Projection of Rainfed Rice Yield Using CMIP6 in the Lower Lancang–Mekong River Basin

: Climate change has had a strong impact on grain production in the Lower Lancang–Mekong River Basin (LMB). Studies have explored the response of LMB rice yield to climate change, but most of them were based on climate projection data before CMIP6 (Coupled Model Intercomparison Project Phase 6). Based on the latest CMIP6 climate projection data and considering three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), this study used the crop growth model (AquaCrop) to simulate and project the LMB rice yield and analyzed the correlation between the yield and the temperature and precipitation during the growth period. The results show that the output of rice yield will increase in the future, with greater yield increases in the SSP5-8.5 scenario (about 35%) than in the SSP2-4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. The average temperature of the rice growth period will increase by 1.6 ◦ C, 2.4 ◦ C, and 3.7 ◦ C under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The rice yield was predicted to have a signiﬁcant positive response to the increase in temperature in the near future (2021–2060). In the far future (2061–2100), the rice yield will continue this positive response under the high-emission scenario (SSP5-8.5) with increasing temperature, while the rice yield under the low-emission scenario (SSP1-2.6) would be negatively correlated with the temperature. There will be a small increase in precipitation during the rice growth period of LMB in the future, but the impact of the precipitation on the rice yield is not obvious. The correlation between the two is not high, and the impact of the precipitation on the yield is more uncertain. This result is valuable for the management of the rice cultivation and irrigation system in the LMB, and it will help the government to adapt the impact of climate change on the rice production, which may contribute to the food security of the LMB under climate change.


Introduction
In recent years, global climate change has become a key topic, closely monitored by governments, experts, scholars, and society and closely related to human production and life [1]. Under the influence of climate change, the agricultural climate conditions are deteriorating. The occurrence of extreme rainfall events and the increase in drought pressure seriously threaten food production security [2][3][4][5][6][7]. Studies have pointed out that, in order to meet the expected demand for food of the growing global population, world food production must increase by 50% by 2050 [8,9]. Rice is one of the most important food crops in the world. More than half of the world's population has rice as its staple food, and its consumption increases year by year with population growth [10,11]. Climate change has always been one of the major constraints on the production of grain crops such as rice. Especially in developing countries, the planting and production of rice depend on weather conditions in many aspects [12][13][14].
In the Lower Lancang-Mekong River Basin (LMB, including large areas of Cambodia, Laos, Thailand, and Vietnam), rice is the main planting crop, with a planting area of about 15 million hectares, accounting for 28% of the total area of the region. Thailand and Vietnam are among the largest rice exporters in the world [15][16][17]. Nevertheless, there is still widespread poverty in the lower LMB, with millions of poor people facing severe food security risks [18]. The level of agricultural productivity in these countries is not high, and the ability to cope with the negative impact of climate change on food production is limited. The decline in rice production is worrying [15]. Many existing studies have analyzed the impact of climate change on water resources in the LMB, while less attention has been paid to the impact of climate change on rice cultivation [19][20][21][22], especially with the CMIP6 outputs. In addition, the results of the research have been contradictory. Some scholars have pointed out that higher temperatures and spatial and temporal deviations in precipitation will lead to lower rice yields; others believe that increased precipitation and higher CO 2 concentrations will increase rice yields. Some scholars and their views are shown in Table 1. Table 1. Some scholars and their perspectives.

Scholars Perspectives
Yamauchi et al. [16] Climate change increases annual rainfall deviation, and insufficient precipitation in the early rainy season will lead to reduced rice yield.
Kang et al. [23] Rice yield will increase due to increased CO 2 concentration and precipitation.
Jiang et al. [24] Under rainfed conditions, seasonal changes in temperature rise and precipitation will significantly reduce rice yield, while the positive effect of CO 2 rise will significantly increase rice yield.
Poulton et al. [25] Rice yield will decrease by about 4% for each 1 • C increase in air temperature over the baseline temperature.
In general, these studies confirmed the positive impact of CO 2 on rice production, while the impact of the temperature on crops is two-sided. When the temperature exceeds certain thresholds, rice production is restricted, resulting in reduced yield, while the impact of precipitation on rice production is more uncertain.
Most studies on the impact of climate change on rice production in the LMB were not based on the newly available CMIP6 (Coupled Model Intercomparison Project Phase 6) projections. This study aims to explore this topic with the latest CMIP6 results, in order to better capture the impacts of climate change on rainfed rice. Considering three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), the rice yield in the historical period (HIS: 1981-2020) and two future periods, i.e., future short-term (NF) 2021-2060, and future far-term (FF) 2061-2100, of the LMB is evaluated and projected at the provincial level. This study is valuable for adjusting the rice cultivation and irrigation system and may contribute to the food security of the LMB under climate change.

Study Area
The Lancang-Mekong River is the main cross-border river in Southeast Asia. It extends from the Qinghai Tibet Plateau of China to the Lancang-Mekong Delta, with a total length of more than 4500 km and a drainage area of about 795,000 km 2 . It is one of the seven major Agronomy 2023, 13, 1504 3 of 23 rivers in Southeast Asia [29,30]. The Lower Lancang-Mekong River Basin (LMB) is defined as the sub-basin of the Lancang-Mekong River located in Laos, Thailand, Cambodia, and Viatnam, with a total area of about 640,000 km 2 , about three-quarters of the total area of the Lancang-Mekong River Basin. The terrain in the basin is complex; the upstream is mainly mountains and highlands, and the downstream is mainly plain with low altitude [31][32][33].
The farmland in the LMB is mainly distributed in the lower reaches of the basin. The study area includes 60 provinces in Cambodia, Laos, and Thailand. There are 20 provinces in Cambodia, 17 provinces in Laos, and 23 provinces in Thailand. With the high irrigation rate in the Vietnam Delta, the yield in the historical period was greatly influenced by irrigation; hence, it was difficult to distinguish the rainfall and irrigation effects without enough data support. Thus, Vietnam was not included in this study. The study area is located in the tropical monsoon zone, with a distinct rainy season and a dry season. The rainy season is from May to October, and the dry season is from November to April [34,35]. The main crop in the basin is rice. The planting dates vary from April to July, and the corresponding harvest dates are from September to November [36]. The study area is shown in Figure 1.

Study Area
The Lancang-Mekong River is the main cross-border river in Southeast Asia. It extends from the Qinghai Tibet Plateau of China to the Lancang-Mekong Delta, with a total length of more than 4500 km and a drainage area of about 795,000 km 2 . It is one of the seven major rivers in Southeast Asia [29,30]. The Lower Lancang-Mekong River Basin (LMB) is defined as the sub-basin of the Lancang-Mekong River located in Laos, Thailand, Cambodia, and Viatnam, with a total area of about 640,000 km 2 , about three-quarters of the total area of the Lancang-Mekong River Basin. The terrain in the basin is complex; the upstream is mainly mountains and highlands, and the downstream is mainly plain with low altitude [31][32][33].
The farmland in the LMB is mainly distributed in the lower reaches of the basin. The study area includes 60 provinces in Cambodia, Laos, and Thailand. There are 20 provinces in Cambodia, 17 provinces in Laos, and 23 provinces in Thailand. With the high irrigation rate in the Vietnam Delta, the yield in the historical period was greatly influenced by irrigation; hence, it was difficult to distinguish the rainfall and irrigation effects without enough data support. Thus, Vietnam was not included in this study. The study area is located in the tropical monsoon zone, with a distinct rainy season and a dry season. The rainy season is from May to October, and the dry season is from November to April [34,35]. The main crop in the basin is rice. The planting dates vary from April to July, and the corresponding harvest dates are from September to November [36]. The study area is shown in Figure 1.

Historical Climate Data (1981-2020)
ERA5-Land is a reanalysis dataset published by the ECMWF (European Center for Medium-Range Weather Forecasts). It provides hourly data of global surface climate variables from 1981 to the near future with high spatial resolution [37]. Compared with the ERA5 and ERA-Interim, the horizontal resolution of the ERA5-Land has been improved from 31 km and 80 km to 9 km, and the ERA5-Land dataset has been extended to 1950 [38,39]. The ERA5-Land has higher accuracy and can be applied to the research of agricultural water resource planning, land use, and environmental management [39,40]. The latest CMIP6 has a higher spatial resolution than the previous climate-coupled comparison projects, and its ability to simulate regional extreme rainfall and temperature has been significantly improved [41][42][43]. Unlike the representative concentration path (RCP) used by CMIP5, CMIP6 advocates emission scenarios according to the shared socioeconomic path (SSP) [44,45]. The research is based on eight models in CMIP6 under three SSPs (SSP1-2.6, SSP2-4.5, and SSP5-8.5); the details are shown in Table 2.

Research Method
The AquaCrop model was adopted in this study to simulate and project the rice yield in the LMB. The AquaCrop was developed by FAO's Water and Soil Division, which is used to solve food security problems and assess the impact of environment and management on crop production. AquaCrop simulates the yield response of herbaceous crops to water, which is especially suitable for the situation in the LMB, where water is the key limiting factor in crop production. Although the model is based on complex biophysical processes to ensure accurate simulation of crop response in the plant soil system, it only uses a few parameters and intuitive variables. This model estimates crop water demand by separating unproductive soil evaporation (E) and productive crop transpiration (Tr). The biomass yield (B) is estimated directly from the actual transpiration of crops using the water productivity parameter, which is then multiplied by the crop harvest index (HI) to obtain the final crop yield (Y). The calculation formula is as follows: where B is the biomass, WP is the water productivity parameter, Tr is the actual transpiration of crops, HI is the harvest index, and Y is the final yield of crops. The input data required by the model include climate data, crop data, soil data, and field management data.
The climate data mainly include the daily maximum temperature T max , the daily minimum temperature T min , the daily rainfall P, the daily reference evapotranspiration E t0 , and the annual average CO 2 concentration. The daily reference evapotranspiration was calculated using the Penman-Monteith formula recommended by the FAO, and the CO 2 data were from the Global Monitoring Laboratory of the National Oceanic and Atmospheric Administration (NOAA) of the USA. The crop data mainly reflect the phenological characteristics of rice crops, including the planting date, harvest date, harvest index (HI), water productivity parameter (WP), crop coefficient (K c ), and some conservative parameters (see Table 3). The soil data include the soil type data of each province in the LMB, which were extracted from the Harmonized World Soil Database provided by the FAO (using the soil classification standard of the US Department of Agriculture, see Table 4). The field management patterns include rainfed and irrigated patterns. Less than one-quarter of the total area is irrigated in the LMB; it is mostly concentrated in the delta plain of Vietnam, and the irrigation efficiency is not high within the area of this study [46,47]. Considering that the purpose of this study is to investigate the effect of climate change on rice, the rainfed model was set uniformly. Figure 2 shows the spatial distribution of the soil types in each province in the study area.

Model Evaluation
According to the rice observation data of each province in the basin (Table 5), the calibration period was from the data start year of each country to 2015, and the verification period was from 2016 to the data end year of each country. Our survey found that rice production in Cambodia and Thailand was mainly in the rainy season, with over 80% of fields planting rice only in the rainy season [48][49][50][51][52][53][54][55]. Considering that the dry-season rice has a similar unit yield to the rainfed rice [49,56], although we could not distinguish the rainy-season rice yield from that of the dry season in the harvest data of Cambodia and Thailand, it is believed that such bias had a very small influence on the calibration of the model.

Model Evaluation
According to the rice observation data of each province in the basin (Table 5), the calibration period was from the data start year of each country to 2015, and the verification period was from 2016 to the data end year of each country. Our survey found that rice production in Cambodia and Thailand was mainly in the rainy season, with over 80% o fields planting rice only in the rainy season [48][49][50][51][52][53][54][55]. Considering that the dry-season rice has a similar unit yield to the rainfed rice [49,56], although we could not distinguish the rainy-season rice yield from that of the dry season in the harvest data of Cambodia and Thailand, it is believed that such bias had a very small influence on the calibration of the model.    According to the observed rice yield, the accuracy of the simulation yield was evaluated, and the root-mean-square error and relative error were calculated to verify the simulation accuracy of the model. The calculation formulas of the root-mean-square error (RMSE) and relative error (RE) are as follows: where S i refers to the simulated yield, and O i refers to the observed yield.

Correlation Analysis
The Pearson correlation coefficient is widely used to measure the degree of correlation between two variables, and its value is between −1 and 1, as proposed by Pearson in the 1880s. In this study, the Pearson correlation coefficient was used to evaluate the correlation between the rice yield and temperature and precipitation during the growth period. The calculation formula is as follows: where x i is the temperature or precipitation, x is its average value, y i is the yield, and y is its average value.

Model Calibration Result
The results showed that the planting dates in the study area ranged from late May to mid-July. The planting dates of provinces in Cambodia and Laos varied widely. The planting dates of provinces in Thailand were relatively concentrated in June. Except for the harvest dates of provinces in Cambodia that spanned October and November, the harvest dates of Laos and Thailand were all concentrated in October. The average growth period of rice in the three countries was 130, 135, and 134 days, respectively, which was basically consistent with the existing research results [57]. The planting date and harvest date of each country are shown in Figure 3.   The calibrated HI and observed and simulated multiyear average yields for each province for the rate period are shown in Figure 4. Cambodia, Laos, and Thailand had     calibration period's RMSE and RE. The RMSE value was within a tolerable rang provinces, ranging from 0.038 to 0.666 (t/ha), accounting for 1.7% to 20.9% of the si rice production; the RE was highest in Cambodia's Otdar Mean Chey province (0. lowest in Laos's Khammuane province (0.0). The results for each province during ibration and validation periods are shown in Tables 6-8.  The average observed yield, simulated yield, RMSE, and RE of each country are shown in Table 9. During the calibration period, the maximum RMSE was 0.233 (t/ha) in Laos, and the minimum was 0.057 (t/ha) in Thailand, accounting for 6% and 2.4% of the simulated average yield, respectively. The maximum RE was 0.018 in Laos, and the minimum was 0.001 in Thailand. During the validation period, the RMSE and RE values of Thailand were the lowest, and the model simulation accuracy was better than that of Cambodia and Laos. Overall, the RMSE and RE values for each country during the validation and rate periods were within 0.4 and 0.05, and the model simulations met the accuracy requirements.

Changes in Rice Yield in History and in the Future
On the basis of the model calibration results, this study simulated the rice yields in the Lower Mekong River Basin for the entire historical period (HIS: 1981-2021) and two future projection periods (NF: 2021-2060 and FF: 2061-2100) for each province. Figures 6-8 show the simulated annual average rice yields for the entire simulation period for the three countries in the basin, where the historical period is the simulated historical rice yield based on the ERA5-Land climate data, and the NF and FF periods are the simulated future yield ranges for rice based on the latest CMIP6 8-model climate data. In the historical period, the simulated rice output of various countries gradually increased. From the first 10 years Over the future projection period, the rice yields also showed an increasing trend, with greater yield increases in the SSP5-8.5 (about 35%) scenario than in the SSP2-4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. Comparing the average simulated yields for the last 20 years of the 21st century with the average simulated yields for the first 20 years of the 21st century, Cambodia, Laos, and Thailand increased by 14.1%, 4.1%, and 11.5%, respectively, in the SSP1-2.6 scenario, by 22%, 11.7%, and 14.4%, respectively, in the SSP2-4.5 scenario, and by 43.8%, 25.6%, and 39%, respectively, in the SSP5-8.5 scenario. first 10 years (1981-1990) to the last 10 years (2011-2020), the average Cambodia, Laos, and Thailand increased from 2.678, 3.480, and 1.995 3.103, 3.877, and 2.343 tons per hectare, increases of 0.425, 0.397, and 0. respectively. Over the future projection period, the rice yields also sh trend, with greater yield increases in the SSP5-8.5 (about 35%) scenar 4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. Comparing the yields for the last 20 years of the 21st century with the average simu first 20 years of the 21st century, Cambodia, Laos, and Thailand increa and 11.5%, respectively, in the SSP1-2.6 scenario, by 22%, 11.7%, and in the SSP2-4.5 scenario, and by 43.8%, 25.6%, and 39%, respectively, nario. first 10 years (1981-1990) to the last 10 years (2011-2020), the average Cambodia, Laos, and Thailand increased from 2.678, 3.480, and 1.995 3.103, 3.877, and 2.343 tons per hectare, increases of 0.425, 0.397, and 0. respectively. Over the future projection period, the rice yields also sh trend, with greater yield increases in the SSP5-8.5 (about 35%) scenar 4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. Comparing the yields for the last 20 years of the 21st century with the average simu first 20 years of the 21st century, Cambodia, Laos, and Thailand increa and 11.5%, respectively, in the SSP1-2.6 scenario, by 22%, 11.7%, and in the SSP2-4.5 scenario, and by 43.8%, 25.6%, and 39%, respectively, nario.  In addition, the average simulated rice yields in all emission scenarios increased more in the NF period than in the FF period; the SSP1-2.6 scenarios showed small decreases in rice yields at the end of the 21st century for all countries, with increases of 4.2%, 4.1%, and 4.8% for each country in the NF period and decreases in rice yields at the end of the FF period of 1.8%, 2.0%, and 0.9%, respectively. Under the SSP2-4.5 scenario, the rice growth in the NF period was 7.2%, 6.7%, and 5.8%, the rice yield in Laos and Thailand decreased by 0.3% and 2.2% at the end of the FF period, and the growth in Cambodia slowed. Under the SSP5-8.5 scenario, the largest increase in rice in the NF period was 13.1% in Cambodia, and the smallest was 9.8% in Laos. During the FF period, the growth of rice production in all countries slowed. In the NF and FF periods, the average yield of the first 10 years (2021-2030 in NF, 2061-2070 in FF) and the last 10 years (2051-2060 in NF, 2091-2100 in FF) is shown in Table 10.

Correlation between the Yield and Temperature under Climate Change
In the future, the average temperature of the rice growth period will increase by 1.6 • C, 2.4 • C, and 3.7 • C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature increase from the historical period to the NF period was greater than that from the NF period to the FF period in the same emission scenario. Compared with the rice yield situation in each country during the same period, the rice yield variation had a high similarity with the temperature variation. Table 10 shows the variation in the temperature and rice yield in different countries under different emission scenarios, where HIS is the mean value of the historical period, NF − HIS is the difference between the near future and the historical period, and FF − NF is the difference between the far future and the near future.   As shown in Table 11, the average temperature of the rice growing period during the historical period in the LMB countries was 26.97 • C in Cambodia, 22.80 • C in Laos, and 25.92 • C in Thailand, with average yields of 2.94 (t/ha), 3.68 (t/ha), and 2.173 (t/ha), respectively. Under the SSP1-2.6 emission scenario, the average temperature increase during the FF was not high in each country, 0.23 • C, 0.31 • C, and 0.30 • C, respectively, and the yield did not increase much during the same period. It was 0.02 (t/ha) in Cambodia, and the increase was about zero in Laos and Thailand. Under the SSP5-8.5 scenario, there was little difference between the short-term and long-term temperature increases, and there was no obvious difference in the change in yield growth in the same period. The yield and temperature change did not follow a completely positive relationship. Under the SSP2-4.5 scenario, the yield increase in Cambodia in the NF period of 0.57 (t/ha) was larger than that in the FF period of 0.21 (t/ha), but the temperature increase of 0.35 • C was smaller than that in the FF period of 0.81 • C. Under the SSP5-8.5 scenario, the temperature rise in Laos in the NF period was 3.06 • C, which was greater than 2.07 • C in the FF period. The output increase in the NF period was only 0.42 (t/ha), which was less than 0.60 (t/ha) in the FF period.
To further explore the response of the rice yield to the temperature, the Pearson correlation coefficients (r) were calculated for the simulated rice yield and average temperature during the growing season in different countries for different periods under different emission scenarios, as shown in Figures 9-11. The results showed that there was a certain linear relationship between the rice yield and temperature. The research found that, in the NF period, the simulated rice yield in all countries was positively correlated with the temperature, and the correlation gradually increased with the increase in the greenhouse gas emissions. The yield of Cambodia had the highest correlation with temperature, and the r-value under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 was 0.518, 0.659 and 0.881, respectively (p < 0.01). The rice yield and temperature in Laos and Thailand were not significantly correlated in the SSP1-2.6 scenario; they were generally correlated in the SSP2-4.5 scenario with r-values of 0.588 and 0.45, respectively (p < 0.01), and they were increased in the SSP5-8.5 scenario with r-values of 0.662 and 0.74, respectively (p < 0.01). In the FF period, the yield and temperature in each country were negatively correlated under the low-emission scenario SSP1-2.6, with r-values of −0.503, −0.741, and −0.747 for Cambodia, Laos, and Thailand, respectively (p < 0.01); the correlation weakened or was not correlated under the medium-emission scenario SSP2-4.5, with r-values (p) of −0.356 (p < 0.05) and −0.489 (p < 0.01) for Laos and Thailand, respectively; positive correlations were found under the SSP5-8.5 scenarios with r-values of 0.869, 0.568, and 0.691, respectively (p < 0.01).

The Correlation between Yield and Precipitation under Climate Change
In addition to temperature, precipitation is one of the important factors influencing yield. Because there are differences in the planting systems of various countries and provinces in the study area, and the planting dates and harvest dates are different, changes in the growth period and rainy season will lead to changes in the precipitation during the

The Correlation between Yield and Precipitation under Climate Change
In addition to temperature, precipitation is one of the important factors influencing yield. Because there are differences in the planting systems of various countries and provinces in the study area, and the planting dates and harvest dates are different, changes in the growth period and rainy season will lead to changes in the precipitation during the growth period, thus affecting the rice yield. In the historical period, the average precipitation of Cambodia, Laos, and Thailand in the growing season was 1027 mm, 1396 mm, and 1028 mm, respectively. The simulated average yield of rice in Laos was also higher than that in Cambodia and Thailand in the same period. In addition, in the years with reduced precipitation during the growth period, the rice yield decreased significantly. Figure 12 shows the total precipitation and rice yield during the growth period in various countries in the historical period. The black dotted circle highlights the dry years [7,20,34,58]. In the future, the rainy season (rainfed rice-growing period) precipitation in the LMB will increase by 12.5%, 13.3%, and 15.3%, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively.
To further explore the response of rice yield to precipitation, Pearson correlation coefficients ®were calculated for the simulated rice yield and average precipitation during the growing season, as shown in Figures 13-15. In the historical period, there was no significant correlation between the rice yield and precipitation in Cambodia and Laos, except for some weak correlation between the simulated average rice yield and precipitation during the growing period in Thailand (r = 0.323, p < 0.05). In the NF period, the correlation between the rice yield and precipitation increased in Cambodia, Laos, and Thailand. The r-values between the yield and precipitation during the growing period under the moderate emission scenario SSP2-4.5 were 0.565 (p < 0.01) and 0.508 (p < 0.01) in Cambodia and Thailand, respectively. The yield and precipitation during the growing period in Laos reached a general correlation (r = 0.6, p < 0.01) under the SSP1-2.6 scenario, and the r-values were higher than 0.5 under both the SSP2-4.5 and the SSP5-8.5 scenarios. In the FF period, the correlation between rice yield and precipitation weakened, and there was no obvious correlation between rice yield and precipitation in Cambodia under the three emission scenarios. In Laos, there was some correlation between the rice yield and precipitation in the SSP1-2.6 and SSP5-8.5 scenarios, with r-values of 0.389 (p < 0.05) and 0.552 (p < 0.01), respectively, but there was no significant correlation in the SSP2-4.5 scenario. The rice yield and precipitation in Thailand were significantly weakly correlated in the SSP2-4.5 and SSP5-8.5 scenarios, with r-values of 0.352 (p < 0.01) and 0.56 (p < 0.01), respectively, but not in the SSP1-2.6 scenario. In general, the rice yield in Laos was more strongly correlated with precipitation than in Cambodia and Thailand; under the same scenario, the correlation between the rice yield and precipitation in the NF period was stronger than that in the FF period, and the correlation in the historical period was the weakest or had no obvious correlation. ronomy 2023, 13, x FOR PEER REVIEW  To further explore the response of rice yield to precipitation, Pea efficients ® were calculated for the simulated rice yield and average p the growing season, as shown in Figures 13-15. In the historical perio nificant correlation between the rice yield and precipitation in Cambod for some weak correlation between the simulated average rice yield an ing the growing period in Thailand (r = 0.323, p < 0.05). In the NF pe between the rice yield and precipitation increased in Cambodia, Laos r-values between the yield and precipitation during the growing per erate emission scenario SSP2-4.5 were 0.565 (p < 0.01) and 0.508 (p < 0.0 Thailand, respectively. The yield and precipitation during the grow reached a general correlation (r = 0.6, p < 0.01) under the SSP1-2.6 scena were higher than 0.5 under both the SSP2-4.5 and the SSP5-8.5 scenari the correlation between rice yield and precipitation weakened, and th correlation between rice yield and precipitation in Cambodia under scenarios. In Laos, there was some correlation between the rice yield the SSP1-2.6 and SSP5-8.5 scenarios, with r-values of 0.389 (p < 0.05) a respectively, but there was no significant correlation in the SSP2-4. yield and precipitation in Thailand were significantly weakly correla and SSP5-8.5 scenarios, with r-values of 0.352 (p < 0.01) and 0.56 (p < but not in the SSP1-2.6 scenario. In general, the rice yield in Laos was related with precipitation than in Cambodia and Thailand; under the correlation between the rice yield and precipitation in the NF period that in the FF period, and the correlation in the historical period was no obvious correlation.

4.Discussion
This study was based on AquaCrop-OSPy, an open-source version in Python of the AquaCrop model, to simulate rice yield in the Lower Lancang-Mekong River Basin. Aq-uaCrop-OSPy is mainly aimed at exploring the impact of climate change on crop yield,

Discussion
This study was based on AquaCrop-OSPy, an open-source version in Python of the AquaCrop model, to simulate rice yield in the Lower Lancang-Mekong River Basin. AquaCrop-OSPy is mainly aimed at exploring the impact of climate change on crop yield, without considering soil fertility and salt stress modules for the time being. However, due to the further increase in the global temperature, the accelerated melting of ice sheets and glaciers has further increased the sea level [59][60][61][62][63]. It is predicted that, by 2050, the sea level in southern Vietnam may rise by 30 cm [64,65], which will lead to salt intrusion in the Lancang-Mekong Delta region, affecting about 1.8 million hectares of land and threatening rice production [64][65][66]. The effects of soil fertility stress and salinity stress were not considered in this study, and further research is needed to explore the potential influences. The AquaCrop model requires that the input temperature data include minimum and maximum temperatures; however, in this paper, the average temperature during the growth period was used to analyze the effect of temperature on rice yield, which may lead to the relationship between temperature and rice yield being blurred. In addition, this paper did not analyze the correlation between CO 2 concentration and rice yield, but many previous studies found that the fertilization effect that will be increased by the increase in the CO 2 concentration will compensate for the rice yield reduction caused by the heat stress from the continuous increase in the temperature and the irregular change in the precipitation [67,68]. This finding happens to be consistent with the results of this study. In the near future, rice yield and temperature will show a significant positive correlation in both the low-emission scenario (SSP1-2.6) and the high-emission scenario (SSP5-8.5). In the far future, rice yield and temperature will be negatively correlated in the low-emission scenario (SSP1-2.6) and positively correlated in the high-emission scenario (SSP5-8.5).

Conclusions
On the basis of the climate data of the historical and future periods, rice yields in the Lower Lancang-Mekong River Basin (LMB) under various scenarios were simulated using the AquaCrop model. The correlation between the temperature and precipitation and the rice yield during the growing period was analyzed. The study drew the below conclusions.
The AquaCrop model had a good capacity for rice yield simulation in the LMB. From 1981 to 2100, the LMB rice yields will increase significantly. The range in the rice yield increase in the future projection period depends on different emission scenarios. The increase in the rice yield under the SSP5-8.5 scenario was the largest (about 35%), followed by the SSP2-4.5 (about 15.8%) and the SSP1-2.6 (about 9.3%) scenarios. The increasing trend in the rice yield in the near future will be stable, and the trend in the far future will slow or decline.
The average temperature of the LMB rice planting period will increase. In the near future, rice yield will be positively correlated with temperature. In the far future, the continued increase in temperature will limit rice production.
In the future, the rainy season (rainfed rice-growing period) precipitation in the LMB will increase, but the effect of increased precipitation on rice yield will be insignificant. The correlation between the two was weak or showed no obvious correlation, and the response of rice yield to precipitation was more uncertain.
The result is valuable for the management of the rice cultivation and irrigation system in the LMB, and it will help the government to adapt the impact of climate change on the rice production. Given that the impact of climate change on the production of LMB rice and other crops is multifaceted and complex, although our results imply a bright future for rice yield increase in general, it is noteworthy that extreme climate events may result in tremendous agricultural losses, and water safety measures should be enhanced to meet the food demand of the increasing population. We suggest carrying out further research to construct a running platform for forecasting the impact of climate change and human activities on rice to propose reasonable and efficient measures to ensure food security.