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Current IssueEditorial BoardOnline First ArticlesArchive

ABSTRACT

Background: This study evaluates the impact of climatic factors (CFs) on rice production and yield in 19 states of India. The elasticities of rice yield and production with CFs were determined. Future projections of rice production and yield were also estimated.

Methods: It uses state-wise balanced panel data of CFs, rice yield and production for the period of between 1966 - 2017. The coefficients of CFs with yield and production were estimated using Cobb-Douglas production model. Marginal impact analysis (MIA) was used to observe the future prediction of rice yield and production in India.

Result: Production and yield of rice decrease due to marginal change in CFs. Rice yield is expected to decline by 1.54%, 1.86%, 2.18% and 2.37%, while its production to be declined by 2.39%, 3.01%, 3.63% and 4.30% in India due to change in CFs by 2040s, 2060s, 2080s and 2100s.

INTRODUCTION

Farming sector has a dependence on climatic factors (CFs). Rice is an important staple food and it is cultivated across India. Thus, it is significant to maintain the food security of Indians (Kumar and Upadhyay, 2019). It is also a requirement to estimate the future prediction of rice yield and production in India. Previous results highlight that climate change is producing a negative impact on world economies, as well as that the agricultural sector faces more risks (Terefe, 2023; Ashkra  et al., 2023). Highly dependent countries on agriculture and related sectors will worsen due to changes in CFs (Kaur et al., 2020; Terefe, 2023). Thereupon, climate change has also affected food security and water availability (Ali  et al., 2017). Livestock productivity, milk production and meat production are also negatively impacted due to climate change (Das, 2018).
       
Water scarcity may be more as temperature increases in Rajasthan (Ravindra  et al., 2024). The incidence of rise in temperature, rainfall, floods, landslides and cyclones have increased significantly in India. Madhya Pradesh has a largest experience of extreme weather events, while Bihar has the highest mortality due to extreme variability in CFs. Moreover, 1.84 million ha. of cropped area is destroyed due to these events in India. In future rainfall is expected to be more severe with less rainy days but with greater intensity. The geographical location and poor health care system of India will make it more vulnerable to spread of communicable diseases like malaria, dengue and chikungunya. The spread of insects and plant-based diseases are harmful for the agriculture sector (Gupta and Pathak, 2016). It is reported an increase in incidence of natural disasters due to changing climate in India.
       
Rice is a highly climate sensitive crop in many countries (Van and Zwart, 2017). The decline in the growing period due to increased temperature will negatively affect rice yield. Ali  et al. (2017) estimated the effect of climatic variables on four major crops (rice, wheat, maize and sugarcane) grown in Pakistan. Chandio  et al. (2019)  assessed the long-term relationship between CFs and rice production in Pakistan. Liu  et al. (2020) analyzed the effect of climatic variability on rice in the world. Li  et al. (2025) noted that growth and yield of rice is sensitive due to change in CO2 concentration and CFs.
       
Rice is an important and worthy staple food grain crop and ensures food and health security of people in India. In India, rice is grown in semi-arid to humid climatic conditions (Aggarwal and Mall, 2002). The production of rice requires high humidity and moderate temperature, extensive water supply and hours of sunshine. Aggarwal and Mall (2002) observed that the rice yield in northern and eastern regions is more vulnerable to climate change. The southern and western regions are less susceptible to changing climates. Mishra  et al. (2013) applied the DSSAT model to analyze the impact of agro-climatic conditions on yield of both the crops and to make future predictions. It was observed that there was variation in crop yield due to the variation in solar availability. Birthal  et al. (2014)  assessed the impact of changing climate and its effect on yield of major food crops in India. Abeysingha  et al. (2016) examined the impact of climate change on rice and wheat in the Gomti river basin of Uttar Pradesh. Vyanatrkao (2017) highlighted the mixed response of climate change to rice production. Kumar and Sidana (2019) observed that the increase in minimum temperature has a negative effect on the rice yield. Thereupon, rice production has high variability across years due to extreme events in India (Gupta and Mishra, 2019). Ashkra  et al. (2023) reported that rice production is significantly associated with rainfall change in India. Therefore, rice crop is highly sensitive due to variability in climatic factors (CFs).
       
Rice crop required extensive water and high humidity during its planting and growing time. However, the extreme variability in maximum temperature and extensive rainfall pattern during harvesting time of rice may reduce its production and yield. Also, water scarcity is caused to diminish growth and development of rice. Earlier economic studies investigated the impact of most CFs excluding water deficit and evapotranspiration on rice yield and production in India. Accordingly, previous findings proved that rice and production decline as CFs increases. However, previous studies could not investigate the impact of CFs (including water deficit and Evapotranspiration) on variability of rice production and yield in India as using district-wise panel data. Thus, rice farming requires scientific cultivation mechanisms, adaptation and mitigation strategies and climate resilience to increase its production and yield in India (Singh et al., 2023). Adaptation strategies can be applied in various aspects like less use of irrigation and minimum quantity of fertilizer to abate CO2 and GHGs emissions from the agricultural sector. Moreover, improving in irrigation facilities, harvesting of rainwater, water conservation and management practices may be proactive to increase rice production and yield.
       
The above-mentioned review stated that rice production and yield decline due to variability in CFs in India (Mishra et al., 2013). Past literature was majorly focused upon the international level for analysing the effect of changing climate on agriculture productivity in general (Liu  et al., 2020). Most studies have focused their investigations at macro, state and regional level in India (Aggarwal and Mall, 2002; Auffhammer  et al., 2011; Birthal  et al., 2014; Abeysingha  et al., 2016; Van and Zwart, 2017; Vyanatrkao, 2017; Pattanayak  et al., 2021; Kumar and Sidana, 2019; Kumar and Singh, 2023). However, earlier researchers could not make future predictions of rice yield and production in the era of global climate change in India. This research has following objectives:

•   To observe the expected change in rice production and  yield due to change in CFs in India.
•   To examine the elasticity of production and yield of rice crop with respect to CFs in India.
•   To observe the future prediction of rice production and yield in India.

MATERIALS AND METHODS

Study area
 
This study is conducted in the Department of Humanities and Social Sciences, Graphic Era (Deemed to be) University. The favorable soil, climatic conditions, geographical location, flora and fauna are beneficial for Indian agriculture. India has diverse regional patterns and climatic conditions. Northern India is covered by the Greater Himalayas. While southern India is surrounded by Sea from all the three sides; towards the East the country is surrounded by Bay of Bengal. The Tropic of Cancer divides the country into two parts. This study includes 19 rice farming intensive states of India (Fig 1). These states have 93% and 93% production and area sown of rice, respectively in India (Reserve Bank of India, GoI, 2017). Hence, these 19 states are major representatives of rice farming in India. West Bengal has a largest contribution in rice production in Indian states. Uttar Pradesh has a largest contribution in area sown of rice in India.

Fig 1: % share of these states in rice production and area sown in India.


 
Sources of data and time period of this Research
 
In this study, yield and production of rice used as dependent variables (DVs), while area sown under rice crop, temperature, precipitation and water deficit are applied as independent variables (IVs) which are compiled in a panel data for 19 Indian states during 1966 - 2017. Production and yield of rice were taken from ICRISAT. ICRISAT provides average monthly data of the mentioned CFs at district level. Thus, the researchers prepare a state-wise crop calendar for rice. Accordingly, monthly average values of the aforesaid CFs during rice crop season are considered as IVs. Moreover, some useful data is also taken from various reports published by the Agriculture Ministry (GoI). The linear interpolation method is used for examining the missing values to make state-wise balanced panel data.
 
Selection of dependent and independent variables
 
Yield of a crop signifies the quality and fertility of soil. Irrigated area, farm management practices, seed quality and use of fertilizer also determine the crop yield. Production and yield of rice are used as dependent variables (DVs) to examine their sensitivity due to variation in climatic factors in this research (Ali et al., 2017; Kumar and Singh, 2023). Finally, monthly average maximum temperature, minimum temperature, precipitation and water deficit during rice crop season are used as climatic factors (CFs). Ali et al., (2017) also included similar kinds of CFs to observe climate change impact on yield of rice, wheat, maize and sugarcane in Pakistan. Auffhammer et al., (2011); Abeysingha et al., (2016); Vyanatrkao (2017); Guntukula (2019); Ashkra et al., (2023); Terefe (2023) also used similar types of CFs in their empirical investigation in India and other nations.
 
Specification of empirical models
 
This research applies a Cobb-Douglas production function (CDPF) model to examine the impact of selected climatic factors (CFs) on rice yield and production. This study assumes that rice yield (RY) and rice total production (RTP) are a function of rice cropped area (RA) and monthly average evapotranspiration (RMAE), monthly average water deficit (RMAWD), monthly average maximum temperature (RMAMAT), monthly average minimum temperature (RMAMIT) and monthly average precipitation (RMAPR) during rice crop season of respective states. The mathematical association of RY and RTP with the CFs are defined as:
 
(RY) = f{(RA), (RMAE), (RMAWD),
                                                                (RMAMAT), (RMAMIT), (RMAPR)}         ....(1)                                               
 
(RTP) = f{(RA), (RMAE), (RMAWD),
                             (RMAMAT), (RMAMIT), (RMAPR)}                ....(2)     
                           
Following equations are used under CDPF model:
 
ln(RY)st = α0 + α1 ln(RA)st + α2 ln(RMAE)st 
+ α3 ln(RMAWD)st + α4 ln(RMAMAT)st +
                           α5 ln(RMAMIT)st + α6 ln(RMAPR)st + Õst                    ....(3) 
                                                                                                                                               
(RTP) = β0 + β1 ln(RA)st + β2 ln(RMAE)st 
+ β3 ln(RMAWD)st + β4 ln(RMAMAT)st 
                  + β5 ln(RMAMIT)st + β6 ln(RMAPR)st + îst               ....(4) 
                                                                                                                                              
Where, 
ln = Natural logarithm of corresponding variables; 
α0 and β0 are intercept terms; α1, …, α6 and are β1, …, β6 are coefficients of associated IVs; f  and î are residual terms; and s and t are the cross-sectional states and years, respectively in the above-mentioned equations. Table 1 explores the description of the variables. While the elasticities of rice yield and production with respect to CFs are also estimated. The coefficients are estimated using PCSEs model for rice yield and production function.

Table 1: Explanation of the variables.


 
Examination of projected rice yield and production
 
This research estimates the projected value of rice production and yields as per the climate change perspectives and methods used by Singh and Jyoti (2021); Kumar and Singh (2023). Marginal impact analysis (MIA) is adopted to examine the predicted values of rice production and yield in different climatic conditions by 2040s, 2060s, 2080s and 2100s. Following equations are adopted for the mentioned investigations:
 
Δ(RTP)2040s =α1×Δ(RMAE) +α2×Δ(RWAWD) +α3×Δ(RMAMAT)
                    +α4×Δ(RMAMIT) +α5 ×Δ(RMAPR)               ....(5) 
          
      Δ(RY)2040s =β1×Δ(RMAE) +β2×Δ(RWAWD)
                         +β3×Δ(RMAMAT) +β4×Δ(RMAMIT) +β5 ×Δ(RMAPR)           ....(6) 
 
            
Where, Δ(RTP)2040s and Δ(RY)2040s are the expected values of rice production and yield, respectively for 2040s and Δ(RMAE), Δ(RMAWD), Δ(RMAMAT), Δ(RMAMIT) and Δ(RMAPR) are the projected change in these CFs by 20240s (Table 2) in equation (5) and equation (6). The projected change in CFs is adopted of the studies of Singh and Jyoti (2021); Kumar and Singh (2023). Thereupon, projected values for rice production and yield are projected for the remaining years. SPSS and STATA software are used for statistical and empirical analysis.

Table 2: Projected trend in CFs in India by the coming years.

RESULTS AND DISCUSSION

The statistical values
 
The statistical values of all variables in given in Table 3. Thereupon, it is noticed a high difference in minimum and maximum values of all variables in the overall sample. Furthermore, most CFs are in abnormal form.

Table 3: Statistical values of variables.


 
Correlation matrix
 
Table 4 show the Spearman’s rank correlation coefficient among the DVs and IVs. RTP is negatively correlated with water deficit. Rice yield (RY) is positively associated with RTP, area sown and all CFs (except, evapotranspiration and precipitation). Thereupon, it is found a significant correlation among the CFs. Maximum temperature is also negatively associated with evapotranspiration and precipitation. While precipitation and evapotranspiration have a positive correlation. The results also signify temperature is positively associated with water deficit.

​

Table 4: Non-parametric correlation coefficients among the DVs and IVs.


 
Rice yield and production
 
Table 5 explore the impact of CFs on rice yield and production. Rice is a dominant foodgrain crop in India. Thus, rice yield and production would be improved as cropped under increases. Despite that, this research suggested that sustainability of cropped areas under rice crop would enhance rice production and yield in India. The R2 and Wald Chi2 values are significantly different for rice yield and production due to their different magnitude. For instance, rice yield is productivity (it is the ratio of total rice production with area sown under this crop), while rice production is gross production. Thus, it is obvious that statistical values of R2 and Wald Chi2 are reported differently. The statistical values of R2 and Wald Chi2 are estimated through regression analysis. Moreover, area sown under rice crop is found conducive to increase its production and yield in India. Thus, protection of the area sown under this crop would be significant to increase production and yield. The results are controversial with the findings of Kumar and Singh (2023) and observe a positive impact of area sown on rice yield and production in Himachal Pradesh.

​

Table 5: Impact of CFs on rice yield and production.


       
Rice yield and production are significantly associated with evapotranspiration. The estimates reveal that rice yield and production may be declined by 0.9172% and 1.3042, respectively as monthly average evapotranspiration increases by 1% during rice crop season. Rice yield and production are adversely impacted due to increase in maximum temperature and evapotranspiration in India. Birthal  et al. (2014); Abeysingha  et al. (2016); Kumar and Sidana (2019); Kumar and Singh (2023) also noted a declining trend in rice yield due to change in CFs in India in future.
       
Controversial impact of water deficit on rice yield and production is found. It may be due to the existence of interaction effects between the CFs. Despite that, the estimates provide a clarity that rice production is likely to decrease by 0.0294% as water deficit increases by 1%. Further, it seems that rice yield and production may have declined by 0.4073% and 1.1563%, respectively as monthly average maximum temperature during rice crop season increases by 1%. While increasing minimum temperature is found favorable to increase rice yield and production. Thereupon, the impact of precipitation on rice yield is observed negatively. Kumar and Sidana (2019) also notified that an increase in temperature by 1oC may reduce rice yield by 1.20% in India. Ashkra  et al. (2023) detect similar predictions about rice yield in India. Therefore, farmers should focus upon rural development, flood control and improving irrigation facilities rice cultivation in India. Appropriate mitigation and adaptation measures are necessary to overcome this challenge of climate change.                           

The government should focus on helping weaker sections, small and marginal farmers of society. Crop insurance schemes, weather forecasting, SMS based information, digital technology, information and communi-cation technologies (ICTs) and climate-smart technologies should be provided to the marginal farmers for improving their trust in the agricultural. Crop management technology and development in technologies enhance sustainable agriculture growth of rice cultivation in India. The use of climate-resilient technology must be adopted in cultivation to increase crop productivity in India (Kumar and Upadhyay, 2019). Public-private partnership is also required to increase sustainable development and resolve the food security related issues in India (Kumar and Upadhyay, 2019).
 
Elasticities
 
Table 6 provide the elasticities of rice yield and production with CFs. Rice yield and production may increase by 0.0157% and 0.5859% due to 1% change in cropped area under this crop. 1% change in monthly average evapotrans-piration is caused to reduce rice yield and production by 0.0037 and 0.0319, respectively. The results imply that changes in CFs are accountable to make significant changes in rice yield and production in India.

Table 6: Elasticities of rice yield and production with CFs.



Expected trend in rice yield and production
 
Fig 2 infer the integrated marginal impact of CFs on rice yield and production. Rice yield and production declined by 0.37% and 1.42% as marginal increases in all CFs. Thereupon, projected results highlighted that rice yield to be decreased by 1.54%, 1.86%, 2.18% and 2.37% by 2040s, 2060s, 2080s and 2100s due to substantial change in CFs in India. Accordingly, rice production would decline by 2.39%, 3.01%, 3.63% and 4.30% for the same years in India. Gupta and Pathak (2016) argued that cereal crop productivity may decline by 10 to 40% by 2100s due to variation in temperature and water availability. Hence, India should apply appropriate water management and conservation policies. Table 7 provided the prediction of rice yield and production in India as per the existing studies.

Fig 2: Projected rice yield and production in India.



Table 7: Prediction of rice crop in India.

CONCLUSION

Rice yield and production were positively correlated with area sown. Water deficit is negatively and positively correlated with rice production and yield, respectively. Rice yield and production are declining due to increase in monthly average evapotranspiration, maximum temperature and precipitation increase in India. While rice production and yield increase as monthly average minimum temperature increases. Marginal increase in CFs is accountable for decreasing rice yield and production in India. Rice yield is expected to decline by 1.54%, 1.86%, 2.18% and 2.37% in India by 2040s, 2060s, 2080s and 2100s due to change in CFs. Rice production is likely to decline by 2.39%, 3.01%, 3.63% and 4.30% in India by the mentioned years.
 

ACKNOWLEDGEMENT

The prime author is thankful to the host institute for providing resources to undertake this research.
 
Disclaimers
 
The authors take all responsibilities of data accuracy and statistical analysis.
 
Informed consent
 
This article does not include any experimental design based on animal and human. 

CONFLICT OF INTEREST

 The authors do not have any conflict of interest for publishing this article. Authors did not receive any financial grant for writing this article.

REFERENCES


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    ABSTRACT

    Background: This study evaluates the impact of climatic factors (CFs) on rice production and yield in 19 states of India. The elasticities of rice yield and production with CFs were determined. Future projections of rice production and yield were also estimated.

    Methods: It uses state-wise balanced panel data of CFs, rice yield and production for the period of between 1966 - 2017. The coefficients of CFs with yield and production were estimated using Cobb-Douglas production model. Marginal impact analysis (MIA) was used to observe the future prediction of rice yield and production in India.

    Result: Production and yield of rice decrease due to marginal change in CFs. Rice yield is expected to decline by 1.54%, 1.86%, 2.18% and 2.37%, while its production to be declined by 2.39%, 3.01%, 3.63% and 4.30% in India due to change in CFs by 2040s, 2060s, 2080s and 2100s.

    INTRODUCTION

    Farming sector has a dependence on climatic factors (CFs). Rice is an important staple food and it is cultivated across India. Thus, it is significant to maintain the food security of Indians (Kumar and Upadhyay, 2019). It is also a requirement to estimate the future prediction of rice yield and production in India. Previous results highlight that climate change is producing a negative impact on world economies, as well as that the agricultural sector faces more risks (Terefe, 2023; Ashkra  et al., 2023). Highly dependent countries on agriculture and related sectors will worsen due to changes in CFs (Kaur et al., 2020; Terefe, 2023). Thereupon, climate change has also affected food security and water availability (Ali  et al., 2017). Livestock productivity, milk production and meat production are also negatively impacted due to climate change (Das, 2018).
           
    Water scarcity may be more as temperature increases in Rajasthan (Ravindra  et al., 2024). The incidence of rise in temperature, rainfall, floods, landslides and cyclones have increased significantly in India. Madhya Pradesh has a largest experience of extreme weather events, while Bihar has the highest mortality due to extreme variability in CFs. Moreover, 1.84 million ha. of cropped area is destroyed due to these events in India. In future rainfall is expected to be more severe with less rainy days but with greater intensity. The geographical location and poor health care system of India will make it more vulnerable to spread of communicable diseases like malaria, dengue and chikungunya. The spread of insects and plant-based diseases are harmful for the agriculture sector (Gupta and Pathak, 2016). It is reported an increase in incidence of natural disasters due to changing climate in India.
           
    Rice is a highly climate sensitive crop in many countries (Van and Zwart, 2017). The decline in the growing period due to increased temperature will negatively affect rice yield. Ali  et al. (2017) estimated the effect of climatic variables on four major crops (rice, wheat, maize and sugarcane) grown in Pakistan. Chandio  et al. (2019)  assessed the long-term relationship between CFs and rice production in Pakistan. Liu  et al. (2020) analyzed the effect of climatic variability on rice in the world. Li  et al. (2025) noted that growth and yield of rice is sensitive due to change in CO2 concentration and CFs.
           
    Rice is an important and worthy staple food grain crop and ensures food and health security of people in India. In India, rice is grown in semi-arid to humid climatic conditions (Aggarwal and Mall, 2002). The production of rice requires high humidity and moderate temperature, extensive water supply and hours of sunshine. Aggarwal and Mall (2002) observed that the rice yield in northern and eastern regions is more vulnerable to climate change. The southern and western regions are less susceptible to changing climates. Mishra  et al. (2013) applied the DSSAT model to analyze the impact of agro-climatic conditions on yield of both the crops and to make future predictions. It was observed that there was variation in crop yield due to the variation in solar availability. Birthal  et al. (2014)  assessed the impact of changing climate and its effect on yield of major food crops in India. Abeysingha  et al. (2016) examined the impact of climate change on rice and wheat in the Gomti river basin of Uttar Pradesh. Vyanatrkao (2017) highlighted the mixed response of climate change to rice production. Kumar and Sidana (2019) observed that the increase in minimum temperature has a negative effect on the rice yield. Thereupon, rice production has high variability across years due to extreme events in India (Gupta and Mishra, 2019). Ashkra  et al. (2023) reported that rice production is significantly associated with rainfall change in India. Therefore, rice crop is highly sensitive due to variability in climatic factors (CFs).
           
    Rice crop required extensive water and high humidity during its planting and growing time. However, the extreme variability in maximum temperature and extensive rainfall pattern during harvesting time of rice may reduce its production and yield. Also, water scarcity is caused to diminish growth and development of rice. Earlier economic studies investigated the impact of most CFs excluding water deficit and evapotranspiration on rice yield and production in India. Accordingly, previous findings proved that rice and production decline as CFs increases. However, previous studies could not investigate the impact of CFs (including water deficit and Evapotranspiration) on variability of rice production and yield in India as using district-wise panel data. Thus, rice farming requires scientific cultivation mechanisms, adaptation and mitigation strategies and climate resilience to increase its production and yield in India (Singh et al., 2023). Adaptation strategies can be applied in various aspects like less use of irrigation and minimum quantity of fertilizer to abate CO2 and GHGs emissions from the agricultural sector. Moreover, improving in irrigation facilities, harvesting of rainwater, water conservation and management practices may be proactive to increase rice production and yield.
           
    The above-mentioned review stated that rice production and yield decline due to variability in CFs in India (Mishra et al., 2013). Past literature was majorly focused upon the international level for analysing the effect of changing climate on agriculture productivity in general (Liu  et al., 2020). Most studies have focused their investigations at macro, state and regional level in India (Aggarwal and Mall, 2002; Auffhammer  et al., 2011; Birthal  et al., 2014; Abeysingha  et al., 2016; Van and Zwart, 2017; Vyanatrkao, 2017; Pattanayak  et al., 2021; Kumar and Sidana, 2019; Kumar and Singh, 2023). However, earlier researchers could not make future predictions of rice yield and production in the era of global climate change in India. This research has following objectives:

    •   To observe the expected change in rice production and  yield due to change in CFs in India.
    •   To examine the elasticity of production and yield of rice crop with respect to CFs in India.
    •   To observe the future prediction of rice production and yield in India.

    MATERIALS AND METHODS

    Study area
     
    This study is conducted in the Department of Humanities and Social Sciences, Graphic Era (Deemed to be) University. The favorable soil, climatic conditions, geographical location, flora and fauna are beneficial for Indian agriculture. India has diverse regional patterns and climatic conditions. Northern India is covered by the Greater Himalayas. While southern India is surrounded by Sea from all the three sides; towards the East the country is surrounded by Bay of Bengal. The Tropic of Cancer divides the country into two parts. This study includes 19 rice farming intensive states of India (Fig 1). These states have 93% and 93% production and area sown of rice, respectively in India (Reserve Bank of India, GoI, 2017). Hence, these 19 states are major representatives of rice farming in India. West Bengal has a largest contribution in rice production in Indian states. Uttar Pradesh has a largest contribution in area sown of rice in India.

    Fig 1: % share of these states in rice production and area sown in India.


     
    Sources of data and time period of this Research
     
    In this study, yield and production of rice used as dependent variables (DVs), while area sown under rice crop, temperature, precipitation and water deficit are applied as independent variables (IVs) which are compiled in a panel data for 19 Indian states during 1966 - 2017. Production and yield of rice were taken from ICRISAT. ICRISAT provides average monthly data of the mentioned CFs at district level. Thus, the researchers prepare a state-wise crop calendar for rice. Accordingly, monthly average values of the aforesaid CFs during rice crop season are considered as IVs. Moreover, some useful data is also taken from various reports published by the Agriculture Ministry (GoI). The linear interpolation method is used for examining the missing values to make state-wise balanced panel data.
     
    Selection of dependent and independent variables
     
    Yield of a crop signifies the quality and fertility of soil. Irrigated area, farm management practices, seed quality and use of fertilizer also determine the crop yield. Production and yield of rice are used as dependent variables (DVs) to examine their sensitivity due to variation in climatic factors in this research (Ali et al., 2017; Kumar and Singh, 2023). Finally, monthly average maximum temperature, minimum temperature, precipitation and water deficit during rice crop season are used as climatic factors (CFs). Ali et al., (2017) also included similar kinds of CFs to observe climate change impact on yield of rice, wheat, maize and sugarcane in Pakistan. Auffhammer et al., (2011); Abeysingha et al., (2016); Vyanatrkao (2017); Guntukula (2019); Ashkra et al., (2023); Terefe (2023) also used similar types of CFs in their empirical investigation in India and other nations.
     
    Specification of empirical models
     
    This research applies a Cobb-Douglas production function (CDPF) model to examine the impact of selected climatic factors (CFs) on rice yield and production. This study assumes that rice yield (RY) and rice total production (RTP) are a function of rice cropped area (RA) and monthly average evapotranspiration (RMAE), monthly average water deficit (RMAWD), monthly average maximum temperature (RMAMAT), monthly average minimum temperature (RMAMIT) and monthly average precipitation (RMAPR) during rice crop season of respective states. The mathematical association of RY and RTP with the CFs are defined as:
     
    (RY) = f{(RA), (RMAE), (RMAWD),
                                                                    (RMAMAT), (RMAMIT), (RMAPR)}         ....(1)                                               
     
    (RTP) = f{(RA), (RMAE), (RMAWD),
                                 (RMAMAT), (RMAMIT), (RMAPR)}                ....(2)     
                               
    Following equations are used under CDPF model:
     
    ln(RY)st = α0 + α1 ln(RA)st + α2 ln(RMAE)st 
    + α3 ln(RMAWD)st + α4 ln(RMAMAT)st +
                               α5 ln(RMAMIT)st + α6 ln(RMAPR)st + Õst                    ....(3) 
                                                                                                                                                   
    (RTP) = β0 + β1 ln(RA)st + β2 ln(RMAE)st 
    + β3 ln(RMAWD)st + β4 ln(RMAMAT)st 
                      + β5 ln(RMAMIT)st + β6 ln(RMAPR)st + îst               ....(4) 
                                                                                                                                                  
    Where, 
    ln = Natural logarithm of corresponding variables; 
    α0 and β0 are intercept terms; α1, …, α6 and are β1, …, β6 are coefficients of associated IVs; f  and î are residual terms; and s and t are the cross-sectional states and years, respectively in the above-mentioned equations. Table 1 explores the description of the variables. While the elasticities of rice yield and production with respect to CFs are also estimated. The coefficients are estimated using PCSEs model for rice yield and production function.

    Table 1: Explanation of the variables.


     
    Examination of projected rice yield and production
     
    This research estimates the projected value of rice production and yields as per the climate change perspectives and methods used by Singh and Jyoti (2021); Kumar and Singh (2023). Marginal impact analysis (MIA) is adopted to examine the predicted values of rice production and yield in different climatic conditions by 2040s, 2060s, 2080s and 2100s. Following equations are adopted for the mentioned investigations:
     
    Δ(RTP)2040s =α1×Δ(RMAE) +α2×Δ(RWAWD) +α3×Δ(RMAMAT)
                        +α4×Δ(RMAMIT) +α5 ×Δ(RMAPR)               ....(5) 
              
          Δ(RY)2040s =β1×Δ(RMAE) +β2×Δ(RWAWD)
                             +β3×Δ(RMAMAT) +β4×Δ(RMAMIT) +β5 ×Δ(RMAPR)           ....(6) 
     
                
    Where, Δ(RTP)2040s and Δ(RY)2040s are the expected values of rice production and yield, respectively for 2040s and Δ(RMAE), Δ(RMAWD), Δ(RMAMAT), Δ(RMAMIT) and Δ(RMAPR) are the projected change in these CFs by 20240s (Table 2) in equation (5) and equation (6). The projected change in CFs is adopted of the studies of Singh and Jyoti (2021); Kumar and Singh (2023). Thereupon, projected values for rice production and yield are projected for the remaining years. SPSS and STATA software are used for statistical and empirical analysis.

    Table 2: Projected trend in CFs in India by the coming years.

    RESULTS AND DISCUSSION

    The statistical values
     
    The statistical values of all variables in given in Table 3. Thereupon, it is noticed a high difference in minimum and maximum values of all variables in the overall sample. Furthermore, most CFs are in abnormal form.

    Table 3: Statistical values of variables.


     
    Correlation matrix
     
    Table 4 show the Spearman’s rank correlation coefficient among the DVs and IVs. RTP is negatively correlated with water deficit. Rice yield (RY) is positively associated with RTP, area sown and all CFs (except, evapotranspiration and precipitation). Thereupon, it is found a significant correlation among the CFs. Maximum temperature is also negatively associated with evapotranspiration and precipitation. While precipitation and evapotranspiration have a positive correlation. The results also signify temperature is positively associated with water deficit.

    ​

    Table 4: Non-parametric correlation coefficients among the DVs and IVs.


     
    Rice yield and production
     
    Table 5 explore the impact of CFs on rice yield and production. Rice is a dominant foodgrain crop in India. Thus, rice yield and production would be improved as cropped under increases. Despite that, this research suggested that sustainability of cropped areas under rice crop would enhance rice production and yield in India. The R2 and Wald Chi2 values are significantly different for rice yield and production due to their different magnitude. For instance, rice yield is productivity (it is the ratio of total rice production with area sown under this crop), while rice production is gross production. Thus, it is obvious that statistical values of R2 and Wald Chi2 are reported differently. The statistical values of R2 and Wald Chi2 are estimated through regression analysis. Moreover, area sown under rice crop is found conducive to increase its production and yield in India. Thus, protection of the area sown under this crop would be significant to increase production and yield. The results are controversial with the findings of Kumar and Singh (2023) and observe a positive impact of area sown on rice yield and production in Himachal Pradesh.

    ​

    Table 5: Impact of CFs on rice yield and production.


           
    Rice yield and production are significantly associated with evapotranspiration. The estimates reveal that rice yield and production may be declined by 0.9172% and 1.3042, respectively as monthly average evapotranspiration increases by 1% during rice crop season. Rice yield and production are adversely impacted due to increase in maximum temperature and evapotranspiration in India. Birthal  et al. (2014); Abeysingha  et al. (2016); Kumar and Sidana (2019); Kumar and Singh (2023) also noted a declining trend in rice yield due to change in CFs in India in future.
           
    Controversial impact of water deficit on rice yield and production is found. It may be due to the existence of interaction effects between the CFs. Despite that, the estimates provide a clarity that rice production is likely to decrease by 0.0294% as water deficit increases by 1%. Further, it seems that rice yield and production may have declined by 0.4073% and 1.1563%, respectively as monthly average maximum temperature during rice crop season increases by 1%. While increasing minimum temperature is found favorable to increase rice yield and production. Thereupon, the impact of precipitation on rice yield is observed negatively. Kumar and Sidana (2019) also notified that an increase in temperature by 1oC may reduce rice yield by 1.20% in India. Ashkra  et al. (2023) detect similar predictions about rice yield in India. Therefore, farmers should focus upon rural development, flood control and improving irrigation facilities rice cultivation in India. Appropriate mitigation and adaptation measures are necessary to overcome this challenge of climate change.                           

    The government should focus on helping weaker sections, small and marginal farmers of society. Crop insurance schemes, weather forecasting, SMS based information, digital technology, information and communi-cation technologies (ICTs) and climate-smart technologies should be provided to the marginal farmers for improving their trust in the agricultural. Crop management technology and development in technologies enhance sustainable agriculture growth of rice cultivation in India. The use of climate-resilient technology must be adopted in cultivation to increase crop productivity in India (Kumar and Upadhyay, 2019). Public-private partnership is also required to increase sustainable development and resolve the food security related issues in India (Kumar and Upadhyay, 2019).
     
    Elasticities
     
    Table 6 provide the elasticities of rice yield and production with CFs. Rice yield and production may increase by 0.0157% and 0.5859% due to 1% change in cropped area under this crop. 1% change in monthly average evapotrans-piration is caused to reduce rice yield and production by 0.0037 and 0.0319, respectively. The results imply that changes in CFs are accountable to make significant changes in rice yield and production in India.

    Table 6: Elasticities of rice yield and production with CFs.



    Expected trend in rice yield and production
     
    Fig 2 infer the integrated marginal impact of CFs on rice yield and production. Rice yield and production declined by 0.37% and 1.42% as marginal increases in all CFs. Thereupon, projected results highlighted that rice yield to be decreased by 1.54%, 1.86%, 2.18% and 2.37% by 2040s, 2060s, 2080s and 2100s due to substantial change in CFs in India. Accordingly, rice production would decline by 2.39%, 3.01%, 3.63% and 4.30% for the same years in India. Gupta and Pathak (2016) argued that cereal crop productivity may decline by 10 to 40% by 2100s due to variation in temperature and water availability. Hence, India should apply appropriate water management and conservation policies. Table 7 provided the prediction of rice yield and production in India as per the existing studies.

    Fig 2: Projected rice yield and production in India.



    Table 7: Prediction of rice crop in India.

    CONCLUSION

    Rice yield and production were positively correlated with area sown. Water deficit is negatively and positively correlated with rice production and yield, respectively. Rice yield and production are declining due to increase in monthly average evapotranspiration, maximum temperature and precipitation increase in India. While rice production and yield increase as monthly average minimum temperature increases. Marginal increase in CFs is accountable for decreasing rice yield and production in India. Rice yield is expected to decline by 1.54%, 1.86%, 2.18% and 2.37% in India by 2040s, 2060s, 2080s and 2100s due to change in CFs. Rice production is likely to decline by 2.39%, 3.01%, 3.63% and 4.30% in India by the mentioned years.
     

    ACKNOWLEDGEMENT

    The prime author is thankful to the host institute for providing resources to undertake this research.
     
    Disclaimers
     
    The authors take all responsibilities of data accuracy and statistical analysis.
     
    Informed consent
     
    This article does not include any experimental design based on animal and human. 

    CONFLICT OF INTEREST

     The authors do not have any conflict of interest for publishing this article. Authors did not receive any financial grant for writing this article.

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