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© 2019 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 6, No. 2, 155-165, 2019 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2019.62.155.165 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

    

 
 
 
Measuring the Climate Variability Impact on Cash Crops Farming in India: An 
Empirical Investigation 

 
 
Ajay Kumar Singh1    

Bhim Jyoti2    

  

 
( Corresponding Author) 

 

 
1Assistant Professor (Economics), Department of Humanities and Social Sciences, DIT University, Dehradun, 
Uttarakhand-248009, India  

 
2Assistant Professor (Seed Science and Technology), V.C.S.G., UUHF, College of Forestry, Ranichauri, Tehri 
Garhwal. Uttarakhand, India 

 

 
Abstract 

This study investigates the climate variability impact on potato, cotton, groundnut and sesame 
crops in Indian states. Thereupon, it estimates the technical efficiency (TE) of climatic and non-
climatic factors in crops farming in Indian states during 1971-2014. Production, yield and area 
sown of aforementioned crops are considered as dependent variables and regressed with socio-
economic and climatic variables using state-wise panel data employing C-D PFM. Sates-wise TE 
of cash crops is estimated using SFPFM under non-parametric condition. Estimates indicate that 
climate variability show negative impact on production, yield and area sown of aforesaid cash 
crops. Thus, it is essential to adopt effective policy to mitigate the negative consequences of 
climate variability in cash crop farming in India. Cropped area, production and yield of cash crops 
are negatively impacted due to climatic variability in India. So there needs to adopt crop specific 
policies to mitigate the negative impact of climate variability in cash crop farming. Finally, it 
estimates the technical efficiency (TE) of cash crop using SFPFM across Indian states. Estimated 
values of TE for production, yield and area sown of associated crops imply that there is significant 
variation in TE due to climate variability and huge diversity in socio-economic characteristic 
across Indian states. Thus, most of states have potential opportunity to increase production and 
yield of cash crops through enlightening technical efficiency of inputs in cultivation.   

 
Keywords: Commercial crops, Yield, Production, Cropped area, Technical efficiency, SFPFA, C-D PFM, Climate change. 

 
Citation | Ajay Kumar Singh; Bhim Jyoti (2019). Measuring the 
Climate Variability Impact on Cash Crops Farming in India: An 
Empirical Investigation. Agriculture and Food Sciences Research, 
6(2): 155-165. 
History:  
Received: 25 March 2019 
Revised: 2 May 2019 
Accepted: 6 June 2019 
Published: 18 July 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: Both authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Background ..................................................................................................................................................................................... 156 
2. Climate Sensitivity and Cash Crops Farming........................................................................................................................... 156 
3. Research Methodology ................................................................................................................................................................. 157 
4. Analytical Viewpoint of Stochastic Frontier Production Function Approach (SFPFA) ................................................. 158 
5. Empirical Analysis ......................................................................................................................................................................... 158 
6. Statistical Inferences on Regression Results ........................................................................................................................... 159 
7. Conclusion and Policy Implications ........................................................................................................................................... 163 
References ............................................................................................................................................................................................ 164 
 

 
 

 

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Contribution of this paper to the literature 
The study contributes to the existing literature by estimating the technical efficiency of production, 
yield and area sown of major cash crops across Indian states.  

 
1. Background 

Agriculture is the backbone for Indian economy and it is prime source to sustain food security and raw material 
for various industries like textile, oilseed, sugar and other industries. Indian agriculture is highly vulnerable due to 
climate change. In this context, numerous of studies have assessed the impact of climate change on yield and 
production of food-grain and cash crops in different regions/states in India [1-11]. In brief: Singh, et al. [12]; 
Kumar and Sharma [1]; Ayyogari, et al. [13]; Kumar, et al. [6]; Kumar, et al. [14]; Yadav, et al. [15]; Singh, et 
al. [9] have investigated the impact of climate change on potato yield, Hundal and Kaur [16]; Ashalatha, et al. 
[17]; Kumar and Sharma [18]; Kumar and Sharma [1]; Birthal, et al. [19]; Kumar, et al. [14]; Yadav, et al. [15]; 
Singh, et al. [9]; Ramachandran, et al. [11] have assessed the climate change impact on groundnut yield, Nath, et 
al. [20]; Kumar and Sharma [1]; Kumar, et al. [14]; Singh, et al. [9] have observed the climate change impact on 
sesame productivity, and Masters, et al. [21]; Sankaranarayanan, et al. [22]; Moorthy, et al. [23]; Deshmukh and 
Lunge [24]; Ashalatha, et al. [17]; Kumar and Sharma [1]; Hebbar, et al. [2]; Raju, et al. [25]; Thakare, et al. 
[26]; Kumar, et al. [14]; Singh, et al. [9] have explored the climate change impact on cotton crop productivity in 
India. Descriptive and empirical results of aforesaid studies were based on time series and district-wise/state-wise 
panel data, which were included crop productivity as a dependent variable. Few studies have incorporated per 
hectare output (in monetary) of a cash crop as a dependent variable and it was regressed with climatic and non-
climatic factors using district-wise/state-wise panel data in India [7, 9, 10, 14, 19, 27].  

Most researchers have used agricultural production per hectare land (in monetary) of selected food-grain and 
cash crops as dependent variables, which was regressed with climatic and non-climatic factors using state-wise 
panel data in India [3, 4, 6, 10, 14, 27, 28]. Few studies have segregated the agricultural productivity (in 
monetary) of Rabi and Kharif crops to assess the impact of climate change on it using state-wise and district-wise 
panel data in India [1, 4-6, 10, 27]. Earlier studies have provided empirical evidence that climate change have a 
negative and significant impact on productivity of cash crops in India. Agricultural productivity is also negatively 
impacted due to climate change in India [1, 5, 6, 27, 28]. However, limited studies assessed the impact of climate 
variability on all aspects (i.e., cropped area, production and yield) of cash crops farming in India. Existing 
researchers also could not estimate the technical efficiency (TE) of climatic and non-climatic factors in cash crop 
farming in India. It would be useful to formulate crop specific policy to increase the TE of inputs in cash crop 
farming. Most studies clearly indicate that agricultural productivity or net revenue of most cash crops decrease due 
to climate change in different regions of India. Most studies analyzed the climate change impact on agricultural 
productivity or net revenue of cash crops for a particular regions of India. Therefore, the present study address the 
few research questions which are given as:  

• How climate variability does affect area sown, production and yield of cash crops in different periods of 
crops in India? 

• Is technical efficiency of cash crop farming varied across Indian states?  

• Is possible to mitigate the adverse effective of climate variability in cash crop farming in India?  
 

Relevance to aforementioned research questions, the present study is achieved following objectives:  

• To investigate the impact of climate variability on various cash crops (i.e., Potato, Cotton, Groundnut 
and Sesame) in India.  

• To estimate the TE of climatic and non-climatic factors in cash crops farming across Indian states.  

• To provide the conclusive policy implications to mitigate the adverse effects of climate change in cash 
crop faming.  

 

2. Climate Sensitivity and Cash Crops Farming  
It is observed that productivity of cash and food-grain crops are negatively impacting due to variability in 

minimum temperature, maximum temperature and change in rainfall pattern in India Ayyogari, et al. [13]; Yadav, 
et al. [15]. Hijmans [29] detected that potato yield decreases by 18% to 32% as temperature increases by 1 to 
1.40C respectively in potato producing economies. Furthermore, potato yield is expected to be increased in Algeria, 
China, Morocco and South Africa [29]. Potato crop is passing through various physiological processes during 
sowing, growing and harvesting time [13]. Hence, variability in climatic factors have a negative impact on potato 
growth and yield [12-15, 29].  

Hundal and Kaur [16] estimated that groundnut yield is likely to be declined by 23.2% and 36.2% as 
temperature increases by 1.2 and 30C respectively in Punjab (India). Nath, et al. [20] investigated the impact of 
climatic variations on sesame productivity during sowing time in Kalyani (India). It shows that climate variability 
during various days in sowing time have a negative impact on sesame yield and the effect of climate variability on 
sesame yield varies across days. Masters, et al. [21] reported that growing season of cotton crop increases in 
moderate temperature, thus cotton yield would be improved. The study show that climate change would have a 
negative impact on cotton yield in near future. Sankaranarayanan, et al. [22] also reviewed that climatic factors 
have a negative impact on production and yield of cotton crop in India.  

Deshmukh and Lunge [24] explored that association of maximum and minimum temperatures and rainfall 
with cotton yield in Vidarbha (Maharashtra) India. It found that climatic factors have a significant impact on cotton 
yield. Moorthy, et al. [23] projected the effect of historical climate change on yield of cotton, sugarcane, rice, wheat 
and sorghum crops. It shows that temperature and precipitation did show insignificant impact on yields of cotton, 
while climate factors have a non-linear relationship with it. Ashalatha, et al. [17] analyzed the drought effect on 
crop yield and farmer’s understanding towards climate change in Dharwad Karnataka (India). It observed that 
groundnut and cotton yields decreases by 34.09 and 59.96 Kg/Ha respectively in rainfed areas due to low rainfall.  



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Kumar and Sharma [1] observed that yield of cotton and sesame crops were negatively associated with 
maximum temperature, while yields of potato and linseed crops are expected to be declined as increase in minimum 
temperature in India. Hebbar, et al. [2] assessed the climate change impact on cotton yield in different climate 
change scenarios (i.e., A2, B2 and A1B). It reported that cotton yield has a probability to be declined by 477Kg/Ha 
and 268Kg/Ha under A2 and B2 scenario respectively, while cotton yield was statistically in-significant under A1B 
scenario. Singh, et al. [12] attain the farmer’s understanding on climate change and assess the climatic impact on 
cotton yield and production in Rajasthan, Andhra Pradesh and Maharashtra. This study concluded that climate 
change have a negative impact on cotton yield, water quality and fabric quality.  

Singh, et al. [30] examined the impact of climate change on groundnut yield in Anantapur and Mahboobnagar 
districts of Andhra Pradesh and Junagadh districts of Gujarat in India. Results of the study based on CROPGRO-
Groundnut model indicate that groundnut yield decreases as increase in temperature. However, the effects of 
temperature, rainfall and CO2 emissions on groundnut yield would vary in aforementioned districts. Thakare, et al. 
[26] assessed the variability in cotton yield due to change in rainfall pattern, and maximum and minimum 
temperature in Surat district of Gujarat in India. It found that rainfall variability has a negative impact on crop 
physiology and cotton yield. Raju, et al. [25] measured the impact of temperature and rainfall on cotton yield in 
Andhra Pradesh (India). It point out that cotton yield decreases by 13Kg/Ha and 0.7Kg/Ha as increase in 10C 
maximum temperature and 10 mm rainfall respectively. Birthal, et al. [19] explored the variability in climatic 
factors (i.e., temperature and rainfall) during 1969-2005 and climate impact on yield of food-grain crops in India. It 
observed that groundnut yield decreases by 9% as 10C increase in maximum temperature, while minimum 
temperature and rainfall showed a positively impact on groundnut yield. However, rainfall could not compensate 
the reduction in groundnut yield due to increase in maximum temperature.  

Kumar, et al. [14] examined the climate change impact on mean yield and yield variability of cotton, 
groundnut, potato, sesame and linseed crops using stochastic production function approach in India. It observed 
that mean yield of cotton and sesame crops are likely to be declined by 2.03% and 2.80% respectively as 10C 
increase in maximum temperature. Productivity of potato and linseed may be declined by 0.47% and 0.84% 
respectively due to 10C increase in minimum temperature. Singh, et al. [9] evaluated the impact of climatic and 
non-climatic factors on cropped area, yield and production of cash crops across Indian states. It observed that 
cropped area under groundnut and sesame crops decrease by 13.87% and 23.50% respectively, while groundnut and 
cotton production declines by 9.83% and 41.09% respectively; and productivity of potato, groundnut, sesame and 
cotton crops declines by 3.71%, 10.31%, 4.51% and 7.20% respectively due to 1% variability in climatic factors. 
Ramachandran, et al. [11] reported that productivity of potato and groundnut crops are expected to be declined by 
3-7% as increase in 10C temperature in India.  
 

3. Research Methodology  
3.1. Introduction of Study Area 

Separate state-wise panel data (during 1971-2014) was compiled for potato, cotton and groundnut crops from 
fifteen states of the country Refer Table 1. Climatic and non-climatic factors are considered in this analysis.  
 

Table-1. Detail of crops which are included in each state-wise panel. 

Name of States Name of crops undertaken from respective states 

Andhra Pradesh Potato, Cotton, Groundnut, Sesame 
Assam Potato, Cotton, Sesame 
Bihar Potato, Cotton, Groundnut, Sesame 

Gujarat Potato, Cotton, Groundnut, Sesame 
Haryana Potato, Cotton, Groundnut, Sesame 

Karnataka Potato, Cotton, Groundnut, Sesame 
Kerala Cotton, Groundnut, Sesame 

Madhya Pradesh Potato, Cotton, Groundnut, Sesame 
Maharashtra Potato, Cotton, Groundnut, Sesame 

Odisha Potato, Cotton, Groundnut, Sesame 
Punjab Potato, Cotton, Groundnut, Sesame 

Rajasthan Potato, Cotton, Groundnut, Sesame 
Tamil Nadu Potato, Cotton, Groundnut, Sesame 

Uttar Pradesh Potato, Cotton, Groundnut, Sesame 
West Bengal Potato, Cotton, Groundnut, Sesame 

                       Source: Author's compilation based on undertaken data of different states for this study.  

 

3.2. Description on Data Sources  
Information on production, yield, area sown, irrigated area and consumption of fertilizer for each crop are 

derived from Centre for Monitoring Indian Economy (CMIE). Forest area is also taken from CMIE. Value of 
production of each crop is estimated at farm harvest prices with constant level as 1993-94 base year. Farm harvest 
price of cash crop is taken from the different publications of Directorate of Economics and Statistics Department of 
Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture (GoI). Agricultural labour and cultivators 
are taken from the different publication of Census (GoI), which is available in decadal periods (i.e. 1971, 1981, 1991, 
2001 and 2011). Rural literate population is taken from the Planning Commission (GoI), which is available 
aforesaid periods. Minimum temperature, maximum temperature and precipitation related data are derived from 
the Indian Meteorological Department (IMD) (GoI) database, Geographic Information System and official website 
of Indian Institute of Tropical Meteorology, New Delhi. Climatic data is available on daily intervals with latitude 
and longitude information of monitoring stations. So, city-wise geographical locations or stations are identified to 
derive the data on climatic factors for all cities in a state. Thereafter, groups of different geographical location in a 
state is integrated to arrive at state-wise data [8, 9]. Thence, these data are converted in monthly averages city-
wise, after that data is transformed in state-wise monthly maximum temperature, minimum temperature and 



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precipitation [9]. Aforesaid process is done through the C++ statistical software, while SPSS statistical software is 
used to extract and bring data to excel format. Minimum temperature, maximum temperature and precipitation in 
three growth periods i.e., sowing time, growing time and harvesting time of each crop is integrated in regression 
models [8-10]. Interpolation and extrapolation techniques are used to fill the missing values to complete the time 
series during 1971-2014 in each state-wise panel data [1, 3, 4, 6-10, 14].  
 

4. Analytical Viewpoint of Stochastic Frontier Production Function Approach 
(SFPFA)  

Existing researchers have used SFPFA to explore the effect of climatic and non-climatic factors on crop 
productivity and to estimate the technical efficiency (TE) in agricultural production analysis in different economies 
[31-37]. SFPFA model can be applied in parametric and non-parametric condition [34, 36, 37]. It comprises 
parameters of production and inefficiency function simultaneously in a model [38]. So, in this study SFPFA model 
is applied to investigate the TE of cash crops farming. Production, yield and area sown of cash crops are considered 
as dependent variables and recursively regressed with climatic and non-climatic factors [5]. Cobb-Douglas 
production function model is used to estimate the regression coefficients of climatic and non-climatic variables in 
empirical models [4, 14].  

It is observed that crop productivity and production are significantly associated with variability in climatic and 
non-climatic factors [1-11, 17, 21, 23, 24]. Thus, the SFPFA model assumes that variability in climate factors have 
a significant association with production efficiency of crops [39]. Climate change have a negative impact on 
technical efficiency of production, yield and cropped area in crop farming [39]. TE of a production unit can be 
measured through SFPFA [31-37]. In the present study SFPFA approach is used, which comprises two terms: 
non-negative and systematic distribution. It may be presented in following functional forms [34, 36, 37]:  
 

Yi = f (Xst, β) exp(est)                                      (1) 
 

Here, Yi is the production or productivity of each crop, Xit is the vector of independent variables; β is the 

vector of regression coefficients of associated explanatory variables; and est is the error term in Equation 1.     
 

Yi = f (Xst, β) exp(vst-ust)                                 (2) 
 

Here, Yst is the output of a specific crop in state (s) in year (t); X is vector of climatic and non-climatic variables; 

β is the vector of regression coefficients of explanatory variables [36, 37]; est (=vst – ust) is random error term that 

includes ust and vst in Equation 2. It is identically and normally distributed [N(0, σ2)] term [36, 37]. ust may be 

considered truncated-normal distribution with zero mean (μ) with constant variance (σ2) while ust and vst are also 
normally distributed for different states [38]. SFPFA model may be used in terms of time-invariant and time-
varying models. This study is used time-invariant model to assess the regression coefficients of variables and to 
estimate TE. 

Assessment of Technical Efficiency (TE): TE of a state can be estimated using following formula [38]:  
 

TEst = exp(-ust)                                                            (3) 
 

Here, TEst is the technical efficiency of a state, and ust is estimated through Equation 3. If the value of TE of a 
states is equal to 1 then it can be concluded that the state has no technical inefficiency in crop production [38].  
 

5. Empirical Analysis  
5.1. Creation of Empirical Model for Crop Farming 
Log-linear (Cobb-Douglas) (C-D) production function model is considered to measure the impact of climate and 
non-climatic on production of undertaken crops. For this, total production of crops are used as dependent variables 
and regressed with climatic and non-climatic factors under the SFPFA. Following functional form of empirical 
model is applied [9, 40]:  
 

ln(tp)st= β0 +β1 ln(as)st+β2 ln(srlpca)st +β3 ln(sfaca)st +β4 ln(ia)st +β5 ln(fcca)st +β6 ln(ralcca)st +β7 ln(vprc)st +β8 ln(vamaxtst)st +β9 

ln(vamaxtgt)st +β10 ln(vamaxtht)st +β11 ln(vamintst)st +β12 ln(vamintgt)st +β13 ln(vamintht)st +β14 ln(vapcpst)st +β15 ln(vapcpgt)st 

+β16 ln(vapcpht)st +β17 ln(cvdapcp)st + β18 ln(cvdamint)st + β19 ln(cvdamaxt)st + (vst- ust)                (4) 
 

Here, tp-Production (in 000 Tonne) of respective crop in state (s) in time (t); ln-Natural logarithms of associated 
variables; as-Area sown (in 000 Ha); srlpca- Share of rural literature population with cropped area (in Number); 
sfaca- Share of forest area with cropped area (in 000 Ha); ia- Irrigated area (in 000 Ha); fcca-Fertilizer consumption 
on cropped area (in 000 Tonnes); ralcca- Share of rural agricultural labour and cultivator with cropped area (in 
Number); vprc-Value of production of respective crop at constant prices of 1993-94 (in INR); vamaxtst, vamaxtgt and 
vamaxtht (in %)-Variability in maximum tempereture during sowing time, growing time and harvesting time 
respectively; vamintst, vamintgt and vamintht (in %)-Variability in minimum temperature during sowing time, 
growing time and harvesting time respectively; vapcpst, vapcpgt and vapcpht (in %)-Variability in precipitation 
during sowing time growing time, growing time and harvesting time respectively; cvdapcp, cvdamint and cvdamaxt 
(in %)-Coefficient variation in daily precipitation, minimum tempereture and maximum temperature respectively 

during crop period. β0 is the constant coefficient; β1,… β19 are the regression coefficients of associated explanatory 
variables; vst and ust are the symmetric error term and non-negative random variable respectively in the Equation 4.  



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Also, land productivity (yield) or production per hectare land of cash crops was used as depended variables and 
regressed with climatic and non-climatic factors. For this, following functional form of empirical model was used 
[9, 40]:  
 

ln(lp)st= α0 +α1 ln(as)st+α2 ln(srlpca)st +α3 ln(sfaca)st +α4 ln(ia)st +α5 ln(fcca)st +α6 ln(ralcca)st +α7 ln(vprc)st +α8 ln(vamaxtst)st +α9 

ln(vamaxtgt)st +α10 ln(vamaxtht)st +α11 ln(vamintst)st +α12 ln(vamintgt)st +α13 ln(vamintht)st +α14 ln(vapcpst)st +α15 ln(vapcpgt)st 

+α16 ln(vapcpht)st +α17 ln(cvdapcp)st + α18 ln(cvdamint)st + α19 ln(cvdamaxt)st + (vst- ust)                            (5) 
 

Here, lp-Land productivity (yield) of respective crop in state (s) in time (t); ln-Natural logarithms of associated 

variables. The detail description of all explanatory variables are presented in earlier Equation 5. α0 is the constant 

coefficient; α1,… α19 are the regression coefficients of corresponding explanatory variables.  
The present study assess the impact of climate variability on cropped area of cash crops, therefore area sown of 

respective crop is considered as dependent variables and regressed with climatic and non-climatic factors. For this, 
the proposed model accept that area sown of a crop get impacted due to variability in climatic factors in sowing 
time only [9, 40]. It also considered that area sown of a crop has a functional relationship with srlpca, sfaca, ia, 
ralcca, vprc, vamaxtst, vamintst, vapcpst, cvdapcpst, cvdamintst and cvdamaxtst. After converting it into C-D production 
functional model, said relationship can be written in following form [9, 40]:   
 

ln(as)st= γ0 +γ1  ln(srlpca)st +γ2 ln(sfaca)st +γ3  ln(ia)st + γ4 ln(ralcca)st +γ5 ln(vprc)st + γ6 ln(vamaxtst)st + γ7 ln(vamintst)st + γ8 

ln(vapcpst)st + γ9 ln(cvdapcpst)st + γ10 ln(cvdamintst)st + γ11 ln(cvdamaxtst)st +(vst- ust)          (6)        

 
Here, as-Area sown of respective crop in state (s) in time (t); cvdapcpst, cvdamintst and cvdamaxtst (in %)-

Coefficient variation in daily precipitation, minimum tempereture and maximum temperature respectively during 

sowing time. γ0 is the constant coefficient; γ1,… γ11 are the regression coefficients of corresponding independent 
variables in Equation 6. Signification on other variables has presented in Equation 5. Regression coefficients and 
technical efficiencies of yield, production and area sown functions of all crops are estimated through STATA 
statistical software.  
 

5.2. Consistency and Viability of Dependent and Explanatory Variables 
Dependent Variables: The present study was used production (tp) (in 000 tonne), yield (LP) (in Kg/Ha) and 

area sown (as) (in 000 Ha) as a dependent variables in three different types of empirical exercises [1, 3, 8, 9, 41].  
Non-climatic and Control Variables: Area sown (as) (in 000 Ha), irrigated area (ia) (in 000 Ha), fertilizer 

consumption on cropped area (fcca) (in tonne), share of rural literature population with cropped area (srlpca) (in 
number), share of rural agricultural labour and cultivator with cropped area (ralcca) (in number) and value of 
production of respective crop (vpcc) (in Rs) are considered as explanatory variables [1, 3, 4, 9, 10, 41].   

Ecological Factor: Forest area is an important factor to maintain sustainability in environmental factors, 
therefore it might be ecosystem adaption based techniques to mitigate the negative consequences on climate change 
in crop farming. Hence, share of forest area with cropped area (sfaca) (in 000 Ha) was used to capture the impact of 
ecological factor on cash crop farming [1, 41].  

Variability in Climatic Factors: Maximum tempereture, minimum tempereture and precipitation are the 
crucial weather factors which are significantly associated with growth, development and productivity of crops. So 
these factors are considered as a climatic factors [1, 5, 8, 10, 19, 25, 26, 41]. To estimate the variability in climatic 
factors, the original value of a particular climatic factor is subtracted from the mean value of respective climatic 
factor for time period 1971-2014. Then, square root of this climatic factor is used to capture the climatic variability 
impact on production, productivity (yield) and cropped area of cash crops [9, 41]. Hence, climate variability in each 
factor is estimated as:  
  

𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = √[(𝑋𝑠𝑡 − 𝑋)] 2                                                                                                                                        (7)        
 

Here, Xit is original value of climatic factor in state (s) and year t; and X is mean value of particular climatic 
factor for time period 1971-2014 in Equation 7 [9, 38]. Coefficient variation is used to assess the impact of daily 
variation in climatic factor on production, yield and cropped area cash crops [9, 10, 14].  
 

5.3. Limitation of the Study 
Solar radiation, sunshine, wind speed, hail storm, humidity, fog, CO2 concentration, soil moisture and other 

weather patterns, which directly or indirectly affect the crop production, yield and cropped area of crops. Though, 
these variables are not included in this study. Furthermore, it also assumes that all varieties of a crop is similarly 
get affected due to climate change, while farmers are cultivating various varieties of a crop. Also, it accepts that a 
particular state lies in the same agro-ecological zone, while India is divided in 15 broad agro-climatic zones with 
127 sub agro-climatic zones. Many states (e.g. Uttar Pradesh, Madhya Pradesh, Bihar, Punjab, Haryana, Orissa, 
Rajasthan etc.) are divided in various agro-ecological zones. However, this study could not capture the zone-wise 
effects on crop productivity, production and cropped area of crops.  
 

6. Statistical Inferences on Regression Results    
6.1. Association of Crops Production and Climatic and Non-climatic Factors 

Regression coefficients of climatic and non-climatic factors in production function of cash crops are estimated 
through time-invariant regression model. Impact of climatic and non-climatic factors on production of potato, 
cotton, groundnut and sesame crops are given in Equations 8, 9, 10 and 11 respectively. Regression coefficients of 
area sown with production of all crops are found positive and statistically significant. Thus, production of these 
crops have a high potential to be increased as area sown increases under [9]. Literate population have a lower 



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interest in cultivation as compared to unskilled labours. So, rural literate population have a negative association 
with crop production. Forest area would be useful to mitigate the adverse effect of climate change in potato and 
groundnut farming [1, 38]. Conversely, forest area have a negative implication on cotton and sesame crop farming. 
Irrigated area have a higher yielding capacity than non-irrigated area [1, 3, 4, 6, 7, 9, 14]. Thus, irrigated area 
have positive association with production of cotton and groundnut crops, it imply that production of these crops 
would be increased as irrigated area increases.  

Extensive fertilizer application in cultivation, contribute high GHGs emission in atmosphere, thus it has a 
negative impact on environmental factors [42].1 Thus, fertilizer application may be significant driver to increase 
variability in climatic factors. Subsequently, fertilizer application have a negative impact on crop production [1, 3, 
4, 6, 7, 9, 14, 27]. Estimates specify that fertilizer consumption on cropped area have a negative impact on 
production of potato, cotton, groundnut and sesame crops. Application of agricultural labour and cultivator show 
positive impact on production of all cash crops. Estimates suggest that production would improve as utilization of 
agricultural labour and cultivator in cash crop farming. As Indian farmers are small and marginal who have farm 
size with less than 1 acre in which mechanization may be costly. Thus, application of human skill would be 
important to increase crop production. Appropriate price of crop is useful to increase crop production as farmer 
gives more preference to those crops that give them greater financial benefits [27]. It also increases the decision 
power of farmers to choose an appropriate crop for cultivation. Value of production is a crucial factor to increase 
the farmer’s consciousness to grow a specific cash crop. It improves farmer’s economic capacity to use another 
inputs like organic fertilizer, pesticides, better quality of seed, advance technology and better irrigation in 
cultivation. Thus, value of production have a positive impact on production of all crops. 

Few climatic factors have a statistically significant impact on production of cash crops. Maximum temperature 
during sowing time is appeared negative impact on production of potato and cotton crops. Estimates can be 
interpreted that germination of these crops decrease as variability in maximum temperature during sowing time, 
therefore production of these crops would decline. Variability in maximum temperature during growing time have 
a negative impact on production of potato and cotton crops [9, 15, 20]. Crop production of potato and cotton crops 
in harvesting time is also negatively impacted due to increase in maximum temperature during harvesting time. 
Minimum temperature during growing period have a positive and negative impact on production of cash crops. 
Estimates specify that minimum temperature during sowing time have a negative impact on potato and groundnut 
crops. It recommended that output of these crop grow in soil, therefore these crops require moderate minimum 
temperature during sowing, growing and harvesting time. Cotton and sesame crops get benefit due to increase in 
variability in minimum temperature during sowing time. Minimum temperature during growing time is seen 
negative impact on productivity of undertaken cash crops. Precipitation during sowing and harvesting time have a 
negative impact on production of cash crops. Estimates are consistent with earlier studies which argued that 
change in rainfall pattern have a negative impact on crop productivity [1, 6, 10, 14, 23, 27]. Coefficient variation in 
daily precipitation also show negative impact on production of cash crops. Coefficient variation in daily maximum 
and minimum temperature have a positive and negative impact on production of these crops.  
 
Potatoln(tp)st= -3.8643* +0.7621* ln(as)st -0.5505* ln(srlpca)st +0.0357 ln(sfaca)st -0.0485 ln(ia)st -0.1503* ln(fcca)st +0.3748* 

ln(ralcca)st +0.5547* ln(vprc)st -0.0049 ln(vamaxtst)st -0.00156 ln(vamaxtgt)st -0.0059 ln(vamaxtht)st -0.0045 ln(vamintst)st -
0.0015 ln(vamintgt)st -0.0094 ln(vamintht)st -0.0141 ln(vapcpst)st -0.0022 ln(vapcpgt)st -0.0029 ln(vapcpht)st -0.0491 
ln(cvdapcp)st + 0.0688 ln(cvdamint)st + 0.0046 ln(cvdamaxt)st +(vst- ust) [(N=578) (Wald Chi2=6858.73*) (Log 
Likelihood = -14.8610)                                                                        (8) 

 
Cottonln(tp)st= -7.4639*+0.4845* ln(as)st -0.6311* ln(srlpca)st -0.0944** ln(sfaca)st + 0.0547*** ln(ia)st -0.1231* ln(fcca)st 

+0.7567* ln(ralcca)st +0.5670*ln(vprc)st -0.0072 ln(vamaxtst)st -0.0189*** ln(vamaxtgt)st -0.0036 ln(vamaxtht)st +0.0025 
ln(vamintst)st -0.0050 ln(vamintgt)st +0.0010 ln(vamintht)st -0.0004 ln(vapcpst)st +0.0145 ln(vapcpgt)st -0.0039 ln(vapcpht)st 
+0.2088** ln(cvdapcp)st -0.1824** ln(cvdamint)st +0.0374 ln(cvdamaxt)st +(vst- ust) [(N=583) (Wald Chi2=6871.17*) (Log 
Likelihood = -66.9939)]                                                                         (9) 

 
Groundnutln(tp)st= -2.1741* +1.0863*ln(as)st -0.6311* ln(srlpca)st +0.0770* ln(sfaca)st +0.0676** ln(ia)st -0.2156* ln(fcca)st 

+0.1982 ln(ralcca)st +0.5730* ln(vprc)st +0.0128 ln(vamaxtst)st -0.0096 ln(vamaxtgt)st +0.0166*** ln(vamaxtht)st -0.0069 
ln(vamintst)st -0.0066 ln(vamintgt)st -0.0006 ln(vamintht)st -0.0046 ln(vapcpst)st -0.0019 ln(vapcpgt)st -0.0065 ln(vapcpht)st -
0.1580** ln(cvdapcp)st +0.0211 ln(cvdamint)st -0.0384 ln(cvdamaxt)st +(vst- ust) [(N= 581) (Wald Chi2= 13115.87*) (Log 
Likelihood = -62.3242)]                                                                  (10) 

 
Sesameln(tp)st= -3.1582* +1.1245* ln(as)st -0.5765* ln(srlpca)st +-0.0332 ln(sfaca)st -0.0333 ln(ia)st -0.2467* ln(fcca)st 

+0.2625* ln(ralcca)st +0.5340* ln(vprc)st +0.0083 ln(vamaxtst)st -0.0019 ln(vamaxtgt)st -0.0028 ln(vamaxtht)st +0.0066 
ln(vamintst)st -0.0145* ln(vamintgt)st -0.0111 ln(vamintht)st -0.0039 ln(vapcpst)st -0.0059 ln(vapcpgt)st -0.0049 ln(vapcpht)st -
0.1682* ln(cvdapcp)st +0.0584 ln(cvdamint)st 0.0864 ln(cvdamaxt)st +(vst- ust) [(N= 626) (Wald Chi2= 6699.65*) (Log 
Likelihood = -71.4120)]                                                                   (11) 
 

6.2. Association of Yield of Crops with Climatic and Non-climatic Factors 
Regression results which estimate the impact of climatic and non-climatic factors on yield of cash crops are 

given in Equation 12, 13, 14 and 15. Area sown is an important factor to decide output per hectare land and yield is 
negatively impacted due to increase in cropped area of cash crops [7, 43]. It has observed that yield have a non-
linear relationship with cropped area of cash crops [9, 43]. So, yield of potato, cotton and sesame crops have a 
negative associated with area sown. Yield of cash crops have a negative relationship with rural literate population. 
It may be due to that literate population have an insignificant contribution in farming. Regression coefficient of 
forest area with cropped area of potato and groundnut crops are positive, which imply that forest area might be 
useful to increase yield of potato and groundnut crops [1, 38].  

 
1 http://www.epa.gov/climatechange/science/causes.html.  

http://www.epa.gov/climatechange/science/causes.html


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Irrigated area would be useful to increase yield of cotton and groundnut crops, while yield of potato and sesame 
crops are negatively associated with irrigated area [1, 6, 9, 10]. Regression coefficient of fertilizer consumption 
with yield of cash crops are found negative, it imply that yield of these crops would be declined as fertilizer 
application increases. Agriculture labour and cultivators and monetary value of crops have a positive impact on 
yield of cash crops. Better return on production of cash crop is helpful to increase the economic capacity of farmers, 
therefore they would be in a better position to apply advance methods in cultivation. Consequently, yield of cash 
crop would be increased as return on per hectare land increases.   

Few climatic factors have statistically significant impact on yield of cash crops. Maximum temperature, 
minimum temperature and precipitation have a negative impact on potato yield. Maximum temperature during all 
crop growth period have a negative impact on cotton yield, while, minimum temperature during sowing and 
growing time are positively associated with cotton yield. In contrary, cotton yield have a negative association with 
precipitation during sowing and harvesting time. Estimates also indicate that daily variation in climatic factors also 
show negative impact on cotton yield. As groundnut crop needs moderate minimum temperature during sowing, 
growing and harvesting time. Thus, any variability in minimum temperature would have a negative impact on 
growth and yield of groundnut crop. Groundnut crop requires less water as compared to other crops, thus increase 
in precipitation have a negative impact on groundnut yield. Sesame yield is negatively influenced due to variability 
in climatic factors during sowing, growing and harvesting time. Furthermore, estimates show that variability in 
climatic factors during sowing, growing and harvesting time have a negative impact on yield of potato, cotton, 
groundnut and sesame crops.  
 
Potatoln(lp)st= 3.0434* -0.2379** ln(as)st -0.5505* ln(srlpca)st +0.0357 ln(sfaca)st -0.0485 ln(ia)st -0.15032* ln(fcca)st 

+0.3748* ln(ralcca)st +0.5547* ln(vprc)st -0.0049 ln(vamaxtst)st -0.0016 ln(vamaxtgt)st -0.0059 ln(vamaxtht)st -0.0045 
ln(vamintst)st -0.0015 ln(vamintgt)st -0.0094 ln(vamintht)st -0.0141 ln(vapcpst)st -0.0022 ln(vapcpgt)st -0.0029 ln(vapcpht)st -
0.0491 ln(cvdapcp)st +0.0688 ln(cvdamint)st + 0.0046 ln(cvdamaxt)st +(vst- ust)  [(N= 578) (Wald Chi2= 1304.91*) (Log 
Likelihood = -14.8654)]          (12) 

 
Cottonln(lp)st= -2.3281* -0.5155*** ln(as)st -0.6311* ln(srlpca)st -0.0944** ln(sfaca)st +0.0548*** ln(ia)st -0.1231* ln(fcca)st 

+0.7567* ln(ralcca)st +0.5670* ln(vprc)st -0.0072 ln(vamaxtst)st -0.0189*** ln(vamaxtgt)st -0.0036 ln(vamaxtht)st +0.0025 
ln(vamintst)st -0.0050 ln(vamintgt)st +0.0010 ln(vamintht)st -0.0004 ln(vapcpst)st +0.0145 ln(vapcpgt)st -0.0039 ln(vapcpht)st 
+0.2088** ln(cvdapcp)st -0.1824** ln(cvdamint)st + 0.0373 ln(cvdamaxt)st +(vst- ust) [(N= 583) (Wald Chi2= 1435.51*) 
(Log Likelihood = -66.9969)                              (13) 

 
Groundnutln(lp)st= 4.7359* +0.087 ln(as)st -0.6313* ln(srlpca)st +0.0771* ln(sfaca)st +0.0667** ln(ia)st -0.2151*   ln(fcca)st 

+0.1978* ln(ralcca)st +0.5730* ln(vprc)st +0.0128 ln(vamaxtst)st -0.0096 ln(vamaxtgt)st +0.0167*** ln(vamaxtht)st -0.0069 
ln(vamintst)st -0.0067 ln(vamintgt)st -0.0006 ln(vamintht)st -0.0046 ln(vapcpst)st -0.0019 ln(vapcpgt)st -0.0065 ln(vapcpht)st -
0.1581** ln(cvdapcp)st +0.0212 ln(cvdamint)st -0.0384 ln(cvdamaxt)st +(vst- ust) [(N= 581) (Wald Chi2= 2154.19*) (Log 
Likelihood = -62.3299)                                           (14) 

 
Sesameln(lp)st= 3.7526* +0.1248*** ln(as)st -0.5762* ln(srlpca)st -0.0332 ln(sfaca)st -0.0331 ln(ia)st -0.2467*   ln(fcca)st 

+0.2619* ln(ralcca)st +0.5340* ln(vprc)st +0.0083 ln(vamaxtst)st -0.0019 ln(vamaxtgt)st -0.0028 ln(vamaxtht)st +0.0066 
ln(vamintst)st -0.0145*** ln(vamintgt)st -0.0111 ln(vamintht)st -0.0039 ln(vapcpst)st -0.0059 ln(vapcpgt)st -0.0049 ln(vapcpht)st 
-0.1681* ln(cvdapcp)st +0.0585 ln(cvdamint)st +0.0864 ln(cvdamaxt)st +(vst- ust)  [(N= 626) (Wald Chi2= 1408.82*) 
(Log Likelihood = -71.4405)                                       (15) 
 
6.3. Association of Cropped Area of Crops with Climatic and Non-climatic Factors 

Regression coefficients of area sown of cash crops with climatic and non-climatic factors are given in Equation 
16, 17, 18 and 19. Estimates indicate that area sown are positively associated with rural literate population, forest 
area, irrigated area, and agricultural labour and cultivators. Irrigated area is a crucial determinant to increase areas 
sown, therefore it have positive impact on area sown. Hence, it is proposed that irrigated area would be useful to 
increase cropped area of cash crops [9, 38]. Agricultural labour and cultivators are useful to prepare land during 
sowing time of crops, thus agricultural labours and cultivators are positively associated with cropped area. Value of 
production of all crops have a negative impact on cropped area of respective crops. Monetary value of crop 
production would be useful to increase the farmer’s economic capacity to buy new technology, high yielding 
varieties of seed, and other inputs, subsequently it may be useful to increase crop productivity. However, high cost 
of cultivation of cash crops may be another reason to reduce the farmer’s consciousness to enlarge area under cash 
crops.  

Regression coefficients of climatic variables shows that variability in maximum temperature and minimum 
tempereture during sowing time have a positive impact on area sown of potato, groundnut and sesame crops. 
Estimates infer that area sown positively reflects due to increase in maximum and minimum temperature during 
sowing time. Variability in precipitation have negative and statistically significant impact on cropped area of 
potato, groundnut and sesame crops. Also, variability in daily precipitation during sowing time is also negatively 
associated with area sown of cash crops. Cropped area of cotton crop is positively associated with precipitation. For 
positive association of precipitation in sowing time for this crop can be interpreted that the large cropped area of 
this crop comes under rainfed areas. While, variation in daily maximum and minimum temperature during sowing 
time show positive impact on cropped area of these crops. 
 

Potatoln(as)st= -3.8397*+0.1453*  ln(srlpca)st +0.1041* ln(sfaca)st +0.0633*  ln(ia)st +0.6113* ln(ralcca) -0.0380* ln(vprc)st 
+0.0033 ln(vamaxtst)st +0.0151* ln(vamintst)st -0.0097** ln(vapcpst)st -0.0177 ln(cvdapcpst)st +0.0784** ln(cvdamintst)st 

+0.1496*  ln(cvdamaxtst)st +(vst - ust)  [(N= 607) (Wald Chi2= 18488.19*) (Log Likelihood = -489.2042)]            (16) 
 



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Cottonln(as)st= -1.4850*+0.1179*  ln(srlpca)st +0.2678* ln(sfaca)st +0.0914*  ln(ia)st +0.4983* ln(ralcca) -0.0380* ln(vprc)st -
0.0056 ln(vamaxtst)st -0.0022 ln(vamintst)st +0.0023 ln(vapcpst)st -0.1012** ln(cvdapcpst)st +0.0165 ln(cvdamintst)st 

+0.1133**  ln(cvdamaxtst)st +(vst - ust)  [(N= 603) (Wald Chi2= 19483.01*) (Log Likelihood = -377.3423)]            (17) 
 

Groundnutln(as)st= -2.2501*+0.0568*** ln(srlpca)st +0.1915* ln(sfaca)st +0.1152* ln(ia)st +0.6293* ln(ralcca) -0.0402* 
ln(vprc)st +0.0022 ln(vamaxtst)st +0.0014 ln(vamintst)st -0.0138* ln(vapcpst)st -0.1208* ln(cvdapcpst)st +0.0601** 
ln(cvdamintst)st +0.0480  ln(cvdamaxtst)st +(vst - ust)  [(N= 604) (Wald Chi2= 21497.47*) (Log Likelihood = -
349.16462)]                                                                                                                                              (18) 

 
Sesameln(as)st= -2.4808*+0.0326*** ln(srlpca)st +0.1551* ln(sfaca)st +0.0837* ln(ia)st +0.6680* ln(ralcca) -0.0256* ln(vprc)st 
+0.0003 ln(vamaxtst)st +0.0006 ln(vamintst)st +-0.0099** ln(vapcpst)st -0.1351* ln(cvdapcpst)st +0.0468*** ln(cvdamintst)st 

+0.0053  ln(cvdamaxtst)st +(vst- ust)  [(N= 649) (Wald Chi2= 15074.98*) (Log Likelihood = -420.6113)]     
                                                                                                                                 (19) 

6.4. Explanation on Estimated Technical Efficiency of Cash Crops  
State-wise mean value of estimated technical efficiency (TE) during 1971-2014 for production, yield and area 

sown for potato, cotton, groundnut and sesame crops are presented in Figure 1, 2, 3 and 4. West Bengal have a 
highest TE (95%) of production and yield of potato crop. Gujarat, Uttar Pradesh and Tamil Nadu have 86%, 75% 
and 71% TE of production in potato crop Refer Figure 1. So, these states are able to produce better return in 
potato farming. It is observed that Rajasthan and Haryana have high efficiency to maintain the area sown under 
potato crop. Estimates conclude that all states have a high variation in technical efficiency to produce potato 
production and to maintain planting area under potato crop. Kerala is observed technically efficient state to 
produce optimum cotton production with TE of 90% Refer Figure 2. Other states have less than 50% TE of cotton 
production. Thus, these states are seen technically inefficient to produce cotton production and maintain higher 
yielding capacity. Haryana and Punjab are seemed most technically efficient states to utilize cropped area under 
cotton crop, while other states are appeared technically inefficient to increase or sustain planting area under this 
crop. Hence, it is suggested that other states need to apply technological advancement to increase technical 
efficiency of production and cropped area in cotton crop. 

The value of TE is found 95% for groundnut production and yield in Kerala, thus the state is technically 
efficient to produce optimum groundnut production Refer Figure 3. Haryana has 87% TE of groundnut, imply that 
Haryana is second technically efficient state to produce groundnut output. Other states have value of TE less than 
62%, thus these states are technically inefficient in groundnut farming. Andhra Pradesh, Gujarat, Karnataka, 
Madhya Pradesh, Maharashtra and Rajasthan have less than 25% value of TE, therefore these states are in poor 
position and unable to produce desire production of groundnut. Haryana have a 97% value of TE for cropped area, 
thus the state is in better position to utilize cropped area in groundnut farming, while Punjab is second technically 
efficient state to maintain cropped area under groundnut crop. Other states have a less than 90% value of TE, thus 
these states needed to increase technical efficiency in production and cropped area of groundnut crop.  

Highest value of TE (94%) for sesame production and yield in Kerala shows that this state is technically 
efficient to provide better return in sesame crop Refer Figure 4. Other states have low value of TE (less than 90%), 
therefore these states are technically inefficient. There is needed to improve technical efficiency of sesame crop in 
these states. Haryana, Madhya Pradesh, Rajasthan, Uttar Pradesh, Maharashtra states have a less than 30% value 
of TE, thus these states are unable to produce desire output. Punjab and Haryana have 92% and 89% value of TE 
respectively for cropped area for sesame crop. So, these states have an ability to maintain cropped area under 
sesame crop. Remaining states have a low value of TE for area sown of sesame crops, thus these are technically 
ineffective to sustain cropped area under this crop.  

  

 
Figure-1. State-wise technical efficiency of potato farming during 1971-2014. 

                     Source: Estimated by authors.  

 



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Figure-2. State-wise technical efficiency of cotton farming during 1971-2014. 

                     Source: Estimated by authors.  

 

 

 
Figure-3. State-wise technical efficiency of groundnut farming during 1971-2014. 

               Source: Estimated by authors.  

 

 
Figure-4. State-wise technical efficiency of sesame farming during 1971-2014. 

                       Source: Estimated by authors.  

 

7. Conclusion and Policy Implications 
This study is assessed the impact of climate variability on production, yield and cropped area of crucial 

commercial crops in India. For this, SFPFA model is employed using state-wise panel data during 1971-2014. 
Regression coefficients of climatic and non-climatic factors in proposed models are estimated through Cobb-
Douglas production function (log-linear regression model). Accordingly, state-wise technical efficiency of 
production, yield and cropped area for each crop was estimated using time in-variant decay model under non-
parametric condition. Empirical results show that variability in maximum temperature, minimum temperature and 
precipitation during sowing, growing and harvesting time have a negative and statistically significant impact on 
cropped area, production and yield of potato, cotton, groundnut and sesame crops. Here, it is concluded that cash 



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crops farming is in stress due to variability in climatic factors in India. As potato, cotton, groundnut and sesame are 
crucial commercial crops which provide the raw material to agro-industries like textile, oilseed, and others. 
Therefore, agro-industries based on these crops would be in alarming position due to climate variability in India. 
Thus, it is advised that agro-industries are essential to take an effective action to mitigate the negative impact of 
climate change on cash crop farming. Otherwise, agro-industries would be in serious position in near future. It 
would also cause to decrease employment opportunities and production in industries, and supply of goods in 
domestic market.  

Estimated state-wise technical efficiency of each crop infer that there is existence high variation in production 
activities of cash crops across Indian states. Most states are found technically inefficient to produce optimum 
production, which are also unable to use cropped area under cash crops efficiently. Kerala is not largely agrarian 
state of the county, despite that the state have highest technical efficiency in cotton, groundnut, and sesame crop 
production. West Bengal and Punjab are technically efficient states to produce optimum potato production. 
Haryana and Punjab are observed technically efficient states to better use of cropped area under potato, cotton, 
groundnut and sesame crops. Thus, it recommended that all states are required to improve their technical efficiency 
to get better returns in cash crop farming.  

 Non-climatic factors such as irrigated area, forest area and appropriate prices of crops would be helpful to 
mitigate the negative impact of climate variability in cash crop farming in India [41]. Also, change in plating time 
and crop varieties may be better adaption technique to reduce climate change impact on cash crop farming [2, 6, 9, 
19, 29]. Moreover, modern breeding techniques of crops also would be favorable to mitigate the adverse effect of 
climate change in cultivation [5]. To sustain forest area, reforestation and water conservation techniques would be 
essential to mitigate the climate change impact in Indian cash crop farming in near future [10, 19]. Also, adoption 
of micro-irrigation techniques through sprinkler and drip irrigation may be useful to sustain water for irrigation in 
near future [3, 8, 10, 19, 44]. Application of renewable energy, green technology and suggested application of 
fertilizer may be valuable to reduce GHGs emission from agricultural activities [9, 10]. Extensive fertilizer 
application in cultivation would be caused to reduce soil nutrients contents. Thus, crop production and productivity 
has a tendency to be declined as extensive application of fertilizer in cultivation. As extensive utilization of fertilizer 
in cultivation contributes more GHGs emission in atmosphere, resulting it would turn as significant cause to 
increase more variability in climatic factors. So, recommended fertilizer application in cultivation would be helpful 
to adapt the climate change impact in cash crop farming. Also, application of technological change in cultivation 
have a better adaptability to resolve the climate change impact in farming [8, 9, 14, 17, 19, 23, 25].  

Moreover, implementation of drip-irrigation would be useful to maintain for soil quality and actual nutrition 
contents of soil [22, 44, 45]. Intercropping, mixed and multi-tier cropping pattern must be preferred by farmers to 
reduce climate change impact in cultivation [10, 19, 22]. To promote more organic farming may be advantageous 
to mitigate the adverse effect of climate change in agriculture [17, 45]. Irrigation facilities are useful to reduce the 
maximum temperature impact in cultivation [3, 5, 8, 10, 19, 44]. Thus, water conservation technologies must be 
adopted by farmers to meet the irrigation requirement in agriculture, which would be useful to mitigate the 
negative consequences of climate change in cultivation in India [25]. There is essential to increase adoption of 
improved quality of seed, and to reduce contamination of soil and water surface to improve cash crops productivity 
[12]. Crop insurance policies would be useful to recover the cost of production for farmers, if crop production 
decreased or damaged due to climate variability [1, 3, 8, 17, 26]. For this, agro-industries desires to provide 
appropriate financial support to farmers to increase their  consciousness to grow commercial and industrial crops 
like cotton, sugarcane, groundnut, potato, sesame and other crops. Government of India is also needs to provide 
financial support to agricultural scientists and researchers to do more research in agricultural related activities. 
Thereby, scientific research community would be in better position to develop drought resistant and high 
temperature tolerant crops to mitigate the negative effect of climate change in Indian cash crop farming [17, 19].  
 

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