Next Article in Journal
Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point
Previous Article in Journal
Piriformospora indica Enhances Rice Blast Resistance and Plant Growth
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 1
10.3390/agronomy15010119
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effective Combination of Advancing Transplantation Date with High-Yielding Cultivars for Paddy Rice Could Increase the Yield Potential Under Climate Warming in China

by
He Zhang
1,2,
Guangsheng Zhou
1,2,* and
Qijin He
3
1
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
2
Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Baoding 072656, China
3
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(1), 119; https://doi.org/10.3390/agronomy15010119
Submission received: 11 December 2024 / Revised: 31 December 2024 / Accepted: 3 January 2025 / Published: 5 January 2025
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
Browse Figures
Versions Notes

Abstract

:
Climate change will have a significant impact on agricultural productivity. Rice is one of the main grains in the world, the stability of its production and supply is directly related to global food security. Based on field observation data from 2000 to 2012 and a biophysical process-oriented CERES-Rice crop model at three typical sites, we investigated the effects of cultivar improvement, different transplanting dates and their interactions on rice yield potential in the major paddy rice production areas of China. Rice planting systems were optimized with an optimal combination of varieties and transplanting dates, and their adaptability under future climate conditions (climate projections from five global climate models under four typical concentration path scenarios) was assessed. The results showed that cultivar improvement could increase the rice yield potential by 18.0–41.4%. The appropriate transplanting date might increase the yield potential of the existing rice by 1.9–6.7%. The advance in the transplanting date combined with the application of high-yielding cultivars would prolong the growth period of rice and increase the rice yield potential by 26.3–51.8%. An effective combination of the transplanting date and cultivar is an efficient approach to increase the yield potential of rice. The results provided an important reference and choice for the scientific management of and yield increase in rice in China.

1. Introduction

Land degradation, water scarcity, the negative impact of climate warming and the food demand from population growth are increasingly affecting the development of sustainable agriculture [1]. Increasing food production has long been one of the most concerning agricultural issues [2]. Rice is an important food crop in China. In 2022, China’s rice production was 208.4948 million tons, accounting for approximately 29.7% of China’s total food production and 26–27% of the world’s total rice yield [3,4]. The increases in China’s rice yield plays an important role in stabilizing the national and even global food supply. Extensive studies have been conducted on how to increase the food production [5,6,7]. The main approaches could be summarized as narrowing the yield gap and increasing the yield potential. For narrowing the yield gap, scientific farmland management practices are used to reduce the yield loss under natural and anthropogenic conditions during the crop growth process [8]. Yield potential generally refers to the yield of a crop that is determined only by meteorological conditions, cultivar and the planting date under ideal conditions without water and nutrient stress or diseases and pests [9]. Increasing the yield potential is equivalent to raising the threshold for the highest yield, which gives more space for the increase in crop yields.
With regard to narrowing the yield gap, the level of farmland management for crops has significantly improved along with progress in science and technology [10]. A 20% yield increase has been shown to be attributable to improvements in farmland management practices [11]. Currently, research on management measures focuses on increasing crop yields while also improving the efficiency of water and nutrient use and reducing the loss of ecological environment [12,13,14]. There are many factors that affect yield potential, including global warming, variety, transplanting date, different farming systems and various amendments [15]. Firstly, by studying the change in climatic conditions, appropriate regions for crop growth can be found to directly increase the yield potential of crops and expand the cropping area to increase the total food production, thereby meeting the population’s demand for food [16]. For example, the arable land area of summer maize in China showed a remarkable trend of moving towards the northeast during recent decades, which indicates that summer maize has greater potential for yield increase in north-eastern China [17]. Secondly, the breeding of high-yielding cultivars plays a key role in increasing crop yields [18,19]. During recent decades, cultivar improvement was the most important reason for the increase in crop yields [20,21,22], and the renewal of rice cultivars mainly focused on “high yield, multi-resistance and good quality”. Thirdly, the change in sowing date alters the available climate resources [23], and an appropriate sowing date can effectively increase crop yields [6,24]. Compared with the effects of climate change, the effect of the sowing date change on the maturity and growth period is more obvious [25,26]. The changes of cultivar and sowing date have an interactive effect on rice yield. For example, the change in sowing date can prolong the appropriate growing season of crops, and cultivar improvement can alter the efficiency for the use of light and heat resources [27]. Therefore, the optimization of these two factors is often performed simultaneously in actual production. Interactions of the cultivar, transplanting date, and other management practices are complicated. However, previous studies have predominantly concentrated on individual practices, like water management, rather than on the integrated impact of various factors. There is little literature on the interaction between transplanting date and genetic traits of cultivars [28]. The crop model is an effective tool for studying crop yield potential and optimization measures [29]. Most crop models have been developed to provide decision-making services for farmland management. In addition, the crop model can simulate an ideal environment without water and nitrogen stress, which cannot be realized under actual conditions, and thus is a favorable tool for studying the yield potential.
The rice-growing regions in China mainly include single rice in the northeast and single rice and double rice in the south. These regions span a large range of geographical locations, with marked differences in cultivar characteristics and management practices [30]. Therefore, it is necessary to find optimal management practices and high-yielding cultivar characteristics with regard to various cultivars in different regions.
In this study, we hypothesized that by adjusting the transplanting date of rice and adopting improved varieties, the potential yield of rice could be significantly increased and that there was an interactive effect between the two, which together influence the yield. Three typical sites were selected from the different rice-growing regions in China, and the following objectives were addressed through the yield potential effects of agricultural management and climate scenarios simulated by the CERES-Rice model: (1) to evaluate the effects of transplanting date changes and improved varieties and their interaction on the potential yield of rice; (2) to screen the optimal planting system of rice in different regions and summarize the genetic characteristics of high-yield varieties; (3) to assess the adaptability of optimal cropping systems under future climate scenarios.

2. Materials and Methods

2.1. Study Sites

Three sites with representative characteristics were selected from the main rice-producing regions of China (Figure 1). These included Wuchang station in Heilongjiang Province in northeastern China, as well as Yixing station in Jiangsu Province and Wugang station in Hunan Province, in the middle and lower reaches of the Yangtze River. Wuchang has a temperate continental monsoon climate, with a daily average temperature for the rice-growing season of 20.1 °C and an average annual precipitation of 500–800 mm. With abundant sunshine and fertile soil, Wuchang is a well-known land of high-quality rice in China. Rice cropping is dominated by single-cropped japonica rice in Wuchang (WCS). Yixing has a subtropical monsoon climate with an average annual temperature of 15.7 °C and average annual precipitation of 1177 mm. The four seasons are distinct, and the rainfall is abundant at Yixing. The typical cropping pattern is a rice–wheat rotation, with japonica rice as the dominant rice type (YXS). Wugang has a subtropical monsoon climate, with sufficient heat and concentrated rainfall. Rice cropping is dominated by early indica rice (WGE) and late indica rice (WGL) at Wugang. The climate, soil and management measures at each site are listed in Table 1.

2.2. CERES-Rice Model

The CERES-Rice model is a rice model developed by Michigan State University and the University of Hawaii. This model is also nested in the Decision Support System for Agrotechnology Transfer (DSSAT), one of the most widely used crop models in the world [31]. By considering multiple factors (weather, soil, cultivar and management), the DSSAT-CERES-Rice model can dynamically simulate the growth, development and yield of rice, as well as simulate soil water balance and nitrogen balance. So far, this model has been extensively used to assess the relationship between rice growth and the environment [32,33]. Mainly eight cultivar parameters exist in this model (Table 2). The CERES-Rice model uses daily thermal time to calculate the development rate of rice. The meteorological data required for model operation includes the daily maximum temperature, minimum temperature, precipitation and solar radiation. The soil data include the soil texture, bulk density, pH and organic matter content. The field experimental data include planting date, planting density, fertilization date and fertilization level. In addition, phenological and yield data are required for model calibration.
The data required by the model mainly came from three aspects: meteorological data, soil data and field observation data. Meteorological data included historical data and climate scenario data. Historical meteorological data of the selected sites (2000–2012) were from the Chinese Meteorological Data Service Center (CMDC): http://data.cma.cn/en (accessed on 2 January 2025), including sunshine hours, daily maximum temperature, daily minimum temperature, daily mean temperature and daily precipitation. The daily solar radiation required for model simulation was derived from sunshine hours using the Angstrom equation [34,35].
Climate scenario data were derived from climate projections of 5 global climate models under 4 representative concentration pathway (RCP) scenarios: RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 in phase five of the Coupled Model Intercomparison Project (CMIP5) (Table 3). Our intention was to employ a comprehensive array of climate models to define the spectrum of uncertainty in climate projections, subsequently bridging the uncertainty gap regarding the impact of climate change on rice production. These 4 RCP scenarios represented greenhouse gas emission pathways that could lead to atmospheric radiative forcing levels of 2.6 w m−2, 4.5 w m−2, 6.0 w m−2 and 8.5 w m−2, respectively, by the end of the 21st century [36]. RCP 2.6 was considered a low emission scenario, RCP 4.5 and RCP 6.0 were intermediate emission scenarios and RCP 8.5 represented a high emission scenario. The primary distinctions among these pathways lay in their radiative forcing alterations and greenhouse gas concentration levels, with RCP 8.5 resulting in the most severe global warming. Climate scenario data were revised [37] to correct for potential climate model bias, and the generated climate scenario data were used as the driving meteorological data of the CERES-Rice model. With reference to the RCP database, the average CO2 concentration in different periods (base period (1985–2004), the 2030s (2021–2040), the 2060s (2051–2070), and the 2090s (2081–2100)) under four RCP scenarios was shown in Table 4.
The soil profile data, including soil texture, bulk density, pH and organic matter content, were derived from the Harmonized World Soil Database (HWSD) version 1.2 [38,39].
Rice observation data of the study sites (2004–2012) were obtained from the China Meteorological Administration (CMA). These data including phenology, yield and yield factor, cultivar and management data were used to calibrate and validate the CERE-Rice model. Since irrigated rice was cropped at all selected sites, the water supply was usually sufficient during the whole growth period; thus, automatic irrigation treatment was selected in model simulation.
In this study, the cultivars that had been cropped for the most years at each site during recent years were selected as the current rice cultivars. Genetic parameters of the cultivars were adjusted using 1- to 2-year phenological and yield data by Generalized Likelihood Uncertainty Estimation (GLUE) [40], and the model’s simulation capability was validated with phenological and yield data of the remaining years. The model used the normalized root mean square difference (RMSE%), the index of agreement (Dindex) and the correlation of determination (R2) to test the degree of deviation and goodness of fit between the simulated and measured values. In general, if the RMSE% was less than 15% and the values of Dindex and R2 were close to 1, the simulated and measured values agreed well.

2.3. Cultivar Optimization

Cultivar optimization aims to find the genetic characteristics of high-yielding cultivars and thus provides a reference for breeding work. Eight genetic parameters exist in the CERES-Rice model. To clarify the effect of each parameter on rice yield, a sensitivity analysis was first performed on the eight parameters. The transplanting date was kept unchanged, and each parameter was reduced by 20% and 10% and then increased by 10% and 20% based on existing cultivar parameters to reflect five different genetic levels of the parameter. For the effect of one of these parameters on the yield, first the remaining seven parameters were kept unchanged, then the rice yield potential at each site during 2000–2009 were then simulated at the five genetic levels of this specific parameter with the other seven parameters to obtain the sensitivity of the target parameter for yield.
Based on the parameter sensitivity analysis, the most sensitive parameters on yields were identified. A total of 5n (n is the number of selected parameters) cultivars were obtained through free combination of the different levels of the parameters. The yield potential of all the 5n cultivars during 2000–2009 was simulated, and the cultivar corresponding to the highest yield was identified as the optimal cultivar for the station.

2.4. Transplanting Date Optimization

To find the optimal transplanting date, the current transplanting date was advanced and delayed by 3, 6, 9 and 12 days. The rice yield potential was simulated at each station during 2000–2009 under current cultivar with the different transplanting dates. The transplanting date corresponding to the highest yield was selected as the optimal transplanting date for each station.

2.5. Rice Planting System Optimization

In this study, the optimization of rice planting system in different regions is to find the optimal combination of varieties and transplanting date. Based on the latest maturity date and climate change trends in history, we assumed that the latest maturity date could be delayed by 10 days. For single rice, we simulated the yield potential combined 5n cultivars with 9 transplanting date during 2000–2009. The cultivar in combination with the transplanting date that produced the highest yield potential was identified as the optimal management measure. For double rice, we first ensured the earliest transplanting date of early rice and the latest maturity date of late rice and then set the latest maturity date of early rice according to the transplanting date of the late rice (Figure 2). Within the range of the late rice transplanting dates (i.e., the range of the early rice maturity date), the yield of 5n cultivars was simulated for 10 years (2000–2009), and the cultivar with the highest yield was identified as the optimal cultivar matching each transplanting date. The late rice cultivar and the early rice cultivar in combination with the late rice transplanting date (i.e., the range of the early rice maturity date) that produced the highest yield potential for the sum of early rice and late rice were identified as the optimal management measures for double rice.

2.6. Adaptability Evaluation of Optimal Rice Planting System

The validated CERES-Rice model was used to simulate the potential rice yield under different climate scenarios (climate projections of 5 global climate models under the 4 typical concentration path scenarios of RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) in the next three periods (the 2030s, the 2060s and the 2090s) after rice planting system optimization at 3 sites. The potential yield of rice under the current planting system under the future climate scenario was compared to evaluate the adaptability of the optimal planting system under the future climate conditions.

3. Results

3.1. Model Calibration and Validation

The validation results of the CERES-Rice model showed that the RMSE% between the simulated and measured values of heading and maturity dates was 3.9% and 3.2%, respectively, with each being lower than 5%, with the Dindex of 0.99. The RMSE% between the simulated and measured yields was 7.8%, with the Dindex of 0.97 (Figure 3). The simulated results of the phenology and yield agreed well with the measured data, indicating that the validated CERES-Rice model could effectively simulate rice growth and yield at the study stations.

3.2. Sensitivity of Genetic Parameters on Rice Yield

For the four genetic parameters reflecting the phenotypic characteristics, i.e., P1, P2R, P2O and P5, their effects on yield showed considerable differences across the three sites and generally lacked consistent trends (Figure 4). Yield was the least sensitive to P2R, generally varying in the range of ±4% at the three sites. Yield was most sensitive to P2O, generally ranging from −22 to 13% at the three sites. Single rice yield was not sensitive to the change in P1 and P5, varying in the range of ±5%. Conversely, double rice yield was sensitive to the change in P1 and P5, varying from −13 to 10% and −8 to 6%, respectively. Yields of early rice and late rice had positive correlations with P1, P2R and P5, whereas a significant negative correlation was observed with P2O.
As for the four parameters reflecting the yield characteristics, i.e., G1, G2, G3 and G4, yield changes showed remarkable consistency with their changes at the three sites. With increasing G1 and G2, rice yield markedly increased from −32 to 19% and −14 to 14%, respectively. With increasing G3, the rice yield gradually decreased in the range between 9 and −7%. For G4, the yield also gradually increased with an increasing G4 value. The difference was that the yield in YXS, WGE and WGL had little response to change in G4, varying between −5 and 10%. In WCS, little change occurred in the yield when G4 changed within ±10%; however, the rice yield changed significantly when G4 changed above ±20%. Therefore, for the yield parameters, high-yielding rice cultivars were characterized by high G1, G2 and G4 and low G3.

3.3. Optimal Cultivar for Current Transplanting Date

According to the sensitivity analysis results of the rice cultivar parameters in Section 3.2, the genetic characteristics of the high-yielding cultivars were set as follows: G1 and G2 were set to the maximum values of the existing cultivars, 80 and 0.03, respectively; G3 was set to the minimum value, 0.6; and G4 was also set to the maximum value, 1.2. For the remaining four parameters, each parameter had five levels (Figure 4). All parameter values were subjected to free combination, producing 54 (625) rice cultivars. Then, the yield potential of all 625 cultivars was simulated at each site during the years 2000–2009.
With the transplanting date kept unchanged, some differences were observed in the genetic parameters between high-yielding cultivars and current cultivars (Table 5). As P2O and P2R jointly affect the photosensitive stage of rice, the characteristics of the photosensitive stage cannot be determined through simultaneous P2O and P2R changes. However, the characteristics of the basic vegetative growth and reproductive growth periods in high-yielding rice cultivars can be determined through P1 and P5. For single rice, P1 and P5 increased in the high-yielding rice in WCS, whereas both parameters decreased in the high-yielding rice in YXS. For double rice, P1 and P2R decreased, whereas the P5 increased in both early and late rice.
Therefore, the high-yielding cultivars and existing cultivars had markedly different growth period lengths. The vegetative growth period of rice in WCS, YXS, WGE and WGL changed by −2, 8, −7 and 4 days, respectively; their reproductive growth period changed by 6, −4, 4 and 8 days, respectively. Compared with existing cultivars, the whole growth period of rice in WCS, YXS and WGL was prolonged by 4, 3 and 9 days, respectively; conversely, the whole growth period in WGE was shortened by 1 day. Table 5 also shows that cultivar improvement increased the rice yield potential by 12.4–41.4%.

3.4. Effects of Different Transplanting Dates on Rice Phenology and Yield

Figure 5 illustrates the changes in various growth periods of existing cultivars with different transplanting dates at the three sites. The change in the transplanting date affected the length of the rice growth period; the greater the change in the transplanting date, the larger the uncertainty of the change in the length of the rice growth period. In WCS, the whole growth period of rice could be prolonged by advance or delay in the transplanting date. The advance in the transplanting date mainly prolonged the vegetative growth period and thus extended the whole growth period; the delay in the transplanting date prolonged the reproductive growth period and thus extended the whole growth period. In YXS, the change in the transplanting date had little effect on the vegetative growth period, whereas it strongly affected the reproductive growth period. The advance in the transplanting date shortened the reproductive growth period; conversely, the delay in the transplanting date significantly prolonged the reproductive growth period, resulting in a marked increase in the whole growth period. In WGE, the advance in the transplanting date prolonged the vegetative growth period and thus extended the overall growth period. In WGL, the advance in the transplanting date had little effect on the phenology, whereas the delay in the transplanting date had a marked effect on the phenology. The delay in transplanting date significantly prolonged the reproductive growth period of late rice and thus significantly extended the whole growth period. Therefore, with the same cultivar, the advance in the transplanting date prolonged the vegetative growth period of single rice in northeastern China and early rice in the middle and lower reaches of the Yangtze River; however, no marked change occurred in the growth period of single rice and late rice in the middle and lower reaches of the Yangtze River. With the delay in the transplanting date, the reproductive growth period was markedly prolonged in single rice and late rice, except for early rice.
For the fixed cultivars, the rice yield increased with the advance in the transplanting date, especially for single rice (Figure 6). The yield increased by 6.7% in WCS and by 4.8% in YXS with a 12-day advance in the transplanting date. With the delay in the transplanting date, the single rice yield markedly decreased and the uncertainty increased. The delay in the transplanting date by 12 days caused yield decreases in the single rice by 11.7% in WCS and by 7.9% in YXS.
The effect of the change in the transplanting date on the double rice yield was not as obvious as that on single rice. For early rice, the advance in the transplanting date markedly affected yield, whereas the delay in the transplanting date had little effect. The yield of early rice increased by 2.4% with the advance in the transplanting date by 12 days. For late rice, the delay in the transplanting date had a greater effect on yield, whereas the advance in the transplanting date had no marked effect. The yield of late rice decreased by 6.0% when the transplanting date was delayed by 12 days. Therefore, the advance in the transplanting date resulted in a yield increase in single rice and early rice, with no significant change in the yield of the late rice; the delay in the transplanting date resulted in a marked yield decrease in single rice and late rice, with no significant yield change in the early rice.

3.5. Interaction of Cultivar and Transplanting Date

Figure 7 shows that the optimal cultivars had a longer whole growth period with an advanced transplanting date. The change in the transplanting date was generally consistent with the change in the whole growth period of optimal cultivars. Meanwhile, the changes in the vegetative and reproductive growth periods were also significant. With the advance or delay in the transplanting date by 1 day, the whole growth period of the optimal cultivars increased or decreased by 0.9–1.2 days. Therefore, high-yielding cultivars always had a longer whole growth period during the different growing seasons, which is mainly due to the change in the vegetative growth period. High-yielding cultivars had a long vegetative growth period with different transplanting dates in the various regions; despite the significant change in reproductive growth period, the extent of the change was small. Like single rice in YXS, the vegetative growth period was almost unchanged. The change in the transplanting date mainly affected the length of the vegetative growth period, thus influencing the length of the whole growth period. Notably, for the late rice in WGL, both the vegetative and reproductive growth periods decreased simultaneously after several days delayed in the transplanting date, which jointly affected the whole growth period.
The optimal cultivars had a higher yield potential with an advanced transplanting date (Figure 8, left). For single rice in WCS and YXS, the optimal combination was to advance the transplanting date and choose cultivars with a long growth period. For double rice at Wugang, the combination of early rice and late rice should be considered simultaneously, and the maturity date of the early rice should occur ahead of the transplanting date of late rice. Therefore, by fixing the transplanting date of early rice, we found trends in the yield of early rice based on the change in the late rice transplanting date (Figure 8, right). It shows that when the early rice transplanting date was advanced by 12 days and the late rice transplanting date was advanced or delayed by no more than 12 days, the yield of the early rice gradually increased with the delay in late rice transplanting date (same as the early rice harvesting date), but the yield of late rice gradually decreased with the delay of the transplanting date. We determined the management practice that maximized the overall yield by summing up the yields of early rice and late rice in the same season. The results showed that the highest total yield of double rice was obtained with a 12-day advance in early rice transplanting date and no change in the late rice transplanting date. Moreover, prolonging the early rice growth period was more beneficial to the total yield compared with prolonging the late rice growth period.
Figure 9 shows the contributions of optimizing the transplanting date and cultivar separately and their interaction on rice yield potential at all the stations. Changing the transplanting date alone had the least effect on the yield increase. The yield increases with the optimal transplanting date at the three sites were 6.7% in WCS, 4.8% in YXS, 2.4% in WGE, 1.5% in WGL and 1.9% in the total yield of double rice. The standard deviation was between 2.0% and 3.5%. Cultivar improvement markedly increased the rice yield potential. High-yielding cultivars at the three sites increased the rice yield by 27.1% WCS and 41.4% in YXS, as well as 12.4% in WGE, and 23.9% in WGL and the total yield of double rice by 18.0%. The standard deviation was between 2.2% and 9.0%.
After the above two factors were combined, the single rice yield in WCS and YXS increased by 37.2% and 51.8%, respectively, 3.4% and 5.7% higher than the sum of improving the transplanting date alone and improving the cultivar alone, respectively. The combined effect significantly increased the yield of early rice in WGE by 28.4%, which was 13.6% higher than the sum of improving the transplanting date and cultivar alone. Due to the late rice transplanting date being unchanged in the double rice optimal combination, the combined effect of the transplanting date and cultivar was equivalent to the effect of the cultivar, which increased the yield by 23.9%, 1.5% lower than the sum of improving the transplanting date and cultivar alone. The total yield of the double rice increased by 26.3%, indicating that the advantage of early rice was expanded and that the advantage of late rice was reduced in the optimal combination.

3.6. Impacts of Optimal Cultivar and Transplanting Date on Rice Potential Yield Under Future Climate

By analyzing the optimal combination of the rice cultivar and transplanting date in different regions under future climate scenarios, it was found that compared with the current combination of the rice cultivar and transplanting date, the optimized combination of the rice cultivar and transplanting date could significantly increase the potential yield of rice under future climate scenarios. Under the scenarios of RCP2.6, RCP4.5 and RCP6.0, the potential yield of WC single rice, YX single rice, WGE and WGL under the future climate scenario increased by 33.9%, 49.5%, 29.4% and 24.6%, respectively, compared with the current combination of cultivars and transplanting dates. However, under the RCP8.5 scenario in the 2090s, except YX, the potential yield of rice in WC and WG under the optimal combination of the cultivar and transplanting date was significantly lower than that in the 2060s, and the uncertainty was increased (Figure 10). It can also be seen that the positive effect of future climate conditions on rice yield is smaller than that of adjusting varieties and the transplanting date. The above analysis shows that the negative effects of climate change on agricultural production can be effectively mitigated through variety replacement, the adjustment of sowing date and field management.

4. Discussion

4.1. Effects of Cultivar and Transplanting Date on Phenology and Yield of Rice

In this study, the characteristics of the high-yielding rice cultivars were mainly reflected in high G1, G2 and G4, i.e., high potential spikelet number, grain weight and high-temperature tolerance. A high potential spikelet number and grain weight indicate a high harvest index, which is beneficial to a yield increase [41]. High-temperature tolerance enables crops to adapt to climate change and thus stabilizes the yield [42]. While maintaining these characteristics, high-yielding cultivars are further characterized by a prolonged whole growth period. The prolonging of the growth period allows the crop to fully perform photosynthesis and accumulate more biomass [41]. In the present study, cultivar improvement resulted in increases of 12.4–41.4% in the rice yield, which agrees with previous results [22]. Rice cultivar improvement is one of the most important factors for yield increase [20,42]. Our findings also showed consistent characteristics with the improvement history of rice cultivars. Due to cultivar improvement, the growth period of single rice and early rice has been prolonged, whereas the late rice growth period has been shortened in recent decades [23,43]. In tropical or subtropical areas outside China, climatic conditions and planting patterns should be taken into account when selecting the best varieties so as to select varieties with suitable ripening and photosensitivity. Comparing these findings with similar studies of other crops in different regions also yielded consistent results. For example, the dominant varieties of winter wheat in Shandong Province, China, are also characterized by long photoperiodic response and high grain weight [44].
The advance in the transplanting date can prolong the vegetative growth period and increase the yield of the original cultivars, whereas a delay in the transplanting date can prolong the reproductive growth period and reduce the yield of the original cultivars. This reduction occurs because the delay in the transplanting date affected the accumulated temperature in the grain-filling stage and may increase the probability of cold damage to rice. Therefore, in recent decades, the growth period of late rice has been shortened to avoid cold damage and ensure high yield under the impact of climate warming by the selection of early-maturing cultivars in China [43]. The change in the transplanting date has a greater effect on the rice growth period compared with climate change [26]. The results of the present study show that the yield potential of existing rice cultivars increased by 1.9–6.7% with a 12-day advance in the transplanting date. Therefore, the advance in the transplanting date is beneficial to the increase in rice yield. The effect of the change in the transplanting date on the double rice yield was not as obvious as that on single rice because double-cropping rice has a shorter growth cycle, usually requiring two plantings and harvests in one year, and therefore requires greater adaptability to climatic conditions. For the original varieties of single rice and late rice in the middle and lower reaches of the Yangtze River, there was no significant change in the vegetative growth period after early transplanting. This may be due to the fact that the early transplanting window of single rice and late rice in the middle and lower reaches of the Yangtze River was in June and July, the high temperature lasted for a long time and the variation range was small, so when the transplanting date was advanced, the change of vegetative growth period was not obvious. In tropical areas outside China, double-cropping rice and triple-cropping rice are the main planting patterns so the transplanting date of triple-cropping rice must be more detailed so as to ensure that the transplanting date of triple-cropping rice is arranged more accurately to maximize the crop yield and efficiency.

4.2. Optimal Managements for Different Rice Systems

With the advance in transplanting date, rice cultivars had a longer growth period and thus exhibited a higher yield potential. Therefore, the future direction is to advance the transplanting date and select cultivars with a long growth period [23]. The highest total yield in the double rice was achieved with the advance in the early rice transplanting date by 12 days while keeping the late rice transplanting date unchanged. In addition, prolonging the early rice growth period was more conducive to the increase in total yield compared with prolonging the late rice growth period. This finding also indicates that the advance in early rice transplanting date is favorable for the rice yield. The late rice transplanting date was kept unchanged because the yield increase was larger after prolonging the early rice growth period; thus, it is necessary to provide more space in the growth period for the early rice. It also suggested that the rotational combination of the double rice was also related to the yield growth rate and base yield. During recent decades, the whole growth period of single rice and early rice has been consistently prolonged as a result of the combined effect of the transplanting date and cultivar improvement [25,43]. On the one hand, the advance or delay in the transplanting date changed the meteorological conditions during the rice growth period and thus prolonged the rice growth period [25]. On the other hand, the selection of rice cultivars with a high demand for thermal time could prolong the rice growth period, stabilize or increase the aboveground biomass, and consequently offset the negative impact of shortening the growth period caused by climate warming [24,45]. In the current study, an advanced transplanting date combined with high-yielding cultivars could increase the rice yield potential by 26.3–51.8%. Therefore, the simultaneous consideration of the transplanting date and cultivar could increase the rice yield to its maximum, which is a future direction for the improved management and increase in yield. To carry out the above practice on a large scale, more consideration should be given to regional climatic conditions, soil types, water resources distribution, etc., which will affect the selection of rice varieties and transplanting strategies in the whole region. On a small scale, specific field management and special varieties should be emphasized to improve rice yield through fine management.

4.3. Limitations and Suggestions

The limitations of this study are as follows: First, the CERES-Rice model used in this study is widely used in the international rice process model, but it may have limitations for crop growth simulation under extreme climate conditions. In the future, a combination of crop models can be used to reduce the uncertainty of simulation results. Second, the RCP scenarios are models used to assess the effects of climate change that provide climate projections under different greenhouse gas emission scenarios. However, there are some limitations to the RCP scenario in long-term forecasting. For example, there are great uncertainties, and the simulation ability of the earth’s biological and chemical processes is insufficient. In the future, climate models will need to be continuously improved. Third, the selection of only three sites is not enough to capture the change of rice cultivation in the whole region of China. Therefore, it is suggested that future studies should provide more detailed and comprehensive high-resolution regional analysis to better understand the effects of regional variety and transplanting date interactions on rice yield in China. Last, this study focused on the influence of the transplanting date and variety coordination on rice yield, without considering other factors such as farming methods, water, fertilizer management and economy. It is suggested that more factors can be taken into account in the future to comprehensively evaluate the influence of total factors on rice yield.

5. Conclusions

This study focused on revealing the effect of change in the transplanting date, cultivar improvement and their interaction at increasing yield potentials in rice. The results showed that high-yielding rice cultivars were characterized by a higher potential spikelet number, grain weight and temperature tolerance and a longer growth period, especially the vegetative growth period. The advance in the transplanting date had a positive effect on the rice yield increase, whereas delaying this date resulted in marked yield reduction in single rice and late rice, with increased variability. If the advances in the transplanting date and cultivar improvement were properly matched, the rice yield potential was improved more than the result obtained with the advance in the transplanting date or cultivar improvement alone. Therefore, the rational combination of various measures can be an effective approach for increasing rice yield potential. The results of this study can provide reference for sustainable agricultural intensification and the promotion of food security; they can also provide effective choices for farmers and decision makers. The planting system optimization method proposed in this study is also applicable to other basic food crop models.

Author Contributions

Conceptualization, H.Z., G.Z. and Q.H.; methodology, H.Z.; validation, H.Z.; formal analysis, H.Z., G.Z. and Q.H.; writing—original draft preparation, H.Z. and G.Z.; writing—review and editing, H.Z., G.Z. and Q.H. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the Key Innovative Team of Agricultural Meteorology of the China Meteorological Administration (CMA2024ZD02).

Data Availability Statement

Presented data in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Long, S.P.; Ainsworth, E.A.; Leakey, A.D.B.; Nosberger, J.; Ort, D.R. Food for thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 2006, 312, 1918–1921. [Google Scholar] [CrossRef] [PubMed]
  2. Becker, M.; Clavero, R.; Khin, O.M.; Kong, S.; Maung, Z.N.; Men, P.; Pariyar, S.; Regalado, M.J.C.; Ro, S.; Win, K.K. System shift in rice: Processes and pathways of change in rice-based production systems of Southeast Asia. Agric. Syst. 2024, 217, 103917. [Google Scholar] [CrossRef]
  3. NBS. National Bureau of Statistics of China. 2022. Available online: https://data.stats.gov.cn/english/easyquery.htm?cn=C01 (accessed on 2 January 2025).
  4. FAO. World Food and Agriculture—Statistical Yearbook 2024; World Food and Agriculture: Rome, Italy, 2024. [Google Scholar] [CrossRef]
  5. Challinor, A.J.; Watson, J.; Lobell, D.B.; Howden, S.M.; Smith, D.R.; Chhetri, N. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 2014, 4, 287–291. [Google Scholar] [CrossRef]
  6. Waongo, M.; Laux, P.; Kunstmann, H. Adaptation to climate change: The impacts of optimized planting dates on attainable maize yields under rainfed conditions in Burkina Faso. Agric. For. Meteorol. 2015, 205, 23–39. [Google Scholar] [CrossRef]
  7. Zhou, H.; Tao, F.; Chen, Y.; Yin, L.; Wang, Y.; Li, Y.; Zhang, S. Climate change reduces agricultural total factor productivity in major agricultural production areas of China even with continuously increasing agricultural inputs. Agric. For. Meteorol. 2024, 349, 109953. [Google Scholar] [CrossRef]
  8. Mueller, N.D.; Gerber, J.S.; Johnston, M.; Ray, D.K.; Ramankutty, N.; Foley, J.A. Closing yield gaps through nutrient and water management. Nature 2012, 490, 254–257. [Google Scholar] [CrossRef] [PubMed]
  9. Evans, L.T.; Fischer, R.A. Yield Potential: Its Definition, Measurement, and Significance. Crop Sci. 1999, 39, 1544–1551. [Google Scholar] [CrossRef]
  10. Peng, S.; Cassman, K.G.; Virmani, S.S.; Sheehy, J.; Khush, G.S. Yield Potential Trends of Tropical Rice Since the Release of IR8 and the Challenge of Increasing Rice Yield Potential. Crop Sci. 1999, 39, 1552–1559. [Google Scholar] [CrossRef]
  11. Lobell, D.B.; Asner, G.P. Climate and management contributions to recent trends in US agricultural yields. Science 2003, 299, 1032. [Google Scholar] [CrossRef] [PubMed]
  12. Peng, S.; Buresh, R.J.; Huang, J.; Zhong, X.; Zou, Y.; Yang, J.; Wang, G.; Liu, Y.; Hu, R.; Tang, Q.; et al. Improving nitrogen fertilization in rice by sitespecific N management. A review. Agron. Sustain. Dev. 2010, 30, 649–656. [Google Scholar] [CrossRef]
  13. Nhamo, N.; Rodenburg, J.; Zenna, N.; Makombe, G.; Luzi-Kihupi, A. Narrowing the rice yield gap in East and Southern Africa: Using and adapting existing technologies. Agric. Syst. 2014, 131, 45–55. [Google Scholar] [CrossRef]
  14. Mueller, N.D.; Lassaletta, L.; Runck, B.C.; Billen, G.; Garnier, J.; Gerber, J.S. Declining spatial efficiency of global cropland nitrogen allocation. Glob. Biogeochem. Cycles 2017, 31, 245–257. [Google Scholar] [CrossRef]
  15. Amirahmadi, E.; Moudrý, J.; Konvalina, P.; Hörtenhuber, S.J.; Ghorbani, M.; Neugschwandtner, R.W.; Jiang, Z.; Krexner, T.; Kopecký, M. Environmental Life Cycle Assessment in Organic and Conventional Rice Farming Systems: Using a Cradle to Farm Gate Approach. Sustainability 2022, 14, 15870. [Google Scholar] [CrossRef]
  16. Yang, X.; Chen, F.; Lin, X.; Liu, Z.; Zhang, H.; Zhao, J.; Li, K.; Ye, Q.; Li, Y.; Lv, S.; et al. Potential benefits of climate change for crop productivity in China. Agric. For. Meteorol. 2015, 208, 76–84. [Google Scholar] [CrossRef]
  17. He, Q.; Zhou, G. Climate-associated distribution of summer maize in China from 1961 to 2010. Agric. Ecosyst. Environ. 2016, 232, 326–335. [Google Scholar] [CrossRef]
  18. Peng, S.; Khush, G.S.; Virk, P.; Tang, Q.; Zou, Y. Progress in ideotype breeding to increase rice yield potential. Field Crops Res. 2008, 108, 32–38. [Google Scholar] [CrossRef]
  19. He, Q.; Zhou, G.; Liu, J. Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming. Agronomy 2022, 12, 3183. [Google Scholar] [CrossRef]
  20. Yu, Y.; Huang, Y.; Zhang, W. Changes in rice yields in China since 1980 associated with cultivar improvement, climate and crop management. Field Crops Res. 2012, 136, 65–75. [Google Scholar] [CrossRef]
  21. Xiao, D.; Tao, F. Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. Eur. J. Agron. 2014, 52, 112–122. [Google Scholar] [CrossRef]
  22. Bai, H.; Tao, F.; Xiao, D.; Liu, F.; Zhang, H. Attribution of yield change for rice-wheat rotation system in China to climate change, cultivars and agronomic management in the past three decades. Clim. Change 2015, 135, 539–553. [Google Scholar] [CrossRef]
  23. Hu, X.; Huang, Y.; Sun, W.; Yu, L. Shifts in cultivar and planting date have regulated rice growth duration under climate warming in China since the early 1980s. Agric. For. Meteorol. 2017, 247, 34–41. [Google Scholar] [CrossRef]
  24. Bai, H.; Tao, F. Sustainable intensification options to improve yield potential and eco-efficiency for rice-wheat rotation system in China. Field Crops Res. 2017, 211, 89–105. [Google Scholar] [CrossRef]
  25. Zhao, H.; Fu, Y.H.; Wang, X.; Zhao, C.; Zeng, Z.; Piao, S. Timing of rice maturity in China is affected more by transplanting date than by climate change. Agric. For. Meteorol. 2016, 216, 215–220. [Google Scholar] [CrossRef]
  26. Wang, X.; Ciais, P.; Li, L.; Ruget, F.; Vuichard, N.; Viovy, N.; Zhou, F.; Chang, J.; Wu, X.; Zhao, H.; et al. Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 1991–2012. Agric. For. Meteorol. 2017, 233, 1–11. [Google Scholar] [CrossRef]
  27. Chen, X.; Chen, F.; Chen, Y.; Gao, Q.; Yang, X.; Yuan, L.; Zhang, F.; Mi, G. Modern maize hybrids in Northeast China exhibit increased yield potential and resource use efficiency despite adverse climate change. Glob. Change Biol. 2013, 19, 923–936. [Google Scholar] [CrossRef] [PubMed]
  28. Jalota, S.K.; Singh, K.B.; Chahal, G.B.S.; Gupta, R.K.; Chakraborty, S.; Sood, A.; Ray, S.S.; Panigrahy, S. Integrated effect of transplanting date, cultivar and irrigation on yield, water saving and water productivity of rice (Oryza sativa L.) in Indian Punjab: Field and simulation study. Agric. Water Manag. 2009, 96, 1096–1104. [Google Scholar] [CrossRef]
  29. Grassini, P.; Yang, H.; Cassman, K.G. Limits to maize productivity in Western Corn-Belt: A simulation analysis for fully irrigated and rainfed conditions. Agric. For. Meteorol. 2009, 149, 1254–1265. [Google Scholar] [CrossRef]
  30. Li, Y.; Tao, F. Rice yield response to climate variability diverges strongly among climate zones across China and is sensitive to trait variation. Field Crops Res. 2023, 301, 109034. [Google Scholar] [CrossRef]
  31. Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.A.; Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT Cropping System Model. Eur. J. Agron. 2003, 18, 235–265. [Google Scholar] [CrossRef]
  32. Saseendran, S.A.; Hubbard, K.G.; Singh, K.K.; Mendiratta, N.; Rathore, L.S.; Singh, S.V. Optimum Transplanting Dates for Rice in Kerala, India, Determined Using Both CERES v3.0 and ClimProb. Agron. J. 1998, 90, 185–190. [Google Scholar] [CrossRef]
  33. Timsina, J.; Humphreys, E. Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review. Agric. Syst. 2006, 90, 5–31. [Google Scholar] [CrossRef]
  34. Angstrom, A. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quart. J. R. Meteorol. Soc. 1924, 50, 121–126. [Google Scholar] [CrossRef]
  35. Prescott, J.A. Evaporation from a Water Surface in Relation to Solar Radiation. Trans. Roy. Soc. S. Aust. 1940, 64, 114–118. [Google Scholar]
  36. van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.-F.; et al. The representative concentration pathways: An overview. Clim. Change 2011, 109, 5–31. [Google Scholar] [CrossRef]
  37. Gourdji, S.M.; Sibley, A.M.; Lobell, D.B. Global crop exposure to critical high temperatures in the reproductive period: Historical trends and future projections. Environ. Res. Lett. 2013, 8, 024041. [Google Scholar] [CrossRef]
  38. Shi, X.Z.; Yu, D.S.; Warner, E.D.; Pan, X.Z.; Petersen, G.W.; Gong, Z.G.; Weindorf, D.C. Soil Database of 1:1,000,000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System. Soil Surv. Horiz. 2004, 45, 129–136. [Google Scholar] [CrossRef]
  39. FAO; IIASA; ISRIC; ISS-CAS; JRC. Harmonized World Soil Database (Version 1.2); FAO: Rome, Italy; IIASA: Laxenburg, Austria, 2012. [Google Scholar]
  40. He, J.; Jones, J.W.; Graham, W.D.; Dukes, M.D. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric. Syst. 2010, 103, 256–264. [Google Scholar] [CrossRef]
  41. Pal, R.; Mahajan, G.; Sardana, V.; Chauhan, B.S. Impact of sowing date on yield, dry matter and nitrogen accumulation, and nitrogen translocation in dry-seeded rice in North-West India. Field Crops Res. 2017, 206, 138–148. [Google Scholar] [CrossRef]
  42. Zhang, H.; Tao, F.; Xiao, D.; Shi, W.; Liu, F.; Zhang, S.; Liu, Y.; Wang, M.; Bai, H. Contributions of climate, varieties, and agronomic management to rice yield change in the past three decades in China. Front. Earth Sci. 2016, 10, 315–327. [Google Scholar] [CrossRef]
  43. Tao, F.; Zhang, Z.; Shi, W.; Liu, Y.; Xiao, D.; Zhang, S.; Zhu, Z.; Wang, M.; Liu, F. Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981–2009 in China, and late rice was just opposite. Glob. Change Biol. 2013, 19, 3200–3209. [Google Scholar] [CrossRef]
  44. Liu, Y.; Zhang, H.; Li, G.; Sun, X.; Wang, M. A comprehensive method to increase yield and narrow the yield gap of winter wheat for sustainable intensification. J. Sci. Food Agric. 2022, 102, 4238–4249. [Google Scholar] [CrossRef]
  45. Liu, L.; Wang, E.; Zhu, Y.; Tang, L.; Cao, W. Quantifying three-decade changes of single rice cultivars in China using crop modeling. Field Crops Res. 2013, 149, 84–94. [Google Scholar] [CrossRef]
Agronomy 15 00119 g001
Figure 1. Locations of the study sites.
Figure 1. Locations of the study sites.
Agronomy 15 00119 g001
Agronomy 15 00119 g002
Figure 2. Growing season boundary of early rice and late rice in simulation scenarios.
Figure 2. Growing season boundary of early rice and late rice in simulation scenarios.
Agronomy 15 00119 g002
Agronomy 15 00119 g003
Figure 3. Comparison of simulated and measured values for heading date (a), maturity date (b) and yield (c) of rice. dap: days after transplanting.
Figure 3. Comparison of simulated and measured values for heading date (a), maturity date (b) and yield (c) of rice. dap: days after transplanting.
Agronomy 15 00119 g003
Agronomy 15 00119 g004
Figure 4. Sensitivity of cultivar parameters to rice yield at the study sites.
Figure 4. Sensitivity of cultivar parameters to rice yield at the study sites.
Agronomy 15 00119 g004
Agronomy 15 00119 g005
Figure 5. Changes in the vegetative growth period (transplanting–heading, TH), reproductive growth period (heading–maturity, HM) and the whole growth period (transplanting–maturity, TM) of existing cultivars under different conditions of transplanting date.
Figure 5. Changes in the vegetative growth period (transplanting–heading, TH), reproductive growth period (heading–maturity, HM) and the whole growth period (transplanting–maturity, TM) of existing cultivars under different conditions of transplanting date.
Agronomy 15 00119 g005
Agronomy 15 00119 g006
Figure 6. Yield changes of existing rice cultivars with different transplanting dates.
Figure 6. Yield changes of existing rice cultivars with different transplanting dates.
Agronomy 15 00119 g006
Agronomy 15 00119 g007
Figure 7. Change in the vegetative growth period (transplanting–heading, TH), reproductive growth period (heading–maturity, HM) and total growth period (transplanting–maturity, TM) of the optimal rice cultivars with different transplanting dates. * Significant at p < 0.05; ** Significant at p < 0.01.
Figure 7. Change in the vegetative growth period (transplanting–heading, TH), reproductive growth period (heading–maturity, HM) and total growth period (transplanting–maturity, TM) of the optimal rice cultivars with different transplanting dates. * Significant at p < 0.05; ** Significant at p < 0.01.
Agronomy 15 00119 g007
Agronomy 15 00119 g008
Figure 8. Yield change in optimal cultivars with different transplanting dates (left) and yield change in double rice under a combination of different management practices (right).
Figure 8. Yield change in optimal cultivars with different transplanting dates (left) and yield change in double rice under a combination of different management practices (right).
Agronomy 15 00119 g008
Agronomy 15 00119 g009
Figure 9. Effects of transplanting date, cultivar and their interaction on rice yield change.
Figure 9. Effects of transplanting date, cultivar and their interaction on rice yield change.
Agronomy 15 00119 g009
Agronomy 15 00119 g010
Figure 10. Effects of optimal cultivar and transplanting date combination (top whisker line of single box) and current cultivar and transplanting date combination (bottom whisker line of single box) on potential rice yield under future climate scenarios. The dots in the whisker line represent the average potential yield; the bottom and top whisker lines indicate minimum and maximum potential yields, respectively. The black five-pointed star represents the potential rice yield in the baseline period (1985–2004).
Figure 10. Effects of optimal cultivar and transplanting date combination (top whisker line of single box) and current cultivar and transplanting date combination (bottom whisker line of single box) on potential rice yield under future climate scenarios. The dots in the whisker line represent the average potential yield; the bottom and top whisker lines indicate minimum and maximum potential yields, respectively. The black five-pointed star represents the potential rice yield in the baseline period (1985–2004).
Agronomy 15 00119 g010
Table 1. The information for the study stations.
Table 1. The information for the study stations.
StationsWCSYXSWGEWGL
Latitude (°N)44.9031.3326.7326.73
Longitude (°E)127.10119.82110.63110.63
Altitude (m)194.616.4341.0341.0
Cropping systemSingle riceRice–wheatDouble riceDouble rice
CultivarWYD4WYJTY706YX88
Period of date2007–20122004–20102007–20102007–2010
Planting date (m/d 1)4/135/273/286/22
Transplanting date (m/d)5/166/175/17/20
Soil textureLoamClay loamSandy loamSandy loam
Total N (%)0.240.160.180.18
Soil organic carbon (%)0.821.121.241.24
1 m/d means month/date.
Table 2. Genetic parameters in DSSAT-CERE-Rice model v 4.7.
Table 2. Genetic parameters in DSSAT-CERE-Rice model v 4.7.
Genetic ParametersDefinition (Units)Impact Phase
P1Time period for basic vegetative phase (growing degree days [GDD] in °C above base 9 °C)Phenology
P2RPhotoperiod sensitivity parameter (GDD in °C)Phenology
P2OCritical photoperiod (hours)Phenology
P5Time period for grain-filling phase (GDD in °C above base 9 °C)Phenology
G1Potential spikelet number coefficientYield
G2Potential single grain weight (g)Yield
G3Tillering coefficientYield
G4Temperature tolerance coefficientYield
Table 3. List of 5 global climate models (GCMs) in this study.
Table 3. List of 5 global climate models (GCMs) in this study.
IDGCMInstitutionCountry
1GFDL-ESM2MNOAA Geophysical Fluid Dynamics LaboratoryUSA
2HadGEM2-ESMet Office Hadley CentreUK
3IPSL-CM5A-LRInstitute Pierre-Simon LaplaceFrance
4MIROC-ESM-CHEMJapan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute and National
Institute for Environmental Studies		Japan
5NorESM1-MNorwegian Climate CenterNorway
Table 4. Average CO2 concentration in different periods under four RCP scenarios.
Table 4. Average CO2 concentration in different periods under four RCP scenarios.
PeriodsRCP 2.6RCP 4.5RCP 6.0RCP 8.5
Base period360 360 360 360
2030s429 434 428 448
2060s441 507 510 602
2090s426 534 633 841
Table 5. Comparison of parameters and yield between the high-yield cultivars and existing cultivars (bold).
Table 5. Comparison of parameters and yield between the high-yield cultivars and existing cultivars (bold).
StationsP1P2RP5P2OT-HH-MT-MYieldYield Change (%)
WCS246.747.4328.912.1804212210,469
296.037.9394.713.3784812613,30327.1
YXS653.1107.7583.011.779521328562
522.5107.7466.411.7874813512,11241.4
WGE330.441.9349.411.35227787245
264.337.7384.311.3453277814412.4
WGL331.190.6232.911.95729866866
298.081.5279.511.9613797850423.9
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, H.; Zhou, G.; He, Q. Effective Combination of Advancing Transplantation Date with High-Yielding Cultivars for Paddy Rice Could Increase the Yield Potential Under Climate Warming in China. Agronomy 2025, 15, 119. https://doi.org/10.3390/agronomy15010119

AMA Style

Zhang H, Zhou G, He Q. Effective Combination of Advancing Transplantation Date with High-Yielding Cultivars for Paddy Rice Could Increase the Yield Potential Under Climate Warming in China. Agronomy. 2025; 15(1):119. https://doi.org/10.3390/agronomy15010119

Chicago/Turabian Style

Zhang, He, Guangsheng Zhou, and Qijin He. 2025. "Effective Combination of Advancing Transplantation Date with High-Yielding Cultivars for Paddy Rice Could Increase the Yield Potential Under Climate Warming in China" Agronomy 15, no. 1: 119. https://doi.org/10.3390/agronomy15010119

APA Style

Zhang, H., Zhou, G., & He, Q. (2025). Effective Combination of Advancing Transplantation Date with High-Yielding Cultivars for Paddy Rice Could Increase the Yield Potential Under Climate Warming in China. Agronomy, 15(1), 119. https://doi.org/10.3390/agronomy15010119

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop