food Energy Secur. 2020;9:e235. https://doi.org/10.1002/fes3.235 2 of 8 | MENG et al. of 0.74 MJ/m2 per decade, represented one of the largest To identify the individual effect from solar radiation or tem- trends in solar dimming globally (Ye, Li, Sun, & Guo, 2010). perature on maize yield potential, we used scenario analyses During the 1990s to 2000, China experienced a slight bright- as described later. We also collected the PM2.5 concentration ening trend, but since 2000, China has experienced a renewed data for each site in 2014 and 2015 to examine the relation- dimming trend. Brightening or dimming has profound impli- ships among changes in solar radiation, yield potential, and cations for ongoing and future efforts to improve crop pro- aerosol emission (PM2.5 concentration). duction in changing climates. Climate-change researchers have paid substantial attention on the effects of high temperature on crop production 2 | MATERIALS AND METHODS in both the past and future (Challinor et al., 2014; Lobell, Schlenker, & Costa-Roberts, 2011; Peng et al., 2004; Wild, 2.1 | Study area 2012). Moreover, recent studies have quantified the effects of extreme heat on crops in the USA and France (Hawkins The NCP included seven provinces or municipalities (Hebei, et al., 2013; Lobell et al., 2013). These studies on the effects Shandong, Henan, the northern part of Anhui and Jiangsu of climate change on crop, however, generally presume that provinces, Beijing and Tianjin). In this area, the major agri- the solar radiation at the decadal scale has kept and will keep cultural system is a winter wheat and summer maize rotation. constant. Although the potential effects of solar radiation Winter wheat is sown in early October and harvested the next increase and decrease on crop yield have been frequently June. The summer maize is sown in early June after the har- discussed, quantitative research remains very limited. One vest of the winter wheat and is harvested at the beginning of recent quantitative study attributed 27% of the increase in October. Maize is irrigated to obtain high yield. yield in the USA Corn Belt from 1984 to 2013 to solar brightening (Tollenaar, Fridgen, Tyagi, Stackhouse, & Kumudini, 2017). In general, however, it is still poorly understood for 2.2 | Climate and crop phenology the response of crop yield to decadal-scale changes in solar radiation. In this study, we collected climate data from 19 sites from In this study, we examined how changes in solar radia- China Meteorological Agency (CMA, 2020) (Table S1). It tion have affected maize production in the North China Plain provided records of sunshine hours, temperatures, and pre- (NCP). Maize in the NCP is mainly irrigated and accounts cipitation for each day from 1961 to 2015. Solar radiation for one-third of the national maize production and about 6% was calculated according to an equation such as Ångström of the global maize production (FAO, 2020; MOA, 2020). formula (Black, Bonython, & Prescott, 1954; Jones, 1992), Although the NCP is an important agricultural area, it has be- which has been widely used (Liu, Yang, Hubbard, & come one of the most developed regions in China. The rapid Lin, 2012). Taken Beijing station as an example, it indicated economic growth and urbanization have generated severe high consistency between calculated and measured daily air pollution caused by aerosol emission (Hu, Wang, Ying, solar radiation from 1961 to 2015 (Figure S1). & Zhang, 2014). The PM2.5 (fine particulate matter with of The dates of sowing, silking, and physiological matu- ≤2.5 μm of an aerodynamic diameter) concentration in 2013, rity of maize were obtained from 1961 to 2015 from the 19 for example, was 77.0 µg/m3 (Hu et al., 2014), which greatly Agrometeorological Experimental Stations, which located exceeded the threshold of 10 µg/m3 of the World Health in the same places as the meteorological sites or very near Organization (WHO, 2005) and which would lead to a sub- the sites. The phenological information was verified by in- stantial decrease in solar radiation. As a case study, research terviewing 15 agronomists from the National Maize System on maize in the NCP would offer a model for quantifying the of the NCP. Total growing degree days (GDD ≥ 10°C) was effects of decadal changes in solar radiation on crop yields in used to quantify variety maturity and for the model simula- other rapidly developing regions of the world. tion. The details of the GDD information for each site were The relationship between solar radiation changes during shown in Table S1. whole maize growing season from the 1960s to 2015 and the related yield potential in the NCP was investigated in this study. As defined by Evans (1993), yield potential is the yield 2.3 | Crop modeling and simulation of a crop variety when grown in an adapted environment with sufficient supplies of nutrient and water, whereas pests and The Hybrid-Maize model (https://hybridmaize.unl.edu/), diseases are effectively controlled. To quantify the effect of developed by the University of Nebraska-Lincoln (Yang changes in solar radiation on maize yield potential at 19 sites et al., 2004, 2006), was used in this research. The simulations across the NCP, we used the Hybrid-Maize model (Yang, for organ growth by the process-based model and assimila- Dobermann, Cassman, & Walters, 2006; Yang et al., 2004). tion and respiration functions by the generic crop models MENG et al. | 3 of 8 were both taken into accounts. It can simulate grain yield temperature data from 1961–2015 but held solar radiation at with irrigated and rainfed conditions. In the previous stud- a constant value equal to the average of individual days of ies, we have calibrated the model and found it could simulate the 1960s, which could enable us to estimate the effects of well for maize yield in NCP (Bai, 2009). In this study, most temperature change in the absence of solar radiation change. parameters for maize growth were set as the maximum for Scenario S2 used the estimated solar radiation data from varieties in North China Plain to simulate the yield potential sunshine duration from 1961–2015 but held temperature at (Table S2). a constant value equal to the average of individual days of For irrigated maize, the model requires daily solar ra- the 1960s, which could enable us to estimate the effects of diation and temperatures. Meanwhile, variety's GDD, date solar radiation change in the absence of temperature change. of sowing, and plant population density were also needed. Comparison of the three scenarios enabled us to estimate the In the simulations, the sowing dates for each area was ac- separate effects of temperature change and solar dimming on cording to the record in Table S1. Plant density was set as yield potential. 90,000 plants/ha at all sites. Grain yield with the climate of the 1960s, 1970s, 1980s, 1990s, and 2001–2015 was the average of the decade (or 15-year from 2001 to 2015) simulation 3 | RESULTS from 1961 to 1970, 1971 to 1980, 1981 to 1990, and 2001 to 2015, respectively. 3.1 | Solar dimming since the 1960s

According to meteorological data from the 19 sites across the 2.4 | PM2.5 concentration NCP, solar radiation decreased (i.e., solar dimming intensified) from the 1960s to 2001–2015, and the rate of dimming In this study, we used PM2.5 measurements in 2014 and 2015 was greatest between the 1990s and 2001–2015 (Figure 1a). from the 19 sites to analyze the relationships among PM2.5, From the 1960s to the 1980s, we estimated that solar radia- solar radiation change, and yield potential change for the tion decreased across this region by 2.8% or 56 MJ/m2 per NCP. We also used the estimated long-term (1973–2013) decade. From the 1980s to the 1990s, solar radiation stabi- PM2.5 concentration using meteorological visibility data lized in the NCP. From the 1990s to 2001–2015, solar ra- (Han, Zhou, & Li, 2016) to analyze the above relationships diation decreased by an average of 3.4% or 70 MJ/m2 per at the typical Beijing site. The trends were similar for the decade in the NCP. Overall, solar dimming since the 1960s long-term data at the Beijing site and other sites in NCP resulted in a 17% decrease of solar radiation during the whole (Figure S2). maize season across the 19 NCP sites (range = 9 to 24%) The average PM2.5 concentrations for the maize grow- (Figures 1a and S3). ing season (June to September) for each month of 2014 and 2015 were obtained from the Chinese Air Quality Monitoring Platform (CAQMP, 2017). Accordingly, we calculated the 3.2 | Impacts for yield potential average of PM2.5 concentration during the whole maize growing season for each of the 19 sites. From the 1960s to 2001–2015, our analyses using the Hybrid-Maize model indicated that climate change reduced maize yield potential for the entire NCP by an average of 2.5 | Data analysis 19% (2.66 t/ha), with a range of 5 to 26% across all 19 sites (Figures 1b and S4). For irrigated maize, model simulations For each site, we used linear-regression to analyze time trends indicated that the decrease in yield potential resulted from from 1961 to 2015 for the following variables: solar radia- both solar dimming and temperature change (Figures 1c and tion, temperatures, and simulated grain yield. The following S4–S7). relationships were also analyzed by the linear regression: To separate the effects of solar dimming and temperature changes in cumulative solar radiation versus yield potential change on the decline in yield potential, the Hybrid-Maize from the 1960s to 2014–2015; changes in cumulative solar model was used to simulate yield potential with three sce- radiation from the 1960s to 2014–2015 versus PM2.5 concen- narios. The scenario analysis showed that solar dimming tration as an average of 2014 and 2015; and changes in yield accounted for 87% of the yield potential decrease (2.31 t/ potential from the 1960s to 2014–2015 versus PM2.5 concen- ha) between the 1960s and 2001–2015, while temperature tration as an average of 2014 and 2015. change accounted for the left of the yield potential decrease Three scenarios were considered in the simulation. In (Figure 1c). The contribution of solar dimming to the decrease scenario 0 (S0), both actual solar radiation and tempera- in yield potential ranged from 32% to 170% among the 19 sites ture of the 55 years of were used. Scenario S1 used actual (Figure S8). However, the contributions of solar dimming and 4 of 8 | MENG et al.

FIGURE 1 Cumulative solar radiation, maize yield potential in different periods, and yield potential decrease from the 1960s to 2001–2015 on the North China Plain. (a) Cumulative solar radiation during the maize growing season. (b) Maize yield potential. (c) Decrease in yield potential caused by climate change from the 1960s to 2001–2015 (S0, yield potential decrease caused by changes in both temperature and solar radiation. S1, yield potential decrease caused by temperature change alone. S2, yield potential decrease caused by solar radiation change alone). (d) Yield potential decrease caused by climate change from the 1960s to 1980s and from the 1980s to 2001–2015 (see definition of S0, S1, and S2 in c). In a, b, and c, solid black lines indicate the medians, and dashed red lines indicate the means. The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the circles indicate the 95th and 5th percentiles. Columns labeled with the same letter are not statistically different at p < .05 temperature to changes in yield potential differed between the 3.3 | PM2.5, solar dimming, and periods from the 1960s to the 1980s versus the period from yield potential the 1980s to 2001–2015 (Figure 1d). From the 1960s to 1980s, yield potential was decreased by dimming but enhanced by We collected data of PM2.5 concentrations during the maize temperature change. Because the negative effect of dimming growing season (June to September) at the 19 sites in the was greater than the positive effect by temperature change, NCP in 2014 and 2015. Over all sites and both years, the 3 yield potential was decreased. From the 1960s to 1980s, yield PM2.5 concentration averaged 56 μg/m . The spatial distri- potential decreased by 4% (a 0.54 t/ha decrease), solar dim- bution of solar dimming values was consistent with PM2.5 ming caused a 177% decrease (i.e., dimming decreased yield concentrations, decreases in yield potentials among the 19 potential by 0.96 t/ha), and temperature change caused an sites, and decreases in yield potential and solar radiation and 82% increase (i.e., temperatures increased yield by 0.44 t/ha). PM2.5 concentration were all highest in the northern part of From the 1980s to 2001–2015, yield potential was decreased the NCP (Figure 2a–c). Based on regression analysis of av- 3 by both dimming and temperature change, and the decrease in erages for the 19 sites, an increase of 10 μg/m PM2.5 is to- yield potential was much greater than in the previous period. gether with a solar dimming of 55 MJ/m2 during the maize From the 1980s to 2015, yield potential decreased by 16% (a season (Figure 2e) and a 0.90 t/ha decrease in yield poten- 2.12 t/ha decrease), solar dimming caused a 64% of the de- tial (Figure 2f). The historical data (1973–2013) of PM2.5 crease (i.e., dimming decreased yield potential by 1.36 t/ha), concentrations at the Beijing site showed similar relation- and temperature change caused a 39% of the decrease (i.e., ships between solar dimming and yield potential decrease increasing temperatures decreased yield by 0.82 t/ha). (Figure S2). MENG et al. | 5 of 8

FIGURE 2 Spatial distribution of decreases in yield potential and cumulative solar radiation from the 1960s to 2014–2015 and PM2.5 concentrations in 2014–2015, and relationships among yield potential decrease, cumulative solar radiation decrease, and PM2.5 on the North China Plain. (a) Spatial distribution of decrease in yield potential from the 1960s to 2014–2015. (b) Spatial distribution of the decrease in cumulative solar radiation decrease from the 1960s to 2014–2015. (c) Spatial distribution of PM2.5 as an average of 2014 and 2015. (d) Relationship between decreases in yield potential and cumulative solar radiation from the 1960s to 2014–2015. (e) Relationship between cumulative solar radiation decrease from the 1960s to 2014–2015 and PM2.5 concentration as an average of 2014 and 2015. (f) Relationship between yield potential decrease from 1960s to 2014–2015 and PM2.5 concentration as an average of 2014 and 2015. *, **, and *** indicate significant at p < .05, <.01, and <.001, respectively

4 | DISCUSSION Solar dimming decreased maize yield substantially. Due to climate change (both solar radiation and temperature We found solar dimming resulted in a 17% decrease in solar change), maize yield potential was reduced by 19% in NCP radiation during the whole maize season since the 1960s in since 1960s (Figure 1b), which was substantially higher than the NCP (Figure 1). However, the change among different the decrease for global maize yield (3.8%) due to climate periods varied largely compared with the global areas. From change (Lobell et al., 2011). Solar dimming explained 87% of the 1960s to the 1980s, the solar dimming is consistent with this decrease in the NCP (Figure 1b). Although many factors the global decrease in solar radiation (Wild, 2009, 2012). influenced the solar dimming, the further analysis showed From the 1980s to the 1990s, solar radiation stabilized in the solar dimming was closely related to the level PM2.5 pollu- NCP, while many parts of Europe and the USA saw the mid- tion (Figure 2). The PM2.5 concentration in the NCP averaged 1980s as a turning point from dimming to brightening (Wild 56 μg/m3 in 2014 and 2015, nearly three times higher than the et al., 2009). From the 1990s to 2001–2015, solar radiation global average (van Donkelaar et al., 2010). Furthermore, a 3 decreased (3.4% per decade) while the global trend was the 10 µg/m increase in PM2.5 concentration in this region would opposite with an increase from 1.2% to 2.8% per decade. be together with a 55 MJ/m2 decrease in solar radiation. During this period, the USA Corn Belt saw an increase in Our findings indicate that the effects of solar dimming or solar radiation during the whole maize season by a total of brightening on crop yield warrant increased attention. Based on 112 MJ/m2 between 1984 and 2013 (Tollenaar et al., 2017). the effects of changes in both solar dimming/brightening and For solar brightening in USA since 1980s, governmental temperature on maize yields since the 1980s, maize-producing policies such as the Clean Air Act have been argued to play a countries can be classified into three groups. For countries like prominent role (Ruckstuhl et al., 2008; Wild, 2012). the USA, solar brightening (Tollenaar et al., 2017) and a lack of 6 of 8 | MENG et al. warming (Lobell et al., 2011) have resulted in increased maize Despite these observations, some aspects of the effects yield. For countries in Europe and Southern Hemisphere, the of air pollutants on maize production should be further ad- combined effects of solar brightening (Wild, 2012) and sig- dressed. The global insolation in this study is the total in- nificant warming (Lobell et al., 2011) on crop production re- solation of direct, diffuse, and reflected light. The effects of quire further study. For countries like China and India, solar change in the diffuse fraction on yields was not distinguished. dimming combined with significant warming have resulted in Beside reducing total insolation, air pollutants increase the reduced grain yields. While much research has focused on cli- fraction of sunlight which is scattered, which may, in turn, mate warming, our results indicate that solar dimming should increase the RUE of crops (Gu et al., 2002; Proctor, Hsiang, be lessened or stopped to ensure the food security of the very Burney, Burke, & Schlenker, 2018). Furthermore, the chang- large populations in China and India. ing pollution would also alter temperature and precipitation, A main cause of solar dimming is atmospheric aerosols which can impact yield (Burney & Ramanathan, 2014). resulting from human activities (Ruckstuhl et al., 2008; Finally, the transparency of the atmosphere due to the clouds, Stern, 2006; Streets et al., 2009; Wild et al., 2005). aerosols, and radiatively active gases also influenced the ob- Anthropogenic aerosol emissions increased from the 1960s served solar radiation variations (Kim & Ramanathan, 2008) to 1980s but then decreased in the Northern Hemisphere. and they should be fully considered for the impacts of crop This decrease resulted from the air quality controls as well production in the future study. as the reduced industrial activities. In Western industrial countries, such as the USA and Europe, brightening is un- likely to become more pronounced because aerosol emissions 5 | CONCLUSION have already been at relatively low values. Aerosol emissions in China and India, in contrast, have been increasing In conclusion, we found that, of the total decrease in maize (Auffhammer, Ramanathan, & Vincent, 2006; Burney & yield potential due to climate in the NCP, 87% can be attrib- Ramanathan, 2014). Because of rapid economic development uted to solar dimming from the 1960s to 2015. The substantial and industrial expansion, the NCP has higher aerosol emis- worsening in solar dimming is together with the substantial sions (e.g., PM2.5 concentration) than other parts of China increases in PM2.5 concentrations. This study highlights the (Zhang & Cao, 2015). China and other countries with high importance of regulating fine particulate matter pollution not levels of anthropogenic aerosol emissions should now imple- only for China, but also for the world. It provides a quantita- ment policies that reduce pollution and that therefore support tive evidence that Clean Air Action is not only beneficial to sustainable development. human health in NCP (Chen, Ebenstein, Greenstone, & Li, In regions experiencing solar dimming, securing food 2013; Ebenstein, Fan, Greenstone, He, & Zhou, 2017), but supplies in the short-term will depend on increasing solar also conducive to crop production. Finally, agricultural tech- radiation use efficiency such as RUE, which shows the ef- nology must be improved to offset the yield decreases caused ficiency with which solar radiation is transformed into grain by solar dimming. yield (Gosse et al., 1986). RUE in crop systems can be increased by novel agronomic strategies (e.g., fertilization, irri- ACKNOWLEDGMENTS gation, and high plant densities). RUE can also be increased This work was financially supported by The National by developing new varieties through breeding. Some new Key Research and Development Program of China (No. varieties of wheat and soybean, for example, have signifi- 2018YFD0200601 and 2016YFD0300300), The National cantly improved RUE (Koester, Skoneczka, Cary, Diers, & Maize Production System in China (CARS-02-24), and Ainsworth, 2014; Shearman, Sylvester-Bradley, Scott, & Fundamental Research Funds for the Central Universities. Foulkes, 2005). For maize in USA, plant growth rate for new hybrid was 33% higher than the old, approximately 80% of CONFLICT OF INTEREST the difference could be attributed to a higher RUE of the new The authors declare that there is no conflict of interest. hybrid (Tollenaar & Aguilera, 1992). 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Solar dimming and matter, ozone, nitrogen dioxide and sulfur dioxide, Geneva, its impact on estimating solar radiation from diurnal temperature Switzerland: World Health Organization. range in China, 1961–2007. Theoretical and Applied Climatology, Wild, M. (2009). Global dimming and brightening: A review. Journal of 101, 137–142. https://doi.org/10.1007/s00704-009-0213-y Geophysical Research, 114, D00D16. https://doi.org/10.1029/2008J Zhang, Y. L., & Cao, F. (2015). Fine particulate matter (PM2.5) in China D011470 at a city level. Scientific Reports, 5, 14884. https://doi.org/10.1038/ Wild, M. (2012). Enlightening global dimming and brightening. srep14884 Bulletin of the American Meteorological Society, 93, 27–37. https:// doi.org/10.1175/BAMS-D-11-00074.1 SUPPORTING INFORMATION Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. Additional supporting information may be found online in G., … Tsvetkov, A. (2005). From dimming to brightening: Decadal changes in solar radiation at earth's surface. Science, 308, 847–850. the Supporting Information section. https://doi.org/10.1126/science.1103215 Wild, M., Trussel, B., Ohmura, A., Long, C. N., Konig-Langlo, G., Ellsworth, G., … Tsvetkov, A. (2009). Global dimming and bright- How to cite this article: Meng Q, Liu B, Yang H, Chen ening: An update beyond 2000. Journal of Geophysical Research, X. Solar dimming decreased maize yield potential on 114, D00D13. https://doi.org/10.1029/2008JD011382 the North China Plain. Food Energy Secur. 2020;9:e235. Yang, H. S., Dobermann, A., Cassman, K. G., & Walters, D. T. (2006). https://doi.org/10.1002/fes3.235 Features, applications, and limitations of the hybrid-maize simulation model. Agronomy Journal, 98, 737–748. https://doi. org/10.2134/agronj2005.0162 Yang, H. S., Dobermann, A., Lindquist, J. L., Walters, D. T., Arkebaur, T. J., & Cassman, K. G. (2004). Hybrid-maize-a maize simulation

) -2 y = 0.908x + 1.37 R2 = 0.918*** 30

Calculated daily solar radiation (MJm radiation solar daily Calculated 0 0 10 20 30 40

Measured daily solar radiation (MJ m-2)

Figure S1 Relationship between measured and calculated daily solar radiation at Beijing from 1961 to 2015.

25 25 2200

)

-2 20 20

)

-1 2000

-1 y=-4.40x+1974 y=0.0124x-10.48 y=-0.081x+14.87 r=0.457** r=0.718*** 15 r=0.486** 15 1800 10 10

Yield potential (t ha potential Yield potential haYield (t 1600 5 1973-1980 5 1980s 1990s 2001-2013 Cumulativesolar radiation m (MJ 0 0 1400 0 20 40 60 80 1400 1600 1800 2000 2200 0 20 40 60 80 -2 -3 -3 PM2.5 (ug m ) Cumulative solar radiation (MJ m ) PM2.5 (ug m )

Figure S2 Relationships among yield potential, cumulative solar radiation and PM2.5 at Beijing site from 1973 to 2013. *Significant at p<0.05. ** Significant at p<0.01. *** Significant at p<0.001.

2800 2600 Beijing Tianjin Tangshan Shijiazhuang Langfang 2400 2200 2000 1800 1600 1400 y=-6.542x+14876 y=-8.607x+18968 y=-4.00x+9808 y=-10.748x+23309 y=-7.526x+16847 1200 r=-0.674*** r=-0.807*** r=-0.531*** r=-0.738*** r=-0.718*** 1000 2800 2600 Baoding Weifang Huimin Anyang Yiyuan 2400

) 2200 -2 2000 1800 1600 1400 y=-7.917x+17561 y=-5.579x+12984 y=-4.893+11617 y=-9.927x+21614 y=-6.760x+15256 1200 r=-0.680*** r=-0.608*** r=-0.564*** r=-0.756*** r=-0.680*** 1000 2800 2600 Yanzhou Xuzhou Ganyu Shangqiu Bozhou 2400 2200 2000 1800

Cumulative solar radiation (MJ m 1600 1400 y=-5.574x+12973 y=-5.017x+11859 y=-6.712x+15337 y=-11.982x+25684 y=-8.570x+18839 1200 r=-0.523*** r=-0.486*** r=-0.653*** r=-0.797*** r=-0.683*** 1000 1960197019801990200020102020 2800 Year 2600 Fuyang Nanyang Bengbu Xuchang 2400 2200 2000 1800 1600 1400 y=-10.405x+22340 y=-8.483x+18680 y=-3.382+8465 y=-8.420x+18561 1200 r=-0.789*** r=-0.670*** r=-0.351** r=-0.682*** 1000 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 Year Figure S3 Cumulative solar radiation during maize growth season at 19 sites from 1961 to 2015. ** Significant at p<0.01. *** Significant at p<0.001.

) Beijing Tianjin Tangshan Shijiazhuang Langfang -1 20

5 y=-0.107x+225.04 y=-0.0712x+154.78 y=-0.0623x+136.34 y=-0.105x+222.32 y=-0.0903x+192.83 potentialYield (t ha r=0.709*** r=0.711*** r=0.500*** r=0.637*** r=0.653*** 0 25

) Baoding Weifang Huimin Anyang Yiyuan -1 20 15

10 y=-0.0729x+157.79 y=-0.0672x+146.99 y=-0.0600x+133.190 y=-0.0621x+135.65 y=-0.0738x+159.30 5

Yield potentialYield (t ha r=0.568*** r=0.676*** r=0.620*** r=0.582*** r=0.632***

0 25 Yanzhou Xuzhou Ganyu Shangqiu Bozhou ) -1 20

10 y=-0.0496x+112.79 y=-0.0444x+100.74 y=-0.0542x+120.87 y=-0.0774x+166.20 y=-0.0767x+166.20 5

potentialYield (t ha r=0.459*** r=0.420** r=0.532*** r=0.669*** r=0.649*** 0 1960197019801990200020102020 25 Year

) Fuyang Nanyang Bengbu Xuchang -1 20

5 y=-0.0629x+138.18 y=-0.0631x+137.79 y=-0.0537x+118.93 potentialYield (t ha r=0.668*** r=0.648*** r=0.423** 0 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 Year Year Year Year Figure S4 Yield potential simulated by Hybrid-Maize model at 19 sites from 1961 to 2015. ** Significant at p<0.01. *** Significant at p<0.001. Panels without regression indicate insignificant trend.

30 Beijing Tianjin Tangshan Shijiazhuang Langfang 25

15 y=0.0518x-83.39 y=0.0215x-23.57 y=0.0462-71.68 y=0.0449x-69.69 r=0.832*** r=0.367** r=0.783*** r=0.668*** 10 30

Baoding Weifang Huimin Anyang Yiyuan ) 25

℃

15 y=0.0282x-35.99 y=0.0209x-22.03 y=0.0289x-37.83 y=0.0216x-22.73 y=0.0317x-44.09 r=0.512*** r=0.445*** r=0.612*** r=0.482*** r=0.655***

10 30 Yanzhou Xuzhou Ganyu Shangqiu Bozhou 25

Average minimum temperature ( 20

15 y=0.0288x-35.98 y=0.0239x-26.45 y=0.0149x-8.97 y=0.0266x-31.28 r=0.612*** r=0.521*** r=0.358** r=0.522*** 10 30 1960197019801990200020102020 Fuyang Nanyang Bengbu Xuchang Year 25

15 y=0.0216x-21.68 y=0.0189x-15.01 r=0.477*** r=0.423** 10 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 Year

Figure S5 Annual average daily minimum temperature during maize growth season at 19 sites from 1961 to 2015. ** Significant at p<0.01. *** Significant at p<0.001. Panels without regression indicate insignificant trend. 32 Beijing Tianjin Tangshan Shijiazhuang Langfang ) 30

℃

Average mean temperature ( temperature Average mean 22 y=0.0354x-45.98 y=0.0137x-2.27 y=0.0212x-18.03 y=0.0293x-33.10 y=0.0267x-28.51 r=0.682*** r=0.358** r=0.407** r=0.596*** r=0.514***

) 30 Baoding Weifang Huimin Anyang Yiyuan

℃

y=0.0143x-3.44 y=0.0157x-6.58 y=0.0167x-8.54 y=0.0113x+2.81 y=0.0200x-16.10 Average mean temperature ( temperature Average mean 22 r=0.307* r=0.387** r=0.440*** r=0.295* r=0.506***

32 Yanzhou Xuzhou Ganyu Shangqiu Bozhou ) 30

℃

y=0.0134-1.09 y=0.0139x-2.96

Average mean temperature ( temperature Average mean 22 r=0.342** r=0.344**

20 1960 1970 1980 1990 2000 2010 2020 32 Year

) 30 Fuyang Nanyang Bengbu Xuchang

℃

Average mean temperature ( temperature Average mean 22

20 1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020 Year Year Year Year Figure S6 Annual average mean temperature during maize growth season at 19 sites from 1961 to 2015. *Significant at p<0.05. ** Significant at p<0.01. *** Significant at p<0.001. Panels without regression indicate insignificant trend.

36 Beijing Tianjin Tangshan Shijiazhuang Langfang 34

28 y=0.0191x-8.58 y=0.0191x-8.48 y=0.0208x-12.48 26 r=0.359** r=0.400** r=0.396** 24 36 Baoding Weifang Huimin Anyang Yiyuan 34 ) ℃ 32

24 36 Yanzhou Xuzhou Ganyu Shangqiu Bozhou 34 32 Average maximum temperatureAverage ( 30 28

24 1960197019801990200020102020 36 Year 34 Fuyang Nanyang Bengbu Xuchang

32 30 28 26 24 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 Year Figure S7 Annual average maximum temperature during maize growth season at 19 sites from 1961 to 2015. ** Significant at p<0.01. Panels without regression indicate insignificant trend.

25 Beijing Tianjin Tangshan Shijiazhuang Langfang

) -1 20 S0 S1 S2 15 10 y=-0.0787x+170.51 r=-0.669*** y=-0.0278x+70.07 r=-0.334* y=-0.0412x+95.42 r=-0.404** y=-0.0554x+125.79 r=-0.523*** y=-0.0577x+129.02 r=-0.537*** 5 y=-0.0286x+69.22 r=-0.303* y=-0.0471x+106.81 r=-0.515*** y=-0.0539x+119.89 r=-0.427** y=-0.0299x+72.21 r=-0.320* Yield potential (t ha y=-0.107x+225.04 r=-0.709*** y=-0.0712x+154.78 r=-0.711*** y=136.34-0.0623x r=-0.500*** y=-0.105x+222.32 r=-0.637*** y=192.83-0.0903x r=-0.653*** 0 25 Baoding Weifang Huimin Anyang Yiyuan

) -1 20 15 10 y=-0.0296x+73.63 r=-0.305* y=-0.0288x+72.25 r=-0.371** y=-0.0379x+90.72 r=-0.445** y=-0.0370x+87.61 r=-0.473*** 5 y=-0.0434x+98.95 r=-352** y=-0.0414x+95.94 r=-0.489*** y=-0.0330x+79.56 r=-0.384** y=-0.0574x+126.34 r=-0.486*** y=-0.0346x+81.42 r=-0.426**

Yield potential (t ha y=-0.0729x+157.79 r=-0.568*** y=-0.0672x+146.99 r=-0.676*** y=-0.0600x+133.19 r=-0.621*** y=-0.0621x+135.65 r=-0.502*** y=-0.0738x+159.30 r=-0.633*** 0 25

) Yanzhou Xuzhou Ganyu Shangqiu Bozhou -1 20 15

y=-0.0367x+87.18 r=-0.397*** y=-0.0401x+92.10 r=-0.317* y=-0.0454x+102.97 r=-0.448*** y=-0.0854x+182.12 r=-0.651*** y=-0.0628x+138.93 r=-0.542*** 5 Yield potential (t ha y=-0.0496x+112.79 r=-0.459*** y=-0.0444x+100.74 r=-0.420** y=-0.0542x+120.87 r=-0.533*** y=-0.0774x+166.20 r=-0.669*** y=-0.0767x+166.20 r=-0.649*** 0 1960197019801990200020102020 25 Fuyang Nanyang Bengbu Xuchang Year

) -1 20

15 Year 10

y=0.0661x+144.65 r=-0.595*** y=-0.0658x+143.37 r=-0.552*** y=-0.0520x+115.55 r=-0.439*** 5 Yield potential (t ha y=0.0629x+138.18 r=-0.668*** y=-0.0631x+137.79 r=-0.648*** y=-0.0578x+127.17 r=-0.537*** 0 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 1960197019801990200020102020 Year Year Year Year Figure S8 Yield potential in different climate change scenarios from 1961 to 2015 (S0 with red dots, yield potential due to both temperature and solar radiation change. S1 with green dots, yield potential due to temperature change. S2 with blue dots, yield potential due to solar radiation change). *Significant at p<0.05. ** Significant at p<0.01. *** Significant at p<0.001. Panels without regression indicate insignificant trend.

Table S1 Locations, management and variety GDD information at 19 sites.

Elevation, Planting Maturity Plant density Sites Latitude Longitude GDD (m) date data (1000 ha-1) Beijing 39.5 116.3 31 6.15 10.1 90 1533 Tianjin 39.1 117.0 3 6.15 10.1 90 1588 Tangshan 39.4 118.1 28 6.15 10.1 90 1488 Shijiazhuang 38.0 114.3 81 6.15 10.1 90 1615 Langfang 39.1 116.2 9 6.15 10.1 90 1561 Baoding 38.5 115.3 17 6.15 10.1 90 1596 Weifang 36.5 119.1 22 6.15 10.1 90 1543 Huimin 37.3 117.3 12 6.15 10.1 90 1582 Anyang 36.0 114.2 63 6.10 10.1 90 1613 Yiyuan 36.1 118.1 305 6.15 10.1 90 1454 Yanzhou 35.3 116.5 129 6.15 10.1 90 1626 Xuchang 34.0 113.5 67 6.10 10.1 90 1654 Ganyu 34.5 119.1 3 6.10 10.1 90 1564 Shangqiu 34.3 115.4 50 6.10 10.1 90 1632 Bozhou 33.5 115.5 38 6.15 9.28 90 1688 Fuyang 32.5 115.4 33 6.15 9.28 90 1707 Nanyang 33.0 112.4 129 6.10 10.1 90 1661 Bengbu 32.6 117.2 22 6.15 9.28 90 1738 Xuzhou 34.2 117.1 41 6.10 10.1 90 1659

Table S2 Crop model parameters for yield potential simulation.

Items Value Unit

Potential numbers of kernels per ear 675

Potential kernel grain filling rate 8.70 Mg kernel-1 day-1

Light extinction (k) 0.55

Maximum photosynthetic rate 7.0 g CO2 m-2 leas area hr-1

Initial light use efficiency 12.5 g CO2 MJ PAR