E ﬀ ects of Sunshine Hours and Daily Maximum Temperature Declines and Cultivar Replacements on Maize Growth and Yields

: In this study, the crop environment resource synthesis maize (CERES-Maize) model was used to explore the e ﬀ ects of declining sunshine hours (SSH), decreasing daily maximum temperature (T max ), and cultivar replacements on growth processes and yields of maize in Northern China, a principal region of maize production. SSH were found to decrease at 189 of 246 meteorological stations in the northern provinces of China over the period of 1994–2012, and a decrease in T max was also seen at many of these stations. The most signiﬁcant decrease in these two climate variables occurred during June to September, a period for summer maize growth. For this study, seven crop ﬁeld stations in the ShaanXi province, in the Guanzhong Plain, were selected, all of which showed a downward trend in SSH and T max over the period of 1994–2012. The CERES-Maize model was ﬁrst calibrated and validated against yield observations for these stations over the same period, and the yield simulations matched very well with observations. The model was then driven by the detrended SSH and T max data, and the simulations were compared with those with a trend in these two input variables. The decline in SSH was found to reduce the maize yield by 8% on average over these stations due mostly to limited root growth, and the decline for shorter SSH reduced the yield more than that for longer SSH. Meanwhile, the decrease in higher T max increased the yield by extending the growth period, while the decrease in lower T max reduced the yield by lowering the thermal time. In addition, the observed yield showed a signiﬁcant upward trend, and our modeling results indicate that this increase can be attributed mainly to the frequent cultivar replacements over our study period. The replaced cultivars usually had a longer growth period than the prior ones, which compensated for the yield loss due to fewer SSH. Net maize production decreased with the combined e ﬀ ects of the declines in SSH and T max on yields. This study quantiﬁes the contribution of changes in climate and cultivars to maize growth processes and yields and provides strong insights into maize production under a complex dynamic climate system.


Introduction
Agricultural production is significantly affected by climate change [1][2][3][4][5][6][7][8]. Global average temperatures have risen by 0.13 • C/decade since 1950. This temperature increase could shorten crop growth periods, leading to a profound impact on crop yields [9,10]. Research also indicates that daily maximum temperature (T max ) has shown a downward trend in some regions [11][12][13][14], which generates uncertainties in predicting crop growth periods. Meanwhile, increased temperatures enhance surface evaporation, often resulting in greater cloud cover and thus, weaker incoming solar radiation [15,16]. The latter is also related to increased pollutant loads in the atmosphere [17][18][19]. The decline in solar radiation could weaken crop photosynthesis and affect production. Therefore, building a link between climate variables and crop growth processes could provide better understanding and prediction of agricultural production. Solar radiation is an essential variable for crop growth, and it is often represented by sunshine hours (SSH) in agriculture studies. Decreasing solar radiation is found to be a dominant phenomenon in many regions of the world along with increasing temperatures [17,[19][20][21][22][23][24][25][26][27]. Many studies have shown that declining SSH reduces crop yield by weakening photosynthesis [10,26,[28][29][30]. However, these studies do not provide details as to how and to what extent SSH decline affects crop yield.
Temperature is a variable used to calculate the thermal time that determines crop growth stages. Many studies show that a temperature increase can reduce yields by shortening grain-filling time [2,3,9,[31][32][33][34][35][36][37][38][39][40][41][42][43]. However, it is still uncertain whether a temperature increase would decrease or increase the yield for different crops [44][45][46]. Research has also found a decrease in daily temperature in some regions, a cooling resulting mostly from a lowered T max , which is associated with the weakened solar radiation caused by more aerosols in the atmosphere [11][12][13][14]. The cooling climate could also affect crop growth processes by extending the growth period [47][48][49], but its significance to crop growth and yield needs to be further investigated.
Cultivar replacement is an important measure to adapt to climate change and improve crop production [10,50,51]. Crop cultivar replacement could compensate for yield loss due to climate change [52][53][54]. In addition, the effects of cultivar replacement on crop growth under a warming climate have been investigated by many researchers [2,4,10,50,51,55,56]. How these effects change in a cooling climate with reduced solar radiation is not very well understood and is explored in this study.
Due to its rapid economic development and increasing population, China is experiencing unprecedented climate change and higher levels of atmospheric aerosols than ever before [57,58]. The latter reduces incoming solar radiation and shortens SSH [59], possibly also lowering the temperature in some regions [60]. Maize is the number one crop in terms of growing area and total yield among all the crops in China [61], and it was selected in this study to examine the effects of changes in climate and cultivars on growth and yield using a crop model.
Based on the facts mentioned above, we aimed to quantify the contribution of cultivar replacement and declines in SSH and T max to maize growth and yield in a selected region of China using the crop environment resource synthesis maize (CERES-Maize) model [62,63]. Our results showed that cultivar replacement was a dominant factor increasing the maize yield in our study region. We also analyzed the trends of SSH and T max for the period of 1994-2012 and separated the effect of SSH on maize growth and yield from that of T max by comparing the differences between the model simulations with the original and detrended SSH and T max . We found that the SSH decline showed a negative effect on maize growth, while the T max reduction produced both negative and positive effects on maize growth. This study provides an improved understanding of maize growth processes under a complex dynamic climate system and provides clues for better prediction of maize yield under different climate change conditions.

Data
Total maize area accounts for more than half of the total crop area in Northern China, and the production of maize in this area accounts for more than 60% of the nation's total [61]. Therefore, this area plays a vital role in securing food production in China. However, it has also experienced a more serious downward trend in SSH than other areas in China [22]. We compared the number of The study area focuses on ShaanXi Province in Northern China, which is located in an arid and semiarid region with a temperate monsoon climate. The SSH data were from 19 meteorological stations in the ShaanXi Province (Figure 1), and maize phenology dates, yield, and management data were collected from the National Meteorological Information Centre of China. Soil data were obtained from the Chinese soil database (website: http://vdb3.soil.csdb.cn/).

Model and Input Data
We explored the effects of SSH decline on maize and its yield using the CERES-Maize model [62]. The model is one of the most widely used crop models in the world, and it is embedded in the Agricultural Technology Transfer Decision Support System (DSSAT) [63]. It can simulate maize growth and development processes in daily time steps, and it can reflect the response of maize to many factors, such as genetic, environmental, and management characteristics. The potential growth of maize Agronomy 2020, 10, 1862 4 of 21 each day in the model depends on the photosynthetically active radiation intercepted by the maize canopy, and the actual biomass production is then calculated considering temperature stress, soil water deficits, and nitrogen deficiencies [62,64,65]. Weather data, field management information, and soil and crop parameters are needed to run the CERES-Maize model. Weather input variables are daily minimum and maximum air temperature ( • C), daily sum of solar radiation (MJ/m 2 ), and daily sum of precipitation (mm). Crop management information includes tillage, planting date, planting density, planting depth, irrigation date and volume, application of fertilizer, etc. Crop genetic parameters are given in three genetic files: cultivar, ecotype, and species, and these parameters control crop growth and development. Users are only allowed to adjust cultivar parameters (Table 2). Soil input variables include soil particle composition, physical and chemical properties, and hydrodynamic characteristics in each layer [62,64]. In the model, solar radiation is estimated with daily SSH by the Angstrom empirical formula [66], which performs well in calculations of solar radiation with SSH in our study region ( Figure 2): where R s is total solar radiation (MJ/m 2 ); R max is astronomical radiation (MJ/m 2 ); a s and b s are the empirical coefficients associated with atmospheric quality (an a s value of 0.25 and a b s value of 0.50 are recommended by the Food and Agriculture Organization) [67]; n is actual day SSH (h); and N is maximum SSH (h).  (Table 2). Soil input variables include soil particle composition, physical and chemical properties, and hydrodynamic characteristics in each layer [62,64]. In the model, solar radiation is estimated with daily SSH by the Angstrom empirical formula [66], which performs well in calculations of solar radiation with SSH in our study region ( Figure 2): where Rs is total solar radiation (MJ/m 2 ); Rmax is astronomical radiation (MJ/m 2 ); as and bs are the empirical coefficients associated with atmospheric quality (an as value of 0.25 and a bs value of 0.50 are recommended by the Food and Agriculture Organization) [67]; n is actual day SSH (h); and N is maximum SSH (h).

Model Calibration and Validation
For this study, we calibrated six cultivar parameters with the CERES-Maize model using GLUE, a software package attached to the model ( Table 2). Our observed data show that every maize cultivar used in the experiments was planted at least two years at each station. We performed model calibration and validation with our field data for each cultivar, and the data for the last year were always retained for validation and the earlier years were used for calibration. Detailed information for model validation and calibration is shown in Table 3. In this study, the absolute relative error (ARE) between the simulation and observation were used to evaluate the accuracy of the model output where S i is the i-th simulated value and O i is the i-th observed value. We examined the significance of the trends with the Mann-Kendall trend test [68,69] in which the correlation was calculated between the ranks of a time series and their time order. For the n time series values X = {x 1 , x 2 , . . . , x n }, the statistic S is computed as follows: where R i and R j are the ranks of observations x i and x j of the time series, respectively. If the null hypothesis H 0 (i.e., there is no trend in the data set) is true, then S is approximately normally distributed with: The Z-statistic is therefore: A positive (negative) value of S indicates an upward (a downward) trend. The trend test was conducted for SSH, maximum and minimum temperatures, average temperature, and summer maize growth period.

Model Settings
CERES-Maize was run with historical climate data (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) to quantify the effects of changes in SSH, temperature, and cultivars on maize growth periods and yields in the ShaanXi Province. These effects were examined with five sets of simulations. The first set (original) was the control simulations, and the second (SSH) and third sets (T max ) were conducted with detrended SSH and T max . The fourth set (cultivar) was run with the cultivars for 1994 for all stations throughout the 19-year simulation period ( Table 4). The last set (climate) was conducted with both detrended SSH and T max . Actual field management information including planting, irrigation, and fertilization were applied to all these runs. To understand the seasonal variations in SSH at these stations, we used the SSH data for two periods, 1994-2003 and 2003-2012, for our analysis. Averaged monthly SSH for these periods was computed, with results shown in Figure 4. The figure clearly shows that averaged monthly SSH for the period 2003-2012 was consistently lower than for 1994-2003 (Figure 3b), with the exception of December, where a very minor increase was seen in the later period. The most remarkable decline, with a value of 0.9 h, occurred from June through September, a maize growth period.
The time-series of the averaged SSH of the maize growth period is presented in Figure 3c. The SSH at all 19 stations shows a trend with a value of −0.7 h/decade for the averaged data for the summer maize growth period over the 19-year period. The maximum trend was −1.3 h/decade at the ShiQuan station, and the minimum trend was −0.1 h/decade at the WuQi station. To understand the seasonal variations in SSH at these stations, we used the SSH data for two periods, 1994-2003 and 2003-2012, for our analysis. Averaged monthly SSH for these periods was computed, with results shown in Figure 4. The figure clearly shows that averaged monthly SSH for the period 2003-2012 was consistently lower than for 1994-2003 ( Figure 3b), with the exception of December, where a very minor increase was seen in the later period. The most remarkable decline, with a value of 0.9 h, occurred from June through September, a maize growth period.
The time-series of the averaged SSH of the maize growth period is presented in Figure 3c. The SSH at all 19 stations shows a trend with a value of −0.7 h/decade for the averaged data for the summer maize growth period over the 19-year period. The maximum trend was −1.3 h/decade at the ShiQuan station, and the minimum trend was −0.1 h/decade at the WuQi station. the 19 stations (light gray lines), which all show a declining trend over time. The lowest Tmax was for the HuaShan station where the elevation was 2064.9 m. The averages from these stations decreased by a trend of −0.5 °C/decade (dark thick line). The maximum and minimum trends were −1.1 °C/decade and −0.04 °C/decade, which appeared at the YuLin and WuQi stations, respectively. The trends of minimum temperature for all stations were insignificant (figure not shown). We selected seven crop experiment stations in the Guanzhong Plain for our study, including DaLi, FengXiang, LinTong, ShangLuo, WeiNan, WuGong, and XianYang stations, due to the availability of detailed long-term observations and field management data for 19 years (1994-2012). Table 5 shows the trends of SSH, maximum and minimum temperature, and precipitation at the seven selected stations for the maize growth period over the study period. For our selected stations, the trend of SSH was from −0.9 to −1.3 h/decade for the period of 1994-2012, all with a p-value of less than 0.05. The trends of Tmax for the seven stations ranged between −0.1 and −1.1 °C/decade, but those for DaLi and WeiNan did not pass the 95% significance test. The minimum temperature was essentially unchanged at all stations, and the change in average temperature was caused by the change in Tmax. The trend of precipitation varied among the seven crop stations (7.7-17 mm/year), while the mean precipitation over the study period ranged from 350 to 450 mm at these stations.

Trends of Weather Variables over the Maize Growing Period
Daily maximum and minimum temperatures and precipitation, the other three forcing variables for CERES-Maize, may also have effects on maize growth. However, we focused only on maximum and minimum temperatures and excluded precipitation in this study, since our study cases all used full irrigation. Figure 4 shows the time-series of the averaged T max over the maize growth period for the 19 stations (light gray lines), which all show a declining trend over time. The lowest T max was for the HuaShan station where the elevation was 2064.9 m. The averages from these stations decreased by a trend of −0.5 • C/decade (dark thick line). The maximum and minimum trends were −1.1 • C/decade and −0.04 • C/decade, which appeared at the YuLin and WuQi stations, respectively. The trends of minimum temperature for all stations were insignificant (figure not shown).
We selected seven crop experiment stations in the Guanzhong Plain for our study, including DaLi, FengXiang, LinTong, ShangLuo, WeiNan, WuGong, and XianYang stations, due to the availability of detailed long-term observations and field management data for 19 years (1994-2012). Table 5 shows the trends of SSH, maximum and minimum temperature, and precipitation at the seven selected stations for the maize growth period over the study period. For our selected stations, the trend of SSH was from −0.9 to −1.3 h/decade for the period of 1994-2012, all with a p-value of less than 0.05. The trends of T max for the seven stations ranged between −0.1 and −1.1 • C/decade, but those for DaLi and WeiNan did not pass the 95% significance test. The minimum temperature was essentially unchanged at all stations, and the change in average temperature was caused by the change in T max . The trend of precipitation varied among the seven crop stations (7.7-17 mm/year), while the mean precipitation over the study period ranged from 350 to 450 mm at these stations.

Analysis of Trends in the Maize Growth Period
For this study, we examined the trends in the phenology and growth period of maize at our selected seven stations. Table 6 shows that the trends that pass the significance test for the phenology and length of each developmental phase were quite randomly distributed. However, the lengths of the entire growth period for all seven stations show an upward trend, all of which passed the significance test. The maximum trend of 14.9 days/decade appeared at ShangLuo, while the minimum trend of 4.8 days/decade was seen at WeiNan.

Results of Model Calibration and Validation
The cultivar parameter and yield results are shown in the Table 7. These parameters are compatible with the cultivar description (website: http://www.chinaseed114.com/seed/26/). The simulations with our calibrated cultivar parameters are shown in Figure 5. The simulations of flowering and maturity date were very close to the corresponding observations, and their coefficients for all seven stations were above 0.9 (Figure 5a). The simulations of yield were very close to the corresponding observations in each year at each station, except for DaLi and LinTong stations in 1994 (Figure 5b). Due to a serious pest outbreak at DaLi and LinTong in that year, the record was not detailed enough. Such a pest effect was not included in our simulations with CERES-Maize. Thus, a large gap between observations and simulations is seen for those two stations in 1994 (Figure 5b insect pest points). Furthermore, we also evaluated the soil water content simulations. Figure 5c shows the soil water content simulations compared against observations, and the coefficients for the seven stations range from 0.64 to 0.85, all of which pass the 99% significance level. Note: some data were not included in calculating absolute relative error (ARE) due to a serious pest outbreak in those years and very large errors.

The Cultivars and Cultivar Parameter Change
Cultivar replacement is a way to adapt to changes in environmental conditions such as climate change, and increase crop yield [50,51,55,56,70,71], as shown in Table 6. Our optimized cultivar parameters show significant changes, which are consistent with the cultivar observations. Figure 6 indicates that three cultivar parameters (P1, P5, and G3) had significant upward trends over our study period, corresponding to extended growth periods and heavier kernel weight. The latter led directly to a yield increase. The other three cultivar parameters (P2, G2, and PHINT) did not show similar changes over the same period, implying that these parameters were insensitive to cultivar selection. Agronomy 2020, 10, x FOR PEER REVIEW 12 of 23

The Cultivars and Cultivar Parameter Change
Cultivar replacement is a way to adapt to changes in environmental conditions such as climate change, and increase crop yield [50,51,55,56,70,71], as shown in Table 6. Our optimized cultivar parameters show significant changes, which are consistent with the cultivar observations. Figure 6 indicates that three cultivar parameters (P1, P5, and G3) had significant upward trends over our study period, corresponding to extended growth periods and heavier kernel weight. The latter led directly to a yield increase. The other three cultivar parameters (P2, G2, and PHINT) did not show similar changes over the same period, implying that these parameters were insensitive to cultivar selection.

Maize Yield Affected by Climate and Cultivar
To further understand how a decline in SSH and T max affect maize yield, we removed the trends of SSH and T max over the maize growth stage for the seven crop stations, respectively. Figure 7 shows the averaged SSH and T max over those stations with and without the trend. In this figure, we can see that the averaged SSH changed from about seven hours to about five hours for the maize growth stage over the period of 1994-2012, while T max decreased by about 1 • C over the same period. Without the trends, the averaged SSH fluctuated around 7.1 h, and the averaged T max varies around the 28.1 degree level.
For this study, we performed our simulations again with the new SSH and T max data without the long-term trend to simulate maize yield for our seven selected stations. We also conducted a simulation using only the cultivar for 1994 throughout the whole 19-year simulation period. By comparing these new simulations with our original modeling results, we quantified the contributions of cultivar selection and the reduction in temperature and SSH to the yield increase ( Figure 8). In Figure 8, we can see that the cultivar changes and decline in T max increased the maize yield quite significantly over our study period, while the decline in SSH reduced the maize yield over the same period. The changes in annual yield due to changes in temperature, cultivar, and SSH were 25, 81, and −51 kg/ha/year, respectively. Compared with that for 1994, the averaged yield for 2012 increased by 498 and 1747 kg/ha due to the respective changes in temperature and cultivar and decreased by 857 kg/ha due to the SSH reduction. The averaged yield increased by 5% and 25% due to the temperature changes and cultivar replacement, respectively, and decreased by 8% due to the SSH reduction at these stations during 1994-2012.  Table 7.

Maize Yield Affected by Climate and Cultivar
To further understand how a decline in SSH and Tmax affect maize yield, we removed the trends of SSH and Tmax over the maize growth stage for the seven crop stations, respectively. Figure 7 shows the averaged SSH and Tmax over those stations with and without the trend. In this figure, we can see that the averaged SSH changed from about seven hours to about five hours for the maize growth stage over the period of 1994-2012, while Tmax decreased by about 1 °C over the same period. Without the trends, the averaged SSH fluctuated around 7.1 hours, and the averaged Tmax varies around the 28.1 degree level.   Table 7.

Maize Yield Affected by Climate and Cultivar
To further understand how a decline in SSH and Tmax affect maize yield, we removed the trends of SSH and Tmax over the maize growth stage for the seven crop stations, respectively. Figure 7 shows the averaged SSH and Tmax over those stations with and without the trend. In this figure, we can see that the averaged SSH changed from about seven hours to about five hours for the maize growth stage over the period of 1994-2012, while Tmax decreased by about 1 °C over the same period. Without the trends, the averaged SSH fluctuated around 7.1 hours, and the averaged Tmax varies around the 28.1 degree level.  and −51 kg/ha/year, respectively. Compared with that for 1994, the averaged yield for 2012 increased by 498 and 1747 kg/ha due to the respective changes in temperature and cultivar and decreased by 857 kg/ha due to the SSH reduction. The averaged yield increased by 5% and 25% due to the temperature changes and cultivar replacement, respectively, and decreased by 8% due to the SSH reduction at these stations during 1994-2012. We used a representative cultivar in each year to simulate maize yield for all 19 stations across the study area. Figure 9 shows the simulated yields averaged over those stations for the period of 1994-2012 against observations. The trend of observed yield was 988 kg/ha per decade, while the trend of the simulated total yield was 882 kg/ha per decade, indicating that the model accurately reproduced the observations. Climate change (the SSH and Tmax reductions) had a negative contribution to the total yield. The Tmax and SSH trends together accounted for a downward yield trend of −201 kg/ha per decade, reducing the trend of the total simulated yield by 28%. The cultivar replacement accounted for a yield trend of 588 kg/ha per decade, contributing to 67% of the total simulated trend (Figure 9). Therefore, although cultivar replacement is a dominant factor increasing the maize yield in our study region, the negative effects of climate change could not be neglected. We used a representative cultivar in each year to simulate maize yield for all 19 stations across the study area. Figure 9 shows the simulated yields averaged over those stations for the period of 1994-2012 against observations. The trend of observed yield was 988 kg/ha per decade, while the trend of the simulated total yield was 882 kg/ha per decade, indicating that the model accurately reproduced the observations. Climate change (the SSH and T max reductions) had a negative contribution to the total yield. The T max and SSH trends together accounted for a downward yield trend of −201 kg/ha per decade, reducing the trend of the total simulated yield by 28%. The cultivar replacement accounted for a yield trend of 588 kg/ha per decade, contributing to 67% of the total simulated trend ( Figure 9). Therefore, although cultivar replacement is a dominant factor increasing the maize yield in our study region, the negative effects of climate change could not be neglected. In this study, different climate change trends affected the maize yield differently across all our study stations. The yield reduction ranged from 67 to 1674 kg/ha due to SSH varying from 4.9 to 7.9 hours, with a similar downward trend for all stations (Figure 10a). Generally, the larger yield In this study, different climate change trends affected the maize yield differently across all our study stations. The yield reduction ranged from 67 to 1674 kg/ha due to SSH varying from 4.9 to 7.9 h, with a similar downward trend for all stations (Figure 10a). Generally, the larger yield reduction corresponded to shorter daily SSH and vice versa. For shorter SSH, solar radiation may be a limiting factor in photosynthesis, and even a small decrease in SSH could have a remarkably adverse effect on the maize yield (Figure 10a). For longer SSH, solar radiation might not restrict photosynthesis. Thus, the effect of a small decrease in SSH on the yield is sometimes minor. Moreover, we explored the reasons for the yield changes caused by the above three variables. Figure 11a shows the difference in the maximum leaf area index, kernel weight, aboveground biomass, and root biomass between the original and detrended simulations. We can see that kernel weight had the largest increase under the Tmax decline and cultivar replacement, while root biomass declined the most due to the SSH reduction. The decrease in Tmax more significantly prolonged the reproductive period than the vegetative period (Figure 11b), leading to an increase in kernel weight. Since the grain number (G2) did not show a meaningful change, the yield increases were due mostly to the kernel weight increase. When compared with the prior cultivars, the replaced cultivars usually had a longer growth period and kernel weight. Yield increase with cultivar replacement accounted for 30% due to a longer growth period and 70% due to heavier kernel weight based on our two additional tests (fixed phenology or kernel weight parameters, data and figure not shown).  As aforementioned, the T max reduction increased the mean maize yield over the 19 study stations for the period of 1994-2012. However, our detailed analysis indicated that the yield decreased over six stations due to the T max reduction (Figure 10b), where T max was equal to or lower than 27.3 • C. When T max was above this temperature, the yield increased due to the reduction in T max over the rest of the 13 stations. No matter which situation occurred, the reduction in T max decreased the thermal time, tending to lower the yield. Hence, the increase or decrease in the final yield was determined by temperature stress. For lower T max (≤27.3 • C), the daily mean temperature was unable to reach the lower boundary of the temperature triggering heat stress (33.0 • C in CERES-Maize), and a decrease in Tmax did not have an impact on the yield. Thus, we see a reduction in the final yield. Meanwhile, for higher Tmax (>27.4 • C), the daily mean temperature often surpassed the heat stress threshold temperature, lowering the yield. A decrease in T max tended to alleviate the heat stress, increasing the yield. In this study, we can see that the alleviation of heat stress played a more important role in affecting the maize yield than the reduction in thermal time, and both were caused by the decrease in T max . Therefore, an increase in yield was seen for the reduction in the higher T max . The generic criterion to distinguish between a lower and a higher T max still needs to be determined with a larger dataset.
Moreover, we explored the reasons for the yield changes caused by the above three variables. Figure 11a shows the difference in the maximum leaf area index, kernel weight, aboveground biomass, and root biomass between the original and detrended simulations. We can see that kernel weight had the largest increase under the T max decline and cultivar replacement, while root biomass declined the most due to the SSH reduction. The decrease in T max more significantly prolonged the reproductive period than the vegetative period (Figure 11b), leading to an increase in kernel weight. Since the grain number (G2) did not show a meaningful change, the yield increases were due mostly to the kernel weight increase. When compared with the prior cultivars, the replaced cultivars usually had a longer growth period and kernel weight. Yield increase with cultivar replacement accounted for 30% due to a longer growth period and 70% due to heavier kernel weight based on our two additional tests (fixed phenology or kernel weight parameters, data and figure not shown).
to the kernel weight increase. When compared with the prior cultivars, the replaced cultivars usually had a longer growth period and kernel weight. Yield increase with cultivar replacement accounted for 30% due to a longer growth period and 70% due to heavier kernel weight based on our two additional tests (fixed phenology or kernel weight parameters, data and figure not shown). To further understand how declining SSH reduces root biomass and yield, the annual and seasonal time series of the root biomass and aboveground biomass are included in Figure 12. We can see that the difference in root biomass between the simulations with and without the SSH trend was remarkably larger than that in aboveground biomass. Starting from 10 days after planting, the To further understand how declining SSH reduces root biomass and yield, the annual and seasonal time series of the root biomass and aboveground biomass are included in Figure 12. We can see that the difference in root biomass between the simulations with and without the SSH trend was remarkably larger than that in aboveground biomass. Starting from 10 days after planting, the difference in root biomass dramatically decreased, while the aboveground biomass difference had a much gentler decrease at both seasonal and long-term scales. As we know, maize develops its roots in the early seedling stage, and the growth of maize roots is very sensitive to the external environment during this period [72,73], when less photosynthesis leads to a decrease in root mass, beginning with less sunshine. Decreased root mass in the early growth stage greatly hinders the development of aboveground biomass during the later growth period due to weaker water and nutrient absorption.
Agronomy 2020, 10, x FOR PEER REVIEW 17 of 23 difference in root biomass dramatically decreased, while the aboveground biomass difference had a much gentler decrease at both seasonal and long-term scales. As we know, maize develops its roots in the early seedling stage, and the growth of maize roots is very sensitive to the external environment during this period [72,73], when less photosynthesis leads to a decrease in root mass, beginning with less sunshine. Decreased root mass in the early growth stage greatly hinders the development of aboveground biomass during the later growth period due to weaker water and nutrient absorption.

Causes of SSH Trends
It is believed that the downward trend of SSH is largely caused by the increases in cloud cover and manmade aerosols [74,75]. In this study as illustrated in Figure 4, the decline in SSH was highest during the summer while it is lowest during the winter. In the meanwhile, the manmade aerosol emission was strongest during the winter and weakest during the summer [76][77][78][79]. Therefore, we believe that the increase in cloud cover was most likely a strong reason for the SSH decline.
To further understand the reason for the decrease in SSH, we analyzed precipitation and rainy days for the summer maize growth period (June to September) over 1994-2012 ( Figure 13). There is a general rising trend of 85.5 mm/decade and 4.5 day/decade in precipitation and rainy days, respectively, during the summer maize growth period. The average monthly rainy days for the same two periods (1994-2003 and 2003-2012) was computed, with results shown in Figure 14. The difference in rainy days between these two periods is very similar to that in SSH. The increase in

Causes of SSH Trends
It is believed that the downward trend of SSH is largely caused by the increases in cloud cover and manmade aerosols [74,75]. In this study as illustrated in Figure 4, the decline in SSH was highest during the summer while it is lowest during the winter. In the meanwhile, the manmade aerosol emission was strongest during the winter and weakest during the summer [76][77][78][79]. Therefore, we believe that the increase in cloud cover was most likely a strong reason for the SSH decline.
To further understand the reason for the decrease in SSH, we analyzed precipitation and rainy days for the summer maize growth period (June to September) over 1994-2012 ( Figure 13). There is a general rising trend of 85.5 mm/decade and 4.5 day/decade in precipitation and rainy days, respectively, during the summer maize growth period. The average monthly rainy days for the same two periods (1994-2003 and 2003-2012) was computed, with results shown in Figure 14. The difference in rainy days between these two periods is very similar to that in SSH. The increase in rainy days was also highest from June to September, as reflected in SSH (Figures 3 and 14). The increase in precipitation and rainy days led to a decrease in sunny days and a decrease in SSH. Essentially, the connection between SSH and rainy days resulted from cloud cover. This strongly suggests that the increase in precipitation and rainy days was the main reason for the decrease in SSH.

Effects of Precipitation on the Yield
Studies show that precipitation affects crop production under full irrigation treatments [80,81]. This could occur when precipitation significantly changes solar radiation, and air moisture or water stress occurs between irrigation events. In this study, we examined the effect of precipitation on maize yield with observations. Figure 15a shows the precipitation amount against maize yield for the

Effects of Precipitation on the Yield
Studies show that precipitation affects crop production under full irrigation treatments [80,81]. This could occur when precipitation significantly changes solar radiation, and air moisture or water stress occurs between irrigation events. In this study, we examined the effect of precipitation on maize yield with observations. Figure 15a shows the precipitation amount against maize yield for the

Effects of Precipitation on the Yield
Studies show that precipitation affects crop production under full irrigation treatments [80,81]. This could occur when precipitation significantly changes solar radiation, and air moisture or water stress occurs between irrigation events. In this study, we examined the effect of precipitation on maize yield with observations. Figure 15a shows the precipitation amount against maize yield for the 19 study stations where precipitation ranged remarkably from 252 to 583 mm. Our results indicate that the change in precipitation did not have a significant impact on maize yield. Furthermore, we produced the yield difference between the simulations with the original and detrended precipitation for the WuGong station (one of the 19 stations), with an upward precipitation trend of 18 mm/year (Figure 15b). Therefore, in our study region with remarkable precipitation changes, we did not see a significant effect of precipitation on maize yield.
Agronomy 2020, 10, x FOR PEER REVIEW 19 of 23 ( Figure 15b). Therefore, in our study region with remarkable precipitation changes, we did not see a significant effect of precipitation on maize yield.

Conclusions
A decline in SSH was observed across most of the meteorological stations in Northern China. We found this decline to be highest in the warm season, when summer maize grows. A downward trend of −0.068 h/day for SSH was observed over the seven selected crop stations in central China for the summer maize growth period of 1994-2012. A downward trend of −0.5 °C/decade in Tmax was observed for the same period, which is likely associated with the decline in SSH. The CERES-Maize model was first calibrated and validated against observed yields, and the simulations agreed very well with observations. The model was then used to quantify the effects of the declines in SSH and Tmax on the maize growth period and yields, which were examined with and without the trends of these two climate forcing variables. CERES-Maize was also used to examine how cultivar replacements affected maize yields by comparing the results with and without cultivar replacement. Our modeling results indicated that the decline in SSH reduced the maize yield by 8% on average over our study stations by limiting root growth, and the decline for shorter SSH played a more important role in affecting the yield than that for longer SSH. In the meantime, the decrease in higher Tmax increased the yield where the extended growth period generated a dominant effect, while the decrease in lower Tmax reduced the yield where the lowered thermal time was most important. In addition, the observed yield showed a significant upward trend, which can be attributed mainly to the frequent cultivar replacements over the study period based on our modeling results. When compared with the prior cultivars, the replaced cultivars usually had a longer growth period, prolonging grain-filling time. Net maize production increased with the combined effects of cultivar replacements and the declines in SSH and Tmax on yields.
Temperature increases have become more significant on a global scale since the beginning of this century [82]. In this dynamic climate system, the feedback resulting from temperature increase is very complex. In some regions, the temperature shows a downward trend over certain seasons, which may be related to the reduced solar radiation due to greater cloud cover and higher aerosol loads as discussed above. These complex changes in the climate system probably have different effects on crop growth and yields. This study explored the issue at a relatively small spatial scale.

Conclusions
A decline in SSH was observed across most of the meteorological stations in Northern China. We found this decline to be highest in the warm season, when summer maize grows. A downward trend of −0.068 h/day for SSH was observed over the seven selected crop stations in central China for the summer maize growth period of 1994-2012. A downward trend of −0.5 • C/decade in T max was observed for the same period, which is likely associated with the decline in SSH. The CERES-Maize model was first calibrated and validated against observed yields, and the simulations agreed very well with observations. The model was then used to quantify the effects of the declines in SSH and T max on the maize growth period and yields, which were examined with and without the trends of these two climate forcing variables. CERES-Maize was also used to examine how cultivar replacements affected maize yields by comparing the results with and without cultivar replacement. Our modeling results indicated that the decline in SSH reduced the maize yield by 8% on average over our study stations by limiting root growth, and the decline for shorter SSH played a more important role in affecting the yield than that for longer SSH. In the meantime, the decrease in higher T max increased the yield where the extended growth period generated a dominant effect, while the decrease in lower T max reduced the yield where the lowered thermal time was most important. In addition, the observed yield showed a significant upward trend, which can be attributed mainly to the frequent cultivar replacements over the study period based on our modeling results. When compared with the prior cultivars, the replaced cultivars usually had a longer growth period, prolonging grain-filling time. Net maize production increased with the combined effects of cultivar replacements and the declines in SSH and T max on yields.
Temperature increases have become more significant on a global scale since the beginning of this century [82]. In this dynamic climate system, the feedback resulting from temperature increase is very complex. In some regions, the temperature shows a downward trend over certain seasons, which may be related to the reduced solar radiation due to greater cloud cover and higher aerosol loads as discussed above. These complex changes in the climate system probably have different effects on crop growth and yields. This study explored the issue at a relatively small spatial scale. Further studies should focus on a much larger scale or even a global scale, and on different crops.
Author Contributions: L.S. conducted the experiment, designed the experiment, performed the analysis, and drafted the manuscript; J.J. interpreted the results, supervised the research, contributed ideas during analysis and interpretation, and edited the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the National Natural Science Foundation of China (grant number 91637209).

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