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Agricultural & Rural Studies 2025, 3, 0004. https://doi.org/10.59978/ar03010004 https://sccpress.com/ars
Article
Exploring the Effects of Climate Change on Rice Yields in Andhra
Pradesh, India
Kotamraju Nirmal Ravi Kumar 1, Tatineni Ramesh Babu 2 , Kavanadala Rangaraju Hamsa 3, Adinan Bahahudeen
Shafiwu 4,* and Ishaque Mahama 5
1 Department of Agricultural Economics, Acharya NG Ranga Agricultural University, Bapatla 522034, India;
kn.ravikumar@angrau.ac.in
2 Vignan’s Foundation for Science, Technology and Research, Guntur 522213, India; dean_saft@vignan.ac.in
3 College of Agriculture and Research Station, Indira Gandhi Krishi Vishwavidyalaya, Kurud 493663, India;
hmmshamsa@gmail.com
4 Faculty of Agriculture, Food, and Consumer Sciences, University for Development Studies, Tamale P.O. Box TL1350,
Ghana
5 Faculty of Social Science and Arts Departments, Simon Diedong Dombo University of Business and Integrated
Development Studies, Wa P.O. Box 36, Ghana; imahama@ubids.edu.gh
*Correspondence: shafiwu@uds.edu.gh
Abstract: This study investigates the influence of climate change variables—namely rainfall, maximum tem-
perature, and minimum temperature—on mean rice yields and yield variability across different agro-climatic
zones in Andhra Pradesh during the Kharif and Rabi seasons. Utilizing Just and Pope production function, the
research focuses on rice, a crucial crop for both seasons in the region. Drawing from panel data spanning 1998
to 2022, the study offers significant insights. During the Kharif season, increased rainfall, along with favorable
maximum and minimum temperatures, positively correlates with higher mean rice yields and reduced yield
variability. In contrast, during the Rabi season, only increased rainfall showed a significant impact on enhanc-
ing yields and minimizing variability, while temperature variables did not exhibit a substantial effect. Addi-
tionally, the time trend variable showed a positive and significant association with mean yield and yield vari-
ability in both seasons. Thus, technological advancement has contributed to improved rice yields and reduced
variability. These findings underscore the importance of informed decision-making in sustainable rice culti-
vation, enabling farmers to effectively manage the impacts of climate change on yield and variability. By
utilizing this knowledge, farmers can adapt their crop management strategies to optimize productivity and
bolster the resilience of rice production in the face of evolving climatic conditions.
Keywords: rice; Andhra Pradesh; panel data; Just and Pope production function; elasticities
1. Introduction
India, much like numerous other nations, faces significant challenges due to climate change.
Alterations in rainfall patterns have led to increased precipitation and severe rainfall events in cer-
tain regions, resulting in floods and landslides. Conversely, other areas experience reduced rainfall,
leading to droughts and water scarcity. Climate change has emerged as a critical global concern,
attracting the attention of environmentalists because of its long-term adverse effects on agricultural
production, food and water security, and rural livelihoods (Baig et al., 2022). Its impacts extend to
the socio-economic and environmental realms, potentially causing widespread famines, migration,
natural resource depletion, and economic instability. Agriculture is particularly vulnerable, bearing
up to 80 percent of direct consequences, which significantly affect water availability, agricultural
output, food security, and rural livelihoods. This multifaceted crisis highlights the need for a com-
prehensive strategy to mitigate the effects of climate change on agriculture and the broader socio-
economic landscape. The ramifications transcend regional boundaries, permeating every house-
hold, as agricultural production and water resources are intrinsically linked in facilitating a plethora
of goods and services. Consequently, climate change emerges as a formidable impediment to
achieving sustainable agricultural development and ensuring food security. Regrettably, India is
one of the susceptible nations to climate change, as evidenced by projections from earlier studies
(Chaturvedi et al., 2012; Krishnan et al., 2020) indicating escalated rainfall and extreme tempera-
tures, impeding timely crop sowing, growth, yields, and food security. According to Chaturvedi et
Citation:
Kumar, K. N. R.; Babu, T. R.;
Hamsa
, K. R.; Shafiwu, A. B.; Mahama,
I.
Exploring the Effects of Climate
Change on Rice Yields in Andhra
Pradesh, India
. Agricultural & Rural
Studies
, 2025, 3, 0004.
https://doi.org/
10.59978/ar03010004
Received:
25 August 2024
R
evised: 18 October 2024
Accepted:
24 December 2024
Published:
3 March 2025
Copyright:
© 2025 by the authors. Licen-
see
SCC Press, Kowloon, Hong Kong
S.A.R.,
China. This article is an open ac-
cess article distributed under the
terms and
conditions of the Creative Commons At-
tribution
(CC BY) license (https://crea-
tivecommons.org/license/by/4.0/).
A&R 2025, Vol. 3, No. 1, 0004 2 of 21
al. (2012), mean warming across India is anticipated to range between 1.7 to 2.0 °C by the 2030s
and 3.3 to 4.8 °C by the 2080s, with precipitation projected to surge by 4 to 5 percent by the 2030s
and 6 to 14 percent by 2080s compared to the 1961–1990 baseline. Moreover, a consistent positive
trend in extreme precipitation days (e.g., exceeding 40 mm/day) is anticipated for the decades be-
yond the 2060s. While climate variability is a global predicament, its impact on agriculture is par-
ticularly acute for emerging economies, notably Asian and African nations (Chandio et al., 2022b).
Given farmers limited financial resources to mitigate environmental impacts on agriculture, climate
change presents a formidable challenge for economists, agronomists, and policymakers (Chandio
et al., 2022d). This highlights the necessity for a rigorous examination of climate change's impact
and variability on crop yields and the consequent development of climate-resilient crop varieties
and technologies tailored to evolving climatic scenarios. Addressing these issues is crucial to safe-
guarding agricultural sustainability and ensuring the resilience of rural communities amidst the
growing threat of climate change. Agriculture is particularly vulnerable, with climate change dis-
rupting crop growth, water availability, and pest dynamics. The State of Andhra Pradesh boasts
diverse agro-climatic zones, encompassing coastal regions, upland areas, hot and humid regions,
and semi-arid regions. Rice, a staple food crop in Andhra Pradesh, is cultivated across these diverse
zones. It constitutes approximately 40 percent of India’s total foodgrain production and accounted
for 16 percent of global rice production in 2021–22 (Directorate of Economics and Statistics, 2022).
Andhra Pradesh ranked eighth in India in rice production, contributing 7.79 million tonnes
accounting for 5.98 percent of the country’s rice production during 2021–22. Notably, the average
rice yield in Andhra Pradesh (3470 kg/ha) surpasses the national average (2809 kg/ha) in 2021–22
(Figure 1; Directorate of Economics and Statistics, 2022). Rice cultivation in Andhra Pradesh spans
both the Kharif and Rabi seasons across diverse agro-climatic zones, including the Scarce Rainfall
Zone, Southern Zone, Krishna Zone, Godavari Zone, and North Coastal Zone. Rice plays an indis-
pensable role in the agricultural economy of Andhra Pradesh, serving as both a staple food and a
critical source of livelihood for millions of farmers. Its importance is accentuated by the state’s
diverse agro-climatic zones, each uniquely suited for rice cultivation. The crop’s prevalence in both
the Kharif and Rabi seasons underscores its significance, as it sustains food security and economic
stability throughout the year. Rice cultivation is heavily dependent on climatic factors, particularly
rainfall and temperature, making these variables crucial for understanding seasonal crop dynamics.
In the Kharif season, which aligns with the monsoon, rainfall is the primary determinant of rice
growth, affecting both water availability and soil conditions necessary for optimal yields. Con-
versely, the Rabi season relies on residual moisture and supplemental irrigation, making tempera-
ture a more critical factor, as it influences crop maturation and water requirements. Any fluctuations
in these climate variables, such as altered precipitation patterns or increased temperature extremes,
can have profound effects on rice productivity, leading to variability in yields. Thus, analyzing the
impacts of climate change on these variables across both seasons is essential for ensuring the resil-
ience of rice farming, safeguarding food security, and formulating effective adaptation strategies in
the face of climate-induced risks. However, the rice cultivating agro-climatic zones experience het-
erogeneous impacts from climate change (Mendelsohn & Williams, 2004; Gbetibouo & Hassan,
2005), necessitating research that employs more disaggregated climate, area, and yield data to com-
prehensively understand its impact on rice yields. Rice is a staple crop, making it crucial to under-
stand climate change effects on its yields to evaluate potential disruptions to food security and
availability. Investigating these impacts helps identify specific challenges and vulnerabilities, guid-
ing the development of adaptation strategies and policies to mitigate adverse effects and enhance
resilience. Additionally, given the substantial water usage in rice cultivation, understanding climate
change’s impact on yields can inform effective water management strategies. Scientific research in
this domain provides valuable evidence for policymakers and decision-makers, aiding the formu-
lation of climate-resilient agricultural policies and sustainable farming practices. This research spe-
cifically aims to scrutinize variations in climate change variables and rice yields across selected
districts during the Kharif and Rabi seasons. It seeks to understand how changes in climate varia-
bles affect both the average and variability of rice yields throughout these seasons. Furthermore,
the study endeavors to estimate the elasticities of climate change configurations to forecast future
rice yields for both seasons, thereby offering insights for proactive agricultural planning and policy
formulation.
A&R 2025, Vol. 3, No. 1, 0004 3 of 21
Figure 1: Trends in Area, Production and Productivity of Rice in India and Andhra Pradesh (2001–02 to
2021–22).
This study aims to address specific gaps in existing research on the impact of climate change
on rice yields in Andhra Pradesh, with a focus on the Kharif and Rabi seasons. While several studies
have examined the broader impacts of climate change on agriculture in India, few have conducted
an in-depth, district-level analysis that considers the unique agro-climatic zones of Andhra Pradesh.
Many existing studies, such as those by Gupta and Mishra (2019), Saud et al. (2022), and Singh et
al. (2024), have explored the heterogeneous impacts of climate change on agriculture across differ-
ent regions, but this research brings a more granular focus by disaggregating climate, area, and
yield data across specific zones within Andhra Pradesh. A key gap this study addresses is the ab-
sence of detailed insights into how climate variables such as rainfall and temperature uniquely af-
fect rice yields across both seasons in the state’s diverse agro-climatic zones. For instance, while
previous research may have assessed the overall vulnerability of crops to climate change, there is
limited analysis of how Kharif and Rabi season-specific climate variations influence rice produc-
tivity, especially with regard to water availability and temperature fluctuations. This research also
seeks to contribute uniquely by estimating the elasticities of climate variables, offering a forecast
of future rice yields under different climate change scenarios. By doing so, it provides critical in-
sights for policymakers and agricultural planners, particularly with respect to formulating targeted
adaptation strategies for different agro-climatic regions. In comparison to broader studies that may
not focus on the seasonal and spatial complexities of rice cultivation in Andhra Pradesh, this re-
search adds value by delivering district-specific, data-driven recommendations, ensuring more pre-
cise and regionally tailored policy interventions. This nuanced approach fills an existing gap by not
only exploring the variability of rice yields across seasons but also offering forward-looking solu-
tions for mitigating the adverse effects of climate change on rice production in the region.
This study significantly advances existing research by incorporating several novel elements.
Firstly, it enhances the current literature by utilizing homogeneity and inhomogeneity calculations
to identify breakpoints in climate change data, specifically analyzing rainfall patterns and maxi-
mum and minimum temperatures over a period of two and a half decades. Secondly, by focusing
on Andhra Pradesh, the research addresses a critical gap and provides valuable insights for formu-
lating climate-resilient strategies in rice cultivation. Thirdly, the study employs an extensive panel
dataset spanning nearly two and a half decades (1998–2022). This extended timeframe is particu-
larly valuable for capturing the gradual impacts of climate change, which manifest over long peri-
ods. The dataset surpasses those used in previous studies, such as Mandal and Singha (2020) and
0
500
1000
1500
2000
2500
3000
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
Area (m.ha)Produc�on (m. tonnes)Produc�vity (kg/ha)
India
0
1000
2000
3000
4000
5000
6000
7000
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Area (m.ha)Produc�on (m. t onnes)Produc�vity (kg/ha)
Andhra Pradesh
A&R 2025, Vol. 3, No. 1, 0004 4 of 21
Carew et al. (2017), thereby offering a more comprehensive and reliable assessment of climate
change’s impact on rice yields. Fourthly, the research delves into the granular level of districts
specializing in rice cultivation across diverse agro-climatic zones in Andhra Pradesh, providing a
nuanced analysis. Lastly, the study focuses exclusively on the major rice-cultivating districts in
Andhra Pradesh, ensuring that the findings are highly relevant and targeted. This comprehensive
approach enables policymakers to formulate effective climate-resilient agricultural policies. By do-
ing so, it overcomes the limitations of aggregation anomalies that may arise considering country-
level panel data. Given the significant climate divergence across different states in India, general-
izing findings at the all-India level would not yield meaningful results. This approach is in tune
with the studies such as Gadedjisso-Tossou et al. (2021), Mandal and Singha (2020), and Carew et
al. (2017).
2. Review of literature
Many researchers have analyzed the impact of climate change variables on crop yields at the
global level and in India, in particular (Table 1).
A&R 2025, Vol. 3, No. 1, 0004 5 of 21
Table 1. Summary of empirical investigations.
Authors Country
Time
period
Methodology
employed
Major research findings
Gadedjisso-
Tossou et al.
(2021)
Northern Togo,
West Africa
1977–
2012
Multiple regres-
sion analysis
There exists a non-linear and significant relation-
ship between rainfall and temperature on the
yields of cereals.
Squared terms of both rainfall and temperature
have a positive influence on yield.
Mandal & Singha
(2020) Assam, India
Panel data
(1991–
2013)
Just & Pope
(1978)
approach
Rising temperatures can have harmful impacts on
the average yields of summer rice and mustard.
Daily average mean temperature has a non-linear
impact on yield variability of summer rice, winter
rice, and potato.
Shayanmehr et al.
(2020) Iran 1961–
2010
Just and Pope
Production Func-
tion – Quadratic
and Cobb-Douglas
forms
Minimum temperature showed a positive influence
on mean yield of spring potato.
Increase in rainfall exerted a negative influence on
potato yield.
Maximum temperature showed a negative associa-
tion with potato yield.
Verma et al.
(2020) India 1966–
2011
Just and Pope sto-
chastic production
function
Rice yields are reduced by rainfall extremes.
Extremely high temperatures negatively influ-
enced the yields of millets.
Mulungu et al.
(2021) Zambia 1981–
2011
Just and Pope sto-
chastic production
function
Negative impact of temperature rise on maize and
beans yields.
Positive impact of rainfall rise on yields.
Saei et al. (2019) Iran
Panel data
(1983 to
2014)
Just and Pope
Production Func-
tion – Quadratic
and Cobb-Douglas
forms
Rainfall showed a positive influence on yields of
maize and wheat.
Minimum temperature is yield risk decreasing fac-
tor.
Both time trends and regional dummies were
statistically significant in boosting maize and
wheat yields.
Carew et al.
(2017)
Manitoba, Can-
ada
1996–
2012
Just and Pope
Production Func-
tion – Cobb-Doug-
las form
Variety richness reduces yield variability in wheat,
unlike varieties protected by plant breeders’ rights.
Application of Phosphorus fertilizers showed a
positive influence on mean yield
Total precipitation, June and July temperatures
had a negative influence on mean yield.
The studies reviewed above employ a variety of methodologies to investigate how climate
variables affect crop yields. These methodologies offer a nuanced examination of the intricate re-
lationships between climate factors—such as rainfall and temperature—and agricultural productiv-
ity. With this background, researchers present the complex dynamics of climatic change impact on
rice yields in Andhra Pradesh. Panel data analysis proves invaluable in several studies by consid-
ering temporal trends and regional variations. This approach facilitates a deeper understanding of
how climate impacts evolve over time and vary across different geographic areas.
3. Methodology
3.1. Study Area and Data Collection
Five distinct agro-climatic zones in Andhra Pradesh were deliberately selected for this study.
These zones include the Scarce Rainfall zone, Southern zone, Krishna zone, Godavari zone, and
North Coastal zone (Figure 2). Specific districts were purposefully chosen from each zone during
the Kharif season (Table 2). These districts—Kurnool, Kadapa, Krishna, West Godavari, and Sri-
kakulam—collectively account for 51.97 percent of the total rice-cultivated area (1.53 m. hectares)
A&R 2025, Vol. 3, No. 1, 0004 6 of 21
in Andhra Pradesh. Similarly, for the Rabi season, the study selected Kurnool, Chittoor, Krishna,
West Godavari, and Srikakulam districts, one from each of the aforementioned agro-climatic zones.
These districts collectively represented 40.24 percent of the total rice cultivated area (0.83 m. hec-
tares). The study relied on a diverse set of data sources to capture historical climate and agricultural
variables, facilitating a detailed analysis of the relationship between climate change and rice yield
variability in Andhra Pradesh. Climate observations from 1998 to 2022 were collected, focusing
on monthly rainfall, maximum and minimum temperatures, which are key variables in determining
crop yield. These data were gathered from the Statistical Abstract of Andhra Pradesh and the Hand-
book of Statistics of the selected districts, available through the Directorate of Economics and Sta-
tistics, Andhra Pradesh Additionally, supplementary climate data were sourced from the NASA
POWER web portal (https://power.larc.nasa.gov/data-access-viewer/), which provides global data
on surface meteorological and solar energy parameters. The panel structure of the study covered
five distinct agro-climatic zones—Scarce Rainfall, Southern, Krishna, Godavari, and North
Coastal—spanning over five selected districts: Kurnool, Kadapa, Krishna, West Godavari, and Sri-
kakulam during the Kharif season, and Kurnool, Chittoor, Krishna, West Godavari, and Srikakulam
during the Rabi season. The panel consists of 25 years of data (1998–2022) across these five dis-
tricts in two seasons, offering a comprehensive dataset to examine both temporal and spatial vari-
ations in climate factors and their impact on rice yield variability.
Figure 2. Selected agro-climatic zones in Andhra Pradesh.
i 2 S A Ci i i A
A&R 2025, Vol. 3, No. 1, 0004 7 of 21
Table 2. Seasons of cultivation of rice across different agro-climatic zones in Andhra Pradesh.
Seasons
Sowing period
Harvesting period
Period of growth#
Source
Kharif rice June to August October to November June to October
Regional Agricultural
Research Stations
(RARSs) of ANGRAU
Rabi rice October to November February to March October to February
Note: # - Considering the period from middle of sowing to middle of harvesting period.
The data sources provided historical monthly rainfall and temperature data, crucial for ana-
lyzing climate impacts on rice yields. NASA POWER, for instance, offers high-resolution global
data on surface meteorological parameters, which were used to complement the state and district-
level data when local records were incomplete or inconsistent. However, obtaining accurate and
granular rainfall and temperature data at the district level posed some challenges. First, while the
Directorate of Economics and Statistics of Andhra Pradesh provides detailed historical climate data
and rice yields, there were occasional gaps or inconsistencies in district-level records, particularly
in earlier years. This was especially problematic for some agro-climatic zones where local data
collection methods were less robust. To address these gaps, climate data from NASA POWER was
used as supplementary information, as it provides consistent, high-resolution climate data derived
from satellite observations and models. This ensured continuity in the climate records and allowed
for the creation of a more complete dataset. Furthermore, any discrepancies between different data
sources were resolved through cross-referencing and validation against available local weather sta-
tion records, where possible. Data processing involved cleaning, standardizing, and organizing the
climate variables (monthly rainfall, maximum and minimum temperatures) to create a panel dataset
compatible with the districts and time periods under study. Advanced statistical techniques, such
as interpolation, were employed to fill minor gaps in the dataset, ensuring consistency across years
and districts. The processed data were then integrated with the agricultural yield data, creating a
robust dataset for further econometric analysis, facilitating the exploration of how climate variabil-
ity has affected rice production across different agro-climatic zones and time periods.
3.2. Descriptive Statistics
Mean, Standard Deviation (SD), and Coefficient of Variation (CV) are employed to examine
the variability among climate change variables viz., rainfall, maximum and minimum temperatures
and yields of rice during both Kharif season (June to September) and Rabi season (October to Jan-
uary).
3.3. Panel Unit Roots and Stationarity
Unit root for each variable was tested (with trend and without trend) through employing
Fisher-type test (Maddala & Wu, 1999); Levin, Lin, Chu (LLC) test (Barnwal & Kotani, 2010) and
Harris-Tzavalis test (Harris & Tzavalis 1999; to ensure the robustness of the results), as non-sta-
tionary data set might yield spurious results (Chen & Chang, 2005; Granger & Newbold, 1974).
3.4. Just and Pope Production Function
Earlier studies (Rao et al., 2016; Rao et al., 2017) have furnished climate change in Andhra
Pradesh. The projections until 2050 in Andhra Pradesh encompass temperature increases of up to
1.5 degrees Celsius in summer and 2 degrees Celsius in winter, with Kharif rainfall anticipated to
rise by 13 to 34 percent and Rabi rainfall by 6 to 45 percent. This climate shift is characterized by
escalating temperatures, particularly nocturnal temperatures, alterations in rainfall patterns, and a
heightened frequency of extreme weather events (droughts, floods, heatwaves, and cold spells).
Just and Pope’s (1978; 1979) production function was employed to analyze the impact of climate
change variables on the mean yield and yield variability of rice during both Kharif and Rabi sea-
sons. Two functional forms of the Just and Pope production function viz., Quadratic and Cobb-
Douglas forms are considered.
3.4.1 Mean Function
This is specified as:
Linear-Quadratic form:
2
0 12 ()
yt jj jj jkjk
j j j kj
T x x xx
αα α α α
≠
=++ + +
∑ ∑ ∑∑
Cobb-Douglas form:
j
j
j
y Tx
α
αα
=++
∏
where represent explanatory variables, “T” represents time trend and ′ are co-
efficients to be estimated.
3.4.2. Variance Function
A&R 2025, Vol. 3, No. 1, 0004 8 of 21
The variability function h(.) is modelled as a Cobb-Douglas form (Just & Pope, 1978; 1979;
Kumbhakar & Tveteras, 2003; Koundouri & Nauges, 2005):
()
j
hx T x
ββ
=∏
or
123
()
n
h x Tx x x x
ββ β β β
= ⋅ ⋅ ⋅⋅
123
ln ( ) ln( )
n
h x T tx x x x
βββ β β β
= ⋅ ⋅ ⋅⋅
123
ln ( ) ln ln ln ln ln ln
n
hx T t x x x x
ββ ββ β β
= + + + + ++
0 112 2 3 3
ln ( ) ln ln ln ln ln ln
nn
hx t T x x x x
ββ β β β β
= + + + + ++
Specification tests are conducted to ensure the reliability, accuracy, and interpretability of
estimated relationships and predictions (Judge et al., 1985; Cameron & Trivedi, 2009). Addition-
ally, elasticities of climate change variables and future predictions of rice yields during both the
Kharif and Rabi seasons are analyzed (Kabir, 2015; Sanjay et al., 2017).
Employing two functional forms, namely the Quadratic and Cobb-Douglas forms, enhances
robustness, accommodating different responses of yield to climate change. Moreover, previous re-
search supports the efficacy of this function in diverse agricultural contexts (Cabas et al., 2010;
Kim & Pang, 2009; Tveterås, 1999; Tveterås & Wan, 2000; Chen et al., 2004; Isik & Devadoss,
2006; Koundouri & Nauges, 2005).
The Just and Pope production function is well-suited for analyzing risk and uncertainty in
agricultural production, particularly in the context of climate change. This model separates the
mean yield from the yield variability, allowing for a clearer distinction between the average effects
of climate variables and the risks associated with their variability. By incorporating both the mean
function (which captures the systematic impact of inputs and climate factors on yield) and the var-
iance function (which models the variability of yields), the model directly addresses uncertainty in
agricultural outcomes. The variance function, specified in a Cobb-Douglas form, links variability
to climate and other input variables, capturing how changes in factors such as temperature, rainfall,
and extreme weather events affect not just the mean yield but also its stability. This allows for an
estimation of how sensitive yields are to different climate risks, providing insights into the risk
management strategies needed for both the Kharif and Rabi seasons.
As for the application of the model, it is typically applied separately for the two seasons to
capture the unique climatic and agronomic conditions present during Kharif and Rabi. Each season
has distinct rainfall patterns, temperature fluctuations, and crop management practices, making it
necessary to estimate the mean and variance functions independently for each. This season-wise
approach ensures a more accurate representation of the climate-yield relationship and accounts for
seasonal variations in risk and uncertainty.
This study hypothesizes that climate change impacts rice yields in Andhra Pradesh in several
ways. First, variations in seasonal rainfall, particularly during the Kharif and Rabi seasons, signif-
icantly affect rice yields, with excessive rainfall during Kharif leading to waterlogging and reduced
yields, while inadequate rainfall during Rabi creates water stress that diminishes output. Second,
extreme temperatures, especially during critical growth stages, are expected to have a significant
negative impact on rice yields, with higher temperatures during the Rabi season being particularly
detrimental due to the crop’s reliance on irrigation and temperature-sensitive growth phases. Third,
the impact of climate change on rice yields is likely to vary across districts, with coastal regions
more vulnerable to increased rainfall and flooding, while inland areas are more prone to tempera-
ture fluctuations and droughts, resulting in varying degrees of climate sensitivity across different
agro-climatic zones. Finally, long-term projections of climate variables, such as rainfall and tem-
peratures, are expected to increase rice yields in both seasons, and the study aims to estimate the
elasticity of these climate variables to forecast future rice productivity and inform targeted adapta-
tion strategies.
4. Results and Discussion
4.1. Summary Statistics
Table 3 shows that Srikakulam exhibited the highest mean rainfall of approximately 767 mm
during the Kharif season spanning from 1998 to 2022. Following closely is West Godavari with
747 mm and Krishna with 693 mm. In stark contrast, Kadapa and Kurnool, situated in the arid
Rayalaseema region, recorded the lowest mean rainfall of 436 mm and 439 mm, respectively. The
substantial coefficient of variation (CV) values (> 90%) underscores the erratic nature of rainfall
distribution across all surveyed districts. Conversely, minimal variation is observed for both maxi-
mum temperature and minimum temperature among the selected districts. When examining agri-
cultural yields, Kurnool exhibited the highest variability at 45.56 percent, trailed by Kadapa at
22.02 percent. In contrast, the coastal districts of Srikakulam, West Godavari, and Krishna,
A&R 2025, Vol. 3, No. 1, 0004 9 of 21
benefitting from perennial rivers (Nagavali, Godavari, and Krishna respectively), displayed lower
yield variability due to consistent water supply.
Table 3. Summary statistics of selected variables (1998–2022).
District
Variables
Mean
CV(%)
Minimum
Maximum
Kharif season
Kurnool
Yield (t/ha)
4.01
45.56
0.23
1.15
Rainfall (mm)
438.90
129.89
263.90
872.70
Max. Temp (
°
C)
33.92
2.83
32.24
35.66
Min. Temp (
°
C)
24.65
2.12
23.84
25.81
Kadapa
Yield (t/ha)
4.09
22.02
0.17
9.69
Rainfall (mm)
435.50
90.70
270.40
572.50
Max. Temp (
°
C)
36.25
1.42
32.67
38.21
Min. Temp (
°
C)
25.88
1.66
21.06
27.14
Srikakulam
Yield (t/ha)
2.63
0.94
0.81
4.46
Rainfall (mm)
766.60
125.40
571.00
1055.00
Max. Temp (
°
C)
32.69
0.88
31.39
34.62
Min. Temp (
°
C)
26.55
0.47
25.85
27.87
Krishna
Yield (t/ha)
4.23
1.28
2.07
6.24
Rainfall (mm)
693.00
160.70
418.90
1090.10
Max. Temp (
°
C)
34.07
0.83
32.86
36.20
Min. Temp (
°
C)
24.13
1.53
21.75
26.17
West Godavari
Yield (t/ha)
3.39
1.34
1.88
6.24
Rainfall (mm)
746.70
171.40
418.90
1090.10
Max. Temp (
°
C)
33.43
1.01
31.54
36.20
Min. Temp (
°
C)
25.26
1.79
21.75
27.55
Rabi season
Kurnool
Yield (t/ha)
3.89
28.26
2.8
5.9
Rainfall (mm)
131.10
48.43
14.60
257.00
Max. Temp (
°
C)
31.82
3.26
30.07
33.70
Min. Temp (
°
C)
20.66
4.27
19.54
22.34
Chittoor
Yield (t/ha)
3.79
32.74
2.44
5.80
Rainfall (mm)
349.60
46.06
158.10
753.00
Max. Temp (
°
C)
31.40
2.75
30.45
33.50
Min. Temp (
°
C)
22.00
3.40
20.52
23.66
Srikakulam
Yield (t/ha)
3.07
30.47
2.11
4.80
Rainfall (mm)
235.00
67.50
29.80
620.00
Max. Temp (
°
C)
30.75
2.92
29.21
31.90
Min. Temp (
°
C)
23.15
3.99
21.80
25.00
Krishna
Yield (t/ha)
4.31
25.90
3.01
6.68
Rainfall (mm)
247.20
52.98
60.60
498.00
Max. Temp (
°
C)
30.82
4.17
29.10
33.70
Min. Temp (
°
C)
18.78
6.17
16.70
20.23
West Godavari
Yield (t/ha)
4.73
10.58
3.93
5.83
Rainfall (mm)
212.90
58.53
44.30
493.10
Max. Temp (
°
C)
30.47
3.41
29.03
32.00
Min. Temp (
°
C)
22.03
6.13
17.30
23.10
Note: Figures in parentheses indicate “Z-cal” value, ** - Significant at 1% level, * - Significant at 5% level.
An interesting observation is that Chittoor, a district in the Rayalaseema region, received the
highest mean rainfall (349.60 mm) during the Rabi season. Furthermore, Chittoor exhibited the
lowest variability in rainfall, as it receives most of its rain from the northeast and retreating mon-
soons during the winter season. Frequent low-pressure systems in the Bay of Bengal during this
period also lead to heavy rainfall. However, despite the higher mean rainfall, Chittoor also exhibited
A&R 2025, Vol. 3, No. 1, 0004 10 of 21
higher yield variability in Rabi season. The same was higher in all three coastal districts (Srikaku-
lam, West Godavari, and Krishna) compared to Kharif season. Furthermore, both maximum and
minimum temperatures exhibited higher magnitudes and variability during Rabi season compared
to Kharif season.
Regarding rice productivity during the Kharif season, Krishna demonstrated the highest yield
at 4.23 t/ha, followed by Kadapa at 4.09 t/ha and Kurnool at 4.01 t/ha. In contrast, Srikakulam had
the lowest productivity with only 2.63 t/ha. However, in the Rabi season, rice productivity increased
across all three coastal districts: Srikakulam (3.07 t/ha), Krishna (3.94 t/ha), and West Godavari
(4.73 t/ha), as these districts enjoy assured irrigated conditions facilitated by the three perennial
rivers in the coastal regions. While the coastal districts outperformed the others in terms of yield
during Rabi season, they also exhibited considerable yield variability.
4.2. Pre-Estimation Specification Tests
ADF-Fisher-type, LLC test, and Harris-Tzavalis test (Poudel & Kotani, 2013; Sarker et al.,
2019) are employed to assess stationarity, considering both constant and trend specifications for
the respective series. The results (Table 4) showed that selected variables were stationary for all
equations (McCarl et al., 2008; Kim & Pang, 2009). The findings from the modified Wald test,
Breusch-Pagan/Cook-Weisberg test, Breusch-Pagan-Godfrey (BPG), and White heteroscedasticity
tests (Table 5) indicated the presence of heteroscedasticity and this does not impede the application
of Just-Pope model. Furthermore, Breusch-Pagan LM test of independence and Wooldridge test
indicated the absence of aggregation bias and contemporaneous correlation. The Variance Inflation
Factor test revealed the absence of multicollinearity among independent variables. Hausman test
revealed fixed effect model was more appropriate than the random effect model.
Table 4. Panel unit root test results (1998–2022).
Variables
Fisher-ADF (Modified inv.
Chi-squared)
LLC (Adjusted t*) Harris-Tzavalis (rho)
Trend Without trend Trend
Without
trend
Trend Without trend
Kharif season
Yield (t/ha) 4.6587** 5.0554** −3.3811** −3.9972** 0.0201
(−6.7679)**
0.3237
(−8.0643)**
Rainfall (mm) 16.9481** 17.5451** −2.3230* −2.5407** −0.2460
(−9.5864)**
−0.1083
(−14.5939)**
Maximum Temp
(
°
C)
7.1499** 8.5659** −4.0732** −4.1641** 0.0859
(−6.0710)**
0.2686
(−8.8960)**
Minimum Temp
(
°
C)
4.7012** 4.7153** −12.9912** −8.6574** 0.3832
(−2.9222)**
0.4607
(−5.9935)**
Rabi season
Yield (t/ha) 3.8943** 4.1083** −3.2118** −3.8936** 0.0311
(−6.9113)**
0.3518
(−9.3815)**
Rainfall (mm) 14.9581** 17.3439** −3.4779** −3.6349** −0.1785
(−8.8708)**
−0.1332
(−14.9711)**
Maximum Temp
(
°
C)
5.2897** 6.3307** −3.4404** −2.6426** 0.1657
(−5.2257)**
0.5741
(−4.2784)**
Minimum Temp
(
°
C)
3.7880** 3.3359** −3.1552** −2.7703** 0.1463
(−5.4315)**
0.4182
(−6.6353)**
Note: Figures in parentheses indicate “Z-cal” value, ** - Significant at 1% level, * - Significant at 5% level.
A&R 2025, Vol. 3, No. 1, 0004 11 of 21
Table 5. Panel data model specification tests (1998–2022).
Heteroscedasticity
Aggregation
bias
(Contempora-
neous Correla-
tion (CC))
VIF Autocorre-
lation
Fixed ef-
fect vs
Random
effect
Modified
Wald test for
group-wise
heteroskedas-
ticity
Breusch-Pa-
gan / Cook-
Weisberg test
Breusch-Pa-
gan-Godfrey
(BPG) Test
White
test
Breusch-Pagan
LM test of in-
dependence
Wooldridge
test
Hausman
test
Kharif season
χ2 (5) =
1613.56** χ2(1) = 5.25** F(3, 121) =
4.01**
F(2, 122)
=
5.07**
χ2(10) =
6.2793NS
< 2.31 for
all inde-
pendent
variables
F(1, 4)
= 0.039 NS
χ2(3) =
8.26**
(Fixed ef-
fect is ap-
propriate)
Rabi season
χ2 (5) =
1262.69** χ2(1) = 3.94** F(3, 121) =
5.33**
F(2, 122)
=
4.27**
χ2(10) =
4.1109NS
< 1.149 for
all inde-
pendent
variables
F(1, 4)
= 0.157NS
χ2(3) =
4.16**
(Fixed ef-
fect is ap-
propriate)
Note: ** - Significant at 1% level, NS - Non-Significant
4.3. Just and Pope Production Function
4.3.1. Determinants of Mean Yield and Variability During Kharif Season
The findings from Table 6 suggest that climate variables showed a significant influence on
the mean yield of rice, as observed in both quadratic and Cobb-Douglas models. Specifically, rain-
fall, maximum and minimum temperatures exerted positive and significant influences on mean
yield of rice. An increase in rainfall, unless it reaches high-intensity levels leading to floods and
subsequent crop inundation, can enhance the production and productivity of Kharif rice. Similarly,
higher maximum and minimum temperatures indicate a cloud-free climate, increased sunshine
hours, and higher night temperatures, which promote photosynthetic activity and assimilation, ul-
timately leading to improved rice yields.
A&R 2025, Vol. 3, No. 1, 0004 12 of 21
Table 6. Estimates of Just and Pope function during Kharif season.
S.No
Variables
Quadratic model
Cobb-Douglas model
Mean Yield
Yield Variability
Mean Yield
Yield Variability
Coeffi-
cient
SE
Coeffi-
cient
SE
Coeffi-
cient
SE
Coeffi-
cient
SE
1
Rainfall
0.0049**
0.0011
−0.0044*
0.0021
0.9143**
0.2096
−0.8199**
0.1503
2
Max.Temp
0.3922**
0.0903
−0.0059*
0.0029
0.0353**
0.0098
−0.0519**
0.0082
3
Min.Temp
0.2257**
0.0416
−0.0007*
0.0003
0.0251*
0.0117
−0.0011**
0.0003
4
Time trend
0.0583**
0.0139
0.0636**
0.0157
0.1305**
0.0492
0.0105**
0.0029
5
Rain2
−0.0006**
0.0002
0.0014*
0.0006
-
-
-
-
6
Max.Temp2
−0.0848**
0.0368
0.1054**
0.0199
-
-
-
-
7
Min.Temp2
0.0226
0.0453
−0.2789
0.1730
-
-
-
-
8
Rain*Max.Temp
0.0011**
0.0003
−0.0027**
0.0009
1.0474**
0.2248
21.0972
32.4152
9
Rain* Min.Temp
−0.0003
0.0002
−0.0015
0.0012
−5.8737
3.7877
−32.7552
18.9765
10
Max.Temp *
Min.Temp
−0.2601 0.1625 0.1133 0.1876 −99.1974 54.6398 −113.9324 377.2511
11
D2-Kadapa
1.9734**
0.4979
0.7233**
0.2454
12
D3-Srikakulam
1.4797**
0.5103
1.1108**
0.2977
13
D4-Krishna
3.1585**
0.2861
1.5304**
0.3271
14
D5-West
Godavari
2.3445** 0.4705 1.3375** 0.2952
Constant
335.3549
60.4899
312.0852
172.098
1194.2115
489.4903
1.0825
3.3584
Model statistics
Observations (n)
125
125
125
125
F test (14, 110) 53.41** F(14, 110) = 2.75**
F test (11, 113) =
42.26**
F(11, 113) = 3.42**
Prob > F
0.0000
0.0022
0.0000
0.0004
R2 Adj
0.8811
0.7631
Note: ** - Significant at 1% level, * - Significant at 5% level.
The significant time trend variable (p < 0.00) in the mean functions indicates that technolog-
ical progress, including improved varieties, better seed, agronomic practices, and plant protection
measures, has positively influenced rice yield over the reference period (Isik & Devadoss, 2006;
Sarker et al., 2014; Sinnarong et al., 2019). However, the lower magnitude of the time trend variable
is due to the excessive use of resources such as seeds, fertilizers, pesticides, and weedicides beyond
the scientific recommendations. Additionally, all four district dummies showed a positive influence
on mean yield. This suggests that the yield of rice in these districts significantly differs from the
benchmark mean yield of Kurnool.
In the quadratic model, the quadratic terms for rainfall and maximum temperature exhibited
negative coefficients, indicating a threshold, beyond which these variables adversely affect the
mean yield of rice. Specifically, excessive rainfall leading to prolonged submergence of crops for
more than a week during its growth stage will adversely affect productivity. This suggests proper
water management and drainage practices to mitigate potential crop damage from excessive rain-
fall. Conversely, the influence of maximum temperature on mean yield is positive when tempera-
tures remain below 40 °C during the Kharif season. However, prolonged exposure to high maximum
temperatures, particularly during dry spells, can result in decreased leaf area, increased senescence
rate, shortened growing periods, and ultimately reduced rice yields (Kumar et al., 2015; Srivastava
et al., 2019; Vashisht et al., 2015; Resop et al., 2014). In contrast, minimum temperature during
sowing and growth stages demonstrates a positive influence on mean yield. In yield variability/risk
functions of both quadratic and Cobb-Douglas models, negative and significant effects of rainfall,
maximum and minimum temperatures are observed. These factors contribute to decreased varia-
bility in rice yield, resulting in a more consistent and stable production. The positive association of
the time trend variable with yield variability underscores the role of technological advancements
and other temporal factors in enhancing production stability.
In quadratic model, squared terms of rainfall and maximum temperature show positive and
significant influences on yield variability, indicating that beyond certain thresholds, higher levels
of these variables lead to increased yield variability. This suggests a higher degree of uncertainty
A&R 2025, Vol. 3, No. 1, 0004 13 of 21
and instability in rice production under such conditions. These findings emphasize the presence of
threshold effects and the necessity for considering non-linear relationships in understanding the
dynamics of rice yield variability (Chen et al., 2004; Kumar et al., 2015).
4.3.2. Determinants of Mean Yield and its Variability of Rice During Rabi Season
As per the findings presented in Table 7, both the quadratic and Cobb-Douglas models indi-
cate that only rainfall exhibits a positive and significant influence on the mean yield of rice. This
underscores the critical role of adequate moisture in the soil, alternating wet and dry periods, and
favorable conditions during crucial growth stages such as tillering and panicle initiation in enhanc-
ing rice yields. Additionally, reducing relative humidity can positively impact the microclimate,
thereby reducing the susceptibility of rice crops to pests and diseases. However, increased maxi-
mum temperature, particularly during flowering, adversely influences rice yield. Additionally, a
fall in minimum temperature between 15 °C to 18 °C during early November negatively impacts
seed germination in nurseries. The time trend variable is significant (p < 0.00), indicating that ad-
vancements in technology, such as improved varieties, better seed, agronomic practices, and plant
protection measures, have contributed to increased rice yields over time. The interaction between
rainfall and maximum temperature showed positive and significant associated with mean yield.
Among the four district dummies, Krishna and West Godavari districts showed a positive influence
on mean yield compared to the benchmark district, Kurnool. However, in Cobb-Douglas model,
Chittoor district also exhibits significant influence.
Table 7. Estimates of Just and Pope function during Rabi season.
S.No Variables
Quadratic model
Cobb-Douglas model
Mean Yield
Yield Variability
Mean Yield
Yield Variability
Coeffi-
cient
SE
Coeffi-
cient
SE
Coeffi-
cient
SE
Coeffi-
cient
SE
1
Rainfall
0.0321**
0.0093
−0.0035**
0.0007
1.8783**
0.5126
−0.3167**
0.1029
2
Max.Temp
−0.0018**
0.0003
0.0017**
0.0004
−0.0003**
0.0001
0.0195**
0.0061
3
Min.Temp
−0.0042**
0.0004
0.0026**
0.0008
−0.0004**
0.0001
0.0129**
0.0041
4
Time trend
0.1266**
0.0294
0.3313**
0.0691
0.1069**
0.0258
0.3208**
0.0846
5
Rain
2
−0.0003
0.0021
0.0004
0.0011
-
-
-
-
6
Max.Temp
2
−0.2137
0.0215
0.0626
0.1593
-
-
-
-
7
Min.Temp
2
−0.0004**
0.0001
0.0052**
0.0008
-
-
-
-
8
Rain*Max.Temp
0.0012**
0.0004
0.0022
0.0024
0.2234*
0.0974
−0.0031
0.0058
9
Rain*Min.Temp
0.0002
0.0002
−0.0017
0.0011
0.1729
0.3324
8.4964
5.7082
10 Max.Temp *
Min.Temp
0.0212 0.0276 0.0256 0.1678 0.0157 7.1752 −8.6340 7.3043
11
D2-Chittoor
−0.1885
0.1579
−0.2064*
0.0881
12
D3-Srikakulam
0.2537
0.1918
−0.0672
0.0658
13
D4-Krishna
0.1609**
0.0435
0.0327**
0.0051
14 D5-West
Godavari
0.6994** 0.1739 0.2970** 0.0577
Constant
232.7963
31.8768
28.6526
4.0067
3.7442
80.1436
50.955
9.5342
Model statistics
Observations (n)
125
125
125
125
F test (14, 110) 61.88** F(14, 110) = 2.06* F test (11, 113) =
14.77**
F(11, 113) = 2.71**
Prob > F
0.0000
0.0211
0.0000
0.0048
R
2
Adj
0.8959
0.6047
Note: ** - Significant at 1% level, * - Significant at 5% level
In the quadratic model, quadratic term for minimum temperature exerted a significant nega-
tive influence on mean yield, implying that below a threshold level, minimum temperature ad-
versely affects the mean yield. This finding aligns with the results of Joshi et al. (2011), indicating
that lower minimum temperatures during the Rabi season exerted a negative impact on rice yield.
The quadratic term of rainfall, although non-significant, exerts a negative influence on mean yield
if it leads to submergence for more than a week during crop growth. According to Peng et al. (2004),
even minor increases in night temperatures can adversely affect the yield of irrigated rice during
A&R 2025, Vol. 3, No. 1, 0004 14 of 21
the Rabi season, indicating the potentially detrimental impact of higher night temperatures on rice
yield. These findings underscore the significance of recognizing temperature thresholds and ac-
knowledging the adverse effects of excessive rainfall and elevated night temperatures on rice yield
during the Rabi season. Moreover, the findings align with those of Chandio et al. (2021; 2022a;
2022e; 2023) in various regions such as Pakistan, Asian-7 countries, China, and South Asia, as well
as the study by Chandio et al. (2022c) in SAARC countries.
In terms of yield variability, both the quadratic and Cobb-Douglas models identify rainfall as
a risk-mitigating factor, suggesting that higher levels of rainfall contribute to lower yield variability,
unlike maximum and minimum temperatures. Additionally, time trend is associated with increased
yield variability likely due to various factors such as shifting climate patterns, evolving agricultural
practices, and technological progress. Notably, in the quadratic model, the squared minimum tem-
perature emerges as a risk-increasing factor for rice yield. These findings underscore the importance
of adequate rainfall, optimal temperature conditions, and ongoing technological advancements in
reducing yield variability and bolstering the stability of rice production.
The differential impact of temperature on rice yield between the Kharif and Rabi seasons can
be attributed to several agronomic and climatic factors, suggesting potential threshold effects. The
Kharif season, characterized by the monsoon, presents optimal conditions for rice growth, as higher
temperatures combined with abundant rainfall promote photosynthesis and plant development.
During this season, rice plants benefit from maximum temperatures that remain below a critical
threshold of approximately 35–40 °C; exceeding this limit can induce heat stress, adversely affect-
ing yields. In contrast, the Rabi season features cooler and drier weather, which may slow down
metabolic processes and affect plant development. Cooler temperatures are generally favorable for
germination and early growth, with a critical threshold for minimum temperatures. Specifically,
temperatures below 15 °C can hinder germination and seedling vigor, while the optimal range is
between 18–25 °C. If temperatures drop significantly below these levels, especially at night, growth
can be stunted, leading to reduced yields. Additionally, the risk of frost in colder regions can further
compromise young rice plants. The photoperiod sensitivity of rice also plays a role in this differen-
tial impact. During the Kharif season, longer days and warmer temperatures enhance growth, while
the shorter days of the Rabi season may not have the same beneficial effect. Moreover, the interac-
tion between temperature and other climatic factors, particularly rainfall, is crucial. Adequate mois-
ture during the Kharif season can help mitigate the negative effects of elevated temperatures, em-
phasizing the importance of rainfall in conjunction with temperature.
The significant time trend variable observed in the analysis underscores the impact of various
technological advancements on rice yield over time. Key improvements include the development
of high-yielding varieties (HYVs) and hybrid rice strains that enhance productivity, disease re-
sistance, and adaptability to varying climatic conditions. Precision agriculture techniques, such as
satellite imagery and soil moisture sensors, allow farmers to optimize irrigation, fertilization, and
pest management, maximizing resource efficiency and minimizing waste. Innovations in water
management, including drip irrigation and rainwater harvesting, ensure adequate moisture supply,
particularly during critical growth stages, while reducing reliance on unpredictable rainfall patterns.
Integrated pest management (IPM) strategies combine biological, cultural, and chemical control
methods to reduce crop losses from pests and diseases. Additionally, practices that enhance soil
health, such as organic amendments and cover cropping, improve soil fertility and structure, pro-
moting better root development and nutrient uptake. Agricultural mechanization has also increased
operational efficiency, allowed timely planting and harvesting. These advancements not only boost
yields but also interact with climate change by enhancing resilience; for example, HYVs resilient
to temperature extremes help mitigate climate impacts. Precision agriculture optimizes inputs based
on real-time data, aiding adaptation to climate variability. However, intensive use of chemicals can
lead to soil degradation and water pollution, posing sustainability challenges. Overall, integrating
these technological innovations enhances rice yields and plays a vital role in adapting to climate
change, highlighting the need for ongoing research and sustainable practices to ensure long-term
production stability.
4.3.3. Elasticities (Marginal effects) of Climate Variables
According to Table 8, both the Quadratic and Cobb-Douglas models reveal positive associa-
tions between rainfall, maximum and minimum temperatures with mean rice yield during the Kha-
rif season. The reported elasticities denote the percentage change in rice yield resulting from a one
percent change in the respective climate variables. Specifically, for rainfall, the elasticities range
from 0.837 to 0.914. This signifies that a one percent increase in rainfall corresponds to an average
increase in rice yield by approximately 0.837 to 0.914 percent. Hence, higher rainfall positively
affects rice yield during the Kharif season. Regarding maximum temperature, elasticities range
from 0.035 to 0.042 implying that one percent rise in maximum temperature corresponds to an
average increase in rice yield by approximately 0.035 to 0.042 percent. Thus, higher maximum
temperatures also have a positive influence on rice yield during the Kharif season. Similarly, for
A&R 2025, Vol. 3, No. 1, 0004 15 of 21
minimum temperature, the elasticities range from 0.025 to 0.032. Hence, higher minimum temper-
atures contribute to higher rice yields during the Kharif season. Furthermore, these climate change
variables—rainfall, maximum temperature, and minimum temperature—also exhibit risk-decreas-
ing characteristics with elasticities of 0.752 to 0.819 percent, 0.052 to 0.055 percent, and 0.001 to
0.005 percent, respectively. So, these variables play a role in alleviating the risk and uncertainty
inherent in rice yield, thereby fostering more steady and reliable production. Moreover, these vari-
ables serve as risk-mitigating factors, bolstering the resilience of rice production amidst the back-
drop of climate change (Kim & Pang, 2009).
Table 8. Elasticities of climate change variables.
Yield function Climate variables
Quadratic
model
Cobb-Douglas
model
Kharif season
Mean yield
Rainfall
0.8371
0.9143
Maximum Temperature
0.0424
0.0353
Minimum Temperature
0.0325
0.0251
Yield variability
Rainfall
−0.7516
−0.8199
Maximum Temperature
−0.0546
−0.0519
Minimum Temperature
−0.0049
−0.0011
Rabi season
Mean yield
Rainfall
1.9447
1.8783
Maximum Temperature
−0.0002
−0.0003
Minimum Temperature
−0.0008
−0.0004
Yield variability
Rainfall
−0.2120
−0.3167
Maximum Temperature
0.0136
0.0195
Minimum Temperature
0.0143
0.0129
However, during Rabi season, for Quadratic and Cobb-Douglas models, only rainfall exhibits
a positive association with mean yield with elasticities ranging between 1.878 to 1.945 percent.
However, the elasticities for maximum temperature ranged between −0.0002 to −0.0003, while the
elasticities for minimum temperature ranged between −0.0004 to −0.0008. So, a one percent rise in
maximum temperature or minimum temperature corresponds to an average decrease in rice yield
by approximately 0.0002 to 0.0003 percent and 0.0004 to 0.0008 percent, respectively. The re-
ported elasticities for yield variability ranged between 0.014 to 0.019 percent, and 0.013 to 0.014
percent with respect to maximum and minimum temperatures respectively. On the contrary, the
elasticities are considerably lower for rainfall ranging between −0.212 to −0.317 percent implying
that higher rainfall reduces yield variability.
The robustness and reliability of the model used in this study were ensured through several
methods, despite the absence of explicit out-of-sample tests. The analysis employed both quadratic
and Cobb-Douglas functional forms to capture the nonlinear and interactive effects of climate var-
iables—rainfall, maximum temperature, and minimum temperature—on rice yields. The con-
sistency of results across these two models provided an initial indication of robustness, especially
as both models revealed similar trends and significance for key variables, including potential
threshold effects identified by the quadratic model. Additionally, the estimated elasticities of cli-
mate variables showed consistent effects on rice yields, aligning with previous studies conducted
in comparable agro-climatic contexts (e.g., Isik & Devadoss, 2006; Sarker et al., 2014). This align-
ment with prior research was further reinforced by the inclusion of significant time trend variables
and district dummies, which accounted for technological advancements in rice cultivation and re-
gional differences in yield. Residual analysis and diagnostic tests for heteroscedasticity and auto-
correlation were conducted to ensure the internal consistency of the models, thus bolstering their
reliability. Although out-of-sample validation was not performed, the comprehensive historical da-
taset spanning from 1998 to 2022 provided a sufficiently broad foundation for estimating reliable
relationships between climate variables and rice yield. This multifaceted approach to model vali-
dation enhances confidence in the findings’ applicability and reliability, showcasing a well-rounded
methodology for assessing the impact of climate on rice production.
4.3.4. Effects of Future Climate Change
In Kharif season, projected rice yields (Table 9) are expected to increase by 28.29 percent
(quadratic model) and 25.66 percent (Cobb-Douglas model) by the year 2080. The quadratic model
predicts a higher increase in mean yield over the Cobb-Douglas model. This led to a reduction in
A&R 2025, Vol. 3, No. 1, 0004 16 of 21
yield variability for rice (quadratic and Cobb-Douglas) over the selected four periods. Interestingly,
a decrease in yield variability is observed to increase over time and is higher in the quadratic model
compared to the Cobb-Douglas model (Kabir, 2015). In Rabi season, the projected rice yields are
expected to increase by 23.08 percent (quadratic model) and 22.36 percent (Cobb-Douglas model)
by the year 2080. However, it is noted that yield variability is projected to slightly increase over
four periods, albeit at a slow increasing rate. So, climate change showed a positive impact on rice
yields, with higher projected increases in the quadratic model. Additionally, the study highlights a
decrease in yield variability over time, indicating a potentially more stable rice production system
in the future.
Table 9. Projected change for rice yields during 2030, 2040, 2050, and 2080.
Years & Climate
projections*
Quadratic Model
Cobb-Douglas model
Mean Yield
(%)
Yield Variability
(%)
Mean Yield
(%)
Yield Variability
(%)
Kharif season
2030
[∆R = 5%; ∆MaxT = 1.26
°C;
∆Mint = 1.36
°
C]
13.94 −11.30 12.43 −10.789
2040*
[∆R = 7%; ∆MaxT = 1.50
°C;
∆Mint = 1.75
°
C]
17.90 −14.30 16.09 −13.717
2050
[∆R = 10%;∆MaxT = 1.81
°C;
∆Mint = 2.14
°
C]
22.99 −18.44 20.90 −17.828
2080
[∆R = 12%;∆MaxT = 2.29
°C;
∆Mint = 2.63
°
C]
28.29 −22.80 25.66 −22.013
Rabi season
2030
[∆R = 5%; ∆MaxT = 1.26
°C;
∆Mint = 1.36
°
C]
9.59 2.60 9.30 2.62
2040*
[∆R = 7%; ∆MaxT = 1.50
°C;
∆Mint = 1.75
°
C]
13.45 3.06 13.03 2.96
2050
[∆R = 10%;∆MaxT = 1.81
°C;
∆Mint = 2.14
°
C]
19.24 3.40 18.64 3.11
2080
[∆R = 12%;∆MaxT = 2.29
°C;
∆Mint = 2.63
°
C]
23.08 4.33 22.36 4.04
*- Singh et al., 2020
The statistical findings underscore the critical role that rainfall and temperature play in influ-
encing rice yield during both the Kharif and Rabi seasons, with significant implications for farmers
and policymakers aiming to enhance agricultural resilience in the face of climate change. For farm-
ers, understanding the positive impact of adequate rainfall and optimal temperature ranges on rice
yields enables them to adopt more effective cultivation practices. For instance, investing in water
A&R 2025, Vol. 3, No. 1, 0004 17 of 21
management techniques, such as rainwater harvesting and efficient irrigation systems, can help
ensure sufficient moisture availability during crucial growth stages, particularly in the Kharif sea-
son, where rainfall significantly enhances yield. Moreover, farmers can implement heat-resilient
rice varieties that are better adapted to withstand temperature extremes, particularly during the Rabi
season, when lower minimum temperatures can impede seed germination.
Policymakers, on the other hand, can leverage these insights to formulate targeted support
programs that promote the adoption of sustainable agricultural practices and technologies. Initia-
tives could include providing training and resources on climate-smart agriculture, facilitating ac-
cess to high-yielding and climate-resilient rice varieties, and improving agricultural extension ser-
vices to disseminate information on best practices. Additionally, establishing local agricultural co-
operatives can help farmers share knowledge, access shared resources, and implement collective
water management strategies. Enhancing local infrastructure for storage and transport can also mit-
igate post-harvest losses and improve market access, enabling farmers to maximize the benefits of
favorable climatic conditions. Ultimately, these adaptation strategies will not only bolster rice pro-
duction but also contribute to broader food security goals in the context of a changing climate,
ensuring that agricultural practices remain viable and sustainable for future generations.
The findings from this study align with and contrast with various global studies examining
the impact of climate variables on rice yields. For instance, studies in India, such as those by Kumar
et al. (2015) and Srivastava et al. (2019), have similarly identified significant relationships between
rainfall and temperature on rice productivity, highlighting the critical role of these climatic factors
in influencing yields during both Kharif and Rabi seasons. Moreover, the threshold effects noted
in this study, particularly the detrimental impacts of excessive rainfall and high maximum temper-
atures, corroborate the findings of research conducted in other rice-growing regions, such as the
Philippines and China, where adverse climate conditions have been shown to negatively affect rice
yields (Stuecker et al., 2018; Saud et al, 2022). Globally, the variability in rice yield due to climatic
factors has been extensively documented, with studies indicating that higher temperatures can sub-
stantially affect growth stages, particularly during flowering (Li & Tao, 2023). For example, the
findings regarding the negative influence of elevated minimum temperatures during the Rabi sea-
son echo concerns raised in the literature highlighting the importance of optimal temperature ranges
for effective rice germination and growth. Additionally, the beneficial impact of technological ad-
vancements, as indicated by the significant time trend variable, resonates with global initiatives
aimed at improving rice yield through innovation and adaptive practices in the face of climate
change, such as the development of heat-tolerant varieties (Hollósy et al., 2023). However, while
some studies emphasize the direct effects of climate change on yield reductions, the nuanced find-
ings of this study, particularly concerning the risk-mitigating role of rainfall and temperature inter-
actions, suggest a complex interplay between climatic variables that requires further investigation
and targeted agronomic strategies to enhance resilience in rice production systems globally.
5. Conclusions and Suggestions
This study showed climate change has significant implications for rice yields in selected agro-
climatic regions of Andhra Pradesh. The selected districts represent different agro-climatic zones,
accounting for a significant proportion of rice cultivation. Historical climate data, including rainfall
and temperature, were collected during 1998–2022, along with corresponding rice yield data. Unit
root tests conducted ensured that the data were stationary. The findings indicate that Srikakulam
district received the highest mean rainfall during the Kharif season, followed by West Godavari
and Krishna districts. On the other hand, Kadapa and Kurnool districts in the dry Rayalaseema
region received the lowest mean rainfall. In terms of rice yields, Kurnool district had the highest
variability, followed by Kadapa. In contrast, the three coastal districts—Srikakulam, West Goda-
vari, and Krishna—exhibited lower yield variability, attributed to the presence of perennial rivers.
Notably, Chittoor district in the Rayalaseema region recorded the highest mean rainfall during the
Rabi season, coupled with the lowest variability. This phenomenon is attributed to Chittoor receiv-
ing rainfall from the northeast and retreating monsoons during the winter season. Moreover, the
Rabi rice yield variability was observed to be higher in coastal districts compared to Kharif season.
Additionally, maximum and minimum temperatures registered higher levels during the Rabi season
relative to Kharif season. Krishna district demonstrated the highest rice productivity in the Kharif
season, whereas Srikakulam reported the lowest. Conversely, during the Rabi season, rice produc-
tivity surged across all three coastal districts in comparison to the Kharif season. Pre-estimation
specification tests affirmed the stationarity of the climate change variables and rice yields. Further-
more, tests for heteroscedasticity, autocorrelation, and contemporaneous correlation were con-
ducted, lending support for the application of the Just-Pope model. Findings from this model un-
veiled that rainfall, maximum temperature, and minimum temperature significantly influenced the
mean yield of rice across both seasons. Moreover, the time trend variable, indicative of technolog-
ical progress, exhibited a positive influence on rice yield. Regarding yield variability, rainfall,
A&R 2025, Vol. 3, No. 1, 0004 18 of 21
maximum temperature, and minimum temperature are considered as variance-decreasing factors.
The rainfall elasticity ranges from 0.837 to 0.914 during Kharif season. Maximum temperature
elasticity ranges from 0.035 to 0.042, and minimum temperature elasticity ranges from 0.025 to
0.032. These variables also reduce yield variability by approximately 0.752 to 0.819 percent, 0.052
to 0.055 percent, and 0.001 to 0.005 percent, respectively, mitigating production risks and enhanc-
ing stability amidst climate change. In Rabi season, rainfall exhibits a positive association with
mean rice yield, with elasticities ranging from 1.878 to 1.945 percent per one percent increase.
However, maximum temperature and minimum temperature showed negative associations, with
elasticities ranging between −0.0002 to −0.0003 and −0.0004 to −0.0008 percent, respectively.
These variables also increase yield variability by approximately 0.014 to 0.019 percent and 0.013
to 0.014 percent, respectively. In contrast, rainfall decreases yield variability by approximately
0.212 to 0.317 percent. In future, rice yields would increase both in Kharif and Rabi seasons by
2080. However, yield variability would slightly increase in the Rabi season, while decreasing in
the Kharif season over time.
These findings emphasize directing research efforts towards the development of new cultivars
that are capable of tolerating multiple biotic and abiotic stresses, rather than focusing on a limited
number of stresses. Increasing access of farmers to agro-meteorological information will help farm-
ers make informed choices and adopt sustainable practices in rice production in Andhra Pradesh.
Additionally, the research highlights the significance of collecting reliable climate data and ensur-
ing regular updates in the study area. Accurate and up-to-date climate data are crucial for conduct-
ing effective research, monitoring climate patterns, and planning adaptation strategies. In view of
these findings, farmers can mitigate risks associated with climate variability in rice cultivation by
adopting a multifaceted approach centered on climate-smart agricultural practices. Key strategies
include crop diversification, such as intercropping with drought-resistant crops during the Kharif
season and transitioning to resilient crops like pulses and oilseeds in the Rabi season. Improved
water management practices, such as rainwater harvesting and the use of efficient irrigation systems
like drip or sprinkler irrigation, are essential for optimizing water use in water-scarce regions. Ad-
ditionally, enhancing soil health through organic amendments and cover crops will boost fertility
and moisture retention, while the adoption of climate-resilient rice varieties tolerant to heat and
drought is crucial. Access to agro-meteorological information via mobile apps and local weather
stations will empower farmers to make informed decisions. Policymakers can support these efforts
by investing in research for resilient rice varieties, enhancing agro-meteorological services, and
providing financial incentives for adopting sustainable practices. Furthermore, investing in rural
infrastructure, facilitating workshops on sustainable farming, and establishing collaborative frame-
works among agricultural stakeholders will foster resilience in the sector. Ongoing monitoring and
evaluation of climate impacts on rice production will enable adaptive management strategies, ulti-
mately contributing to food security in Andhra Pradesh.
This study identifies several limitations, particularly regarding data constraints and model as-
sumptions. One significant limitation is the omission of non-climate variables, such as edaphic
conditions, cropped area, irrigation practices, fertilizer application, adoption of high-yielding vari-
ety seeds, and occurrences of extreme natural events, in the Just-Pope production function. This
omission may result in an incomplete understanding of rice production dynamics and yield varia-
bility, as these factors can significantly influence agricultural outcomes. Additionally, the reliance
on historical climate data (1998–2022) may not adequately capture the rapidly changing climate
conditions and their impacts on rice yields, potentially affecting the robustness of the findings. This
study also acknowledges a limitation in its approach by not incorporating non-climate variables
(such as edaphic conditions, cropped area, irrigation practices, fertilizer application, adoption of
high-yielding variety seeds, and occurrences of extreme natural events) into the utilized Just and
Pope production function. By omitting these variables, the findings may offer an incomplete un-
derstanding of rice production dynamics and yield variability. Integrating such variables into the
production function would facilitate a more comprehensive analysis, enabling a nuanced assess-
ment of their individual contributions to production risk and yield outcomes. Previous research, as
evidenced by studies conducted by Guttormsen and Roll (2013), Rosegrant and Roumasset (1985),
Roumasset et al. (1989), Ramaswami (1992), and Di Falco et al. (2006), underscores the signifi-
cance of non-climate variables in agricultural production, including rice cultivation. Thus, future
studies stand to benefit from incorporating these variables, thereby fostering a more accurate com-
prehension of the multifaceted dynamics influencing rice production.
Future research should aim to address these limitations by incorporating a broader range of
climate variables, such as humidity and wind speed, which can further elucidate the impacts of
climate change on rice cultivation. Exploring the interactions between multiple climate factors and
their cumulative effects on yields could enhance the comprehensiveness of the analysis. Further-
more, expanding the scope of research to include other crops affected by climate change, such as
pulses, oilseeds, or vegetables, would provide valuable insights into the resilience of various
A&R 2025, Vol. 3, No. 1, 0004 19 of 21
agricultural systems. Investigating the role of adaptive management practices and technological
innovations in mitigating climate risks will also be crucial in developing effective strategies for
sustainable agricultural production in the face of ongoing climate challenges.
CRediT Author Statement: Kotamraju Nirmal Ravi Kumar: Conceptualization, Methodology and Writ-
ing initial draft; Tatineni Ramesh Babu: Expert suggestions; Adinan Bahahudeen Shafiwu: Expert sug-
gestions; Kavanadala Rangaraju Hamsa: Analysis of data; Ishaque Mahama: Analysis of data and editing.
Data Availability Statement: Not applicable
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Acknowledgments: Not applicable.
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