Climate Change Impact Assessment on Rice Yield Using Dssat and Geospatial Methods

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It grows in a variety of agro-climatic conditions and is mostly cultivated in Asia. Sudden increase in rainfall and temperature adversely impact on crop yield which leads to economical loss to farmers. The current research aims to assess climate change impact on rice yield produced in Hanumakonda district, Telangana, India where agriculture area is predominant. To achieve this, the yield was simulated over a 17-year (2007–2023) period for five different agricultural fields of Hanumakonda which has crop management information used by farmers in both Kharif and Rabi seasons using DSSAT (Decision Support System for Agrotechnology Transfer) CERES (Crop Environment Resource Synthesis)-Rice model. RMSE (Root Mean Square Error) and NRMSE (Normalized Root Mean Square Error) are the metrics used to verify the accuracy of simulated yield. The simulated yield is compared with observed data which was collected from Directorate of Economics and Statistics, Telangana. It is observed that the pattern of temporal variation of simulated yield over the years 2007-23 is aligning with the trend of observed yield variation for the same time period. Average NRMSE of all the sites is 13.02% for Kharif and 17.12% for Rabi. The average RMSE of all the sites is 408.07 Kg/ha for Kharif and 584.398 Kg/ha for Rabi. For impact assessment a second-degree polynomial regression model is fitted between climate variables and rice yield. The convincing coefficient of determination values of present fitted model confirms the predicting future rice yield for future climatic conditions. CERES-Rice Module Climate Crop Yield DSSAT NRMSE Regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction Climate variability significantly influences global food production, particularly in developing regions, as changes in trends of temperature and rainfall pose challenges for crop growth. Various simulation studies indicate that increased temperatures, either alone or in combination with elevated CO₂ levels, can drastically reduce maize yield, thereby highlighting the urgent need for adaptive measures (Lone et al., 2020). To support agricultural decision-making, the Extended Range Forecast System (ERFS) provides seasonal and sub-seasonal rainfall forecasts, which are crucial for planning Kharif rice cultivation in India. In this regard, this study evaluates the reliability of ERFS forecasts in predicting Kharif rice yield using the CERES (Crop Environment Resource Synthesis)-Rice model, thereby enhancing better agricultural planning (Dhekale et al., 2018 ). Ensuring global food security amid climate change requires an integrated approach that combines climate-hydrological-crop models to assess agricultural impacts. Consequently, improving data reliability and improving modelling techniques are crucial for developing effective adaptation strategies, particularly for rice production in vulnerable regions such as Indonesia (Ansari et al., 2023 ). Similarly, the diverse agro-ecological conditions of the North-western Himalaya support varied farming systems. However, climate change and biotic stresses are increasingly impacting traditional crop cultivation. Rising temperatures, shifting rainfall patterns, and pest challenges are prompting a transition toward fruit and cash crop farming in the region (Patel et al., 2019 ). Further, Harinarayanan et al. (2022) integrated ERFS data with the DSSAT model. However, improvements in both forecasting and modelling remain necessary to enhance yield prediction reliability. Likewise, the study conducted by Rajasivaranjan et al. ( 2022 ) highlights the effectiveness of the DSSAT model in assessing the impact of water stress on rice yield. By integrating climate simulations with crop yield modelling, the findings showing that DSSAT can be a valuable tool for the early prediction of water stress effects, thereby aiding in better agricultural planning and management. Geethalakshmi et al. ( 2023 ) further observed that future climate projections indicate adverse impacts on key crops in Tamil Nadu. Moreover, multi-model climate assessments emphasize the need for adaptive strategies to mitigate potential yield declines and ensure long-term food security. While climate change may reduce rice yield stability, adaptive measures and enhanced CO₂ levels could help mitigate some of the adverse effects on crop productivity (Mulla et al., 2024 ). Understanding crop water requirements is also essential for optimizing yield, as water stress during critical growth stages significantly affects productivity. In this regard, the DSSAT-based crop model effectively simulates water stress, thereby improving irrigation strategies and aiding sowing decisions for rainfed crops (Gohain et al., 2021). Li et al. ( 2024 ) further emphasize that climate change significantly influences rice growth. Their study highlights the need for robust analytical approaches to better understand and reduce uncertainties in future rice yield projections. In Bangladesh, climate change affects rice food security differently across regions. Ara et al. ( 2017 ) highlighted the importance of region-specific adaptation strategies to mitigate rice yield variability and ensure long-term food security. Similarly, Kephe et al. ( 2021 ) used a hybrid approach by combining remote sensing and field experiments to overcome data availability limitations, such as those related to crop management, climate, and soil conditions. Rice yield in Bangladesh is influenced by various environmental factors, with rising temperatures leading to significant yield reductions in most districts. While increased CO₂ levels provide some benefits, they remain insufficient to offset the negative impacts of temperature variations. This emphasizes the need for improved cultivation strategies (Hasan et al., 2017 ). Likewise, optimizing nitrogen fertilizer management is essential for improving rice yield and economic efficiency in the Vietnamese Mekong Delta. The DSSAT-CERES-Rice model effectively simulates rice productivity, indicating that a moderate nitrogen application rate offers the best balance between economic and environmental sustainability (Dang et al., 2023 ). Similarly, the CERES-Rice model effectively simulated rice yield under various climatic conditions in Pakistan, providing valuable insights into future challenges and adaptation strategies. Agronomic and genetic adaptations were found to mitigate the negative impacts of changing temperatures and rainfall, highlighting the importance of climate-resilient practices for sustaining rice production (Nasir et al., 2020 ). Future climate projections indicate shifts in yield across different growing seasons, and simulation results highlight the need for adaptation strategies (Nicolas et al., 2020 ). Similarly, rice production in Central Java is projected to decline due to shifting rainfall patterns, rising temperatures, and increased solar radiation, presenting challenges for food security. To mitigate these impacts, strategies such as adjusting cropping calendars, modernizing irrigation systems, and optimizing nutrient management are essential for sustaining rice yields (Ansari et al., 2017). Beyond rice, grain production in Ethiopia is expected to be significantly affected by changing temperatures and rainfall patterns, potentially reducing yields. However, adaptive strategies such as early sowing, increased planting density, and higher fertilizer application can help mitigate these impacts and sustain production (Gardi et al., 2022 ). Furthermore, Rahman et al. ( 2018 ) used the DSSAT model to assess the impact of changing climate conditions on major crops, revealing a decline in yield due to shortened growing seasons and reduced water-use efficiency. Climate change impact on crop yield has been observed from the years, extreme events like high temperature and heavy rainfalls adversely effected crop yield, understanding climate and crop yield relation helps in mitigating such effects. Numerous studies show how climate change is affecting agriculture, and there is legitimate fear that poverty and sustainable development, particularly in developing nations, may be threatened by climate change (Lone et al., 2019). Rice is one of the mostly grown crops in Telangana state of India and its cultivation has been increasing year by year, the gross sown area in the state has in increased from 22.04% in 2014 to 43.79% in 2021 in Kharif season, whereas in Rabi the gross sown area has increased from 43.42% in 2014 to 63.46% in 2021 (TSEO, 2023 ). Many studies in India shown that the rice yield is vulnerable to changes in climate, Subhankar et.al,2020 observed that around 22% of rice yield is reduced in future in major rice growing states of India. Significant effect of climate variables like rainfall, minimum and maximum temperature was found on crop yield when empirically examined the climate change impact on major food crops grown in India, Out of these three variables increased rainfall showed adverse effect on food crops (Guntukula, 2020 ). Change in temperature pattern in different future scenarios resulted in decrease in rice yield (Dang et.al 2023 ). Yield simulation is an important step in assessing climate impact, The DSSAT software is used to simulate rice yield which are matched when later checked with observed yield (Dias et.al, 2016 ). Through distinct independent programs working in tandem, the DSSAT allows the user to forecast the potential outcomes of various managerial aspects and methods (Rahman et al. 2018 ). The model considers natural procedures like photosynthesis, nutrient absorption and respiration in simulating crop yield (Zhao el.al,2024). Out of different yield estimation methods, the yield simulation using crop growth model like DSSAT showed prominent results (Pazhanivelan et. al, 2022 ), RMSE (Root Mean Square Error) is the metric used to check the agreement between observed and simulated yield using DSSAT (Liu et al., 2017 ). In this study CERES-Rice module of DSSAT is used for rice yield estimates, because CERES module is used for cereals crop yield simulations like rice and this module computes daily plant growth rates using a crop-specific ecotype coefficient obtained from Radiation Use Efficiency (RUE), which translates daily collected photosynthetically active radiation into plant dry matter (Ahmed et.al,2015). Since the study area is more dependent on agriculture studying the climate-crop relationship helps in mitigating the adverse effects of climate on crop yield, so this research aims to study the role of climate in rice yield variation. Crop Management data which is input to DSSAT model is collected from experimental fields which are in controlled environment. This may not resemble actual field conditions (Guntukula, R,2020). To overcome this gap, the research is carried out at regional scale (at field-level) and crop management data is collected from the actual field. After understanding simulation capability of DSSAT and climate threat to crop yield from existing literature this research aims to carry out yield simulations in DSSAT and determine the significance of climate in influencing rice yield. 2. Materials and Methods 2.1 Study area Five agricultural fields in Hanumakonda district, Telangana State, India were chosen to conduct the study (Fig. 1 ). The location details of the sites are mentioned in Table 1. As per Koppen climate classification, the district comes under tropical region with the normal annual rainfall, normal daily temperature of five agricultural fields as 936.83 mm and 27.74°C respectively. The slope of the study area is 1.09%, this low slope and climatic conditions made the study area more suitable for rice cultivation. Based on sentinel-2 land use and land cover images acquired 71.6% of land in district covered by crop land in Kharif season and 30.11% land covered crop land in Rabi season, this states the district where the five agricultural sites are selected for climate-impact study is dependent more on agriculture which provides valuable insights to farmers in that area. The majority farmers are involved in rice production not just for the market but also for their own survival because it is the area's primary food source. Average yield of rice produced in Hanumakonda district is 3273Kg/ha (DES, 2024). As per recent statistical abstract released by the Government of Telangana, the rice production in district constitutes 2.4% of rice produced in the state (TSSA, 2022 ). Table.1 Locations of agricultural field on which study is conducted Site. No Village Name Mandal Name 1 Bheemdevarapalli Bheemdevarapalli 2 Bheemdevarapalli Bheemdevarapalli 3 Mulkanoor Bheemdevarapalli 4 Mulkanoor Bheemdevarapalli 5 Mallikudurla Velair 2.2 Data Collection Historical climate data which includes daily datasets of minimum temperature (°C), maximum temperature (°C), and rainfall (mm) from the year (2007 to 2023) is collected from Indian Meteorological Department (IMD). Daily Solar Radiation data is collected from NASA POWER (Prediction of Worldwide Energy Resources). Crop management data includes sowing and harvesting dates, type and amount of fertilizers, availability of irrigation facility is collected from Crop Cutting Experiments (CCE) that were conducted in 2022, from Chief Planning Office, Hanumakonda. Historical observed yield data of rice obtained from Directorate of Economics and statistics (DES), Telangana for the year 2007 to 2023. The yield represents the average yield of all the cultivars used in Hanumakonda. Soil data which includes Clay content (%), Silt content (%), PH in soil water, Cation exchange capacity (cmol/kg) at various depths is collected from ISRIC (International Soil Reference and Information Centre) for six different depths (0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm). 2.3 Methodology Methodology Flowchart for the rice yield impact assessment using DSSAT Model is represented in Fig. 2 . The data collected from various sources were pre-processed before feeding into DSSAT. The processed data includes daily rainfall, daily minimum and maximum temperature, daily solar radiation which are the minimum weather inputs required for crop yield simulations in DSSAT. Daily weather database created in python notebook of ArcGIS Pro using Arcpy module for geospatial operations and python Jupyter notebook for systematic arrangement and data stored in CSV format. The prepared database is imported in WeatherMan of DSSAT which is a tool available to handle weather data. Similarly soil database is prepared from ISRIC soil profile data in ArcGIS Pro and Jupyter Notebook and entered in SBUILD of DSSAT which is a tool for handing soil data. The detailed procedure of weather and soil database creation are represented in Figs. 3 and 4 . The data imported in WeatherMan and entered in SBUILD are saved in their respective directory. Table.2 Sowing and harvest dates in Kharif and Rabi Site. No Kharif Rabi Sowing Date Harvest Date Sowing Date Harvest Date 1 30-07-2022 04-11-2022 25-12-2022 18-04-2023 2 15-07-2022 04-11-2022 20-12-2022 24-04-2023 3 23-07-2022 28-10-2022 05-01-2023 20-05-2023 4 04-08-2022 03-11-2022 11-01-2023 26-05-2023 5 10-06-2022 25-11-2022 10-12-2022 12-05-2023 DSSAT has separate GUI for creating experiment named XBUILD which is used to create a new experiment for crop yield simulation. Various inputs are given to experiment that includes weather file, soil file, crop management data which includes sowing dates, harvest dates, irrigation and fertilizer details. The sowing and harvest dates of all the sites in both seasons are in Table 2, the irrigation details in CCE data was qualitative, it is mentioned in the CCE form of each site that sufficient amount of water is supplied to the site, but DSSAT requires actual amount of water supplied. To achieve this the qualitative data of irrigation is converted into quantitative data using CROPWAT software as mentioned by Bokke et al., 2020. The additional water supplied is mentioned in Table 3 . Table 3 Irrigation to all the sites in Kharif and Rabi Site. No Irrigation (mm) Kharif Rabi 1 332 1404 2 332 1492 3 264 1651 4 481 1648 5 590 1647 All the crop management data which are collected from CCE is for year 2022 and kept constant for remaining years. From the available cultivars of rice, a cultivar is selected whose characteristics are similar to the cultivars used in the sites, to reduce the deviation between simulated and observed yield a cultivar is selected and calibration is done in sensitivity analysis tool of DSSAT to find out best cultivar coefficients. The resultant coefficients from sensitivity analysis are mentioned in Table 4 . In the simulation options CERES-RICE module is selected and yield of the rice is obtained by running the experiment file for the years 2007 to 2023. In this way, rice yield is simulated for remaining sites in both Kharif and Rabi seasons. RMSE and NRMSE are calculated using Eqs. ( 1 ) and ( 2 ). $$\:RMSE\:=\:\:\sqrt{\frac{{\sum\:}_{i=1}^{n}{\left({Obs}_{i}-{Sim}_{i}\right)}^{2}}{N}}$$ 1 Where N = Number of Observations (Number of years in this case), Obs i = Observed Yield in the i th Year and Sim i = Simulated Yield in the i th Year $$\:NRMSE=\:\frac{RMSE}{\stackrel{-}{O}}$$ 2 Where \(\:\stackrel{-}{O}\) = Mean of Observed Yield. Lower RMSE and NRMSE values indicates better results. NRMSE range lies between 0 and 100%, 30% poor (Moroozeh et.al, 2023 ), The NRMSE values obtained for each site is tabulated in Table 5 . With the simulated yield a 2nd order polynomial regression which is another form of multi linear regression as mentioned in Eq. ( 3 ) is fitted to each site between yield and climatic variables to check the significance of the climate in influencing the rice yield. The included climatic variables are Average Rainfall, Average Temperature, and Average Solar Radiation during growing period. $$\:Crop\:Yield=\:{\beta\:}_{0}+{{\beta\:}_{1}x}_{1}+{\beta\:}_{2}*{x}_{2}+{\beta\:}_{3}*{x}_{3}+{\beta\:}_{4}*{x}_{1}^{2}+{\beta\:}_{5}*{(x}_{1}*{x}_{2})+{\beta\:}_{6}*{(x}_{1}*{x}_{3})+{\beta\:}_{7}*{x}_{2}^{2}+{\beta\:}_{8}*{(x}_{2}*{x}_{3})+{\beta\:}_{9}*{x}_{3}^{2}$$ 3 Where x 1 = Average rainfall received in growing season (mm), x 2 = Average temperature during growing season ( 0 C), x 3 = Average solar radiation during growing season (MJ/m 2 /day). and β 0 , β 1 ,……….., β 9 are the coefficients. Table 4 Cultivar coefficients obtained through sensitivity analysis of each site in Kharif and Rabi Cultivar Coefficient Site-1 Site-2 Site-3 Site-4 Site-5 Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi P1 544 490 500 500 673 559 672 536 550 500 P2R 200 200 283 283 120 120 200 162 300 180 P5 200 200 261 261 276 276 264 187.8 280 200 P20 12.6 12.6 12.28 12.28 12.74 12.74 12.6 12.6 12.6 12.59 G1 59 59 68 68 80 80 70 59 59 59 G2 0.03 0.03 0.03 0.03 0.034 0.034 0.03 0.03 0.03 0.03 G3 1.14 1.14 1.04 1.04 0.79 0.79 0.84 0.5 0.72 0.72 Note: P1, P2R, P5, P2O are related to thermal and photoperiod sensitivities of crop during its development phase. G1, G2, G3 related yield of crop Table 5 NRMSE values for all the sites in Kharif and Rabi Site No. NRMSE (%) Kharif Rabi 1 17.25 15.28 2 17.95 14.3 3 6.99 14.52 4 12 27.87 5 10.91 13.63 3. RESULTS AND DISCUSSIONS Figure 5 and 6 shows the simulated yield in DSSAT of each site from year 2007 to 2023 which are represented as stacked columns where each column size in the stacked column is proportional to yield obtained in that particular year. Average simulated yield over the years 2007 to 2023 are tabulated in Table 6 . NRMSE for all the sites are within acceptable range. Site-3 got better NRMSE than remaining sites which is equal to 6.99%. All the sites are experiencing sudden fall in crop yield when there are fluctuations in weather events, especially rainfall received in growing period of crop. It is observed that in the Kharif season the rice yield value in the year 2020 is slightly low at all the sites in the study area and rainfall was higher in that year. In the Rabi season, except site-2 all the remaining has lower crop yield in the year 2007 and rainfall in that year was comparatively higher than subsequent years. The fitted regression model for each site in Kharif and Rabi season is explained well about the variability of crop yield. By keeping one of the independent variables (Solar Radiation) constant, variation of the rice yield with change in remaining climate variables are represented in Figs. 7 and 8 . Out of ten models which includes five agricultural sites with each site has two seasons, agricultural site-4 in Rabi season got highest R-Square value of 0.96. The values of coefficient of determination for remaining sites in both seasons are mentioned in Table 7 . From fitted models, it is observed that for all the sites rainfall has inverse relation with rice yield whereas yield increasing with solar radiation. Table 6 Average yield of all the for both seasons from the year 2007 to 2023 Site. No Average Yield in Kharif (2007-23) Average Yield in Rabi (2007-23) 1 2749.65 3120.88 2 3293.06 3229.29 3 3154.65 3227.53 4 2945.59 2873.24 5 3007.18 3506.06 Table 7 R 2 in Kharif and Rabi Site No. R 2 Kharif Rabi 1 0.78 0.52 2 0.54 0.52 3 0.53 0.64 4 0.5 0.96 5 0.76 0.5 Figure 9 depicts the distribution of total rainfall received during growth period in all the sites from the year 2007 to 2023, average rainfall during growth period in all the sites was 566.63 mm for site-1, 727.66 mm for site-2, 630.64 mm for site-3, 507.18 mm for site-4, and 877.43 mm for site-5. Highest rainfall for all the sites was observed in year-2020 which is deviated on an average of 65% from mean rainfall of the sites, the highest deviation was observed in site-1 (91% from mean), in this year in all sites the crop yield was low when compared to previous years. Among all sites, site-5 has recorded highest rainfall of 1369.52 mm, the sudden drop in the rainfall was observed in the year 2011 and 2023 Figure 10 describes the variation of daily average temperature in Kharif season during growth period in each site from the year 2007 to 2023. 27.48 0 C is the average daily temperature of all the sites during growth period, site-5 records highest average temperature 27.8 0 C than all sites. The peak temperature in all the sites was observed in the year 2015, in this year the rainfall was decreased on an average of 35% of mean rainfall, site-2 observed with lowest decrease in rainfall in that year which is around 9%. The variation in daily average solar radiation in sites were described by graph (Figure-11), lower solar radiation was observed during the period where rainfall was high. The study of rainfall, temperature solar radiation is necessary not only in climate-impact point of view because these are the minimum weather datasets required to DSSAT for crop-yield simulation. Figure 12 describes the comparison of observed yield and simulated yield from DSSAT. Despite few deviations most the simulated yield is closer to the observed yield, this indicates simulating capability of DSSAT model. In all the sites the year 2020 has observed with lower yield, in the same year the rainfall received in the sites were very high, which are 76% more than average rainfall of all the sites, this implies the negative impact of rainfall on crop yield in that year, also year-2013 has lower yield a rainfall in that year was relatively higher than sites mean rainfall. In the 2023 all the sites are observed with highest yield and rainfall received in the year was low and moderate in the sites, this reveals the negative relation of rainfall with rice yield. The average yield of all the sites during Kharif season from the year 2007–2023 was tabulated in Table 9 Figure 13 shows yearly variation in total rainfall received during growth period in Rabi season, 64.92 mm is the average total rainfall received in all the sites with highest average total rainfall of 79.25 mm received in site-4 whereas site-5 in Kharif season. Site-1 and 2 received highest rainfall in 2007, site-3 and 4 in 2008, site-5 in 2022, rice yields in those years were less which demonstrated in Fig. 13 , the similar pattern was observed in Kharif season, this implies sudden increase in rainfall is showing adverse effect of rice yield for all the sites. Figure 14 shows the yearly variation of average daily temperature in Rabi season. 27.83 0 C was the average temperature of all the sites, site-4 has recorded highest average temperature of 29.22 0 C followed by site-3 28.68 0 C, and this is because for these sites the growth periods include April and May months. Figure 15 demonstrates the yearly variation of average solar radiation during growth period in Rabi season. Average value in all the five sites observed was 20.36 MJ/m 2 /day. Site-3 and 4 has higher solar radiation than other three sites, in the year 2016 all sites received similar average solar radiation value, the lowest solar radiation was observed in the year 2013 at site-1 and 2, year 2007 all sites have relatively lower solar radiation value and rainfall was high in that year. Figure 16 shows the temporal variation of simulated crop yield along with the observed yield. All the sites showed larger deviations of simulated yield from observed in the year 2017. Sudden increase in rainfall was observed in site-2 and 3 in the year 2008. All the sites witnessed the lower yield in year 2007, in the same year the rainfall was very high, the similar trend was observed in Kharif season also the yield was lower when there is sudden increase in rainfall. The non-linear relation between rice yield and climate variables made to fit second degree polynomial equation for both Kharif and Rabi seasons. The resultant rice yield over two different time periods (1970–2000 and 2000–2023) are calculated from developed equation to assess the change in rice yield due to change in climate. In the Kharif season, the average decrease in yield was around 6% (186.16 Kg/ha). Out of five agriculture sites, site-1 showed higher decrease in crop yield of 18.1% (555.14 Kg/ha) followed by site-5 of 8.96% (311.45 Kg/ha) in 2000 to 2023 time period when compared with 1970 to 1973. In Rabi season, highest yield reduction also observed at site-1 of 13.37% (455.54 Kg/ha) followed by site-2 of 5% (158 Kg/ha). In both the seasons, site-5 has highest average crop yield of 3007.18 Kg/ha in Kharif, 3506.06 Kg/ha in Rabi. The yield simulations using CERES-Rice model of DSSAT are reliable based on calculated metrics. In Kharif, for site-1 both temperature and rainfall has shown negative impact on rice yield whereas solar radiation showed strong positive correlation, the interaction terms in equation indicating combination of rainfall and temperature, temperature and solar radiation reducing crop yield, in the case of site-2 solar radiation also showed negative effect on yield, positive correlation is observed between yield and solar radiation, rainfall in site-3 but the combined effect of rainfall and temperature, temperature and solar radiation impacted negatively on yield, for site-4 temperature and solar radiation has shown inverse relation, similar to site-1, site-5 also observed negative relation of rainfall and temperature with rice yield. In Rabi, in site all three independent variables showed inverse relation with rice yield, among three independent variables rainfall has strong negative relation also the similar trend was observed in site-5. For site-2 temperature and solar radiation are reducing rice yield. In site-4 rainfall and temperature impacting negatively on rice yield out which temperature has more impact. 4. Conclusions The yield simulations using CERES-Rice model of DSSAT are reliable based on calculated metrics. Simulated yield over the years 2007 to 2023 for five different agriculture fields are close to each other indicating the precision of simulations performed in DSSAT. Hence, performing these simulations based on available climate and crop management data helps farmers to identifying yield before sowing or harvesting based on which they can modify sowing dates to mitigate negative impact of climate on yield. Rainfall is showing strong negative correlation with rice yield, in both Kharif and Rabi season at least any one of the climate variable adversely effecting rice yield which provides the need of adaptation strategies in mitigating these adverse effects. The methodology developed in the present research work is useful for understanding climate change influence on the crop yield and helps in planning adaptation strategies. Declarations Author Contribution Raviteja Kudikala: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing—original draft, Writing—review & editing. Venkata Reddy Keesara: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. Eswar Sai Buri: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing—original draft, Writing—review & editing. References Ahmed, K. F., Wang, G., Yu, M., Koo, J., & You, L. (2015). Potential impact of climate change on cereal crop yield in West Africa. Climatic Change, 133 , 321–334. Ansari, A., Lin, Y. P., & Lur, H. S. (2021). Evaluating and adapting climate change impacts on rice production in Indonesia: A case study of the Keduang subwatershed, Central Java. Environments, 8 (11), 117. Ansari, A., Pranesti, A., Telaumbanua, M., Alam, T., Wulandari, R. A., & Nugroho, B. D. A. (2023). Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach. Heliyon, 9 (9). Ara, I., Lewis, M., & Ostendorf, B. (2017). Understanding the spatially variable effects of climate change on rice yield for three ecotypes in Bangladesh, 1981–2010. Advances in Agriculture, 2017 , 6287156. Bokke, A. S., & Shoro, K. E. (2020). Impact of effective rainfall on net irrigation water requirement: The case of Ethiopia. Water Science, 34 (1), 155–163. Dang, L. T. T., Tran, A. H., Vu, T. L. T., & Nguyen, T. A. T. (2023, April). Assessment of the impact of climate change and cultivation conditions on rice yield in Vietnamese Mekong Delta. A case study in Vinh Thanh District, Can Tho city. In IOP Conference Series: Earth and Environmental Science (Vol. 1170, No. 1, p. 012001). IOP Publishing. Debnath, S., Mishra, A., Mailapalli, D. R., Raghuwanshi, N. S., & Sridhar, V. (2021). Assessment of rice yield gap under a changing climate in India. Journal of Water and Climate Change, 12 (4), 1245–1267. Dhekale, B. S., Nageswararao, M. M., Nair, A., Mohanty, U. C., Swain, D. K., Singh, K. K., & Arunbabu, T. (2018). Prediction of Kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts. Theoretical and Applied Climatology, 133 , 1075–1091. Dias, M. P. N. M., Navaratne, C. M., Weerasinghe, K. D. N., & Hettiarachchi, R. H. A. N. (2016). Application of DSSAT crop simulation model to identify the changes of rice growth and yield in Nilwala river basin for mid-centuries under changing climatic conditions. Procedia Food Science, 6 , 159–163. Gardi, M. W., Memic, E., Zewdu, E., & Graeff‐Hönninger, S. (2022). Simulating the effect of climate change on barley yield in Ethiopia with the DSSAT‐CERES‐Barley model. Agronomy Journal, 114 (2), 1128–1145. Geethalakshmi, V., Gowtham, R., Gopinath, R., Priyanka, S., Rajavel, M., Senthilraja, K., Dhasarathan, M., Rengalakshmi, R., & Bhuvaneswari, K. (2023). Potential impacts of future climate changes on crop productivity of cereals and legumes in Tamil Nadu, India: A mid‐century time slice approach. Advances in Meteorology, 2023 , 4540454. Gohain, G. B., Singh, K. K., Singh, R. S., Dakhore, K. K., & Ghosh, K. (2022). Application of CERES-sorghum crop simulation model DSSAT v4.7 for determining crop water stress in crop phenological stages. Modeling Earth Systems and Environment, 8 (2), 1963–1975. Guntukula, R. (2020). Assessing the impact of climate change on Indian agriculture: Evidence from major crop yields. Journal of Public Affairs, 20 (1), e2040. Harinarayanan, M. N., & Dheebakaran, G. (2022). Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu. Journal of Applied and Natural Science, 14 (SI), 244. Hasan, A. S., Islam, A. K. M. S., Bala, S. K., & Khan, M. J. (2017). Impact of extreme climate change on the production of BORO rice in Bangladesh using DSAAT 4.6. In Proceedings of the 6th International Conference on Water and Flood Management (ICWFM 2017) (pp. 384–397). IWFM, BUET, Dhaka, Bangladesh. ISRIC, https://data.isric.org/geonetwork/srv/eng/catalog.search#/home Kephe, P. N., Ayisi, K. K., & Petja, B. M. (2021). Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa. Agriculture & Food Security, 10 (1), 1–24. Li, N., Zhao, Y., Han, J., Yang, Q., Liang, J., Liu, X., Wang, Y., & Huang, Z. (2024). Impacts of future climate change on rice yield based on crop model simulation—A meta-analysis. Science of The Total Environment, 949 , 175038. Liu, H. L., Liu, H. B., Lei, Q. L., Zhai, L. M., Wang, H. Y., Zhang, J. Z., Zhu, Y. P., Liu, S. P., Li, S. J., Zhang, J. S., & Liu, X. X. (2017). Using the DSSAT model to simulate wheat yield and soil organic carbon under a wheat-maize cropping system in the North China Plain. Journal of Integrative Agriculture, 16 (10), 2300–2307. Lone, B. A., Fayaz, A., Singh, P., Qayoom, S., Dar, Z. A., Kumar, S., AndRabi, N., Manzoor, M., & Rasool, F. (n.d.). Impact of climate change on growth and yield of maize using CERES-Maize model under temperate Kashmir. Moroozeh, A. D., Bansouleh, B. F., Ghobadi, M., & Ahmadpour, A. (2023). Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions. Spanish Journal of Agricultural Research, 21 (3), e1201. Mulla, S., Singh, S. K., Ahmed, R., Rana, M., & Singh, N. P. (2024). Assessing climate change impacts on rice yield using bias correction and DSSAT modeling with scenario projections: A case study of Dapoli, Maharashtra. Journal of Pharmacognosy and Phytochemistry, 13 (5), 574–585. NASA POWER. https://power.larc.nasa.gov/ Nasir, I. R., Rasul, F., Ahmad, A., Asghar, H. N., & Hoogenboom, G. (2020). Climate change impacts and adaptations for fine, coarse, and hybrid rice using CERES-Rice. Environmental Science and Pollution Research, 27 , 9454–9464. Nicolas, F., Migliaccio, K. W., Hoogenboom, G., Rathinasabapathi, B. R., & Eisenstadt, W. R. (2020). Assessing the potential impact of climate change on rice yield in the Artibonite Valley of Haiti using the CSM-CERES-Rice model. Transactions of the ASABE, 63 (5), 1385–1400. Pai, D. S., Rajeevan, M., Sreejith, O. P., Mukhopadhyay, B., & Satbha, N. S. (2014). Development of a new high spatial resolution (0.25× 0.25) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65(1), 1-18. Patel, N. R., Akarsh, A., Ponraj, A., & Singh, J. (2019). Geospatial technology for climate change impact assessment of mountain agriculture. In Remote Sensing of Northwest Himalayan Ecosystems (pp. 381–400). Pazhanivelan, S., Geethalakshmi, V., Tamilmounika, R., Sudarmanian, N. S., Kaliaperumal, R., Ramalingam, K., Sivamurugan, A. P., Mrunalini, K., Yadav, M. K., & Quicho, E. D. (2022). Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model. Agronomy, 12 (9), 2008. Rahman, A., Mojid, M. A., & Banu, S. (2018). Climate change impact assessment on three major crops in the north-central region of Bangladesh using DSSAT. International Journal of Agricultural and Biological Engineering, 11 (4), 135–143. Rajasivaranjan, T., Anandhi, A., Patel, N. R., Irannezhad, M., Srinivas, C. V., Veluswamy, K., Surendran, U., & Raja, P. (2022). Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield. Scientific Reports, 12 (1), 16985. Srivastava, A. K., Rajeevan, M., & Kshirsagar, S. R. (2009). Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Science Letters, 10(4), 249-254. TSEO. (2023). Socio-economic Outlook . https://legislature.telangana.gov.in/socioEconomic (Accessed September 9, 2024) TSSA. (2022). Statistical Abstract. https://tgdps.telangana.gov.in/ (Accessed September 9, 2024) Zhao, R., Ma, Y., & Wu, S. (2024). A review of the research status and prospects of regional crop yield simulations. Agronomy, 14 (7), 1397. Additional Declarations No competing interests reported. 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13:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6557175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6557175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82164294,"identity":"767e0fcf-8ae1-4d64-933f-d8383a7dce34","added_by":"auto","created_at":"2025-05-07 09:05:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198755,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of Study Area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/7e4ac0d0c7cc9f889d0bf0fe.png"},{"id":82166429,"identity":"839791aa-fd87-4515-ae81-c20552c25989","added_by":"auto","created_at":"2025-05-07 09:13:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24556,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology 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processing.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/0f26bd525a8b60aaa0aeab0e.png"},{"id":82164292,"identity":"163e48cf-072c-4153-95f4-872add57668f","added_by":"auto","created_at":"2025-05-07 09:05:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86517,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated Rice Yield in Kharif Season\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/028c0a9027edd45bd4c5cfd5.png"},{"id":82164290,"identity":"5d8a1aee-4524-48de-8656-1d0b4da12d9e","added_by":"auto","created_at":"2025-05-07 09:05:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88183,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated Rice Yield in Rabi Season\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/5297452b22f489d0120aa668.png"},{"id":82164297,"identity":"72546559-2794-486a-95bb-4a398562a5f0","added_by":"auto","created_at":"2025-05-07 09:05:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":324913,"visible":true,"origin":"","legend":"\u003cp\u003e3D Regression Surface between rice yield and climate variables in Kharif\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/e58a5d982f15d980d694e44c.png"},{"id":82168462,"identity":"ba3bfa87-e7e8-4bd8-8982-750928b46f92","added_by":"auto","created_at":"2025-05-07 09:29:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":280283,"visible":true,"origin":"","legend":"\u003cp\u003e3D Regression Surface between rice yield and climate variables in Rabi\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/ea4f269c9d3a90eed1696cae.png"},{"id":82167282,"identity":"562d8a2a-c5b8-4ee3-b84f-5dbe12473290","added_by":"auto","created_at":"2025-05-07 09:21:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":125975,"visible":true,"origin":"","legend":"\u003cp\u003eTotal rainfall received in all five agricultural sites during sowing to harvest period in Kharif\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/d4da0f5ca64206a8692695b9.png"},{"id":82166434,"identity":"947ee9ad-59d5-41c4-9841-b9597d12b470","added_by":"auto","created_at":"2025-05-07 09:13:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":141806,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily temperature in all five agricultural sites during sowing-harvest period in Kharif\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/82ffb200488c4f400593896a.png"},{"id":82164298,"identity":"3de686db-c7dc-4fd5-8442-5c785350adf0","added_by":"auto","created_at":"2025-05-07 09:05:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":108290,"visible":true,"origin":"","legend":"\u003cp\u003eAverage solar radiation in all five agricultural sites during sowing-harvest period in Kharif\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/50132d7733926b4f4bbda7ff.png"},{"id":82166436,"identity":"689d6e75-d42e-4818-a692-11eed8e750ba","added_by":"auto","created_at":"2025-05-07 09:13:17","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":224569,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of observed and simulated yield of all five sites in Kharif season\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/3a600dd4f51802babadc9ba3.png"},{"id":82166440,"identity":"cfc6f088-665c-4bab-9694-611e4a4ca59b","added_by":"auto","created_at":"2025-05-07 09:13:17","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":103928,"visible":true,"origin":"","legend":"\u003cp\u003eTotal rainfall received in all five agricultural sites during sowing-harvest period in Rabi\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/ce0e4bc112a8aea35ab2dec8.png"},{"id":82164317,"identity":"5a2df179-6767-47a2-831f-54c640d8a6e3","added_by":"auto","created_at":"2025-05-07 09:05:17","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":137280,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily temperature in all five agricultural sites during sowing-harvest period in Rabi\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/a3a904be49af199d7f34e559.png"},{"id":82164315,"identity":"3132512b-0aed-4cdb-bf47-e1fc71fa9ddd","added_by":"auto","created_at":"2025-05-07 09:05:17","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":126003,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily solar radiation in all five agricultural sites during sowing-harvest period in Rabi\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/1693a377b3c3e49aaf5c8a03.png"},{"id":82166443,"identity":"f2cde7d9-0d0b-483b-b7d5-363243d2245e","added_by":"auto","created_at":"2025-05-07 09:13:17","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":207724,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of observed and simulated yield of all five sites in Rabi season\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/e188b154954e8ead4059845e.png"},{"id":86031448,"identity":"0c0e1c20-ee8c-4f58-a720-9f4680adc78f","added_by":"auto","created_at":"2025-07-04 14:23:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2686749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6557175/v1/3871c19d-492a-4ca7-9a68-de00aa5128e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClimate Change Impact Assessment on Rice Yield Using Dssat and Geospatial Methods\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate variability significantly influences global food production, particularly in developing regions, as changes in trends of temperature and rainfall pose challenges for crop growth. Various simulation studies indicate that increased temperatures, either alone or in combination with elevated CO₂ levels, can drastically reduce maize yield, thereby highlighting the urgent need for adaptive measures (Lone et al., 2020). To support agricultural decision-making, the Extended Range Forecast System (ERFS) provides seasonal and sub-seasonal rainfall forecasts, which are crucial for planning Kharif rice cultivation in India. In this regard, this study evaluates the reliability of ERFS forecasts in predicting Kharif rice yield using the CERES (Crop Environment Resource Synthesis)-Rice model, thereby enhancing better agricultural planning (Dhekale et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ensuring global food security amid climate change requires an integrated approach that combines climate-hydrological-crop models to assess agricultural impacts. Consequently, improving data reliability and improving modelling techniques are crucial for developing effective adaptation strategies, particularly for rice production in vulnerable regions such as Indonesia (Ansari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the diverse agro-ecological conditions of the North-western Himalaya support varied farming systems. However, climate change and biotic stresses are increasingly impacting traditional crop cultivation. Rising temperatures, shifting rainfall patterns, and pest challenges are prompting a transition toward fruit and cash crop farming in the region (Patel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, Harinarayanan et al. (2022) integrated ERFS data with the DSSAT model. However, improvements in both forecasting and modelling remain necessary to enhance yield prediction reliability. Likewise, the study conducted by Rajasivaranjan et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlights the effectiveness of the DSSAT model in assessing the impact of water stress on rice yield. By integrating climate simulations with crop yield modelling, the findings showing that DSSAT can be a valuable tool for the early prediction of water stress effects, thereby aiding in better agricultural planning and management. Geethalakshmi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further observed that future climate projections indicate adverse impacts on key crops in Tamil Nadu. Moreover, multi-model climate assessments emphasize the need for adaptive strategies to mitigate potential yield declines and ensure long-term food security. While climate change may reduce rice yield stability, adaptive measures and enhanced CO₂ levels could help mitigate some of the adverse effects on crop productivity (Mulla et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding crop water requirements is also essential for optimizing yield, as water stress during critical growth stages significantly affects productivity. In this regard, the DSSAT-based crop model effectively simulates water stress, thereby improving irrigation strategies and aiding sowing decisions for rainfed crops (Gohain et al., 2021). Li et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further emphasize that climate change significantly influences rice growth. Their study highlights the need for robust analytical approaches to better understand and reduce uncertainties in future rice yield projections. In Bangladesh, climate change affects rice food security differently across regions. Ara et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlighted the importance of region-specific adaptation strategies to mitigate rice yield variability and ensure long-term food security. Similarly, Kephe et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used a hybrid approach by combining remote sensing and field experiments to overcome data availability limitations, such as those related to crop management, climate, and soil conditions. Rice yield in Bangladesh is influenced by various environmental factors, with rising temperatures leading to significant yield reductions in most districts.\u003c/p\u003e \u003cp\u003eWhile increased CO₂ levels provide some benefits, they remain insufficient to offset the negative impacts of temperature variations. This emphasizes the need for improved cultivation strategies (Hasan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Likewise, optimizing nitrogen fertilizer management is essential for improving rice yield and economic efficiency in the Vietnamese Mekong Delta. The DSSAT-CERES-Rice model effectively simulates rice productivity, indicating that a moderate nitrogen application rate offers the best balance between economic and environmental sustainability (Dang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, the CERES-Rice model effectively simulated rice yield under various climatic conditions in Pakistan, providing valuable insights into future challenges and adaptation strategies.\u003c/p\u003e \u003cp\u003eAgronomic and genetic adaptations were found to mitigate the negative impacts of changing temperatures and rainfall, highlighting the importance of climate-resilient practices for sustaining rice production (Nasir et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future climate projections indicate shifts in yield across different growing seasons, and simulation results highlight the need for adaptation strategies (Nicolas et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, rice production in Central Java is projected to decline due to shifting rainfall patterns, rising temperatures, and increased solar radiation, presenting challenges for food security. To mitigate these impacts, strategies such as adjusting cropping calendars, modernizing irrigation systems, and optimizing nutrient management are essential for sustaining rice yields (Ansari et al., 2017). Beyond rice, grain production in Ethiopia is expected to be significantly affected by changing temperatures and rainfall patterns, potentially reducing yields. However, adaptive strategies such as early sowing, increased planting density, and higher fertilizer application can help mitigate these impacts and sustain production (Gardi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Rahman et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used the DSSAT model to assess the impact of changing climate conditions on major crops, revealing a decline in yield due to shortened growing seasons and reduced water-use efficiency. Climate change impact on crop yield has been observed from the years, extreme events like high temperature and heavy rainfalls adversely effected crop yield, understanding climate and crop yield relation helps in mitigating such effects. Numerous studies show how climate change is affecting agriculture, and there is legitimate fear that poverty and sustainable development, particularly in developing nations, may be threatened by climate change (Lone et al., 2019).\u003c/p\u003e \u003cp\u003eRice is one of the mostly grown crops in Telangana state of India and its cultivation has been increasing year by year, the gross sown area in the state has in increased from 22.04% in 2014 to 43.79% in 2021 in Kharif season, whereas in Rabi the gross sown area has increased from 43.42% in 2014 to 63.46% in 2021 (TSEO, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many studies in India shown that the rice yield is vulnerable to changes in climate, Subhankar et.al,2020 observed that around 22% of rice yield is reduced in future in major rice growing states of India. Significant effect of climate variables like rainfall, minimum and maximum temperature was found on crop yield when empirically examined the climate change impact on major food crops grown in India, Out of these three variables increased rainfall showed adverse effect on food crops (Guntukula, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Change in temperature pattern in different future scenarios resulted in decrease in rice yield (Dang et.al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yield simulation is an important step in assessing climate impact, The DSSAT software is used to simulate rice yield which are matched when later checked with observed yield (Dias et.al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Through distinct independent programs working in tandem, the DSSAT allows the user to forecast the potential outcomes of various managerial aspects and methods (Rahman et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The model considers natural procedures like photosynthesis, nutrient absorption and respiration in simulating crop yield (Zhao el.al,2024). Out of different yield estimation methods, the yield simulation using crop growth model like DSSAT showed prominent results (Pazhanivelan et. al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), RMSE (Root Mean Square Error) is the metric used to check the agreement between observed and simulated yield using DSSAT (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study CERES-Rice module of DSSAT is used for rice yield estimates, because CERES module is used for cereals crop yield simulations like rice and this module computes daily plant growth rates using a crop-specific ecotype coefficient obtained from Radiation Use Efficiency (RUE), which translates daily collected photosynthetically active radiation into plant dry matter (Ahmed et.al,2015).\u003c/p\u003e \u003cp\u003eSince the study area is more dependent on agriculture studying the climate-crop relationship helps in mitigating the adverse effects of climate on crop yield, so this research aims to study the role of climate in rice yield variation. Crop Management data which is input to DSSAT model is collected from experimental fields which are in controlled environment. This may not resemble actual field conditions (Guntukula, R,2020). To overcome this gap, the research is carried out at regional scale (at field-level) and crop management data is collected from the actual field. After understanding simulation capability of DSSAT and climate threat to crop yield from existing literature this research aims to carry out yield simulations in DSSAT and determine the significance of climate in influencing rice yield.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eFive agricultural fields in Hanumakonda district, Telangana State, India were chosen to conduct the study (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The location details of the sites are mentioned in Table 1. As per Koppen climate classification, the district comes under tropical region with the normal annual rainfall, normal daily temperature of five agricultural fields as 936.83 mm and 27.74\u0026deg;C respectively. The slope of the study area is 1.09%, this low slope and climatic conditions made the study area more suitable for rice cultivation. Based on sentinel-2 land use and land cover images acquired 71.6% of land in district covered by crop land in Kharif season and 30.11% land covered crop land in Rabi season, this states the district where the five agricultural sites are selected for climate-impact study is dependent more on agriculture which provides valuable insights to farmers in that area. The majority farmers are involved in rice production not just for the market but also for their own survival because it is the area\u0026apos;s primary food source. Average yield of rice produced in Hanumakonda district is 3273Kg/ha (DES, 2024). As per recent statistical abstract released by the Government of Telangana, the rice production in district constitutes 2.4% of rice produced in the state (TSSA, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable.1 Locations of agricultural field on which study is conducted\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVillage Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMandal Name\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulkanoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulkanoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBheemdevarapalli\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMallikudurla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVelair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\n \u003cp\u003eHistorical climate data which includes daily datasets of minimum temperature (\u0026deg;C), maximum temperature (\u0026deg;C), and rainfall (mm) from the year (2007 to 2023) is collected from Indian Meteorological Department (IMD). Daily Solar Radiation data is collected from NASA POWER (Prediction of Worldwide Energy Resources). Crop management data includes sowing and harvesting dates, type and amount of fertilizers, availability of irrigation facility is collected from Crop Cutting Experiments (CCE) that were conducted in 2022, from Chief Planning Office, Hanumakonda. Historical observed yield data of rice obtained from Directorate of Economics and statistics (DES), Telangana for the year 2007 to 2023. The yield represents the average yield of all the cultivars used in Hanumakonda. Soil data which includes Clay content (%), Silt content (%), PH in soil water, Cation exchange capacity (cmol/kg) at various depths is collected from ISRIC (International Soil Reference and Information Centre) for six different depths (0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Methodology\u003c/h2\u003e\n \u003cp\u003eMethodology Flowchart for the rice yield impact assessment using DSSAT Model is represented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The data collected from various sources were pre-processed before feeding into DSSAT. The processed data includes daily rainfall, daily minimum and maximum temperature, daily solar radiation which are the minimum weather inputs required for crop yield simulations in DSSAT. Daily weather database created in python notebook of ArcGIS Pro using Arcpy module for geospatial operations and python Jupyter notebook for systematic arrangement and data stored in CSV format. The prepared database is imported in WeatherMan of DSSAT which is a tool available to handle weather data. Similarly soil database is prepared from ISRIC soil profile data in ArcGIS Pro and Jupyter Notebook and entered in SBUILD of DSSAT which is a tool for handing soil data. The detailed procedure of weather and soil database creation are represented in Figs. 3 and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The data imported in WeatherMan and entered in SBUILD are saved in their respective directory.\u003c/p\u003e\n \u003cp\u003eTable.2 Sowing and harvest dates in Kharif and Rabi\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSite. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKharif\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRabi\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSowing Date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHarvest Date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSowing Date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHarvest Date\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30-07-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e04-11-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25-12-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18-04-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15-07-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e04-11-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20-12-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24-04-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23-07-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28-10-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e05-01-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20-05-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e04-08-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e03-11-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11-01-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26-05-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10-06-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25-11-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10-12-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12-05-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eDSSAT has separate GUI for creating experiment named XBUILD which is used to create a new experiment for crop yield simulation. Various inputs are given to experiment that includes weather file, soil file, crop management data which includes sowing dates, harvest dates, irrigation and fertilizer details. The sowing and harvest dates of all the sites in both seasons are in Table 2, the irrigation details in CCE data was qualitative, it is mentioned in the CCE form of each site that sufficient amount of water is supplied to the site, but DSSAT requires actual amount of water supplied. To achieve this the qualitative data of irrigation is converted into quantitative data using CROPWAT software as mentioned by Bokke et al., 2020. The additional water supplied is mentioned in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIrrigation to all the sites in Kharif and Rabi\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSite. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIrrigation (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKharif\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRabi\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAll the crop management data which are collected from CCE is for year 2022 and kept constant for remaining years. From the available cultivars of rice, a cultivar is selected whose characteristics are similar to the cultivars used in the sites, to reduce the deviation between simulated and observed yield a cultivar is selected and calibration is done in sensitivity analysis tool of DSSAT to find out best cultivar coefficients. The resultant coefficients from sensitivity analysis are mentioned in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In the simulation options CERES-RICE module is selected and yield of the rice is obtained by running the experiment file for the years 2007 to 2023. In this way, rice yield is simulated for remaining sites in both Kharif and Rabi seasons. RMSE and NRMSE are calculated using Eqs.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:RMSE\\:=\\:\\:\\sqrt{\\frac{{\\sum\\:}_{i=1}^{n}{\\left({Obs}_{i}-{Sim}_{i}\\right)}^{2}}{N}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere N\u0026thinsp;=\u0026thinsp;Number of Observations (Number of years in this case), Obs\u003csub\u003ei\u003c/sub\u003e = Observed Yield in the i\u003csup\u003eth\u003c/sup\u003e Year and Sim\u003csub\u003ei\u003c/sub\u003e = Simulated Yield in the i\u003csup\u003eth\u003c/sup\u003e Year\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:NRMSE=\\:\\frac{RMSE}{\\stackrel{-}{O}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{O}\\)\u003c/span\u003e\u003c/span\u003e = Mean of Observed Yield. Lower RMSE and NRMSE values indicates better results. NRMSE range lies between 0 and 100%, \u0026lt;\u0026thinsp;10% implies excellent, 10\u0026ndash;20% good, 20\u0026ndash;30% fair, \u0026gt;\u0026thinsp;30% poor (Moroozeh et.al, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), The NRMSE values obtained for each site is tabulated in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. With the simulated yield a 2nd order polynomial regression which is another form of multi linear regression as mentioned in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) is fitted to each site between yield and climatic variables to check the significance of the climate in influencing\u003c/p\u003e\n \u003cp\u003ethe rice yield. The included climatic variables are Average Rainfall, Average Temperature, and Average Solar Radiation during growing period.\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:Crop\\:Yield=\\:{\\beta\\:}_{0}+{{\\beta\\:}_{1}x}_{1}+{\\beta\\:}_{2}*{x}_{2}+{\\beta\\:}_{3}*{x}_{3}+{\\beta\\:}_{4}*{x}_{1}^{2}+{\\beta\\:}_{5}*{(x}_{1}*{x}_{2})+{\\beta\\:}_{6}*{(x}_{1}*{x}_{3})+{\\beta\\:}_{7}*{x}_{2}^{2}+{\\beta\\:}_{8}*{(x}_{2}*{x}_{3})+{\\beta\\:}_{9}*{x}_{3}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Average rainfall received in growing season (mm), x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Average temperature during growing season (\u003csup\u003e0\u003c/sup\u003eC), x\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Average solar radiation during growing season (MJ/m\u003csup\u003e2\u003c/sup\u003e/day). and \u0026beta;\u003csub\u003e0\u003c/sub\u003e, \u0026beta;\u003csub\u003e1\u003c/sub\u003e,\u0026hellip;\u0026hellip;\u0026hellip;.., \u0026beta;\u003csub\u003e9\u003c/sub\u003e are the coefficients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCultivar coefficients obtained through sensitivity analysis of each site in Kharif and Rabi\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"2\"\u003e\u003cp\u003eCultivar Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eSite-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eSite-2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eSite-3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eSite-4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eSite-5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eP2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e187.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eP20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote: P1, P2R, P5, P2O are related to thermal and photoperiod sensitivities of crop during its development phase. G1, G2, G3 related yield of crop\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNRMSE values for all the sites in Kharif and Rabi\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"2\"\u003e\u003cp\u003eSite No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003eNRMSE (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eKharif\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eRabi\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e17.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\"\u003e\u003cp\u003e15.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e17.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\"\u003e\u003cp\u003e14.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\"\u003e\u003cp\u003e27.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e10.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\"\u003e\u003cp\u003e13.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSIONS","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the simulated yield in DSSAT of each site from year 2007 to 2023 which are represented as stacked columns where each column size in the stacked column is proportional to yield obtained in that particular year. Average simulated yield over the years 2007 to 2023 are tabulated in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. NRMSE for all the sites are within acceptable range. Site-3 got better NRMSE than remaining sites which is equal to 6.99%. All the sites are experiencing sudden fall in crop yield when there are fluctuations in weather events, especially rainfall received in growing period of crop. It is observed that in the Kharif season the rice yield value in the year 2020 is slightly low at all the sites in the study area and rainfall was higher in that year. In the Rabi season, except site-2 all the remaining has lower crop yield in the year 2007 and rainfall in that year was comparatively higher than subsequent years. The fitted regression model for each site in Kharif and Rabi season is explained well about the variability of crop yield. By keeping one of the independent variables (Solar Radiation) constant, variation of the rice yield with change in remaining climate variables are represented in Figs. 7 and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Out of ten models which includes five agricultural sites with each site has two seasons, agricultural site-4 in Rabi season got highest R-Square value of 0.96. The values of coefficient of determination for remaining sites in both seasons are mentioned in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. From fitted models, it is observed that for all the sites rainfall has inverse relation with rice yield whereas yield increasing with solar radiation.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage yield of all the for both seasons from the year 2007 to 2023\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Yield in Kharif (2007-23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Yield in Rabi (2007-23)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2749.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3120.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3293.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3229.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3154.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3227.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2945.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2873.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3007.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3506.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e in Kharif and Rabi\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSite No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKharif\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRabi\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e depicts the distribution of total rainfall received during growth period in all the sites from the year 2007 to 2023, average rainfall during growth period in all the sites was 566.63 mm for site-1, 727.66 mm for site-2, 630.64 mm for site-3, 507.18 mm for site-4, and 877.43 mm for site-5. Highest rainfall for all the sites was observed in year-2020 which is deviated on an average of 65% from mean rainfall of the sites, the highest deviation was observed in site-1 (91% from mean), in this year in all sites the crop yield was low when compared to previous years. Among all sites, site-5 has recorded highest rainfall of 1369.52 mm, the sudden drop in the rainfall was observed in the year 2011 and 2023\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e describes the variation of daily average temperature in Kharif season during growth period in each site from the year 2007 to 2023. 27.48\u003csup\u003e0\u003c/sup\u003eC is the average daily temperature of all the sites during growth period, site-5 records highest average temperature 27.8 \u003csup\u003e0\u003c/sup\u003eC than all sites. The peak temperature in all the sites was observed in the year 2015, in this year the rainfall was decreased on an average of 35% of mean rainfall, site-2 observed with lowest decrease in rainfall in that year which is around 9%.\u003c/p\u003e\n\u003cp\u003eThe variation in daily average solar radiation in sites were described by graph (Figure-11), lower solar radiation was observed during the period where rainfall was high. The study of rainfall, temperature solar radiation is necessary not only in climate-impact point of view because these are the minimum weather datasets required to DSSAT for crop-yield simulation.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e describes the comparison of observed yield and simulated yield from DSSAT. Despite few deviations most the simulated yield is closer to the observed yield, this indicates simulating capability of DSSAT model. In all the sites the year 2020 has observed with lower yield, in the same year the rainfall received in the sites were very high, which are 76% more than average rainfall of all the sites, this implies the negative impact of rainfall on crop yield in that year, also year-2013 has lower yield a rainfall in that year was relatively higher than sites mean rainfall. In the 2023 all the sites are observed with highest yield and rainfall received in the year was low and moderate in the sites, this reveals the negative relation of rainfall with rice yield. The average yield of all the sites during Kharif season from the year 2007\u0026ndash;2023 was tabulated in Table 9\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e shows yearly variation in total rainfall received during growth period in Rabi season, 64.92 mm is the average total rainfall received in all the sites with highest average total rainfall of 79.25 mm received in site-4 whereas site-5 in Kharif season. Site-1 and 2 received highest rainfall in 2007, site-3 and 4 in 2008, site-5 in 2022, rice yields in those years were less which demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e, the similar pattern was observed in Kharif season, this implies sudden increase in rainfall is showing adverse effect of rice yield for all the sites.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e shows the yearly variation of average daily temperature in Rabi season. 27.83\u003csup\u003e0\u003c/sup\u003eC was the average temperature of all the sites, site-4 has recorded highest average temperature of 29.22\u003csup\u003e0\u003c/sup\u003eC followed by site-3 28.68\u003csup\u003e0\u003c/sup\u003eC, and this is because for these sites the growth periods include April and May months.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e demonstrates the yearly variation of average solar radiation during growth period in Rabi season. Average value in all the five sites observed was 20.36 MJ/m\u003csup\u003e2\u003c/sup\u003e/day. Site-3 and 4 has higher solar radiation than other three sites, in the year 2016 all sites received similar average solar radiation value, the lowest solar radiation was observed in the year 2013 at site-1 and 2, year 2007 all sites have relatively lower solar radiation value and rainfall was high in that year.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e shows the temporal variation of simulated crop yield along with the observed yield. All the sites showed larger deviations of simulated yield from observed in the year 2017. Sudden increase in rainfall was observed in site-2 and 3 in the year 2008. All the sites witnessed the lower yield in year 2007, in the same year the rainfall was very high, the similar trend was observed in Kharif season also the yield was lower when there is sudden increase in rainfall.\u003c/p\u003e\n\u003cp\u003eThe non-linear relation between rice yield and climate variables made to fit second degree polynomial equation for both Kharif and Rabi seasons. The resultant rice yield over two different time periods (1970\u0026ndash;2000 and 2000\u0026ndash;2023) are calculated from developed equation to assess the change in rice yield due to change in climate. In the Kharif season, the average decrease in yield was around 6% (186.16 Kg/ha). Out of five agriculture sites, site-1 showed higher decrease in crop yield of 18.1% (555.14 Kg/ha) followed by site-5 of 8.96% (311.45 Kg/ha) in 2000 to 2023 time period when compared with 1970 to 1973. In Rabi season, highest yield reduction also observed at site-1 of 13.37% (455.54 Kg/ha) followed by site-2 of 5% (158 Kg/ha). In both the seasons, site-5 has highest average crop yield of 3007.18 Kg/ha in Kharif, 3506.06 Kg/ha in Rabi. The yield simulations using CERES-Rice model of DSSAT are reliable based on calculated metrics.\u003c/p\u003e\n\u003cp\u003eIn Kharif, for site-1 both temperature and rainfall has shown negative impact on rice yield whereas solar radiation showed strong positive correlation, the interaction terms in equation indicating combination of rainfall and temperature, temperature and solar radiation reducing crop yield, in the case of site-2 solar radiation also showed negative effect on yield, positive correlation is observed between yield and solar radiation, rainfall in site-3 but the combined effect of rainfall and temperature, temperature and solar radiation impacted negatively on yield, for site-4 temperature and solar radiation has shown inverse relation, similar to site-1, site-5 also observed negative relation of rainfall and temperature with rice yield.\u003c/p\u003e\n\u003cp\u003eIn Rabi, in site all three independent variables showed inverse relation with rice yield, among three independent variables rainfall has strong negative relation also the similar trend was observed in site-5. For site-2 temperature and solar radiation are reducing rice yield. In site-4 rainfall and temperature impacting negatively on rice yield out which temperature has more impact.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe yield simulations using CERES-Rice model of DSSAT are reliable based on calculated metrics. Simulated yield over the years 2007 to 2023 for five different agriculture fields are close to each other indicating the precision of simulations performed in DSSAT. Hence, performing these simulations based on available climate and crop management data helps farmers to identifying yield before sowing or harvesting based on which they can modify sowing dates to mitigate negative impact of climate on yield.\u003c/p\u003e \u003cp\u003eRainfall is showing strong negative correlation with rice yield, in both Kharif and Rabi season at least any one of the climate variable adversely effecting rice yield which provides the need of adaptation strategies in mitigating these adverse effects. The methodology developed in the present research work is useful for understanding climate change influence on the crop yield and helps in planning adaptation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRaviteja Kudikala: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing. Venkata Reddy Keesara: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing. Eswar Sai Buri: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, K. F., Wang, G., Yu, M., Koo, J., \u0026amp; You, L. (2015). Potential impact of climate change on cereal crop yield in West Africa. \u003cem\u003eClimatic Change, 133\u003c/em\u003e, 321\u0026ndash;334.\u003c/li\u003e\n\u003cli\u003eAnsari, A., Lin, Y. P., \u0026amp; Lur, H. S. (2021). Evaluating and adapting climate change impacts on rice production in Indonesia: A case study of the Keduang subwatershed, Central Java. \u003cem\u003eEnvironments, 8\u003c/em\u003e(11), 117.\u003c/li\u003e\n\u003cli\u003eAnsari, A., Pranesti, A., Telaumbanua, M., Alam, T., Wulandari, R. A., \u0026amp; Nugroho, B. D. A. (2023). Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach. \u003cem\u003eHeliyon, 9\u003c/em\u003e(9).\u003c/li\u003e\n\u003cli\u003eAra, I., Lewis, M., \u0026amp; Ostendorf, B. (2017). Understanding the spatially variable effects of climate change on rice yield for three ecotypes in Bangladesh, 1981\u0026ndash;2010. \u003cem\u003eAdvances in Agriculture, 2017\u003c/em\u003e, 6287156.\u003c/li\u003e\n\u003cli\u003eBokke, A. S., \u0026amp; Shoro, K. E. (2020). Impact of effective rainfall on net irrigation water requirement: The case of Ethiopia. \u003cem\u003eWater Science, 34\u003c/em\u003e(1), 155\u0026ndash;163.\u003c/li\u003e\n\u003cli\u003eDang, L. T. T., Tran, A. H., Vu, T. L. T., \u0026amp; Nguyen, T. A. T. (2023, April). Assessment of the impact of climate change and cultivation conditions on rice yield in Vietnamese Mekong Delta. A case study in Vinh Thanh District, Can Tho city. In \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e (Vol. 1170, No. 1, p. 012001). IOP Publishing.\u003c/li\u003e\n\u003cli\u003eDebnath, S., Mishra, A., Mailapalli, D. R., Raghuwanshi, N. S., \u0026amp; Sridhar, V. (2021). Assessment of rice yield gap under a changing climate in India. \u003cem\u003eJournal of Water and Climate Change, 12\u003c/em\u003e(4), 1245\u0026ndash;1267.\u003c/li\u003e\n\u003cli\u003eDhekale, B. S., Nageswararao, M. M., Nair, A., Mohanty, U. C., Swain, D. K., Singh, K. K., \u0026amp; Arunbabu, T. (2018). Prediction of Kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts. \u003cem\u003eTheoretical and Applied Climatology, 133\u003c/em\u003e, 1075\u0026ndash;1091.\u003c/li\u003e\n\u003cli\u003eDias, M. P. N. M., Navaratne, C. M., Weerasinghe, K. D. N., \u0026amp; Hettiarachchi, R. H. A. N. (2016). Application of DSSAT crop simulation model to identify the changes of rice growth and yield in Nilwala river basin for mid-centuries under changing climatic conditions. \u003cem\u003eProcedia Food Science, 6\u003c/em\u003e, 159\u0026ndash;163.\u003c/li\u003e\n\u003cli\u003eGardi, M. W., Memic, E., Zewdu, E., \u0026amp; Graeff‐H\u0026ouml;nninger, S. (2022). Simulating the effect of climate change on barley yield in Ethiopia with the DSSAT‐CERES‐Barley model. \u003cem\u003eAgronomy Journal, 114\u003c/em\u003e(2), 1128\u0026ndash;1145.\u003c/li\u003e\n\u003cli\u003eGeethalakshmi, V., Gowtham, R., Gopinath, R., Priyanka, S., Rajavel, M., Senthilraja, K., Dhasarathan, M., Rengalakshmi, R., \u0026amp; Bhuvaneswari, K. (2023). Potential impacts of future climate changes on crop productivity of cereals and legumes in Tamil Nadu, India: A mid‐century time slice approach. \u003cem\u003eAdvances in Meteorology, 2023\u003c/em\u003e, 4540454.\u003c/li\u003e\n\u003cli\u003eGohain, G. B., Singh, K. K., Singh, R. S., Dakhore, K. K., \u0026amp; Ghosh, K. (2022). Application of CERES-sorghum crop simulation model DSSAT v4.7 for determining crop water stress in crop phenological stages. \u003cem\u003eModeling Earth Systems and Environment, 8\u003c/em\u003e(2), 1963\u0026ndash;1975.\u003c/li\u003e\n\u003cli\u003eGuntukula, R. (2020). Assessing the impact of climate change on Indian agriculture: Evidence from major crop yields. \u003cem\u003eJournal of Public Affairs, 20\u003c/em\u003e(1), e2040.\u003c/li\u003e\n\u003cli\u003eHarinarayanan, M. N., \u0026amp; Dheebakaran, G. (2022). Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu. \u003cem\u003eJournal of Applied and Natural Science, 14\u003c/em\u003e(SI), 244.\u003c/li\u003e\n\u003cli\u003eHasan, A. S., Islam, A. K. M. S., Bala, S. K., \u0026amp; Khan, M. J. (2017). Impact of extreme climate change on the production of BORO rice in Bangladesh using DSAAT 4.6. In \u003cem\u003eProceedings of the 6th International Conference on Water and Flood Management (ICWFM 2017)\u003c/em\u003e (pp. 384\u0026ndash;397). IWFM, BUET, Dhaka, Bangladesh.\u003c/li\u003e\n\u003cli\u003eISRIC, https://data.isric.org/geonetwork/srv/eng/catalog.search#/home \u003c/li\u003e\n\u003cli\u003eKephe, P. N., Ayisi, K. K., \u0026amp; Petja, B. M. (2021). Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa. \u003cem\u003eAgriculture \u0026amp; Food Security, 10\u003c/em\u003e(1), 1\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eLi, N., Zhao, Y., Han, J., Yang, Q., Liang, J., Liu, X., Wang, Y., \u0026amp; Huang, Z. (2024). Impacts of future climate change on rice yield based on crop model simulation\u0026mdash;A meta-analysis. \u003cem\u003eScience of The Total Environment, 949\u003c/em\u003e, 175038.\u003c/li\u003e\n\u003cli\u003eLiu, H. L., Liu, H. B., Lei, Q. L., Zhai, L. M., Wang, H. Y., Zhang, J. Z., Zhu, Y. P., Liu, S. P., Li, S. J., Zhang, J. S., \u0026amp; Liu, X. X. (2017). Using the DSSAT model to simulate wheat yield and soil organic carbon under a wheat-maize cropping system in the North China Plain. \u003cem\u003eJournal of Integrative Agriculture, 16\u003c/em\u003e(10), 2300\u0026ndash;2307.\u003c/li\u003e\n\u003cli\u003eLone, B. A., Fayaz, A., Singh, P., Qayoom, S., Dar, Z. A., Kumar, S., AndRabi, N., Manzoor, M., \u0026amp; Rasool, F. (n.d.). Impact of climate change on growth and yield of maize using CERES-Maize model under temperate Kashmir.\u003c/li\u003e\n\u003cli\u003eMoroozeh, A. D., Bansouleh, B. F., Ghobadi, M., \u0026amp; Ahmadpour, A. (2023). Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions. \u003cem\u003eSpanish Journal of Agricultural Research, 21\u003c/em\u003e(3), e1201.\u003c/li\u003e\n\u003cli\u003eMulla, S., Singh, S. K., Ahmed, R., Rana, M., \u0026amp; Singh, N. P. (2024). Assessing climate change impacts on rice yield using bias correction and DSSAT modeling with scenario projections: A case study of Dapoli, Maharashtra. \u003cem\u003eJournal of Pharmacognosy and Phytochemistry, 13\u003c/em\u003e(5), 574\u0026ndash;585.\u003c/li\u003e\n\u003cli\u003eNASA POWER. https://power.larc.nasa.gov/ \u003c/li\u003e\n\u003cli\u003eNasir, I. R., Rasul, F., Ahmad, A., Asghar, H. N., \u0026amp; Hoogenboom, G. (2020). Climate change impacts and adaptations for fine, coarse, and hybrid rice using CERES-Rice. \u003cem\u003eEnvironmental Science and Pollution Research, 27\u003c/em\u003e, 9454\u0026ndash;9464.\u003c/li\u003e\n\u003cli\u003eNicolas, F., Migliaccio, K. W., Hoogenboom, G., Rathinasabapathi, B. R., \u0026amp; Eisenstadt, W. R. (2020). Assessing the potential impact of climate change on rice yield in the Artibonite Valley of Haiti using the CSM-CERES-Rice model. \u003cem\u003eTransactions of the ASABE, 63\u003c/em\u003e(5), 1385\u0026ndash;1400.\u003c/li\u003e\n\u003cli\u003ePai, D. S., Rajeevan, M., Sreejith, O. P., Mukhopadhyay, B., \u0026amp; Satbha, N. S. (2014). Development of a new high spatial resolution (0.25\u0026times; 0.25) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65(1), 1-18.\u003c/li\u003e\n\u003cli\u003ePatel, N. R., Akarsh, A., Ponraj, A., \u0026amp; Singh, J. (2019). Geospatial technology for climate change impact assessment of mountain agriculture. In \u003cem\u003eRemote Sensing of Northwest Himalayan Ecosystems\u003c/em\u003e (pp. 381\u0026ndash;400).\u003c/li\u003e\n\u003cli\u003ePazhanivelan, S., Geethalakshmi, V., Tamilmounika, R., Sudarmanian, N. S., Kaliaperumal, R., Ramalingam, K., Sivamurugan, A. P., Mrunalini, K., Yadav, M. K., \u0026amp; Quicho, E. D. (2022). Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model. \u003cem\u003eAgronomy, 12\u003c/em\u003e(9), 2008.\u003c/li\u003e\n\u003cli\u003eRahman, A., Mojid, M. A., \u0026amp; Banu, S. (2018). Climate change impact assessment on three major crops in the north-central region of Bangladesh using DSSAT. \u003cem\u003eInternational Journal of Agricultural and Biological Engineering, 11\u003c/em\u003e(4), 135\u0026ndash;143.\u003c/li\u003e\n\u003cli\u003eRajasivaranjan, T., Anandhi, A., Patel, N. R., Irannezhad, M., Srinivas, C. V., Veluswamy, K., Surendran, U., \u0026amp; Raja, P. (2022). Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield. \u003cem\u003eScientific Reports, 12\u003c/em\u003e(1), 16985.\u003c/li\u003e\n\u003cli\u003eSrivastava, A. K., Rajeevan, M., \u0026amp; Kshirsagar, S. R. (2009). Development of a high resolution daily gridded temperature data set (1969\u0026ndash;2005) for the Indian region. Atmospheric Science Letters, 10(4), 249-254.\u003c/li\u003e\n\u003cli\u003eTSEO. (2023). \u003cem\u003eSocio-economic Outlook\u003c/em\u003e. https://legislature.telangana.gov.in/socioEconomic (Accessed September 9, 2024)\u003c/li\u003e\n\u003cli\u003eTSSA. (2022). Statistical Abstract. https://tgdps.telangana.gov.in/ (Accessed September 9, 2024)\u003c/li\u003e\n\u003cli\u003eZhao, R., Ma, Y., \u0026amp; Wu, S. (2024). A review of the research status and prospects of regional crop yield simulations. \u003cem\u003eAgronomy, 14\u003c/em\u003e(7), 1397.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CERES-Rice Module, Climate, Crop Yield, DSSAT, NRMSE, Regression","lastPublishedDoi":"10.21203/rs.3.rs-6557175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6557175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice, a staple cereal crop feeding over half of the world's population. It grows in a variety of agro-climatic conditions and is mostly cultivated in Asia. Sudden increase in rainfall and temperature adversely impact on crop yield which leads to economical loss to farmers. The current research aims to assess climate change impact on rice yield produced in Hanumakonda district, Telangana, India where agriculture area is predominant. To achieve this, the yield was simulated over a 17-year (2007\u0026ndash;2023) period for five different agricultural fields of Hanumakonda which has crop management information used by farmers in both Kharif and Rabi seasons using DSSAT (Decision Support System for Agrotechnology Transfer) CERES (Crop Environment Resource Synthesis)-Rice model. RMSE (Root Mean Square Error) and NRMSE (Normalized Root Mean Square Error) are the metrics used to verify the accuracy of simulated yield. The simulated yield is compared with observed data which was collected from Directorate of Economics and Statistics, Telangana. It is observed that the pattern of temporal variation of simulated yield over the years 2007-23 is aligning with the trend of observed yield variation for the same time period. Average NRMSE of all the sites is 13.02% for Kharif and 17.12% for Rabi. The average RMSE of all the sites is 408.07 Kg/ha for Kharif and 584.398 Kg/ha for Rabi. For impact assessment a second-degree polynomial regression model is fitted between climate variables and rice yield. The convincing coefficient of determination values of present fitted model confirms the predicting future rice yield for future climatic conditions.\u003c/p\u003e","manuscriptTitle":"Climate Change Impact Assessment on Rice Yield Using Dssat and Geospatial Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:05:12","doi":"10.21203/rs.3.rs-6557175/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f123670f-3bac-4aab-a806-f595509e753c","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T14:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 09:05:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6557175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6557175","identity":"rs-6557175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}