Climate Change Affects the Utilization of Light and Heat Resources in Paddy Field on the Songnen Plain, China

Abstract

Efficient utilization of light and heat resources is an important part of cleaner production. However, exploring the changes in light and heat resources utilization potential in paddy under future climate change is essential to make full use of the potential of rice varieties and ensure high-efficient, high-yield, and high-quality rice production, which has been seldom conducted. In our study, a process-based crop model (CERES-Rice) was calibrated and validated based on experiment data from the Songnen Plain of China, and then driven by multiple global climate models (GCMs) from the coupled model inter-comparison project (CMIP6) to predict rice growth period, yield, and light and heat resources utilization efficiency under future climate change conditions. The results indicated that the rice growth period would be shortened, especially in the high emission scenario (SSP585), while rice yield would increase slightly under the low and medium emission scenarios (SSP126 and SSP245), it decreased significantly under the high emission scenario (SSP585) in the long term (the 2080s) relative to the baseline of 2000–2019. The light and temperature resources utilization (E_RT), light utilization efficiency (E_R), and heat utilization efficiency (HUE) were selected as the light and heat resources utilization evaluation indexes. Compared with the base period, the mean E_RT in the 2040s, 2060s, and 2080s were −6.46%, −6.01%, and −6.03% under SSP126, respectively. Under SSP245, the mean E_RT were −7.89%, −8.41%, and −8.27%, respectively. Under SSP585, the mean E_RT were −6.88%, −13.69%, and −28.84%, respectively. The E_R would increase slightly, except for the 2080s under the high emission scenario. Moreover, the HUE would reduce as compared with the base period. The results of the analysis showed that the most significant meteorological factor affecting rice growth was temperature. Furthermore, under future climate conditions, optimizing the sowing date could make full use of climate resources to improve rice yield and light and heat resource utilization indexes, which is of great significance for agricultural cleaner production in the future.

1. Introduction

Global climate change, which affects agricultural production and human health, is a central issue of constant concern [1]. It has been estimated that global warming will reach 1.5 °C in the near-term [2]. Agriculture is very sensitive to climate change and is also one of the industries most affected by climate change [3,4]. The United Nations Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) on agriculture extends from crop production systems to food supply systems, and evidence of the adverse effects of climate change on crop production is strengthening [2]. Greenhouse gas emissions have a significant impact on climate warming [5,6,7]. Anthropogenic warming hampers crop yield increase. Moreover, crop yield is decreased with the increase in surface O₃ concentrations and CH₄ emissions exacerbate these adverse effects [8]. Climate change will increase pressure on food production, especially in vulnerable areas [2]. Therefore, for ensuring global food security, assessing the impacts of future climate change on crop growth is one of the most important issues in the 21st century [9].

Rice is a staple food for the global population, and its production is key to global food security. Stable growth of rice production has been an issue in achieving food security, especially in developing countries [10]. A slight decline in rice productivity will have a significant impact on global food security [11]. The Songnen Plain is an important rice producer in China, with its vast area and large inter-annual variability of heat and precipitation. Climate change changes the conditions of light, temperature, and water during the growth and development of crops, affecting the allocation of their heat, radiation, and water resources [12]. The light and heat resources utilization of rice is a decisive factor affecting its yield, and the light and heat resources utilization in a certain time and space range determines the production potential of the agricultural system [13]. Efficient utilization of local light and heat resources is of great importance to provide full play to the potential of crop varieties and ensure high-efficient, high-yield, and high-quality rice production. However, most of the previous studies only focused on crop phenology and yield changes and rarely in terms of light and heat resources utilization. Therefore, it is necessary to analyze the response of light and heat resource utilization efficiency of rice to future climate change on the Songnen Plain and ensure the rice supply.

At this stage, field experiments cannot simulate climate change, such as temperature, precipitation, and solar radiation very well. The decision support system for agrotechnology transfer (DSSAT) can simulate the process of crop growth and yield formation, soil and crop water balance, nutrient and carbon dynamics, etc. Research and application on DSSAT have focused on yield prediction, crop breeding, land use, water and fertility management, climate change, and other areas. It is one of the most widely used crop modeling systems at present [14]. In the context of climate change, the application of crop growth models to study the impacts of climate conditions on crop production status and yield in historical periods has become quite extensive and mature [15]. The use of global climate models (GCMs) or regional climate models (RCMs) to construct future climate change scenarios and then coupled with crop growth models has developed into an important tool to assess the impacts of future climate change on agricultural production [16,17,18]. Kang et al. used an enhanced soil and water assessment tool (SWAT) model in combination with five global climate models to assess the potential impacts of climate change on the yield of two major crops (i.e., potato and barley) in Western Canada [18]. Rosenzweig et al. used seven global grid crop models (GGCMs) combined with five global climate models to analyze crop responses to climate change, showing strong negative effects of climate change, particularly at higher levels of warming and lower latitudes [17]. Crop model yield projections derived from averaging multiple GCM ensembles and different shared socioeconomic pathway and representative concentration (SSP) scenarios can provide a more reliable assessment of climate change impacts.

In our study, we used a calibrated and validated crop model, the CERES-Rice model, driven by future climate data from six GCMs for phase six of the coupled model inter-comparison project (CMIP6) under the scenario of three different shared socioeconomic pathway and representative concentration pathway (SSP), including SSP126, SSP245, and SSP585, to project impacts of future climate change on rice production on the Songnen Plain of China. The objectives of our study were to (1) evaluate the performance of the CERES-Rice model in simulating the rice growth on the Songnen Plain of China, (2) explore the effects of future climate change on rice phenology, yield, and the light and heat resources utilization efficiency in the study area, and (3) investigate the ability to optimize sowing dates to cope with the effects of climate change on rice growth.

2. Materials and Methods

2.1. Study Site

The Songnen Plain is located in the central and western part of Northeast China (Figure 1). We selected the representative site, Qing’an National Irrigation Experimentation Key Station (46°52′41″ N, 127°30′04″ E) on the Songnen Plain for our study. The study site is a typical cold black soil distribution area, that belongs to the cold temperate continental monsoon climate, the annual frost-free period is 128 days, the average annual temperature is 2.5 °C, the average annual rainfall is 566 mm, the rainfall is mostly concentrated in July and August, the geographical environment and natural resources conditions in the area are superior, favorable to the growth of crops, and the crop growing period is 156~171 days.

2.2. Field Experimental Data

The field trials were conducted for 2 years with the rice cultivar “Suijing No.18”. The rice was sown on 17 April, at a planting density of 24 plants/m², row spacing was 30 cm, the area of the test plot was 100 m² (10 × 10 m), and each plot was applied with N (110 kg/hm²), P₂O₅ (45 kg/hm²), and K₂O (80 kg/hm²) in the ratio of base fertilizer/tillering fertilizer/heading fertilizer (5:3:2). The three different irrigation methods, namely control irrigation (CI), wet irrigation (WI), and flood irrigation (FI) were designed [19,20]. The observation data included phenology date and yield, which were used to calibrate and validate the CERES-Rice model. The soil parameters of the study site were obtained from the 1:1 million soil data provided by the Nanjing Soil Institute of the Second National Land Survey in the Harmonized World Soil Database (HWSD) and the empirical data automatically generated by the CERES-Rice model, including soil profile characteristics, soil physical and chemical properties, i.e., soil color, hydrology group, bulk density, organic carbon, soil texture of each layer (clay, silt, and stones), soil nitrogen content, pH in water, etc.

2.3. Climate Data

The historical observed meteorological data of the study site were obtained from the National Meteorological Information Center-China Meteorological Data Network (http://data.cma.cn/, accessed on 1 March 2022), including four meteorological indicators of daily maximum and minimum temperature (°C), precipitation (mm), and solar radiation (calculated based on the sunshine hours using the Ångström-Prescott formula, MJ/m²).

Based on the results of the existing study evaluating the CMIP6 model, six GCMs (

Table 1. Information on the six global climate models (GCMs) selected in our study.

No.	Model Name	Institute and Country	Atmospheric Resolution (lon × lat: Number of Grids, L: Vertical Levels)
1	ACCESS-CM2	Commonwealth Scientific and Industrial Research Organization, Australian	192 × 144, L85
2	BCC-CSM2-MR	Beijing Climate Center, China	320 × 160, L46
3	EC-Earth3	EC-Earth Consortium, Europe	512 × 256, L91
4	GFDL-ESM4	National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, USA	288 × 180, L49
5	MPI-ESM1-2-HR	Max Planck Institute for Meteorology, Germany	384 × 192, L95
6	MRI-ESM2-0	Meteorological Research Institute, Japan	320 × 160, L80

) were selected that were more effective in simulating the study site [21]. Compared with CMIP5, CMIP6 improved projections of climate features, such as extreme temperatures and precipitation [22,23]. Future climate data were derived from monthly-scale meteorological data output from multi-modal ensemble averaging (MME) under three shared socioeconomic pathways and typical concentration pathway combination scenarios SSP126, SSP245, and SSP585 of the CMIP6 (https://esgf-node.llnl.gov/projects/cmip6/, accessed on 17 April 2022), including four meteorological indicators: Daily maximum and minimum temperature, precipitation, and solar radiation for three future periods: 2031–2050 (2040s), 2051–2070 (2060s), and 2071–2090 (2080s). The monthly climate projections from GCMs were down-scaled to the study site using an inverse distance-weighted (IDW) interpolation method, then bias correction was conducted by transferring the resulting monthly site data using functions obtained from analyzing the observed and GCM data for the period of 1961–2000. Daily climate variables were downscaled through the WGEN stochastic weather generator based on the spatially down-scaled monthly model [24].

Statistical down-scaling technique has been widely used for providing daily climate data to drive crop models in the assessment of the impacts of future climate change on agricultural systems in different countries and regions [25,26,27,28]. We used the statistical down-scaling model NWAL-WG provided by Liu and Zuo (2012). The main advantage of this statistical down-scaling approach, especially compared with dynamic down-scaling, is that it can be easily applied to any location where long-term daily historical climate records are available [29].

2.4. Model Simulations

2.4.1. CERES-Rice Model

The CERES-Rice model used in this study is included in decision support system for agrotechnology transfer (DSSAT) version 4.7.5 [30]. The CERES-Rice model is a process-based crop model driven by daily climate data (daily maximum and minimum temperature, precipitation, solar radiation). It can simulate rice growth, development, leaf area index, and dry matter content. Moreover, it can simulate soil water balance and nitrogen balance. Minimum input data include weather, soil, and crop variety genetic parameters. The CERES-Rice model has been widely tested and applied in many countries, including China [31,32,33]. In our study, the CERES-Rice model from DSSAT version 4.7.5 (https://get.dssat.net/, accessed on 21 December 2021) was used to simulate rice growth and development.

2.4.2. Model Calibration and Validation

During model calibration and validation, the varietal genetic parameters of the crop were adjusted using the trial-and-error method and the generalized likelihood uncertainty estimation (CLUE) tuning tool that comes with the DSSAT system. The observed dates of anthesis, maturity, and yield were made close to the CERES-Rice model simulation results. The parameters were calibrated using trial data for anthesis, maturity, and yield in 1 year. The parameters were validated using trial data in another year.

2.4.3. Simulation Scenarios

Rice growth was simulated by the CERES-Rice model, the irrigation was set to automatic when required (i.e., when the moisture content was <50% of water capacity at 30 cm depth, the rice was irrigated with 10 mm of water), the harvest time was set at maturity, and other field management methods were the same as in the field experiment. In our study, rice growth was also simulated under different future sowing dates, including 27 March, 3 April, 10 April, 17 April, 24 April, 1 May, and 8 May. The transplanting dates were 35 days after the sowing date.

2.5. Indicator Calculation Method

2.5.1. Statistical Indices for Model Evaluation

To evaluate the CERES-Rice model performance in simulating rice growth on the Songnen Plain, we used the following three evaluation metrics: (1) Root mean squared error (RMSE), (2) normalized root mean squared error (NRMSE), and (3) determination coefficient (R²):

$$RMSE=\sqrt{\frac{{\sum }_{i=1}^n{\left(S_i-O_i\right)}^2}{n}}$$

(1)

$$\mathrm{NRMSE}(\%)=\frac{RMSE}{\overline{O}}\times 100$$

(2)

$$R^2=\frac{{\sum }_{i=1}^n{\left(S_i-\overline{S}\right)}^2}{{\sum }_{i=1}^n{\left(O_i-\overline{O}\right)}^2}$$

(3)

where $S_i$ is the simulated value; $O_i$ is the observed value; $\overline{S}$ and $\overline{O}$ are the average values of the simulated and observed values, respectively; n is the number of samples.

2.5.2. Light and Heat Resources Utilization Evaluation Index

In our study, we computed the following indices to evaluate the light and heat resources utilization efficiency:

(1)

Light and temperature resources utilization

$${\mathrm{E}}_{RT}=\frac{Y}{Y_P}\times 100\%$$

(4)

$$Y_P=Y_1·f\left(t\right)$$

(5)

$$Y_1=\epsilon \left(1-\alpha \right)\left(1-\beta \right)\left(1-\gamma \right)\left(1-\rho \right)\left(1-\omega \right)f\left(L\right)E\phi {\left(1-\lambda \right)}^{-1}{\left(1-\mathsf{\chi }\right)}^{-1}{H}^{-1}\sum Q\times 10,000$$

(6)

$$f\left(t\right)=\{\begin{array}{c}0\left(t<t_{min},t>t_{max}\right)\\ \frac{t-t_{min}}{t_{opt}-t_{min}}(t_{min}\le t<t_s)\\ \frac{t_{max}-t}{t_{max}-t_{opt}}\left(t_s\le t\le t_{max}\right)\end{array}$$

(7)

where ${\mathrm{E}}_{RT}$ is the light and temperature resources utilization, $Y$ is the crop yield in kg/hm², $Y_P$ is light and temperature potential productivity in kg/hm², i.e., the upper limit of yield per unit area per unit time determined by local solar radiation and temperature at optimum conditions. $Y_1$ is photosynthetic potential productivity in kg/hm², i.e., the upper limit of yield per unit area per unit time determined solely by local solar radiation at optimum conditions. $\sum Q$ is the total solar radiation projected onto the unit area in MJ/m², H is the dry weight calorific value of the crop and rice taking the value of 16.9 MJ/kg, $\mathsf{\epsilon }$ is the ratio of photosynthetically active radiant energy to total radiant energy taking the value of 0.49. $\alpha , \beta , \gamma , \rho , \mathsf{\omega }, \phi , \lambda , \chi , \mathrm{f}\left(\mathrm{L}\right), \mathrm{E}$ are photosynthetic production potential parameters; $\mathsf{\alpha }$ is the plant population reflectance, taking a value of 0.06, $\beta$ is the plant luxuriant population light transmission, taking a value of 0.08, $\mathsf{\gamma }$ is the proportion of light above the light saturation point, taking a value of 0.05, $\rho$ is the proportion of radiation intercepted by non-photosynthetic organs of the crop, taking a value of 0.10, $\omega$ is the depletion rate of respiration, taking a value of 0.33, $\phi$ is the quantum efficiency of photosynthesis, taking a value of 0.22, and $\lambda$ is the inorganic nutrient content of the plant body, taking a value of 0.08, $\chi$ is the water content and takes the value of 0.14, $\mathrm{f}\left(\mathrm{L}\right)$ is the revised positive value of crop leaf area dynamics and takes the value of 0.56, and $\mathrm{E}$ is the economic coefficient and takes the value of 0.45.

(2)

Light utilization efficiency

$${\mathrm{E}}_R=\frac{H\times Y}{{\sum }_{i=SD}^{MD}PAR}\times 100\%$$

(8)

where ${\mathrm{E}}_R$ is light utilization efficiency, i.e., the ratio of the energy stored in the crop harvest per unit of land area during the plant’s reproductive life to the photosynthetically active radiation projected onto that unit area during the same period. $\sum _{i=SD}^{MD}PAR$ is photo-synthetically active radiation during crop growth (seeding to maturity), in MJ/m², i.e., the radiant energy in the solar radiation spectrum that can be used by green plants in the photosynthetic band, it accounts for 49% of the total solar radiation. SD is the sowing date, and MD is the harvest date. The total solar radiation can be obtained by Equations (9)–(14):

$$R_s=(a_s+b_s\frac{n}{N})R_a$$

(9)

$$R_a=24\frac{60}{\pi }{G}_{sc}{d}_r\left[{\omega }_s\sin \left(\phi \right)\sin \left(\delta \right)+\cos \left(\phi \right)\cos \left(\delta \right)\sin \left({\omega }_s\right)\right]$$

(10)

$$d_r=1+0.033\cos (\frac{2\pi }{365}J)$$

(11)

$$\mathsf{\delta }=0.409\sin (\frac{2\pi }{365}J-1.39)$$

(12)

$${\omega }_s=\arccos [-\tan \left(\phi \right)\tan \left(\delta \right)]$$

(13)

$$N=\frac{24}{\pi }{\omega }_s$$

(14)

where $R_s$ is the solar radiation (MJ/m²/d), $a_s$ and $b_s$ are empirical parameters, where $a_s$ indicates the fraction of astronomical radiation reaching the Earth’s surface on cloudy days, and $b_s$ indicates the transport properties (aerosol density) of the cloud-free atmosphere, $a_s$ takes 0.19 and $b_s$ takes 0.54; n is the actual insolation duration (h); N is the maximum possible insolation duration (h); n/N is the relative insolation duration, also called the insolation percentage; $R_a$ is the astronomical radiation (MJ/m²/d); π is the circumference; $G_{sc}$ is the solar constant, taking the value of 0.082; $d_r$ is the reverse solar-terrestrial relative distance; ${\omega }_s$ is the sunset time angle; $\phi$ is the latitude (rad); $\delta$ is the solar declination; J is the number of days.

(3)

Heat utilization efficiency

$$\mathrm{HUE}=\frac{Y}{{\sum }_{i=SD}^{MD}\left(T_{i,mean}-t_{min}\right)}$$

(15)

where HUE is heat utilization efficiency, in kg/(hm²·°C·d); $T_{i,mean}$ is the daily average temperature of the day i; ${\sum }_{i=SD}^{MD}\left(T_{i,mean}-t_{min}\right)$ is the effective cumulative temperature during crop growth that is greater than the biological lower limit temperature t, in °C·d, the $t_{min}$ of rice is 10 °C.

2.6. Data Analysis

A stepwise multiple linear regression model and correlation analysis were used to quantify the effects of climate factors, including mean temperature (average of maximum and minimum temperatures), precipitation, and solar radiation on future rice yield, the light and temperature resources utilization, light utilization efficiency, and heat utilization efficiency. The Pearson correlation coefficient was used for the correlation analysis.

The framework of our study is shown in Figure 2.

3. Results

3.1. Calibration and Validation of the CERES-Rice Model

The calibrated CERES-Rice model was able to simulate rice phenology and yield reasonably well in the study area. The CERES-Rice model was calibrated and validated using 2 years of field trial data, including three irrigation methods. The calibrated parameters for “Suijing 18” are shown in

Table 2. The calibration parameters for “Suijing 18”.

Cultivar Parameter		Physical Significance	Range	Value
Phenology Parameters	P1	The amount of heat hours required during the basic vegetative phase of the plant (°C·d)	150~800	338.4
	P2R	The extent to which phase development leading to panicle initiation is delayed (°C·d)	5~300	57.56
	P5	The amount of heat hours required from the beginning of grout to physiological maturity (°C·d)	150~850	328.3
	P2O	Critical photoperiod or the longest day length at which the development occurs at a maximum rate (h)	11~13	12.73
Growth Parameters	G1	Potential spikelet number coefficient	50~75	74.3
	G2	Single grain weight under ideal growing conditions (g)	0.015~0.03	0.019
	G3	Tillering coefficient relative to IR64 cultivar under ideal conditions	0.7~1.3	0.747
	PHINT	Phyllochron interval (°C·d)	55~90	74.01

, including phenology and growth parameters.

The results of the validation have shown that the simulated dates of anthesis and maturity were consistent with the observed dates (Figure 3a). The error of simulating anthesis and maturity was generally within 5 days. The normalized root means square error (NRMSE) of anthesis and maturity dates between simulated and observed values was 3%, and the R² value was 0.968. The simulated and observed yields were also in general agreement (Figure 3b), with NRMSE = 3.4% and R² close to 1.0. Therefore, the validated results indicated that the CERES-Rice model could effectively simulate rice growth and development on the Songnen Plain of China.

3.2. Projected Climate Change in Rice Growth Period in the Future

The ensemble average of six GCMs under SSP126, SSP245, and SSP585 conditions were selected to reduce the uncertainty of future climate change projections. Compared with the baseline of 2000–2019, the ensemble-mean daily maximum temperature increased 1.06, 1.26, and 1.25 °C under SSP126, respectively, increased 1.31, 1.64, and 1.92 °C under SSP245, respectively, and increased 1.29, 2.36, and 3.54 °C under SSP585 in the 2040s, 2060s, and 2080s (Figure 4a). The ensemble-mean daily minimum temperature increased 1.81, 1.28, and 1.28 °C under SSP126, respectively, increased 1.40, 1.93, and 2.21 °C under SSP245, respectively, and increased 1.55, 2.95, and 4.38 °C under SSP585 in the 2040s, 2060s, and 2080s relative to the baseline (Figure 4b). In general, the ensemble-mean daily temperature showed an increasing trend during the rice growth period in the future. The largest increase in temperature was observed in the SSP585 scenario and the lower increase in temperature in the SSP126 scenario.

The daily solar radiation was also projected to increase relative to the baseline in the future under both SSPs (Figure 4c). The ensemble-mean solar radiation increased 0.66, 0.86, and 0.87 MJ/m² in the 2040s, 2060s, and 2080s under SSP126, respectively, increased 0.46, 0.56, and 0.74 MJ/m² in the future period under SSP245, respectively, and increased 0.32, 0.57, 0.80 MJ/m² in the future period under SSP585, respectively. Solar radiation increased more in the SSP126 scenario than in the SSP245 and SSP585 scenarios. The multi-model mean changes in annual precipitation were similar to other climatic factors (Figure 4d). In the 2040s, 2060s, and 2080s, there could be an increase in 40.71, 60.10, and 61.60 mm under SSP126, respectively, increase in 26.46, 58.94, and 64.87 mm under SSP245, respectively, and increase in 54.61, 69.73, and 95.35 mm under SSP585 relative to the baseline. Overall, the multi-model mean annual precipitation was also increased in the future period, especially in the SSP585 scenario, where the increase was more pronounced.

3.3. Impacts of Climate Change on Rice Phenology and Yield

Climate change could directly affect the rice growth period, including anthesis and maturity, which in turn could have an impact on rice yield. Our simulation results indicated that the rice growth period would be shortened in the future (Figure 5). In the 2040s, 2060s, and 2080s, the multi-model mean changes in anthesis date were −5.2, −4.2, and −5.5 days under SSP126, respectively, and −6.55, −7.4, and −7.15 days under SSP245, respectively, and −7.15, −11.85, and −19.45 days under SSSP585, respectively.

The maturity date would be changed to −5.15, −4.2, and −6.25 days under SSP126, respectively, and −6.35, −7.55, and −9.4 days under SSP245, respectively, and −10.35, −17.05, and −26.8 days under SSP585, respectively. The results showed that the greatest variation in rice growth period under the future climate scenario was observed for SSP585, followed by SSP245, and the least significant for SSP126, which was more similar to the multi-model mean changes in temperature. Correlation analysis showed a significant negative correlation between rice maturity and temperature, with correlation coefficients reaching above 0.9 (Figure 6).

Compared with the baseline of 2000–2019, in the 2040s, 2060s, and 2080s, the multi-model mean changes in rice yield were +1.90%, +4.68%, and +2.46% under SSP126, respectively, and +0.25%, +0.48%, and +0.80% under SSP245, respectively, and −1.60%, −3.80%, and −24.89% under SSSP585, respectively (Figure 7). In general, rice yield varied inconsistently under different future scenarios, with a slight increase under SSP126 and SSP245 and a significant downward trend under SSP585. The result showed the greatest reduction in rice yield in the 2080s under SSP585. We used stepwise multiple linear regression analysis to reflect the relationship between rice yield and climatic variables change (including mean temperature, precipitation, and solar radiation). The value of the determination coefficient (R²) was 0.717. As the regression coefficient was shown in

Table 3. Coefficients of regression analysis of the impacts of climate change in rice growth period (April to September) on rice yield (Y), the light and temperature resources utilization (${\mathrm{E}}_{RT}$ ), light utilization efficiency (${\mathrm{E}}_R$ ), and heat utilization efficiency (HUE) change. Shown in the table are the ΔY (kg·ha⁻¹), $\Delta {\mathrm{E}}_{RT}$ (%), $\Delta {\mathrm{E}}_R$ (%), and $\Delta$ HUE (kg/(hm²·°C·d) as a function of change in mean temperature ($\Delta T_{mean}$, °C), precipitation ($\Delta Prec$, mm), and solar radiation (ΔRad, MJ·m⁻²).

Item	a	b	c	R2
$\Delta Y=a\Delta T_{mean}+b\Delta Prec+c\Delta Rad$
	−7.972 ***	2.904 *	4.569 ***	0.717
$\Delta {\mathrm{E}}_{RT}=a\Delta T_{mean}+b\Delta Prec+c\Delta Rad$
	−7.440 ***	1.905	−1.365	0.779
$\Delta {\mathrm{E}}_R=a\Delta T_{mean}+b\Delta Prec+c\Delta Rad$
	−0.044 ***	0.024	−0.035 *	0.331
$\Delta \mathrm{HUE}=a\Delta T_{mean}+b\Delta Prec+c\Delta Rad$
	−0.619 ***	0.152	0.252 **	0.697

Note: *, **, and *** indicate significance at p < 0.05, p < 0.01, and p < 0.001, respectively.

, rice yield was significantly positively correlated with solar radiation and negatively correlated with mean temperature, while rice yield had a slight positive correlation with precipitation. In addition, temperature, precipitation, and solar radiation could explain 71.7% of the rice yield change.

3.4. Impacts of Climate Change on the Light and Heat Resources Utilization

Within a certain range, the light and heat resources utilization in rice determines the production potential of an agricultural system, and research on the light and heat resources utilization was particularly important but has been less studied in the past. Therefore, we focused on the study of this issue. In the baseline period (2000–2019), the calculated light and temperature resources utilization (${\mathrm{E}}_{RT}$) reached 89.74%. In the 2040s, 2060s, and 2080s, the calculated ${\mathrm{E}}_{RT}$ changes were −6.46%, −6.01%, and −6.03% under SSP126, respectively, and −7.89%, −8.41%, and −8.27% under SSP245, respectively, and −6.88%, −13.69%, and −28.84% under SSP585, respectively (Figure 8a). In general, the calculated ${\mathrm{E}}_{RT}$ showed a decreasing trend in the future, especially in the SSP585 scenario, and the reduction was more apparent. The light utilization efficiency (${\mathrm{E}}_R$) was 1.39% in the baseline period and decreased by 0.13% under SSP585 in the 2080s, while slightly increased in other scenarios and periods (Figure 8b). Compared with the baseline of 2000–2019, in the 2040s, 2060s, and 2080s, the calculated heat utilization efficiency (HUE) changes were −0.17, −0.08, and −0.01 kg/(hm²·°C·d) under SSP126, respectively, and −0.37, −0.40, and −0.39 kg/(hm²·°C·d) under SSP245, respectively, and −0.26, −0.74, and −2.00 kg/(hm²·°C·d) under SSP585, respectively (Figure 8c). In this case, the changes in HUE were similar to the ${\mathrm{E}}_{RT}$.

As shown in Table 3, the results of multiple linear regression analysis showed the relationship between the indexes of rice, light, and heat resources utilization (including ${\mathrm{E}}_{RT}$, ${\mathrm{E}}_R$, and HUE) and changes in climatic variables (including mean temperature, precipitation, and solar radiation). The values of the determination coefficient (R²) were 0.779, 0.331, and 0.697, respectively. All photothermal resource utilization indicators showed a significant negative correlation with mean temperature, while there was no significant correlation with precipitation. The ${\mathrm{E}}_R$ had a slight negative correlation with solar radiation and the HUE had a slight positive correlation with solar radiation.

3.5. The Impact of Different Sowing Dates on Rice Yield and Light and Heat Resources Utilization

To adapt to the impact of future climate change on rice growth, we have adjusted the sowing dates of rice to change the efficiency of light and heat resources utilization in rice. We simulated the light and temperature resources utilization (E_RT), light utilization efficiency (E_R), and heat utilization efficiency (HUE) for different sowing periods under two typical scenarios, SSP245 and SSP585 (Figure 9). The results showed that early sowing was beneficial for improving the E_RT under all emission scenarios, while light energy utilization was different and appropriately delayed sowing was beneficial for improving light energy utilization. Early sowing under the SSP126 and SSP245 scenarios increased the HUE, while the effect of the sowing date on the HUE was different in different periods under the SSP585 scenario.

In the context of climate change, by adjusting the sowing date, rice could use light and heat resources more efficiently during the growth period, thus contributing to rice yield. According to the rice yield changes under different sowing dates in the 2040s, 2060s, and 2080s relative to 2000–2019, we found that advancing or delaying the sowing period in the future period could mitigate the negative effects of climate change to some extent, but could not fully offset them, especially under the SSP585 scenario (Figure 10). Under the SSP126 and SSP245 scenarios, earlier sowing dates would increase rice yield, regardless of the period, while delayed sowing would result in lower rice yield. Our study showed that in the 2040s, the optimum sowing date for rice was before 27 March and would increase yield by 3.35% to 4.76%, while in the 2060s and 2080s, the optimum sowing date was around 3 April and would increase yield by 1.30% to 2.50% compared with sowing on 17 April. Under the SSP585 scenario, in the 2040s and 2060s, early sowing could increase yield by 3.36% to 3.76% compared with the baseline sowing date of 17 April. Whereas, in the 2080s, an appropriate delay in sowing would have a positive impact on rice yield, sowing on 1 May would increase yield by 8.37% compared with sowing on 17 April.

4. Discussion

4.1. Performance of the CERES-Rice Model

The performance of the crop model needs to be validated before it can be applied to future simulations. The results of our calibration indicated that the DSSAT-Rice model could simulate rice growth well in our study area (Figure 3). The simulated phenological periods and yield were in agreement with our experimental observations. Similar findings had been obtained in previous scholarly studies [34,35,36]. For example, Boonwichai et al. [34] validated the DSSAT-Rice model in the Songkhram River Basin, Thailand, with calibrated and validated R² values of 0.84 and 0.78, respectively, and simulated rice yield similar to the observed yield. Kontgis et al. [36] calibrated the DSSAT-Rice and DSSAT-Maize models using experimental data and observed a close agreement between the observed and simulated values for anthesis, maturity, yield, biomass, and N uptake in both crops, with d-values of 0.89 to 0.99, indicating an acceptable performance. Overall, our results showed that the calibrated and validated DSSAT-Rice model had an acceptable error rate and could better simulate the effects of climate change on rice growth on the Songnen Plain of China.

4.2. Impacts of Future Climate Change on Rice Growth

In our study, the future climate variables were averaged from the set of six GCM outputs. We found that under different future scenarios, the daily maximum temperature increased by 1.06–3.54 °C, the daily minimum temperature increased by 1.28–4.38 °C, the average daily solar radiation increased by 0.32–0.87 MJ m⁻², and the average annual precipitation increased by 26.26–95.35 mm (Figure 4). This prediction was consistent with many previous studies [37,38,39,40,41]. Arunrat et al. [37] used GCMs to predict future climate change in Thailand, with an increase in precipitation, as well as maximum and minimum temperatures for the SSP245 and SSP585 scenarios compared with the historical period. The maximum temperature increase for the SSP245 and SSP585 scenarios were 0.7–2.2 °C and 0.7–3.9 °C, respectively, and the minimum temperatures were 0.7–2.1°C and 0.8–3.8 °C, respectively. Precipitation increased by 2.2–3.9% and 1.8–5.8%, respectively. Tan et al. [38] also showed an increasing trend in both temperature and solar radiation in the future climate scenario. Multi-model ensemble averaging methods are widely used in climate modeling to reduce the large bias and uncertainty introduced by model parameterization errors, model structure, assumptions, and input variables [42].

The predicted future climate change resulted in varying degrees of shortening of rice maturity, with the largest magnitude during the 2080s under the SSP585 scenario, with an average shortening of 26.8 days (Figure 5). Many studies indicated that future climate change would shorten the crop growing period [43,44,45]. Our simulations projected that the mean rice yield increased by 1.9–4.68% under SSP126 and by 0.25–0.80% under SSP245 in the future period; however, under the SSP585 scenario, rice yield decreased by 1.6–24.89% (Figure 7). A relevant study showed that a gradual increase in 3.0–4.3% in rice yield in irrigated areas was predicted under the SSP245 scenario in Thailand, while a decrease in 6.0–17.7% in rice yield in irrigated areas in the medium and long term was predicted under the SSP585 scenario [8]. The same findings were found in wheat on the Sichuan basin: Wheat yield was significantly higher under the SSP245 scenario and negatively affected under the SSP585 scenario [46]. Temperature and solar radiation were considered to be the main environmental factors affecting the yield potential of rice [47,48]. Moreover, our results demonstrated that rice yield showed a significant positive correlation with solar radiation and a significant negative correlation with temperature (Table 3).

Furthermore, we simulated and calculated the E_RT, E_R, and HUE for different scenarios in the future period (Figure 8). The E_RT was not common in previous studies, which takes the light and temperature potential productivity as the light-temperature resources available in a region, where the light and temperature potential productivity was calculated using the step-by-step revision method. E_R and HUE are the more common evaluation metrics in solar thermal resource utilization assessment [49]. Our results showed that there was a relatively significant downward trend in E_RT and HUE under future climate scenarios. The main reason for this is the change in light-temperature production potential and effective cumulative temperature during the rice reproductive period due to future temperature and solar radiation increases. The E_R increased in the low and medium emission scenarios and decreased more significantly in the high emission scenario in the 2080s, which was mainly due to the alteration of temperature and photosynthetically active radiation during the reproductive period of rice.

4.3. Adaptive Strategies for Rice Production in Response to Climate Change

In rice production, a suitable sowing date could make the rice fertility process coincide with the local suitable water temperature and climate conditions; adjusting the sowing period is considered an effective strategy to slow down the development of rice and avoid heat damage [50,51,52]. In this study, climate resources could be fully utilized to increase rice yield by adjusting the sowing date, but the negative impact of climate change on rice yield could not be fully offset in the high emission scenario (Figure 9). Therefore, there is a need for more adaptation strategies, and some relevant studies have shown that with advances in technologies, such as breeding and crop management, more diverse rice varieties could provide a greater buffer for rice production to better adapt to environmental anomalies, such as climate change [53].

4.4. Uncertainty and Limitations of This Study

In this study, we evaluated the changes in rice yield and simulated the utilization of light and heat resources by the DSSAT-Rice model in future climate change under SSP126, SSP245, and SSP585 scenarios based on the six GCMs. Different climate change projections may result in different results. To reduce the uncertainty of climate projections, we selected six GCMs with better projection results in the study area for ensemble averaging. Nevertheless, there were still some uncertainties. First, climate change scenarios and subsequently estimated impacts may have some uncertainties due to the inherent systematic biases of GCMs. Second, although the DSSAT-Rice model has been widely used in global climate change assessment studies, it still has uncertainties, and a single crop model may be overconfident and ignore the uncertainty of crop model structure and parameters on climate change. Based on the existing crop models that use different functional relationships to simulate crop growth processes, multi-model ensembles are often considered superior to single-model simulations, and future studies are recommended to use multiple models for simulations. Third, there may be some uncertainty in the timing of varietal adaptation, since we assumed no future adaptation while keeping other agronomic management, including planting density and cropping pattern constant in the future. In addition, we evaluated the performance of only one commonly used variety in the study site, without explicitly considering the impact of other factors that could harm rice yields, such as pests and diseases, extreme climatic events (droughts and floods), irrigation water availability, and socioeconomic conditions.

5. Conclusions

In this study, we used six GCMs in CMIP6 in combination with the CERES-Rice crop model to assess the impact of climate change on the utilization potential of light and heat resources of rice on the Songnen Plain. The main results could be summarized as follows: (1) Under the SSPs, the maximum and minimum temperatures, precipitation, and solar radiation increased in the future period relative to the baseline, (2) the rice growth period showed different degrees of shortening under the future climate scenario, with a slight increase in rice yield under the SSP126 and SSP245 scenarios, while under the SSP585 scenario, rice yield was significantly lower than the baseline. The E_RT and HUE would decrease in the future, while E_R would slightly increase in the rest of the scenarios except under SSP585 in the 2080s, which would decrease. The results of data analysis showed that both rice yield and light and heat resource utilization indicators showed a significant negative correlation with temperature, which was the most dominant meteorological factor, and (3) under future climate conditions, optimizing the sowing date could make full use of climate resources to improve rice yield and light and heat resource utilization indexes.