Impact of Climate Variability on Maize Yield Under Different Climate Change Scenarios in Southern India: A Panel Data Approach

Abstract

The changes in frequency and intensity of rainfall, variation in temperature, increasing extreme weather events, and rising greenhouse gas emissions can together have a varying impact on food grain production, which then leads to significant impacts on food security in the future. The purpose of this study is to quantify how maize productivity might be affected due to climate change in Southern India. The present study examines how the projected changes to the northeast monsoon will affect maize yield in Tamil Nadu during the rabi season, which spans from September to December, by using a three-step methodology. Firstly, global climate models that accurately represent the large-scale features of the mean monsoon were chosen. Secondly, baseline and future climate data were extracted from the selected global models and the baseline data were compared with observations. Thirdly, the panel data regression model was fitted with the India Meteorological Department’s (IMD) observed climate data to generate the baseline coefficients and projected the maize production using future climate data generated from the global climate model. The Representative Concentration Pathways (RCPs) of RCP4.5 and RCP8.5 were used from two global climate model outputs, namely GFDL_CM3 and HadGEM2_CC, to predict the climate change variability on maize yields during the middle (2021–2050) and the end (2071–2100) of this century. The maize yield is predicted to increase by 3 to 5.47 per cent during the mid-century period and it varies from 7.25 to 14.53 per cent during the end of the century for the medium- (RCP4.5) and high-emission (RCP8.5) climate change scenarios. The maize grain yield increasing during the future periods indicated that the increase in rainfall and temperature during winter in Southern India reduced the possibility of a negative impact of temperature on the maize yield.

1. Introduction

The human diet is mostly composed of three major cereals, viz., wheat, rice, and maize, which account for 42 per cent of all food calories and 37 per cent of all protein consumption [1]. Maize is the most significant cereal crop in the world, which is essential to both the present and future food and nutritional security of the world’s food supply. On a global scale, maize is cultivated on about 197 million hectares with an average yield of 6.14 t/ha and a total production of 1210 million tons [2]. Worldwide, the USA leads the production of maize followed by China, Brazil, Argentina, and India. In Sub-Saharan Africa and Latin America, maize is a staple food for over 1.2 billion people. In Asia, it is a vital source of nutritious food and is known as the “queen of the cereals” or “miracle crop” due to its great potential for productivity when compared to other cereal crops.

Maize is the most significant food crop in India after rice and wheat, and is cultivated on 10.8 million hectares with a total production of 35.67 million tons [3]. India has around 4 per cent of the world’s land area for maize cultivation and 2 per cent of its total production, while it ranks fourth in terms of cultivated area and seventh in terms of production. Maize is a highly adaptable crop that grows well in a variety of weather conditions and is extensively cultivated in the states of Karnataka, Andhra Pradesh, Tamil Nadu, Rajasthan, Maharashtra, Bihar, Uttar Pradesh, Madhya Pradesh, and Gujarat, which account for 80 per cent of the cultivated area and 85 per cent of the nation’s total production.

Maize is a crucial cereal crop in Tamil Nadu, constituting 24 per cent of its total food grain production in the state. The demand for maize has risen due to its growing importance in the poultry sector and its popularity for human consumption, attributed to evolving dietary preferences. In Tamil Nadu, around 43 per cent of the agricultural land depends on rainfed conditions and nearly 40 per cent of maize is produced from these areas. Nonetheless, agriculture in Tamil Nadu has a formidable challenge from global warming, which presents a substantial risk to its sustainability.

Climate change is referred to as any change in the climate over time by human activity that modifies the composition of the atmosphere [4]. Atmospheric concentrations of carbon dioxide (CO₂) along with other greenhouse gases have considerably increased due to deforestation, the burning of fossil fuels, and the production of agricultural products. The cumulative rise in India’s annual mean surface temperature from 1930 to 2000 was 0.44 °C, reflecting global patterns [5]. Increases in greenhouse gas emissions have been linked to a 0.85 °C rise in the global average surface temperature between 1880 and 2012 as reported in the IPCC Fifth Assessment Report [6]. The global temperature is projected to increase by 1.5 °C by 2050 and 2 to 4 °C by the end of the 21st century [7]. Lonergan [8] indicated that, with a doubling of CO₂ concentrations, it is expected that the average temperature will rise across the entire nation by 2.33 °C to 4.78 °C. As a result of climate change, the Food and Agriculture Organization predicted that the amount of cultivable rainfed land in developing countries will shrink by 11 per cent by the year 2080, resulting in a loss of nearly 280 million tons of potential cereal production for these countries.

As a consequence of climate change effects, India is also projected to lose around 125 million tons, representing 18 per cent of its total rainfed cereal production by the end of this century [9]. Climate change-related production losses may significantly increase the number of malnourished people, impeding efforts to alleviate poverty and food insecurity. The single largest sector of the Indian economy is agriculture and its related activities [10] continue to be fundamentally dependent on weather variables such as rainfall and temperature. Climate change effects can have either positive or negative effects; farming communities are either at the mercy of these natural forces or can attempt to take advantage of them.

Millions of people around the world, particularly those who reside in hot, dry climates, rely heavily on maize as a dietary food source. Maize is primarily cultivated in marginal areas and has a propensity to flourish in unfavorable climates with little rainfall and maize is a staple meal in many developing nations. Additionally, maize serves as the primary source of protein and energy for millions of individuals and provides a variety of nutritional and therapeutic benefits. In Tamil Nadu, maize is one of the major crops grown in different agro-climatic zones, which are significantly affected by variability in rainfall and temperature, the two most critical climatic parameters that are anticipated to change in the future. Therefore, predicting the effects of climate variability on the productivity of maize will be highly essential to ensure the future food security of populations in Southern India.

The impact of climate change on crop yields has been employed by using statistical models based on historical data to represent the relationship between dependent variables (yield) and independent variables (climatic variables) [11,12]. Crop models have been studied based on simulation data by using crop simulation models (DSSAT, APSIM, CROPGRO) to predict the impact of future climate change on crop yields [13,14,15]. However, the application of statistical models can provide more direct information for the assessment of the impact of climate variables on crop yield [16].

The Representative Concentration Pathways (RCPs) scenarios of CMIP5 climate projection are generated by various modeling groups, which have a huge data set of high spatial resolution till the year 2100 [14]. As different RCP scenarios lead to different projections, and different GCMs produce different projections for the same scenario, uncertainty in future climate scenarios on maize yield needs to be analyzed. Hence, the key objective of the present study is to examine the trends in observations and future climatic variables and to assess the impact of climate change on maize yield in Southern India using panel data statistical models to predict the changes under RCP4.5 (medium-emission) and RCP8.5 (high-emission) scenarios during the mid-century (2021–2050) and end-century (2071–2100) periods.

2. Data and Methodology

2.1. Study Area and Data Sources

Tamil Nadu has been divided into seven agro-climatic zones, viz., northeastern zone, northwestern zone, western zone, Cauvery delta zone, southern zone, high rainfall zone, and hilly zone based on rainfall patterns, irrigation facilities, soil characteristics, cropping system, and other physical and socioeconomic aspects. Maize is the predominant crop cultivated across several agro-climatic zones of Tamil Nadu and its area, production, and productivity distribution are illustrated in Figure 1. Secondary data sources were used for this research study and gathered from a variety of published and unpublished documents, including the Government of Tamil Nadu’s Season and Crop Reports, the Statistical Handbook of Tamil Nadu, and the Tamil Nadu—An Economic Appraisal. The Government of Tamil Nadu’s Season and Crop Reports were used to gather information on maize yield for the seven different agro-climatic zones of Tamil Nadu. Weather information on rainfall and temperature data obtained from the India Meteorological Department (IMD), Pune, were also used.

2.2. Global Climate Models: Historical Data

Climate data for both baseline and future climate change scenarios were available from more than 25 models developed at premier modeling institutes located in different parts of the world. These climate model data were extracted from the global coupled model from CMIP5 (Coupled Model Intercomparison Project Phase 5) and its extensive examination of baseline historical data compared with observations revealed that eight models predicted the regional distribution and variability of mean monsoon precipitation in South Asia [17]. The HadGEM2_CC and the GFDL_CM3 model from the UK Met Office and Geophysical Fluid Dynamics Laboratory, USA, were selected for their exceptional ability to replicate the contemporary climatological features of South Asian monsoons [18,19]. Figure 2 illustrates the schematic diagram employed for downloading climate data pertaining to baseline historical and future climate change scenarios.

2.3. CMIP5 Future Climate Change Scenarios

Representative Concentration Pathways (RCPs) were introduced in CMIP5 to establish a link between radiative forcing and projected CO₂ levels by the year 2100. For the assessment of future climate, the CMIP5 used four RCP scenarios, viz., RCP2.6, RCP4.5, RCP6.0, and RCP8.5, in its Assessment Report 5 (AR5). Among the four climate change scenarios, RCP4.5 and RCP8.5 were chosen for this study to consider medium- and high-emission pathways, respectively. The radiative forcing of ~4.5 W/m² and CO₂ equivalent of ~650 ppm increases up to 2100 are represented by RCP4.5, which stabilizes after 2100 compared to pre-industrial conditions. According to RCP8.5, radiative forcing of >8.5 W/m² and CO₂ equivalent of >1370 ppm continue to rise beyond 2100 [20]. Two time periods, namely mid-century (2021 to 2050) and end-century (2071 to 2100), were chosen to represent the future climate change projection periods.

2.4. Panel Data Regression Models

Generally, there are many models to estimate the impact of climate variability on agriculture and widely used approaches found in the literature are agronomic simulation models, agro-ecological zone models, and cross-sectional models. The agronomic simulation models also known as crop models such as DSSAT [21,22], EPIC [23], and APSIM [24] emphasize the dynamic physiological process of plant growth function. These models need information on crop growth parameters, soil properties, water and fertilizer needs, and meteorological parameters like rainfall, temperature, humidity, and radiation to predict the yield. Agronomic models are specific to each crop and take inputs from field or controlled laboratory conditions to simulate the current yield, compare them with the observed yields, and then use these data for future prediction.

The agro-ecological zone approach is used to measure the overall evaluation of the global food system under various agro-ecological zones considering the future scenarios of population growth, economic expansion, and climate change. Also, this approach is used to study the possible impact of climate change on agricultural resources [25,26] and the effects of climate change on potential arable land, agro-climatic resources, and associated modifications to crop production [27]. This approach also requires inputs such as length of crop growing cycle, yield details, leaf area, harvest index, etc., which are needed to explain plant growth and development. The impact of climate variables combined with these variables is used in the model to determine how the potential agricultural output and cropping patterns are simulated [9].

In the cross-sectional approach, also known as the Ricardian approach, the farm performances are examined by linking land values regressed with a set of climatic and non-climatic inputs to calculate each input’s marginal contribution to farm income [28,29]. The Ricardian approach has been widely applied to many countries with all these studies based on net revenue per hectare. Many of these studies suggest that climate warming will reduce farm net revenues. Although the Ricardian model is widely used to predict climate change impacts on agriculture, it has some limitations. The Ricardian approach overestimates the gains and losses from climate change and assumes that input and product prices will be proportionately constant, and it also ignores price fluctuations [30]. A panel data technique has been used in many recent cross-sectional studies, for instance, the economic impact of climate change on US agriculture [31], impacts on rice yields in Southern India [32,33], impacts on cereals productivity in Afghanistan [34], impacts on rice production in Asian countries [35], and impacts on maize growing in China [36]. Auffhammer et al. [5] studied the impact of greenhouse gases and brown clouds in the atmosphere on rice output in India using the panel data approach.

Hence, the present study was performed by using the panel data approach for different agro-climatic zones of Tamil Nadu, Southern India, to estimate the effect of climate change on maize yields. The high rainfall zone comprising Kanyakumari district and the hilly zone comprising Nilgiris district are not suitable for maize cultivation due to their climatic conditions. To account for unobserved features of each agro-climatic zone, our panel data’s structure incorporates both cross-sectional and time series variation with fixed effects. The specified panel data regression model is as follows:

$$Y_{it}={\beta }_0+{\beta }_1{RF}_{it}+{\beta }_2{T}_{it}+{\beta }_3{RF}_{it}^2+{\beta }_4{T}_{it}^2+{\beta }_5{RF}_{it}\times T_{it}+\varnothing t+Z_i+{\epsilon }_{it}$$

where

• $Y_{it}$ = maize yield for the zone i and at time t;
• ${RF}_{it}$ = rainfall (mm) in zone i and year t;
• $T_{it}$ = temperature (°C) in zone i and year t;
• ${\beta }_0\mathrm{t}\mathrm{o}{\beta }_5\mathrm{a}\mathrm{n}\mathrm{d}\varnothing$ = regression coefficients;
• $t$ = trend variable to capture technological change and adaptation measures;
• $Z_i$ = agro-climatic zone level fixed effect, which equals 1 for observations, from zone i and 0 otherwise;
• ${\epsilon }_{it}$ = error term.

The advantages of the panel data regression with fixed-effect approach control the unobserved district-level heterogeneity [33]; capture the random year-to-year fluctuations in realized weather across agro-climatic zones [37]; and the agro-climatic zone level fixed effect $Z_i$ absorbs zone-specific time-invariant determinants such as soil quality [38,39] to examine the impact of climate change on maize yield.

3. Results and Discussion

3.1. Seasonal Cycle Evaluation of GCM Baseline Climate with Observations

Observational climate data obtained from the meteorological stations controlled by the India Meteorological Department (IMD), Pune, India, provide long-term time series data suitable for analyzing climatological trends. Seasonality is reflected in the temporal patterns of rainfall, as well as minimum and maximum temperatures, which also indicate the timing of their highest and lowest values throughout the year (Figure 3). Rainfall clearly peaks during the northeast monsoon season, which is primary monsoon period in Tamil Nadu, and runs from September to December. The highest maximum and minimum temperatures are recorded in April and May, while the lowest temperatures occur in December and January. This demonstrates that the historical global climate model compared with the observational data effectively captured the seasonal evolution of temperature and rainfall.

Rainfall and temperature climatologies were constructed from two global climate models (GCMs) for the historical period from 1971 to 2000 and their accuracy in reproducing observed patterns of temperature and rainfall was evaluated and this well captured the features of the existing regional climate. The RCP4.5 and RCP8.5 climate change scenarios that correspond to medium and high emissions for the mid-century (2021–2050) and late-century (2071–2100) eras were also analyzed for the future projections. The models effectively captured the seasonal peaks and magnitudes of rainfall, as well as the timing of the highest and lowest temperatures in the annual cycles, indicating good model performance when compared with the observational data.

3.2. Seasonal Cycle of HadGEM2 Model Projections

The historical and projected future rainfall and minimum and maximum temperature climatology of HadGEM2 model are depicted in Figure 4. According to the Hadley Centre model, future rainfall will be expected to increase throughout the year. The rainfall climatology predicts a prominent seasonal peak during the northeast monsoon season through September to December and shows the confidence of the model for projecting future rainfall. The projected temperature climatology for both RCP scenarios indicates an upward trend throughout the year for both the future periods.

3.3. Seasonal Cycle Projections of the GFDL_CM3 Model

The historical and projected rainfall and maximum and minimum temperatures of the GFDL_CM3 model for both the future periods are shown in Figure 5 and all the climate variables indicated an increasing trend throughout the year. Rainfall climatology is the primary indication of model performance for predicting the future climate and it very well captures the seasonal peak during northeast monsoon season. The RCP4.5 and RCP8.5 scenarios predicted the minimum temperature increase of 1.68 °C and 4.65 °C during the mid- and end-century periods.

3.4. Impact of Tempearature and Rainfall on Extreme Weather Events and Pests and Diseases on Maize Yield

Maize crops are affected by drought and floods [40], plant pathogens [41], pest incidence [42], and heat and drought [43] due to changes in temperature. Drought-induced moisture stress causes a yield reduction during the reproductive stage by affecting the release of pollen and silk development by 3 to 8 per cent and by 3 to 6 per cent during the grain-filling stage and extreme moisture stress by up to 30 per cent [44] and 39 per cent [45]; drought stress continued for two weeks before pollination can reduce the yield by 3 to 4 per cent per day [46]. Heat stress of more than 35 °C during the flowering stage also reduces the maize yield by up to 42 per cent in India [47] and a temperature rise can contribute towards increased productivity by increasing photosynthetic activity [48]. Proper adaptation and mitigation strategies such as drought- and heat-tolerant varieties, better irrigation water management, various agronomic managements such as adjusting sowing and harvest dates, mulching, etc., can help to strengthen the resilience of maize production. Delayed planting [49], agro-advisory services to alleviate hydrometeorological disasters, automatic weather stations, climate-smart solutions, diversification and agri-allied sectors [50], shifting sowing date [51], use of farm ponds, effective water conservation technologies, and supplemental irrigation under rainfed maize [52] can be effective adaptation strategies to be followed to cope with climate change and the efficient use of available resources.

Rainfall causes both favorable conditions and damages the crops through pest and disease infestation, impacts on wireworm populations leading to significant crop damage [53], and heavy rainfall washing away tiny pests such as aphids, mites, jassids, and whiteflies can be beneficial [54] and possibly reduce pest manifestations [55]. The planning and formulating of adaptation and mitigation strategies such as integrated pest management practices, pest and disease monitoring for a changing climate, and use of nanomaterials in managing insect pests and disease resistance [56] can help to improve stress tolerance and disease resistance to minimize the yield loss. The emergence of artificial intelligence, machine learning approaches, early warning systems, and remote sensing technologies can be used to control disease and pest outbreaks. Government support is also highly crucial for educational campaigns to create awareness, training programs, implementing crop insurance schemes, infrastructural development, and integrating traditional indigenous practices with modern technology among farmers to reduce the impact of future warming on maize productivity and sustainable food security.

3.5. Estimation of Panel Data Regression: Impact of Climate Variables on Maize Yield

As discussed in the Methodology section, with maize yield as the dependent variable, rainfall, temperature, and their square terms, interaction terms, and dummy variables representing agro-climatic zones were used as independent variables. The results of the estimated panel data models are discussed and the regression results for maize productivity are presented in

Table 1. Regression results: impact of climate variables on maize yield.

Variables	Coefficient	p Value
Constant	4259.176	0.949
Trend	24.058 **	0.022
Rainfall	5.344	0.479
Temperature	−378.972	0.941
Rainfall square	−0.002 *	0.089
Temperature square	9.400	0.924
RF*Temperature	−0.102	0.709
Northwestern zone	83.141	0.871
Western zone	−136.945	0.873
Cauvery delta zone	505.130 ***	0.008
Southern zone	207.099	0.267
F-test	2.58 ***	0.0086
R2	0.23

*, ** and *** significant at 10 per cent, 5 per cent, and 1 per cent.

. The trend variable coefficient is positive and significantly indicates maize productivity increased by 24 kg/ha over a year due to technological developments such as hybrid varieties, improved agronomic practices, integrated pests and diseases management, adaption management for drought, etc. The results show that rainfall has a significant positive effect and temperature has a negative effect on maize yield. The square term corresponding to rainfall was significant, implying that the nonlinear effects of climate variables are significant. The coefficient of the dummies for the Cauvery delta zone is positive and significant (p < 0.01), implying that the mean yields differ statistically compared with other agro-climatic zones. Assuming all other factors remain constant, the difference in mean yield between these two agro-climatic zones is 298.03 kg per ha, with the Cauvery delta zone coefficient being 505.13 and the southern zone’s coefficient being 207.09. The model R² value of 0.23 indicates that the climate variables included in the model explain about 23 per cent of the variations in maize yield. The F-test value can be used to understand, if the given set of independent variables are significant in explaining the variance of the maize yield and our regression model indicated a good fit to the data. The R² value is low in many other similar studies on the economic impact of climate change on agriculture [5,33,35,40]. Even though the R² value is low, the F-value (2.58) is highly significant (p < 0.01), indicating that the overall goodness of fit of the model and explanatory variables in the model are statistically significant.

3.6. Marginal Productivity and Elasticity of Climate Variables

The marginal productivity and elasticity for climatic parameters at mean values for maize crops is shown in

Table 2. Marginal productivity and elasticity of climate variables.

Climate Variables	Marginal Productivity	Elasticity
Rainfall	0.162	0.047
Temperature	51.199	0.736

. The marginal productivity of rainfall at 0.162 indicates that if there is an increase of one mm in rainfall, it results in an increase of 0.162 kg in the mean yield of maize. Based on the marginal productivity estimation from the regression coefficients, a 1 °C increase in the mean temperature may increase the maize yield by 51 kg/ha. The rainfall elasticity of 0.047 implies that a one per cent increase in rainfall will result in an increase of 0.047 per cent in the mean maize yield. Likewise, a one per cent increase in the mean temperature will result in a 0.736 per cent increase in the mean maize yield.

3.7. Prediction of Maize Yield Under Future Climate Change Scenarios

The impacts of climate change variability on maize yield were simulated using the estimated regression coefficients and the predicted maize yield changes are presented in

Table 3. Maize yield prediction under future climate change scenarios.

Climate Change Scenario	Period	GFDL_CM3		HadGEM2_CC
		Change in Maize Yield (kg/ha)	Percentage Increase	Change in Maize Yield (kg/ha)	Percentage Increase
RCP45	2021–2050	86.19	4.82	53.66	3.00
	2071–2100	154.44	8.63	129.73	7.25
RCP85	2021–2050	97.88	5.47	65.84	3.68
	2071–2100	259.90	14.53	253.99	14.20

. The impact of climate change on maize yield is predicted for the mid-century (2021–2050) and end century (2071–2100) under two different climate change scenarios such as RCP4.5 (medium-emission) and RCP8.5 (high-emission). The prediction was compared between two different global climate models, indicating that an increasing trend in both the models gives more reliability and robustness to our prediction. The study indicated that climate change will positively impact on maize yield during the future periods under both RCP4.5 and RCP8.5 climate change scenarios. The rise in the mean maize yield varies by 3 to 5.47 per cent during the mid-century and it varies from 7.25 to 14.53 per cent during the end century as per the two climate change scenarios.

Our results are consistent with a similar study conducted by [52] using the AquaCrop simulation model, which calibrated and simulated maize yield under rainfed conditions. The maize yield was expected to increase by 28.39 to 74.79 per cent under the RCP 4.5 scenario and 34.81 to 85.27 per cent under the RCP8.5 scenario during the near-century (2025) to the end-century (2090) periods in Telangana state. A similar study was conducted by [57] using the DSSAT crop model, which predicted an increased and decreased maize yield under the RCP 4.5 scenario with varying magnitude across Tamil Nadu. Similarly, McDermid et al. [58] used the DSSAT/CERES-Maize crop model to show increasing yields, while the APSIM crop model indicated yield declines in Tamil Nadu. A similar trend of deviation in maize productivity from (−) 22 to (+) 31 per cent was observed in Tamil Nadu during the rabi season by employing the DSSAT v4.7 crop simulation model [50]. Another study conducted by [51] assessed the climate change impact on maize yield in eastern India and predicted that maize cultivated under rainfed conditions showed an increase in yield due to increases in rainfall that reduced the negative impact of temperature rises, whereas maize cultivated in irrigated conditions showed a decreasing trend. The positive effect on maize yields might be due to the increase in rainfall and temperature during the rabi season (September to December), which occurs in the main rainy season of Tamil Nadu [32]. The impact of a rise in temperatures exhibits a greater beneficial positive effect on grain yield in the future [57].

4. Conclusions and Future Strategies

It is crucial to understand the future climate change scenarios and their impact on maize production in Tamil Nadu state to minimize the adverse effects of climate change. Adopting climate-resilient technologies such as the promotion of climate-resilient maize varieties, extension services on climate-smart agricultural practices, investment in research for breeding new maize varieties, developing innovative water management strategies, and improving agricultural practices will be essential to mitigate climate change. A panel data regression model that controls for unobserved features of different agro-climatic zones and takes into account cross-sectional and time series variation with fixed effects has been used to evaluate the variations in maize productivity. Our region of study focused on the northeast monsoon season (September–December) that receives most of the annual rainfall over Tamil Nadu compared to its summer monsoon. The positive aspect of our research is that the global climate model solutions broadly represent the seasonal variations in temperature and rainfall over Tamil Nadu in both the present and projected future climate change scenarios. Overall, the findings of the study show that climate change will boost the productivity of maize during the rabi season in Tamil Nadu under medium- and high-emission scenarios.

The maize yield was projected using the global climate model outputs GFDL_CM3 and HadGEM2_CC by considering two scenarios, viz., RCP4.5 and RCP8.5, for the two periods, viz., 2021–2050 and 2071–2100. The projected maize yield is expected to increase by 3 to 5.5 per cent during the middle of the century and 7.3 to 14.5 per cent by the end of the century. However, the increase in maize yield may be due to the positive effect of projected increases in rainfall and temperature during the winter season, which generate favorable conditions for maize growth and development. Maize is a major crop for small farmers in India; hence, greater awareness about climate change among farmers and the genetic development of crops to withstand drought and high temperatures is needed for the further improvement in yield for ensuring food security and livelihoods for millions of farmers across the country. Further, the study also suggested that future research on various adaptation and mitigation measures for different maize production environments will reduce the climate change impact on maize yield.

This study quantitatively assessed the impact of climate change on maize yield in the southern region of India using panel data analysis, predicting changes under RCP4.5 and RCP8.5 scenarios for the mid-century (2021–2050) and late-century (2071–2100) periods. The results suggest that increased rainfall may have a positive impact on maize productivity, aligning with findings from previous studies on the economic impact of climate change on agriculture.

However, this study has several limitations. Firstly, socioeconomic factors, viz., agricultural subsidies, market price fluctuations, labor availability, and policy changes, were not included, limiting the ability to comprehensively assess the combined effects of climate change and economic conditions on agricultural productivity. Secondly, extreme weather events such as heatwaves, floods, and cyclones were not explicitly analyzed, making it difficult to quantitatively assess their short-term impacts on maize production. Thirdly, precision agriculture technologies and innovative elements, viz., hybrid varieties, climate-resilient crops, and smart farming technologies, were not directly incorporated into the analysis but were only considered indirectly through the trend variable. Fourthly, the lack of quality observations and more reliable future climate projections is also a major obstacle in predicting the future maize production. Developing very-high-resolution regional climate models will capture realistic regional climate features and their variations.

To address these limitations, future research should focus on the integration of socioeconomic variables, explicit inclusion of extreme climate variables, and scenario-based analysis of technological advancements. Additionally, utilizing more detailed agricultural and economic data will enhance the reliability of the study and strengthen its policy implications. Despite these limitations, this study provides a valuable foundation for understanding the complex relationship between climate change and maize productivity, offering key insights for future policy development and climate adaptation strategies.