42 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 Research on World Agricultural Economy https://ojs.nassg.org/index.php/rwae Copyright © 2022 by the author(s). Published by NanYang Academy of Sciences Pte. Ltd. This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/). *Corresponding Author: Ajay Kumar Singh, School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, 248009, India; Email: a.k.seeku@gmail.com; kumar.ajay_3@yahoo.com. DOI: http://dx.doi.org/10.36956/rwae.v3i1.498 Received: 28 February 2022; Received in revised form: 25 March 2022; Accepted: 28 March 2022; Published: 31 March 2022 Citation: Singh, A.K., Kumar, S., Ashraf, S.N., et al., 2022. Implications of Farmer’s Adaptation Strategies to Climate Change in Agricultural Sector of Gujarat: Experience from Farm Level Data. Research on World Agricultural Economy. 3(1), 498. http://dx.doi.org/10.36956/rwae.v3i1.498 1. Introduction Climate change has been increased the high uncertainty in production and vulnerability in the agricultural sector world-wide [1-3]. Recently, climate change has been ob- served in terms of rising minimum and maximum tempera- ture, and change in rainfall pattern and precipitation [1,2,4,5]. High fluctuation in floods, droughts and natural disasters clearly show that climatic factors are changing due to an- thropogenic and natural activities at global level [5,6]. It is likely to be expected that the impact of climate change will be more on socio-economic development and produc- tion activities of the agricultural sector in most developing RESEARCH ARTICLE Implications of Farmer’s Adaptation Strategies to Climate Change in Agricultural Sector of Gujarat: Experience from Farm Level Data Ajay Kumar Singh1*● Sanjeev Kumar1● Shah Nawaz Ashraf2● Bhim Jyoti3● 1. School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, 248009, India 2. Entrepreneurship Development Institute of India Ahmedabad, Gujarat, India 3. V.C.S.G., UUHF, College of Forestry, Ranichauri, Tehri Garhwal, Uttarakhand, India Abstract: This study examined the farmer’s perception on climate change and adaptation strategies to mitigate the adverse effect of climate change in the agricultural sector of Gujarat. It used farm level information of 400 farmers who were purposely selected from 8 districts. Thereupon, linear, non-linear and log-linear production function models were used to examine the impact of climate change, farmer’s adaptation strategies and technological change on agricultural production in Gujarat. The descriptive and empirical results specify that adaptation strategies (i.e., change in showing time of crops, mixed cropping pattern, irrigation facilities, application of green and organic fertilizer, hybrid varieties of seeds, dampening of seed before planting, climate tolerate crops, organic farming and technology) have a positive impact on agricultural production. Thus, farmer’s adaptation strategies are useful to mitigate the negative impact of climate change in the agricultural sector. Maximum temperature and minimum temperature, precipitation and rainfall have a negative impact on agricultural production. However, the impact of these factors seemed positive in the agricultural sector when farmers apply aforementioned adaptation strategies in cultivation. Family size, education level of farmers, annual income of farmers, arable land, irrigated area, cost of technology, appropriate technology and financial support from government have a positive contribution to increase agricultural production in Gujarat. Keywords: Adaptation strategies; Agricultural sector; Technology; Climate change; Gujarat; India; Mitigation approach mailto:kumar.ajay_3@yahoo.com http://dx.doi.org/10.36956/rwae.v3i1.498 https://orcid.org/0000-0003-0660-6743 https://orcid.org/0000-0003-0429-0925 https://orcid.org/0000-0003-0660-6743 https://orcid.org/0000-0002-2074-1071 https://orcid.org/0000-0003-0660-6743 https://orcid.org/0000-0001-5410-5404 https://orcid.org/0000-0003-0660-6743 https://orcid.org/0000-0002-6960-5097 43 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 counties including India. Climate change would be caused to increase the vulnerability of 60% of the population who depend upon the agricultural sector in India [2,5]. There are many reasons which would increase high vulnerability for agricultural sector due to large dependency of population on agricultural sector; large dependency of sugarcane, oilseed and textile industries on agricultural sector in India [5]. India is located at low latitude and it has small size of land hold- ings with low economic capacity of farmers who are unable to maintain their income due to climate change. There exists high illiteracy of farmers, ineffective mechanism of govern- ment policies towards climate change, low technological upgradation of farmers and ineffective supports from agricul- tural extension services in India [5,7,8]. Subsequently, climate change will create several obstacles to increase sustainability in production and yield, food and health security, farmers’ income and trust in farming, price stability, rural develop- ment, and socio-economic development of farming and non- farming communities in India [2,9,10]. Also, poverty, income inequality, food insecurity, nutritional insecurity and hunger may increase due to climate change in India [2,9,10]. Therefore, it would be a major challenge for agronomists, agricultural scientists and policy makers to implement an effective plan to increase agricultural sustainability in the presence of cli- mate change and changing socio-economic activities of the people in India [10,11]. Agriculture sector is useful to ensure food security, nutritional security and poverty alleviation in India [5,8]. It is useful to generate employment for a large segment of society [5]. Agriculture sector also provides the raw mate- rial for several agriculture industries. Thus, it is useful to increase industrial growth and economic development. It also provides fodder for livestock which meet the require- ment of milk and raw material for dairy based industries in India. Moreover, the agricultural sector is useful to pro- duce surplus labour for the industries, provide the raw ma- terial for the agriculture industries, generate revenue for the government as a tax and foreign currency, create capi- tal assets and develop rural infrastructure. Most specifical- ly, in India, agricultural sector is useful to: meet the food requirement of present and growing population; provide jobs to large segment of society and increase the exports of many products such as tea, sugar, jute, coffee, etc. [5,12]. India is also a main producer of several crops in the world. For example, it is the largest producer of milk, jute; sec- ond largest in wheat, rice, groundnut, vegetables, fruits, sugar cane, and potatoes, onion; third in tea, rapeseed and tobacco production in the world. Agriculture and allied sectors are the mainstay of the Indian economy. This sec- tor also creates the demand for many industrial products such as fertilizers, pesticides, agricultural instruments and machines. India has a first position in total pulses, jute, buf- faloes and milk production in the world. India also has a 2nd position in arable land, total cultivated land and participa- tion of economic active population in agriculture. India is a major producer of wheat, rice, groundnut, vegetables & melons, fruits (excluding melons), potatoes, onion (dry), sugarcane, cotton, cattle, and goats in the world. India has a 3rd position in many agricultural products such as total cereals, rapeseed, tea, tobacco leaves, sheep, and eggs pro- duction. India has a 5th position in chicken which meets the nutritional security of most of the population. India also has a 7th position in coffee (green) production in the world. It is also the 2nd largest producer of flowers after China. It is also a leading producer, consumer and exporter for spices and plantation crops like tea and coffee at global level. In India, the agricultural sector has a significant con- tribution to increasing sustainable livelihood security of farming and non-farming communities. However, climate change is causing a high vulnerability for the Indian ag- ricultural sector. In this regard, existing studies estimated the impact of climate change in the Indian agricultural sector in several ways. Most studies have focused to ex- amine the climate change impact on production and yield of food-grain and commercial crops in India [1,5,8,11-30]. Other studies also assessed the influence of climatic and non-climatic factors on productivity and performance of agricultural sector in India [31-38]. Few studies examined the association of climate vulnerability with farmer’s sui- cides; climate change and human health; and agricultural practices and ecosystem services in India [2,9,39]. Some studies have also assessed the role of organic farming in the agricultural sector [40,41]. Existing researchers also ob- served the farmer’s perception and natural disaster, and mitigation approach in the Indian agricultural sector [42-45]. Some studies have examined the importance of organic farming and credit facilities in Indian agricultural sec- tor [46-51]. Descriptive and empirical findings of aforesaid studies concluded that production and yield of food-grain and cash crops, agricultural productivity are expected to decline due to climate change in India. Therefore, it is necessary to apply technological advancement which can be effective to mitigate the negative impact of climate change in the agricultural sector [6]. Also, more practises of agricultural technology will work as an effective adap- tation strategy toward climate change in Indian farming. Technological applications such as biotechnological tools and heat tolerance crops will be also useful to mitigate the negative consequences of climate change in farming. Previous studies have used different proxy variables to capture the influence of technological change in agricul- tural sector using time series, panel data and cross-sec- 44 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 tional data [1,5,7,8,12,19,27,30,52]. However, limited studies could examine the impact of technological change on Indian agriculture using farm level data. Furthermore, there are many socio-economic variables which may have a posi- tive impact on agricultural production. These variables may be used as adaptation strategies to mitigate the cli- mate change impact in the agricultural sector of India and other economies [1,3,6,53-55]. Few studies assessed the role of social-economic factors and climatic factors in agricul- tural sustainability in Indian states [10]. As previous studies have been argued that technology and specific character- istics of farmers can be considered as adaptation strate- gies to climate change in the agricultural sector. Though, limited studies could assess the significance of technology and farmer’s socio-economic variables in the Indian agri- cultural sector [42-44,52]. Hence, this study has a significant contribution to the existing literature which examines the impact of climate change and farmer’s socio-economic profile on agricultural production in Gujarat using a farm level data of 400 farmers. Accordingly, this study assessed the answers on the following research questions: • What is the farmer’s perception towards climate change and adaptation strategies in the agricultural sector? • What is the influence of climatic factors and farmer’s adaptation strategies on agricultural production of Gujarat? • How farmer’s adaptation strategies may be used to mitigate the negative impact of climate change in the agricultural sector of Gujarat? • What may be the role of technology to mitigate the adverse impact of climate change in the agricultural sector of Gujarat? • What may be the policy initiatives to mitigate the negative consequences of climate change in the agricultural sector of Gujarat? With regards to aforesaid research questions, this study achieved following objectives: • To examine the farmers’ perception on climate change and adaptation strategies in context of agri- cultural sector of Gujarat. • To assess the impact of climate change, farmer’s adaptation strategies and technological change on agricultural production in Gujarat. • To provide the practical approaches to mitigate the negative consequences of climate change in agricul- tural sector of Gujarat. 2. Research Methods and Materials 2.1 Study Area and Sources of Data This study comprises the farm level information which was composed through personal interviews of 400 farmers from 8 districts (i.e., Anand, Banas Kantha, Bhavnagar, Ju- nagadh, Kheda, Surat and Vadodara) of Gujarat. These dis- tricts were selected based on their percentage share in ag- ricultural labourers, agricultural district domestic product, gross cropped area and net sown area in Gujarat. These districts also occupied more than 30% cropped area and production of wheat, rice, jowar, bajra, arhar, rapeseed & mustard, sugarcane and potato crops in Gujarat. Two blocks from each district were selected randomly and one village from each block was chosen purposively. Thus, 16 villages were considered in this study. Thereafter, 25 farmers from each village were identified randomly for a personal interview. Total 400 farmers were interviewed, however, only 240 respondents could provide the com- pleted information. A structural questionnaire was used to collect the relevant information from the farmers. The in- terview of farmers was conducted from 01 October 2019 to 31 December 2019. Information of climatic factors was derived from the India Meteorological Department (IMD), Ministry of Earth Sciences (Government of India (GoI)) and website of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). Farm harvest price of each crop was taken from the Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture and Famers Welfare (GoI). 2.2 Formulation of Empirical Model Existing studied have been used different variables like production and yield of individual crop, aggregate produc- tion of food-grain and cash crops, agricultural production and productivity (monetary value) as dependent variables to ex- amine their association with climatic and non-climatic vari- ables in India [13,15-18,21-23,26,28,30,35,36]. Thus, agricultural produc- tion (in monetary terms) of all crops was used as a dependent variable in this study. Monetary value of production of each crop (that was cultivated by farmers during survey year) was estimated based on farm harvest prices. Agricultural production is significantly associated with several climatic factors such as rainfall, wind speed, CO2 concentration, precipitation, maximum and minimum tem- perature, actual evapotranspiration, solar radiation, solar intensity, water availability, soil moisture and relative hu- midity [4,14-17,20,21,24,26,28,31,33-35,56]. Hence, coefficient variation in actual annual evapotranspiration, annual average maxi- mum temperature, annual average minimum temperature and annual average precipitation during 1991-2015 were used as climatic factors in this study. Kumar et al. [12] also used coefficient variation in maximum temperature, mini- mum temperature and rainfall in empirical models. 45 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 Age, family members, education level, annual income of farmer, agricultural land, irrigated area, agricultural labour, application of fertilizer, gender of farmers and main occupation of farmers have significant contribution in the agricultural sector [6,12,19,27,32,34,36,57,58]. Accordingly, these variables also can be used as adaptation strategies to mitigate the climate change impact in the agricultural sector [53-55,59]. Financial support for farmers from the gov- ernment to buy new technology or inputs was also used to examine the impact of government policies on agricultural production. Therefore, aforesaid variables were used as agricultural inputs in this study. A technology has several usages in the agricultural sec- tor. Therefore, it is difficult to examine the impact of tech- nology on the agricultural sector. Previous studies have used different variables such as irrigated area, fertilizers and others to capture the impact of technological change in the agricultural sector. Furthermore, few studies also used time trend factors to examine the influence of tech- nological change in the agricultural sector [5,7,8,12,27]. Hence, in this study the cost of technology was used to capture the impact of technological change in agricultural produc- tion. While, it measures as an aggregate cost of technol- ogy which was used by farmers to grow various crops. Also, farmer’s perception on appropriate technology was included to capture the influence of technological change in agricultural sector. Thus, cost of technology and appro- priate technology was considered as independent variables in this study. Moreover, farmers were used several adapta- tion strategies (e.g., late sowing of crops, more irrigation, high yielding of seed, mixed cropping pattern, wetting of seed before planting, use of green fertilizer, used of climate tolerate crops, increasing intensity of inputs, and use of technology, etc.) to mitigate the negative impact of climate change in cultivation. Thus, this variable was also used an independent variable in the empirical models. Linear, log-linear and non-linear production function models were used to examine the regression coefficients of aforementioned explanatory variables with agricultural production in this study. Several studies have also used similar regression models to examine the influence of cli- matic and non-climatic factors on agricultural production in India [5,8,12,19,25,30,36]. Thus, in this study, linear production function model was used in following form: (ap)i =α0 +α1 (cvaaea)i +α2 (cvaamaxt)i +α3 (cvaamint)i +α4 (cvaapre)i +α5 (cvaarf)i +α6 (agre)i +α7 ( fame)i +α8 (edlere)i +α9 (aninfa)i +α10 (toagla)i, +α11 (irar)i +α12 (usagla)i +α13 (usfe)i +α14 (cote)i + α15 (gere)i +α16 (maocre)i +α17 (apte)i +α18 (fisugo)i +α19 (adstfa)i +µi (1) Here, α0 is constant term; α1, α2, …, α19 are the regres- sion coefficient of corresponding explanatory variables; µi is the error-term; and i is the cross-sectional farmers (1 to 240) in Equation (1). The explanation of remaining vari- ables is given in Table 1. Non-linear production function model was useful to identify the long-term association of independent vari- ables with agricultural production [30]. Also, it measures that up to what extent a specific variable has a positive or negative impact on output. Hence, a non-linear produc- tion function model was also applied to examine the long- Table 1. Summary of the variables Variables Symbol Unit Agricultural production ap Rs. Coefficient variation in annual average evapotranspiration cvaaea mm Coefficient variation in annual average maximum temperature cvaamaxt 0C Coefficient variation in annual average minimum temperature cvaamint 0C Coefficient variation in annual average precipitation cvaapre mm Coefficient variation in annual actual rainfall cvaarf mm Age of respondents agre Years Family members fame Number Education level of respondent edlere Number Annual income of the family aninfa Rs. Total agricultural land toagla Ha. Irrigated area irar Ha. Use of agricultural labour per Ha. usagla Number Use of fertilizer usfe Kg. Cost of technology per hectare cote Rs./Ha. Gender of respondents [1= male; 0 = female] gere Number Main occupation of respondents [1= agriculture; 0= non-agriculture] maocre Number Appropriateness of the technologies [1= Appropriate; 0= Inappropriate] apte Number Financial support from government to buy new technology or inputs [1 = yes; 0 = No] fisugo Number Adaptation strategy of farmers (1=yes; 0 =No) adstfa Number Source: Authors’ compilation. 46 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 term association of explanatory variables with agricultural production in this study. For this, the original and square terms of independent variables were included in non- linear production function model in the following form: (ap)i = γ0 +γ1 (cvaaea)i +γ2 (Sq. cvaaea)i +γ3 (cvaamaxt)i +γ4 (Sq. cvaamaxt)i +γ5 (cvaamint)i +γ6 (Sq. cvaamint)i +γ7 (cvaapre)i +γ8 (Sq. cvaapre)i +γ9 (cvaarf)i +γ10 (Sq. cvaarf )i +γ11 (agre)i +γ12 (Sq. agre)i +γ13 ( fame)i +γ14 (Sq. fame)i +γ15 (edlere)i +γ16 (Sq. edlere)i +γ17 (aninfa)i +γ18 (Sq. aninfa)i +γ19 (toagla)i +γ20 (Sq. toagla)i +α21 (irar)i +γ22 (Sq. irar)i +γ23 (usagla)i +γ24 (Sq. usagla)i +γ25 (usfe)i +γ26 (Sq. usfe)i +γ27 (cote)i +γ28 (Sq. cote)i +γ29 (gere)i +γ30 (maocre)i +γ31 (apte)i +γ32 (fisugo)i +γ33 (adstfa)i +¥i (2) Here, γ0 is constant term; Sq. is the square term of cor- responding variables; γ1, γ2, …, γ23 are the regression coef- ficients of corresponding explanatory variables; ¥i is the error-term in Equation (2). Natural log of all quantitative variables was also considered for the log-linear production function model in this study. The log-linear production function model was used in following form: log (ap)i = β0 +β1 log (cvaaea)i +β2 log (cvaamaxt)i +β3 log (cvaamint)i +β4 log (cvaapre)i +β5 log (cvaarf)i +β6 log (agre)i +β7 log ( fame)i +β8 log (edlere)i +β9 log (aninfa)i +β10 log (toagla)i +β11 log (irar)i +β12 log (usagla)i +β13 log (usfe)i +β14 log (cote)i +β15 (gere)i +β16 (maocre)i +β17 (apte)i +β18 (fisugo)i +β19 (adstfa)i +λi (3) Here, β0 is the constant term; Sq. is the square term of corresponding variables; β1, β2, …, β19 are the regression coefficients of corresponding explanatory variables; λi is the error-term in Equation (3). 2.3 Selection of Appropriate Model This study collects the primary data from the selected farmers. Hence, it was essential to check the validity of data. Previous studies have used Cronbach’s Alpha Test to estimate reliability of primary data [60-62]. If the statistical value of Cronbach’s Alpha Test is greater than 0.70 for an individual variable, then it has validity. Therefore, statistical values of Cronbach’s Alpha Test were estimated for all vari- ables. Thereafter, statistical values of skewness and kurtosis were also estimated for each variable to check the normal- ity. Previous studies have argued that if the statistical values of kurtosis and skewness for a particular variable lie be- tween –1 to +1, then it can be observed that it is in a normal form. Multi-correlation measures the exact linear relation- ship among the explanatory variables [61]. It may be caused to increase misleading in the regression coefficients. Thus, variance inflation factor (VIF) was estimated to identify the existence of multi-correlation among the independent vari- ables. Breusch-Pagan/Cook-Weisberg test was used to iden- tify the presence of heteroskedasticity in the cross-sectional data [63]. As this study used linear, log-linear and non-linear production function models to estimate the regression coef- ficients of independent variables, thus, Ramsey RESET test was used to identify the appropriate function form of the proposed empirical model (8). Akaike information criterion (AIC) and Bayesian information criterion (BIC) tests were applied to check the consistency of regression coefficients in proposed empirical models [8]. 3. Descriptive Results 3.1 Social-economic Profile of the Respondents Sample size of 240 farmers had the significant diversity in term of gender, age, family size, education level, main occupation, annual income, total agricultural land, irri- gated area, use of agricultural labour per hectare, fertilizer application per hectare and cost of technology per hectare (Table 2). The sample included 97.50% males, age of 34.17% respondents were between 30-39 years, 51.67% respondents had the family’s size between 4-5 members, 29.58% respondents were graduate, 26.67% respondents were engaged in farming and livestock rearing sector, 32.50% farmers had annual income between INR550001- 700000, 50.83% respondents had 0-5 hectare irrigated area and 60.42% respondents used 51-60 agricultural labour per hectare. Around 64.2%, 89.2%, 63.3% and 46.67% respondents have understanding on economic vi- ability, social viability, environmental viability and appro- priate technology, respectively. Also, 43.75% respondents received financial support from government and banking sector for cultivation. Only 46.25% respondents were ap- plying practices of adaptation strategies to mitigate the climate change impact in the agricultural sector. 3.2 Explanation of Farmers’ Perception on Climate Change and Technology Based on descriptive results, it was reported that most farmers accepted that agricultural production has declined due to climate change. It was also observed that farmers were applying several adaptation strategies such as change in showing time of crops, more irrigation, application of additional fertilizer, hybrid varieties of seed, wetting of seed before planting, mixed cropping pattern, use of high yielding varieties of seeds, use of green and organic ferti- lizer, use of technology, use of climate tolerate crop, plant- ing date adjustment, and increasing intensity of inputs in cultivation to mitigate the negative impact of climate change in this sector. Furthermore, as per the farmer’s view, application of technology has a crucial contribution to mitigate the negative impact of climate change in the 47 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 Table 2. Sample distribution based on characteristics of farmers Variables Characteristics Frequency % Gender Male 234 97.50 Female 6 2.50 Age (Years) 20 - 29 44 18.33 30 - 39 82 34.17 40 - 49 65 27.08 50 - 59 35 14.58 60 and above 14 5.83 Family size (Number) 0 - 3 18 7.50 4- 5 124 51.67 6 - 8 79 32.92 9 - 10 12 5.00 11 and above 7 2.92 Education level 8th Passed 43 17.92 10th Passed 41 17.08 12th Passed 46 19.17 Graduate 71 29.58 Post graduate 39 16.25 Main occupation Only farming 157 65.42 Farming and livestock rearing 64 26.67 Farming and milk production 12 5.00 Farming and dairy farming 7 2.92 Annual income of the family (in Rs.) 140000 - 250000 12 5.00 250001 -350000 22 9.17 350001 -450000 40 16.67 450001 -550000 55 22.92 550001 -700000 78 32.50 710001 -912000 33 13.75 Total agricultural land (in Ha.) 0 - 6 98 40.83 7 - 12 68 28.33 13 - 18 30 12.50 19 - 25 25 10.42 26 - 30 19 7.92 Irrigated area (in Ha.) 0 - 5 122 50.83 6 - 10 69 28.75 11 - 15 25 10.42 16 - 20 15 6.25 21 - 25 9 3.75 Use of agricultural labour per hectare (Number) 40 - 50 60 25.00 51 - 60 145 60.42 61 - 78 35 14.58 Fertilizer application per hectare (Kg./Ha) 100 - 150 136 56.67 151 - 200 168 70.00 200 - 250 26 10.83 Cost of technology per hectare (Rs./Ha.) 1500 - 2000 18 7.5 2001 - 2500 84 35 2501 - 3000 138 57.5 Economic viability Yes 154 64.2 No 86 35.8 Social viability Yes 214 89.2 No 26 10.8 Environmental viability Yes 152 63.3 No 88 36.7 Appropriate of technologies Yes 112 46.67 No 128 53.33 Financial support from government and banking sector Yes 105 43.75 No 135 56.25 Farmer’s adaptation strategy to climate change Yes 111 46.25 No 129 53.75 Source: Author’s estimation based on farm level information. 48 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 agricultural sector. Change in showing date and use of more technology in the agricultural sector work as a better adaptation strategy to mitigate the climate change impact in cultivation [1]. Mixed cropping patterns, soil conservation practices and crop rotation may be better adaptation strate- gies to cope with climate change in the agricultural sector of Lebanon [3]. Furthermore, technology was effective to increase water conservation, environmental sustainability, farmer’s income, social equity and agricultural productiv- ity. It was also found that poor and small-land holders were unable to use technology in cultivation due to small size of land holdings, low economic capacity of farmers to bear the high cost of technology, low skills of farmers, inappropri- ate financial support from government and banking sector, low association of farmers with various stakeholders (i.e., agricultural entrepreneurs, agricultural universities, agricul- tural extension offices, agriculture cooperative societies), and insignificant skill and technical support from sellers or agricultural technology creator industries. 3.3 Validity of the Variables The validity and consistency of individual variables are checked through Cronbach’s Alpha test. This test is highly effective to examine the internal consistency of a specific variable or set of variables. The statistical values of Cronbach’s Alpha test all variables are given in Table 3. As per the estimated values of Cronbach’s Alpha test, the variables can be segregated in six categories i.e., excellent if the value is greater than 0.90; good if the value lie be- tween 0.80 to 0.89; acceptable if the value lie between 0.70 to 0.79; questionable if the value lie between 0.60 to 0.69; poor if the value lie between 0.50 to 0.59; and acceptable if the value is less than 0.49. As per the estimated value of Test Scale is 0.85 and Alpha values for all variables were found more than 0.80 [60-62]. Thus, the estimates show that the selected set of variables have consistency and rational- ity for considering undertaken indicators in statistical and empirical investigations. The statistical summary (i.e., minimum, maximum, mean, standard deviation, skewness and kurtosis) of the variables is given in Table 4. As per the estimated values of standard deviation, it was perceived that most variables (except agricultural production, age of respondents, annual income of farmers, use of fertilizer, cost of technology) do not have high leverages. Furthermore, most variables (except, agricultural production, use of fertilizer, cost of technology, and gender of respondents) have the skewness values between –1 to +1. Thus, these variables were in nor- mal form. However, values of kurtosis were not between –1 to +1 for all variables. Thus, the natural logarithm of all variables were used to convert them in a normal form. 3.4 Correlation Coefficients among the Variables The correlation coefficients of agricultural produc- tion with explanatory variables are given in Table 5. The correlation coefficients of coefficient variation in annual Table 3. Scale reliability coefficient of variables Variables Sign Item-test correlation Item-rest correlation Average interitem correlation Alpha ap + 0.49 0.41 0.23 0.85 cvaaea + 0.76 0.71 0.21 0.84 cvaamaxt + 0.86 0.83 0.21 0.83 cvaamint + 0.86 0.83 0.21 0.83 cvaapre + 0.85 0.82 0.21 0.83 cvaarf + 0.86 0.83 0.21 0.83 agre – 0.34 0.25 0.24 0.86 fame – 0.27 0.18 0.24 0.86 edlere + 0.54 0.46 0.23 0.85 aninfa + 0.46 0.38 0.23 0.85 toagla + 0.86 0.83 0.21 0.83 irar + 0.83 0.79 0.21 0.83 usagla – 0.10 0.00 0.25 0.87 usfe + 0.61 0.54 0.22 0.84 cote – 0.16 0.06 0.25 0.86 gere + 0.06 -0.04 0.26 0.87 maocre – 0.25 0.15 0.24 0.86 apte + 0.51 0.43 0.23 0.85 fisugo + 0.15 0.05 0.25 0.86 adstfa + 0.52 0.44 0.23 0.85 Test Scale 0.2276 0.85 Source: Estimated by authors. 49 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 average evapotranspiration, coefficient variation in annual average maximum temperature, coefficient variation in annual average minimum temperature, coefficient varia- tion in annual average precipitation, coefficient variation in annual actual rainfall, education level of farmers, annu- al income of the farmers, total agricultural land, irrigated area and use of fertilizer with agricultural production were seemed positive and statistically significant. Hence, the estimates indicate that aforesaid variables have a positive contribution in the agricultural sector. The correlation co- efficients of other variables with agricultural production seemed statistically insignificant. Table 4. Statistical Summary of the Variables Variables Min Max Mean SD Skewness Kurtosis ap 12324 1789244 129299.9 170837 6.91 59.60 cvaaea 0.14 5.02 1.31 0.93 1.06 3.68 cvaamaxt 0.01 0.27 0.10 0.06 0.69 2.64 cvaamint 0.02 0.46 0.17 0.10 0.71 2.78 cvaapre 0.29 8.20 2.71 1.67 0.81 3.09 cvaarf 0.36 9.20 3.51 2.17 0.67 2.51 agre 22.00 65.00 39.98 10.64 0.33 2.19 fame 2.00 12.00 5.83 1.83 0.80 3.75 edlere 7.00 17.00 12.59 3.09 –0.11 1.69 aninfa 140000 912000 531692 159320 –0.02 2.55 toagla 1.00 25.00 9.27 5.57 0.67 2.67 irar 0.50 20.00 6.16 4.12 0.88 3.15 usagla 51.00 86.00 65.47 5.48 0.38 4.07 usfe 143 22452 1897 2398 4.56 32.50 cote 165 2986 2528 325 –2.02 13.57 gere 0.00 1.00 0.98 0.14 –6.71 46.02 maocre 0.00 1.00 0.65 0.48 –0.65 1.42 apte 0.00 1.00 0.72 0.30 –0.51 1.91 fisugo 0.00 1.00 0.44 0.50 0.25 1.06 adstfa 0.00 1.00 0.46 0.50 0.15 1.02 Source: Estimated by authors. Table 5. Correlation coefficients of the variables Variables ap cvaaea cvaamaxt cvaamint cvaapre cvaarf agre fame edlere aninfa ap 1 0.435** 0.454** 0.446** 0.433** 0.453** –0.069 –0.019 0.124* 0.201** cvaaea 0.435** 1 0.858** 0.818** 0.893** 0.864** –0.105 –0.001 0.093 0.200** cvaamaxt 0.454** 0.858** 1 0.995** 0.957** 0.991** –0.112* –0.028 0.180** 0.345** cvaamint 0.446** 0.818** 0.995** 1 0.945** 0.984** –0.116* –0.019 0.185** 0.360** cvaapre 0.433** 0.893** 0.957** 0.945** 1 0.954** –0.115* –0.01 0.173** 0.334** cvaarf 0.453** 0.864** 0.991** 0.984** 0.954** 1 –0.119* –0.024 0.170** 0.348** agre –0.069 –0.105 –0.112* –0.116* –0.115* –0.119* 1 0.252** –0.469** –0.165** fame –0.019 –0.001 –0.028 –0.019 –0.01 –0.024 0.252** 1 –0.448** –0.070 edlere 0.124* 0.093 0.180** 0.185** 0.173** 0.170** –0.469** –0.448** 1 0.322** aninfa 0.201** 0.200** 0.345** 0.360** 0.334** 0.348** –0.165** –0.07 0.322** 1 toagla 0.441** 0.822** 0.977** 0.984** 0.963** 0.978** –0.128* –0.003 0.183** 0.377** irar 0.430** 0.782** 0.927** 0.936** 0.924** 0.924** –0.118* 0.012 0.162** 0.395** usagla –0.030 –0.041 –0.004 0.004 –0.032 –0.012 0.079 0.068 0.091 0.021 usfe 0.228** 0.549** 0.620** 0.619** 0.614** 0.625** –0.118* 0.008 0.115* 0.198** cote –0.049 –0.076 –0.030 –0.018 –0.010 –0.034 0.076 0.081 –0.091 0.147* gere 0.030 0.065 0.023 0.015 0.041 0.030 0.159** 0.162** –0.152** 0.045 maocre –0.030 0.021 –0.067 –0.087 –0.058 –0.053 0.000 0.095 –0.239** –0.035 apte 0.085 0.135* 0.190** 0.190** 0.174** 0.178** –0.369** –0.297** 0.801** 0.262** fisugo 0.051 –0.032 –0.024 –0.011 –0.011 –0.023 0.069 –0.193** 0.144* 0.000 adstfa 0.103 0.127* 0.166** 0.167** 0.152** 0.152** –0.342** –0.352** 0.886** 0.270** 50 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 4. Discussion on Empirical Results Two regression models were run simultaneously to get a better understanding of the impact of climatic factors and other inputs on agricultural production in this study. In the 1st empirical model, climatic and non-climatic fac- tors (Table 6), and 2nd empirical model only climatic fac- tors were included (Table 7). The regression coefficients of explanatory variables with agricultural production were estimated through linear, non-linear and log-linear pro- duction function models. The statistical values of Ramsey RESET test for all models appeared statistically insignifi- cant. Thus, the estimates show that functional forms of aforementioned production function models were seemed correctly-well-defined. The Chi2 values under Breusch- Pagan/Cook-Weisberg test were also found statistically insignificant, thus it infers that cross-sectional data do not have heteroskedasticity. Log-linear production function model has lower values of AIC and BIC as compared to linear and non-linear production function models. Hence, the log-linear production function model produces con- sistent results which were used to provide statistical infer- ences. The regression coefficient of annual average maximum temperature with agricultural production was found posi- tive. Thus, it indicates that agricultural production may be improved as increased in maximum temperature. The es- timate is not consistent with previous studies which have reported negative impact of maximum temperature on agricultural production and yield at state-level estimation in India [25,34]. The regression coefficient of annual aver- age minimum temperature with agricultural production seemed negative. Hence, it is suggested that agricultural production is expected to be declined due to increase in minimum temperature. Annual average precipitation and annual actual rainfall also showed a negative impact on agricultural production. Hence, the aforesaid estimates show that agricultural production declines due to climate change in Gujarat. The R-squared value was found 0.8298, thus, the esti- mate shows that 83% variation in agricultural production can be explained by undertaken explanatory variables. Furthermore, the regression coefficient of family mem- bers with agricultural production appeared positive and statistically significant. Thus, estimates show that agri- cultural production increases as an increase in family size of farmers. Literate farmers have more understanding of Table 5 continued Variables toagla irar usagla usfe cote gere maocre apte fisugo adstfa ap 0.441** 0.430** –0.030 0.228** –0.049 0.03 –0.03 0.085 0.051 0.103 cvaaea 0.822** 0.782** –0.041 0.549** –0.076 0.065 0.021 0.135* –0.032 0.127* cvaamaxt 0.977** 0.927** –0.004 0.620** –0.03 0.023 –0.067 0.190** –0.024 0.166** cvaamint 0.984** 0.936** 0.004 0.619** –0.018 0.015 –0.087 0.190** –0.011 0.167** cvaapre 0.963** 0.924** –0.032 0.614** –0.010 0.041 –0.058 0.174** –0.011 0.152** cvaarf 0.978** 0.924** –0.012 0.625** –0.034 0.030 –0.053 0.178** –0.023 0.152** agre –0.128* –0.118* 0.079 –0.118* 0.076 0.159** 0.000 –0.369** 0.069 –0.342** fame –0.003 0.012 0.068 0.008 0.081 0.162** 0.095 –0.297** –0.193** –0.352** edlere 0.183** 0.162** 0.091 0.115* –0.091 –0.152** –0.239** 0.801** 0.144* 0.886** aninfa 0.377** 0.395** 0.021 0.198** 0.147* 0.045 –0.035 0.262** 0.000 0.270** toagla 1 0.953** –0.012 0.627** –0.010 0.028 –0.085 0.174** 0.005 0.159** irar 0.953** 1 –0.006 0.585** 0.008 0.052 –0.076 0.155** 0.005 0.135* usagla –0.012 –0.006 1 –0.025 0.059 –0.004 –0.013 0.084 0.058 0.068 usfe 0.627** 0.585** –0.025 1 –0.058 0.049 –0.072 0.154** –0.110* 0.109* cote –0.010 0.008 0.059 –0.058 1 –0.014 –0.04 –0.118* 0.033 –0.123* gere 0.028 0.052 –0.004 0.049 –0.014 1 0.017 –0.138* 0.011 –0.157** maocre –0.085 –0.076 –0.013 –0.072 –0.040 0.017 1 –0.250** –0.206** –0.274** apte 0.174** 0.155** 0.084 0.154** –0.118* –0.138* –0.250** 1 0.081 0.875** fisugo 0.005 0.005 0.058 –0.110* 0.033 0.011 –0.206** 0.081 1 0.176** adstfa 0.159** 0.135* 0.068 0.109* –0.123* –0.157** –0.274** 0.875** 0.176** 1 Source: Author’s estimation. Note: **- Correlation is significant at the 0.01 level; * - Correlation is significant at the 0.05 level. 51 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 Table 6. Regression coefficient of explanatory variables with agricultural production Regression Models Linear Regression Log-linear Non-linear Number of obs. 240 240 240 F - Value 3.71 62.34 2.4 Prob > F 0.000 0.000 0.0001 R-squared 0.2429 0.8434 0.2778 Adj. R-squared 0.1775 0.8298 0.1621 Mean VIF 186.80 294.90 1310.21 Breusch-Pagan/Cook-Weisberg test for heteroskedasticity [Chi2] 290.47 21.76 493.00 Ramsey RESET test for (DV) [F - Value] 1.2 2.13 7.39 Ramsey RESET test for (IV) [F - Value] 0.63 0.86 0.85 AIC 6436.572 148.3235 6453.247 BIC 6506.185 217.9363 6571.589 Agricultural production (DV) Reg. Coef. Std. Err. Reg. Coef. Std. Err. Reg. Coef. Std. Err. cvaaea 45841.330 36568.700 0.131 0.104 43998.180 101364.50 (cvaaea)^2 - - - - –188.481 23056.750 cvaamaxt 8349013.00 6354368.00 1.896 1.473 8609792.0 17800000.0 (cvaamaxt)^2 - - - - –5930011.0 51100000.0 cvaamint –4133836.0 3593001.00 –1.652 1.332 –3689721.0 9909242.00 (cvaamint)^2 - - - - 1175084.00 17800000.0 cvaapre –88990.100 50729.710 –0.702 0.385 –96476.60 149641.000 (cvaapre)^2 - - - - 2331.052 13373.450 cvaarf –57433.330 59383.210 –0.543 0.484 –262033.80 177406.400 (cvaarf)2 - - - - 19506.410 14756.020 agre 121.410 1160.805 –0.034 0.093 –1991.738 8151.804 (agre)^2 - - - - 24.954 96.138 fame 5123.179 6571.116 0.035 0.077 23979.730 31353.820 (fame)^2 - - - - –1362.207 2335.016 edlere 11316.060 8522.278 0.137 0.188 42173.560 39270.030 (edlere)^2 - - - - –1490.799 1596.023 aninfa 0.066 0.075 0.030 0.071 –0.139 0.385 (aninfa)^2 - - - - 0.0001 0.0001 toagla 35396.390 31083.000 1.982 0.656 111783.000 86435.090 (toagla)^2 - - - - -2809.722 2860.114 irar 4671.183 8331.863 0.001 0.094 –1657.953 20596.440 (irar)^2 - - - - 262.135 976.602 usagla –1141.709 1902.503 –0.054 0.256 13779.570 28239.620 (usagla)^2 - - - - –111.729 211.720 usfe –5.202 5.503 –0.093 0.057 –17.356 17.709 (usfe)^2 - - - - 0.001 0.001 cote –16.201 32.478 0.018 0.101 193.371 157.366 (cote)^2 - - - - –0.047 0.035 gere –5438.471 73695.370 0.085 0.153 1662.428 77573.580 maocre –8411.764 23138.330 0.006 0.047 –15066.40 24561.350 apte –33323.750 74370.400 0.059 0.152 –21031.70 78118.480 fisugo 24488.180 22326.880 0.053 0.045 20175.710 23500.990 adstfa –38325.670 59798.980 0.046 0.112 –4510.820 65286.770 Con. Coef. –45654.170 189386.200 10.213 1.931 –905897.0 1011192.00 Source: Author’s estimation. 52 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 technology and various inputs and their usages in culti- vation [5,6]. Subsequently, the education level of farmers showed a positive impact on agricultural production. Fur- thermore, educated farmers have more skills as compared to uneducated farmers. Annual income of the farmers has a positive impact on agricultural production. Cultivation is not possible without arable land [12]. Therefore, it is a most significant input for cultivation. The estimate also exhibited the positive impact of total agricultural land on agricultural production. Irrigated area is a crucial input for farming. Subsequently, agricultural production will in- crease as an increase in irrigated area. The regression co- efficient of irrigated area with agricultural production was also observed positive in this study. A group of research- ers have claimed that irrigated area produce has high yield in cultivation [7,5,8,12,18,21]. The regression coefficient of use of fertilizer per hectare land with agricultural production was found positive. Hence, recommended application of fertilizer in cultivation may be effective to increase yield of crops and agricultural production [5]. Otherwise, it may be caused to reduce crop yield due to decline in soil fer- tility and quality in log-run. The cost of technology per hectare of land has a positive impact on agricultural pro- duction. The estimate can be justified that technological advancement would be useful to increase agricultural pro- duction. Previous literature have also observed positive influence of technology on agricultural production [64,65]. Subsequently, agricultural production increases as use of appropriate technology in cultivation increase. The regression coefficient of gender of respondents with agricultural production was found positive. Thus, the estimate provides evidence that male farmers have a more contribution in agricultural production activities as com- pared to females. Occupation of farmers has a significant impact on agricultural production. Age of farmers has a negative impact on agricultural production. It may hap- pen due to decrease in the contribution of farmers when their age increases. The regression coefficient of financial support for farmers from the government with agricultural production appeared positive. It can be useful to increase the economic capacity of the farmers to buy new technol- ogy, seeds, fertilizer and other inputs for farming. There- fore, it is obvious that financial support for farmers from the government and banking sector will be useful to in- crease agricultural production [46]. The descriptive results also specify that the farmers were applying different ad- aptation strategies to mitigate the negative climate change impact in farming. Therefore, this study also assessed the influence of farmer’s adaptation strategies on agricultural production. The regression coefficient of adaptation strat- egies with agricultural production was observed positive. Thus, the estimate implies that adaptation strategies are found useful to mitigate the negative consequences to climate change in the agricultural sector [10,42-44]. Human resources play a crucial role in farming. Therefore, the use of agricultural labor per hectare land has a positive impact on agricultural production. The regression results based on non-linear production function, showed that climatic and non-climatic variables have a non-linear relationship with agricultural produc- tion. This study found U-shaped and hilly association of explanatory variables with agricultural production as per the sign of the regression coefficients of original and square terms of respective variables. Evapotranspiration, maximum temperature, family members, education level of farmers, arable land, use of agricultural labour and cost of technology have an U-shaped relationship with agricul- tural production. While, minimum temperature, precipita- tion, rainfall, age of farmers, annual income of farmers, irrigated area and use of fertilizer have a hilly-shaped as- sociation with agricultural production. The regression coefficients of climatic factors with ag- ricultural production are given in Table 7. The R-squared value was observed 0.8312. Thus, 83% variation in ag- ricultural production depends upon undertaken climatic factors. The regression coefficient of coefficient variation in maximum temperature with agricultural production was appeared negative and statistically significant. The estimate indicates that agricultural production is expected to decline by 1.85% due to 1% increase in maximum tem- perature. Precipitation, minimum temperature and actual rainfall have a positive impact on agricultural production. The estimates demonstrate that agricultural production is expected to be increased by 1.78%, 0.30% and 0.67% as an increase 1% increase in annual average minimum temperature, annual average precipitation and annual ac- tual rainfall, respectively. As ground water increases due to increase in annual actual rainfall. Subsequently, annual rainfall may be useful to meet the water requirement for farming activities and it would be useful to increase the productivity and production of food-grain and cash crops. The regression results based on non-linear production function model, showed that agricultural production has a non-linear association with climatic factors. Evapotran- spiration, minimum temperature and precipitation have an U-shaped relationship with agricultural production. Ag- ricultural production has a hilly-shaped association with maximum temperature and rainfall. Prior studies have also reported non-linear association of climatic factors with crop production and productivity in India [8,19,30]. 53 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 5. Conclusions and Policy Implications The main objective of this study was to detect the farmer’s perspective on adaptation strategies to climate change in cultivation. Thereupon, it examined the impact of climate change, technology, adaptation strategies and socio-economic profile of farmers on agricultural produc- tion using linear, non-linear and log-linear production function models. Farm level data were used, while it was collected through personal interviews of 400 farmers from purposely selected eight districts of Gujarat. However, only 240 farmers could provide the complete information. This study, therefore, provides the statistical inference of descriptive and empirical results based on this sample size of 240 respondents. Descriptive results imply that most farmers were con- scious about climate change and its negative implications in agricultural sector. Therefore, farmers were adopting several methods such as change in showing time of crops, irrigation facilities, application of fertilizer, and use of hybrid varieties of seeds, wetting of seed before planting in soil, climate tolerant crops, improving intensity of in- puts and use of various technologies to cope with climate change in agricultural sector. Few farmers have adopted organic and green fertilizer to increase soil fertility for mitigation the adverse impact of climate change in the agricultural sector. The empirical result also clearly en- forces that adaptation strategies have a positive impact on agricultural production. Hence, aforesaid practices can be considered as adaptation strategies to mitigate the nega- tive consequences of climate change in the agricultural sector. Furthermore, the empirical results indicate that maxi- mum and minimum temperature, precipitation, and rain- fall have a negative impact on agricultural production in the study area. The impact of maximum temperature, minimum temperature and rainfall were seemed positive on agricultural production when farmers were applied different adaptation strategies such as change in showing time of crops, improve irrigation facilities, application of Table 7. Regression coefficients of climatic factor with agricultural production Regression Models Linear Regression Log-linear Non-linear Number of obs. 240 240 240 F - Value 12.66 230.39 6.79 Prob > F 0.000 0.000 0.000 R-squared 0.2129 0.8312 0.2287 Adj. R-squared 0.1961 0.8276 0.195 Mean VIF 119.84 219.7 889.51 Ramsey RESET test for (DV) [F - Value] 0.07 1.22 1.66 Ramsey RESET test for (IV) [F - Value] 0.76 1.09 0.79 Breusch-Pagan / Cook-Weisberg test for heter- oskedasticity [Chi2] 167.54 9.16 251.74 Cameron & Trivedi’s decomposition of IM-test 16.17 8.01 26.28 AIC 6417.886 138.3228 6423.039 BIC 6438.77 159.2066 6461.326 ap Reg. Coef. Std. Err. Reg. Coef. Std. Err. Reg. Coef. Std. Err. cvaaea 47557.66 32340.14 0.1052 0.098 35902.21 87774.03 (cvaaea)^2 –56.79336 18539.55 cvaamaxt 538533.9 2790006 –1.8505 0.712 –8889458 8703948 (cvaamaxt)^2 2.79E+07 2.43E+07 cvaamint 309695.4 1373392 1.7888 0.577 6303996 4414759 (cvaamint)^2 –1.10E+07 7475095 cvaapre –22618.4 23565.76 0.3006 0.178 74503.26 82422.12 (cvaapre)^2 –8941.018 7729.299 cvaarf 4389.973 34330.14 0.6657 0.265 –67738.63 94526.53 (cvaarf)2 6003.597 8127.961 Con. Coef. 5370.183 19154.23 9.4601 1.087 –18326.24 32917.95 Source: Author’s estimation. 54 Research on World Agricultural Economy | Volume 03 | Issue 01 | March 2022 fertilizer, hybrid varieties of seeds, wetting of seed be- fore planting in soil, climate tolerate crops, and maintain intensity of inputs in farming. The empirical results also showed that family size, education level of farmers, an- nual income of farmers, arable land, irrigated area, cost of technology, appropriate technology, financial support for farmers from government and farmer’s adaptation strategies have a positive and significant contribution to increase agricultural production in Gujarat. The estimate also indicates that agricultural production is expected to be declined by 1.85% due to 1% increase in maximum temperature. Precipitation, minimum temperature and actual rainfall have a positive impact on agricultural pro- duction. The estimates demonstrate that agricultural pro- duction is expected to be improved by 1.78%, 0.30% and 0.67% as an increase of 1% increase in annual average minimum temperature, annual average precipitation and annual actual rainfall, respectively. It was also observed that climatic factors have a non-linear association with ag- ricultural production. This study provides several policy suggestions which might be helpful for farmers and policy makers to mitigate the negative impact of climate change on agricultural pro- duction in Gujarat. Application of technology is useful to increase farmer’s income, water sustainability, soil quality and fertility, land productivity, and efficiency of agricul- tural inputs in farming. Policy makers should implement water conservation and management plans to meet the ir- rigation requirement in cultivation and to maintain the ag- ricultural sustainability. Furthermore, small and medium land holding farmers were unable to use technology in cultivation due to their low economic capacity, low litera- cy and skills, weak understanding on technology, and high cost of technology. Thus, the government should provide credit to the small and marginal farmers to increase their economic capacity to bear the high cost of technology and other inputs. Agriculture entrepreneurs, agricultural universities, agricultural extension offices and agricultural cooperative societies should provide the training and tech- nical supports to the farmers to increase their understand- ing on new technology and climate change related issues. Collaboration of agriculture industries with farmers would be effective for farmers to cultivate a specific crop which provides them better return. Farmers should grow com- mercial crops as per the needs of agriculture industries to maintain their profitability in the long-term. There is also a requirement to develop appropriate marketing of agri- cultural products to increase the farmer’s trust in agricul- tural production activities. This study develops the conceptual framework to as- sess the influence of climate change, technological change and other variables on agricultural production using farm level information in Gujarat. Also, it provides several policy proposals to mitigate the negative consequences of climate change in farming based on empirical findings. Hence, the present study is a significant contribution in existing literature. Though, the empirical finding of this study is based on eight districts of Gujarat. Despite that, the estimates of this study are crucial to develop climate action plans and agricultural development policies in Gu- jarat. Further research can be replicated in other districts of Gujarat to check the consistency of this study. Conflict of Interest There is no conflict of interest. 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