36 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 Research on World Agricultural Economy https://ojs.nassg.org/index.php/rwae DOI: http://dx.doi.org/10.36956/rwae.v4i1.788 1. Introduction Climatic factors, geographical location and ecosystem services have a significant contribution to increasing the farmer’s choice to cultivate a particular crop [1-7]. For instance, wheat, mustard and gram crops need minimum temperature during sowing time, and high temperature during harvesting time. Sugarcane crop needs different climatic conditions at various stages of production [8]. Rice crop needs high temperature and an abundance of water during sowing time; moderate temperature and high rain- fall during plant growth; and high temperature, minimal Copyright © 2023 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/). Received: 22 December 2022; Received in revised form: 24 February 2023; Accepted: 1 March 2023; Published: 6 March 2023 Citation: Singh, A.K., Ashraf, S.N., Sharma, S.K., 2023. Farmer’s Perception on Climatic Factors and Social- economic Characteristics in the Agricultural Sector of Gujarat. Research on World Agricultural Economy. 4(1), 788. http://dx.doi.org/10.36956/rwae.v4i1.788 *Corresponding Author: Ajay K. Singh, School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, 248009, India; Email: a.k.seeku@gmail.com; kumar.ajay_3@yahoo.com RESEARCH ARTICLE Farmer’s Perception on Climatic Factors and Social-economic Characteristics in the Agricultural Sector of Gujarat Ajay K. Singh1* Shah Nawaz Ashraf 2 Sandeep Kumar Sharma1 1. School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, 248009, India 2. Entrepreneur Academy, Mohammed VI Polytechnic University, Ben Guerir, 43150, Morocco Abstract: This study investigated the implication of various factors, including climatic conditions, social-economic variables, agricultural inputs, technological development, institutional support, and adaptation strategies in the agricultural sector of Gujarat. A confirmatory factor analysis (CFA) was used to analyse the farm-level data from 240 randomly selected farmers across eight districts. The study found that farm income per hectare is influenced by climate adaptation strategies, appropriate technology, annual income, education level, family size, fertilizer application, farm income from cash crops, financial support from the government, and access to credit. The study recommends the use of appropriate technology and adaptation strategies to mitigate the negative impact of climate change, as well as increase farmers’ access to credit, diversify crops, and encourage technological development in the agricultural sector. In addition, agricultural extension and development agencies should train farmers regularly to improve their understanding of climate adaptation practices and other inputs. Keywords: Agricultural sector; Appropriate technology; Climate change; Farm income; Adaptation strategies http://dx.doi.org/10.36956/rwae.v3i4.803 https://orcid.org/0000-0003-0429-0925 https://orcid.org/0000-0001-5410-5404 37 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 precipitation and no rainfall during harvesting time [9]. The growth and production of other crops like groundnut, sesame, soybean, cotton, sorghum, millet, etc. also depend on different climatic conditions [9]. Agricultural production activities, therefore, primarily depend on climatic factors [8,9]. Whereas, high variability in climatic factors in various seasons may reduce the productivity of crops [2,3,9-12]. Extreme climatic events such as drought, floods, cyclones and heat waves may also produce a negative impact on the agricultural production system [13,14]. Findings of previous literature have concluded that climatic factors are fluctuating due to the rising quantity of GHGs emissions in the atmosphere [3,9,15]. Moreover, ecosystem services such as water and land resources are useful inputs for agricultural production activities. While, the quantity of ecosystem services is declining due to rising population, urbanization, industrialization, infrastructural development, production activities of industries and agriculture and climate change [1,3,14]. Also, the excessive use of pesticides and fertilizer in the agricultural sector also reduces soil fertility, quality of water and air [9,16]. It is stated earlier that agricultural production and its allied activities are adversely affected due to climate change and ecosystem services. Therefore, the sector needs technological advancement, appropriate technology, climate adaptation strategy (CAS) and institutional support to reduce the vulnerability of crops due to climate change [3,5,6,12,17,18]. Technological advancement and appropriate technology will bring significant changes in this sector to cope with climate change [1,5,6,9]. Adaptation strategy is a practice or action which reduces or mitigates the negative consequences of any event in a production process. It can be divided into two categories i.e., autonomous and planned adaptation. Autonomous adaptation is an involuntary and regular incidence that creates a capacity to adjust to climate change impact in a system without taking any action. Planned adaptations strategy is associated with farmers’ practices in a system to reduce the negative impacts of human and natural activities in it [19]. Mitigation means all human interven- tions which abate greenhouse gas emissions from various sources [6,9]. Institutional support also helps to increase the farmer’s understanding to apply different practices to reduce the uncertainty of climate change impacts on crop production [20]. Public policies, adaptation practices and climate action policies support to reduction the nega- tive consequences of climate change in the agricultural sector [4,5,14,19,21]. India is a developing and highly agricultural-intensive country. Thus, the agricultural sector has a greater contri- bution to sustaining the social-economic development of a farming community in India. As India is located at low latitudes, therefore, its agricultural sector is highly vul- nerable due to climate change [9,14,15,22,23]. In India, numer- ous studies have examined the climate change impact on agricultural production, food-grain yield and commercial crops, agricultural productivity and agricultural GDP at district, state and national levels [1,2,8-10,16,24-33]. Previous findings reported that yield, production and cropped area of cash crops decline due to climate change in India. Ac- cordingly, climate change produces a negative impact on sustainable food security (SFS), livelihood security, the income of farmers, rural development and environmental factors and sustainable agricultural development (SAD) in India [12,34,35]. SFS is a state in which the agricultural sector meets the food security of all people to ensure their physical and mental health, and provide fodder to all live- stock as sustaining the quality and quantity of ecosystem services [35]. At present India’s population is around 1.37 billion and its population is projected to be 1.53 billion by the year 2030 [9]. Hence, India would be required to pro- duce 70% more food grains to meet the food security of future generations [36]. Moreover, high population growth, industrialization, urbanization and labour migration would increase the extensive burden on natural and capi- tal resources, and the agricultural sector in India [9]. Most developing countries including India would be unable to maintain irrigation systems and ecosystem services which further hamper sustainable agricultural development [37,38]. India, therefore, needs to protect ecosystem services by using technological advancement, appropriate technology and CAS in the agriculture sector [5,6,10,16]. The use of new technologies, scientific techniques, climate resilient tech- nologies and appropriate technology will reduce the nega- tive impact of climate change [3,5,39-41]. CAS would be use- ful to reduce the risk, and increase the economic capacity of farmers to manage the climate change impact on the agricultural sector. Education level, access to information, electricity for irrigation, agricultural subsidies, water and land management practices, farm income, training, social capital, agroforestry, bio-diversification, and communication are also detected as the most CAS influencing factors [5,12,18]. Changes in planting time, water and nutrient management practices, fertilizer, irrigation management and technology also would work as a CAS [9,13]. Water conservation and management, heat tolerance crops, high yielding of seed, change in cropping pattern, mixed cropping pattern, crop diversification, tree planting, late sowing of seed and ap- plication of green fertilizer can be used as CAS [3,16,19,25]. Crop rotation, drip irrigation, local farming techniques, green technologies and green fertilizer are the various practices of CAS [42]. 38 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 In addition to the above, agricultural technologies would be positive to increase crop productivity and reduce the negative consequences of socio-economic activities on natural resources; decreases the use of water, fertilizer and pesticides in farming; reduce chemicals in the rivers and groundwater [3,5,6,41,43]. It can be used for ploughing, planting seeds in soil, watering, irrigation, and fertilizer and others [6,44]. Subsequently, technological development and agricultural technologies have a positive and signifi- cant impact on the growth of the agricultural sector in India [5,6,44,45]. Applications of traditional technologies have also provided numerous benefits in agricultural activities in India [46-50]. Furthermore, appropriate technology will bring several alternatives to increase the sustainability of ecosystem services as its practices in production activi- ties will abate the GHGs emission in the atmosphere [5,6]. Appropriate technology may be conducive to maintaining economic, social and environmental conditions of avail- able resources [6,51]. The use of appropriate technology in the agricultural sector is helpful to increase the productiv- ity, efficiency and profitability of farmers [5,6,39]. Appropri- ate technology is a new technology or idea or knowledge or knowledge-know-how which reduces the negative impact of social and economic development on the en- vironment [5,6]. Most specifically, appropriate technology and technological development will improve land pattern and management, recovery of surplus land, maintain the cropping pattern in various crop seasons, a technique of farming, marketing facilities, seed germination and seed viability, soil quality and fertility, and land productivity in the agricultural sector [5,6,39,52-54]. Agricultural production activities depend upon different types of indicators such as climatic factors, ecosystem ser- vices, technological advancement, appropriate technology, Agri industries, irrigated area, physical assets, farm man- agement practices, government policies, credit accessibil- ity, geographical location, institutional support and others. Therefore, it is indispensable to assess the most valuable factors which enhance the growth of the agricultural sec- tor. Hence, there is a requirement to apply a confirmatory factor analysis (CFA) to examine the contribution of the above-mentioned indicators to mitigate the adverse impact of climate change in the agricultural sector. Moreover, few studies could observe the significance of mentioned varia- bles in farming activities using CFA. Hence, it is essential to examine the role of highlighted indicators to mitigate the negative impact of climate change in the agricultural sector. Accordingly, this study achieved the answer to the following research questions: ● What is the significance of climatic and non-climatic factors, & climate adaptation strategies (CAS) in the agricultural sector? ● How social-economic factors, agricultural inputs, technological advancement, and institutional supports related activities can be used as CAS in the agricultural sector? This present study realized the following objective: ● To examine the latent variables in five different cat- egories of variables (i.e., climate change, social-eco- nomic, agricultural input, technological change and appropriate technology, and institutional support and CAS) in the agricultural sector using a confirmatory factor analysis (CFA). 2. Research Methods and Materials 2.1 Study Area Gujarat is located on the western coast of India and it is bounded by the Arabian sea in the west and southwest. Figure 1 shows the geographical and administrative loca- tion of Gujarat. The state touches the international border of Pakistan; and Rajasthan, Madhya Pradesh and Maha- rashtra states and the Dadra, Diu and Nager Haveli union territories of India. Geographically, the state is located at a latitude and longitude of 23.00 north and 72.00 east, respectively. The state has a 1,659-kilometre coastline which is the largest among the other Indian states. It oc- cupied a total 196,244 square kilometer geographical area that has a significant share of forest area, grazing land and arable land total geographical area of Gujarat. The state is located in a peninsular region which can be divided into four sub-regions. Administratively, the state has 33 districts that have high diversity in agricultural, industrial and service sector, and social-economic activi- ties of the population. Gujarat is a highly industrialized state and it has appropriate start-ups and entrepreneurship ecosystem among the Indian states [55]. It has a dominant position in the production of many industrial goods such as diamonds, petrochemical, medical devices, medical engineering goods and services, drugs, dairy products, etc. The state also has a significant share of the agricultural sector in India’s gross domestic product. The agricultural and its allied sector meet the requirement of raw materi- als for agro-industrial development in Gujarat. Sugarcane, mustard, groundnut, soybean, cotton, potato, rice, sor- ghum, wheat and maize are the major crops of this state [6]. Gujarat is a climate-sensitive state due to its geographical location, and it has high diversity in ecosystem services, availability of natural resources, demographical change and social-economic development of farmers. Climate change has a diverse negative impact on the livelihood security of farmers in Gujarat [5,6,35,56]. Therefore, this state was considered a study area for the proposed research. 39 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 2.2 Collection of Primary Data A total of 8 districts out of 33 (i.e., Anand, Banas Kan- tha, Bharuch, Bhavnagar, Junagadh, Kheda, Surat, and Vadodara) were selected based on their contribution to the agricultural sector (Figure 2). These districts collectively contribute around 46% of agricultural labour, 36% of agri- cultural district domestic product, 36.6% of gross cropped area, 31% of net area sown and 44% gross irrigated area of Gujarat. Also, these districts have a high share in arable land, agricultural workforce, cropping intensity, irrigated area, and cropped area under food-grain and cash crops in Gujarat. These districts are highly vulnerable due to climate change as compared to other districts of Gujarat [14,35]. Two blocks from each district were chosen purposively, and 16 blocks were considered for a field visit. One village from each block was selected randomly. Thus, 16 villages were considered in this study. Subsequently, 15 farmers from each village were identified randomly for a personal inter- view. Hence, 240 farmers were interviewed. The personal interview of selected respondents was conducted from 1st October 2019 to 31st December 2019. A well structural questionnaire survey was conducted for the personal interview of selected farmers. The question- naire was filled up by the research team during the per- sonal interview of farmers. The questionnaire was divided into four broad sections. The 1st section includes the in- formation associated with the social-economic structure of farmers, gender, age, family size, annual income, educa- tional level and income-generating occupations. The 2nd 5 Figure 1. Geographical location of Gujarat. Source: Author’s formation. 2.2 Collection of Primary Data A total of 8 districts out of 33 (i.e., Anand, Banas Kantha, Bharuch, Bhavnagar, Junagadh, Kheda, Surat, and Vadodara) were selected based on their contribution to the agricultural sector (Figure 2). These districts collectively contribute around 46% of agricultural labour, 36% of agricultural district domestic product, 36.6% of gross cropped area, 31% of net area sown and 44% gross irrigated area of Gujarat. Also, these districts have a high share in arable land, agricultural workforce, cropping intensity, irrigated area, and cropped area under food-grain and cash crops in Gujarat. These districts are highly vulnerable due to climate change as compared to other districts of Gujarat [14,35]. Two blocks from each district were chosen purposively, and 16 blocks were considered for a field visit. One village from each block was selected randomly. Thus, 16 villages were considered in this study. Subsequently, 15 farmers from each village were identified randomly for a personal interview. Hence, 240 farmers were interviewed. Figure 1. Geographical location of Gujarat. Source: Author’s formation. 40 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 section includes information on the gross cropped area, irrigated and non-irrigated area, production of all crops, number of livestock and agricultural inputs. The 3rd sec- tion comprises information on the cost of technologies, appropriate technology, financial support from the gov- ernment and credit accessibility from banks, farmer’s as- sociation with Agri-entrepreneurs, skill supports, farmer’s adaptation strategy and agricultural development agencies. The 4th section contains open-ended questions on various aspects such as government policies, marketing, and pric- ing of products, etc. of the agricultural sector. Qualitative and quantitative information was collected from the farm- ers to achieve the specific objectives of the study. 2.3 Collection of Secondary Data Information related to climatic factors such as actual annual average evapotranspiration, annual average maxi- mum temperature, annual average minimum temperature, annual average precipitation and annual actual rainfall were derived from the India Meteorological Department (IMD), Ministry of Earth Sciences (Government of India) and official website of International Crops Research Insti- tute for the Semi-Arid Tropics. The statistics of mentioned climatic factors were used during 1991-2015. Since the statistics were available in time series. Hence, the coef- ficient of variation (CV) in mentioned climatic variables was included in the statistical analysis. The farm harvest price of each crop was taken from the annual report (2019- 2020) of farm harvest prices of principal crops in India published by the Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers Wel- fare, Ministry of Agricultural and Farmers Welfare, GoI, New Delhi. Figure 2. Location of districts. Source: District wise statistics of CMIE (2019-2020). 41 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 2.4 Data Analysis Analysis of Qualitative and Quantitative Data CFA is a statistical technique that identifies the latent and constructed variables in production activities [57]. The technique is helpful to describe the role of climatic and non-climatic factors in the agricultural sector [57-60]. It makes the group of indicators into constructs or fac- tors and observes the interrelationship among them [38,61]. Hadrich and Olson [58] examined the correlation coeffi- cients between farm size and farm performance, and the role of latent variables in farm performance in the USA using a CFA. Hosseini and Eghtedari [59] detected the fac- tors affecting the development of nanotechnology in the agricultural sector of Iran using CFA. Karakas et al. [62] have determined the factors which were useful to increase the farmer’s knowledge and skills regarding bureaucratic procedures using CFA. Syan et al. [63] applied the CFA technique to examine the farmer’s intention to adopt sus- tainable agriculture practices in Punjab (India). Narmilan et al. [41] used CFA to estimate the relationship between factors with regard to precision agricultural techniques and farmers’ adoption capacity in Sri Lanka. Pakmehr et al. [60] used CFA to determine the factors affecting farm- ers’ adaptation to climate change-induced water pressure in Iran. Laurett et al. [38] examined the SAD-affecting factors in Brazil using exploratory factor analysis. Singh et al. [64] also applied PCA to examine the performance of indicators associated with sustainable livelihood security in Indian states. This study also used the CFA technique to inspect the performance of climatic and non-climatic fac- tors in the agricultural sector. Rationality of Confirmatory Factor Analysis (CFA) The authors of this study collected information on nu- merous factors which were essential for the growth of the agricultural sector. These variables have diverse contribu- tions to the agricultural production system. Therefore, this study used CFA to identify the latent variables which can- not be observable [63]. A latent variable can be decided as per its variance and covariance in the set of variables. For this, CFA helps to reduce the dimensionality of a set of variables and further it may be useful to develop a math- ematical model for different statistical and empirical anal- yses. If the variance of a variable in a specific category of factors is less than 40%, then the variable can be dropped from the statistical analysis [57]. The estimate infers that the variable has an insignificant contribution among the set of variables. In the agricultural sector, there are many variables that can be used as dependent and independent variables. Therefore, it is expected that the contribution of some variables may be latent in farming activities. Hence, CFA was used to examine the latent variables among the selected set of variables. Validity of CFA Results Cronbach’s alpha score was estimated to check the reli- ability of the scale coefficient of individual and group fac- tors [59,65]. If the statistical value of Cronbach’s alpha score is less than 0.50, then undertaken variables cannot con- sider for CFA. Furthermore, Kaiser-Meyer Olkin (KMO) test was also used to check the perfection of the sample and consistency of CFA [38]. If the KMO value is detected as more than 0.5, the sample has adequacy for CFA [57]. Finally, the Chi2 value was also considered to check the viability of CFA. Description of Variables Included for CFA This study applies the CFA technique to observe the la- tent and construct factors in the agricultural sector. There- fore, 31 factors in five categories: (i) Climate change, (ii) Social-economic, (iii) Agricultural input, (iv) Techno- logical development and appropriate technology, and (v) Institutional support and CAS-related variables were used (Table 1). Previous studies have used climatic and non- climatic factors to observe the impact of climate change in the agricultural sector [1,2,8,16,22,32]. Few studies have consid- ered only climatic factors to assess the impact of climatic factors on production, yield and cropped area of food- grain and cash crops [26,28]. As district-level information on climatic factors was available during 1991-2015. Since, the coefficient of variation in climatic factors measures their long-term variability [1,5]. Thus, the coefficient of variation (CV) in a particular climatic factor captures its integrated influence in the agricultural sector. Therefore, CV in climatic factors was considered to examine the significance of climatic factors in the agricultural sector. Kumar et al. [15]; Singh et al. [66] also used CV in climatic factors to observe the climate change impact on sugarcane production in Indian states. A farmer’s social-economic profile also plays a significant role to increase farm in- come [67]. Thus, gender, age, family size, education level, main occupation, annual income, family size and a num- ber of livestock were also included in the statistical analy- sis of this study [3,12,68-71]. Here, education level was used to capture the influence of technical skills, and livestock was used to analyze the impact of physical assets of farmers in the agricultural sector. 42 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 Agricultural output was valued as the monetary value of food grain and cash crops which were cultivated by farmers during the survey year. The monetary value of each crop was estimated as per farm harvest price. Ag- gregate economic values of all crops were considered as gross agricultural production. Accordingly, per hectare farm income was assessed as a ratio of gross agricultural production with the gross cropped area. While, per hec- tare farm income from cash crops was also estimated separately. Gross cropped area, irrigated and non-irrigated area, use of agricultural labour per hectare, farm income from cash crops farming, fertilizer application per hectare, number of livestock and crop diversification index (CDI) were considered as agricultural inputs [4,5,22,45]. Cash crop farming is also useful to increase farmers’ income and economic capacity [1]. Subsequently, farmers can apply various inputs to get a better return in the cultivation of crops in the next season. Crop diversification measures how many crops can be cultivated in a specific area in a year. Agricultural production and income of farmers are to be increased as crop diversification increases. Moreover, crop diversification is also a vital driver to familiarizing Table 1. Summary of the variables. Category Variables Symbol Unit Climate change related variables CV in actual annual average evapotranspiration cvaaea mm CV in annual average maximum temperature cvaamaxtem °C CV in annual average minimum temperature cvaamintem °C CV in annual average precipitation cvaapre mm CV in annual actual rainfall cvaarf mm Social-economic related variables Gender (Male = 1; Female = 0) genres Number Farmer’s age ageres Years Family size famsizres Number Types of family (Joint = 1; Single = 0) typfamres Number Farmer’s education level (years spent in school) edulevres Years Farmer’s main occupation (Farming = 1; Farming and others = 0) maioccres Number Farmer’s annual income annincfam Rs. Number of livestock (Cow, goat, buffalo) nlf Number Agricultural input related variables Farm income/Ha. fiph Rs./Ha. Gross cropped area tagla Ha. Irrigated area irrare Ha. Non-irrigated area nonirrare Ha. Crop diversification index cdi % Use of agricultural labour/Ha. ualph Number/Ha. Farm income from cash crops farming/Ha. ficcph Rs./Ha. Fertilizer application/Ha. faph Kg./Ha. Technological development and appropriate technology related variables Cost of technology/Ha. cotepeha Rs./Ha. Economic viability of technology (Yes = 1; No = 0) ecoviatec Number Social viability of technology (Yes = 1; No = 0) socviatec Number Environmental viability of technology (Yes = 1; No = 0) envviatec Number Appropriate technology (Yes = 1; No = 0) apptec Number Institutional support and climate adaptation strategies related variables Financial problem of farmers (Yes = 1; No = 0) finpro Number Financial support from government and credit accessibility from banks (Yes = 1; No = 0) finsupgov Number Farmer’s association with various stakeholders (i.e., Agri-entrepreneurs, agricultural universities, agricultural extension offices, coo-operative societies, Agri industries) (Yes = 1; No = 0) farassstahol Number Skill and technical support from technology developers (Yes = 1; No = 0) skitecsupfar Number Farmer’s adaptation strategy to climate change (Yes = 1; No = 0) adstfa Number Source: Author’s compilation based on primary and secondary data. 43 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 the climate change impact on cultivation [5,2,72,73]. Hence, CDI was used to examine the influence of crop diversifi- cation on per hectare farm income. While, the crop diver- sification index (CDI) was estimated as: (CDI)i = =1   2 (1) Here, CDI is the crop diversification index of ith farm- ers; CA is the cropped area of a crop (in percentage) under 1 to nth crops during the survey year in Equation (1). Any agricultural technology may have multiple prac- tices in the agricultural sector. Therefore, the impact of technological change in the agricultural sector cannot be observed easily. Prior researchers used the time trend factor, new varieties of seeds and crop diversification as proxy variables to capture the impact of technological development in the agricultural sector [1,12,42,45,74]. Irrigation facilities, fertilizer and high-yielding verities of seeds also reflect the overall technological development in farming activities. In this study, the cost of technology/hectare was used to capture the impact of technological development on farm income. This study assumes that technological development needs more financial resources to bear the cost of the latest technology. Further, it also accepts that if the cost of technology increases then it infers the tech- nological development in the agricultural sector. Cost of technology is the gross amount which is paid by farmers to bear the cost of tractors and another mechanical devices during various stages of crop production, i.e., land prepa- ration, seed planting, electricity or fuel charge for irriga- tion and harvesting. Accordingly, the cost of technology/ hectare was used to examine the significance of techno- logical development in the agricultural sector. Ashraf and Singh [6] also used a similar variable to capture the impact of technological development on the farm income of the agricultural sector. Appropriate technology ensures the economic, social and environmental viability of the resources in the pro- duction process [5,6,39,40,51]. Most studies claimed that an appropriate technology maintains sustainability in the economic, environmental and social aspects [6]. In the context of the agricultural sector, technology can be ap- propriate when it meets said aspects. The economic aspect of technology is concerned with purchasing power of farmers and the social aspect of technology is associated with its acceptability and usability by farmers. Economi- cally viable technology also provides a better return to the farmers. The environmental viability of technology helps to ensure the protection of ecosystem services (i.e., soil fertility, and air and water quality) and natural resources. This aspect of technology is useful to reduce GHG emis- sions from the production sector and it may be highly conducive to reducing more existence of climate change. Hence, the measurement of appropriate technology is not easy [5,6]. Also, the scientific research community could not provide a universally acceptable indicator and develop a model to examine the impact of appropriate technology in the agricultural sector. Therefore, existing researchers could not assess the influence of appropriate technology on the production, yield, and growth of the agricultural sector. Though, previous studies used different variables such as time trend factor, cost of technology, fertilizer in- tensity, tractor, ICT, transplant technique, etc. to perceive the significance of appropriate technology and technologi- cal development in this sector [39,45]. Accordingly, it was difficult to observe the viability of appropriate technology and its components. The authors of this study used some proxy questions to include the farmer’s view on appropri- ate technology and its other aspects. For instance, whether applied technologies are economically feasible for you or not (if yes then 1 otherwise 0)? whether applied technolo- gies are socially acceptable to you or not (if yes then 1 otherwise 0)? whether applied technologies are environ- mentally sound or not (if yes then 1 otherwise 0)? Hence- forth, in this study, farmers’ judgments on economic, so- cial and environmental aspects of appropriate technology were used as proxy variables. In CFA, it uses binary data for mentioned aspects of appropriate technology in men- tioned ways [5]. Financial restrictions of farmers, financial support from the government and credit accessibility from banks, farm- er’s association with different institutions (i.e., Agri-entre- preneurs, agricultural universities, agricultural extension offices, agricultural cooperative societies, Agri industries), skill and technical support from technology developers’ industries, and farmer’s CAS were used as institutional support related variables in CFA. Agricultural extension offices and developmental institutions provide training and technical support to the farmers to increase their un- derstanding of various climate adaptation strategies and new technologies in the agricultural sector [5,6,12,63,75]. 3. Main Results 3.1 Statistical Summary of the Variables Table 2 shows the statistical properties (i.e., minimum, maximum, mean, standard deviation and skewness) of climatic and non-climatic factors. The values of standard deviation (SD) of most variables (except ageres, anninc- fam, fiph, ficcph, faph, cotepeha, edulevres, tagla, irrare, ualph, famsizres, cdi and nlf) were appeared less than 1. Thus, these factors have an insignificant diversity in the sample. The statistical value of skewness describes the 44 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 normality of the respective factor. The skewness values of most factors (excluding cvaapre, cvaarf, genres, nlf, ficcph, faph, socviatec and fiph) appeared between –1 to + 1. Thus, these factors were found in normal form. Moreover, Cronbach’s Alpha score measures the internal viability of individual variable. The Cronbach’s Alpha (α) score of a variable also measures its internal viability for further consideration in CFA [57]. Cronbach’s α score for all factors was found more than 0.70. Hence, the estimates infer that undertaken variables have internal consistency to apply CFA. 3.2 Results Based on CFA The eigenvalues, proportion share and cumulative contribution of all factors were estimated through simple factor analysis, principal-component factors analysis, iterated principal-factor analysis and maximum likelihood factor analysis. As principal-component factor analysis produces better results as compared to other forms of CFA. In the CFA method, the significance of a factor in the group of factors was observed based on eigenvalue, percentage variance and cumulative variance. Thereupon, factor loading and the uniqueness value of a specific variable explain their aggregate variation in the group of variables. This study used five categories of variables to examine latent and construct variables. The CFA results for climate change, social-economic, agricultural input, technological development and appropriate technology, Table 2. Descriptive statistics of factors. Variables Min Max Mean SD Skewness Cronbach’s α score cvaaea 0.081 0.221 0.145 0.051 0.351 0.7614 cvaamaxtem 0.011 0.012 0.012 0.002 0.592 0.7608 cvaamintem 0.021 0.021 0.023 0.003 –0.043 0.7643 cvaapre 0.241 0.391 0.291 0.041 1.124 0.7630 cvaarf 0.340 0.481 0.382 0.043 1.990 0.7803 genres 0.001 1.002 0.981 0.142 –6.712 0.7643 ageres 22.001 65.002 39.982 10.644 0.331 0.7524 famsizres 2.00 12.001 5.831 1.831 0.801 0.7515 typfamres 0.00 1.001 0.631 0.48 –0.55 0.7407 edulevres 7.00 17.00 12.59 3.09 –0.11 0.7338 maioccres 0.00 1.00 0.65 0.48 –0.65 0.7575 annincfam 140000 912000 531692 159320 –0.021 0.7535 nlf 5.00 34.00 12.40 4.17 1.40 0.7483 fiph 10821 23420 12053 1757 3.68 0.7668 tagla 1.00 25.00 9.27 5.57 0.67 0.7507 irrare 0.50 20.00 6.16 4.12 0.88 0.7527 nonirrare 0.00 10.00 3.15 2.00 0.79 0.7529 cdi 2.00 8.00 6.00 1.43 –0.55 0.7649 ualph 40.00 78.00 54.24 6.37 0.39 0.7693 ficcph 7214.17 9816.00 7865.46 597.24 1.48 0.7707 faph 102.00 435.00 167.38 50.58 2.59 0.7623 cotepeha 1765.00 2986.00 2536.39 287.22 –0.68 0.7674 ecoviatec 0.00 1.00 0.64 0.48 –0.59 0.7454 socviatec 0.00 1.00 0.89 0.31 –2.52 0.7619 envviatec 0.00 1.00 0.63 0.48 –0.55 0.7431 apptec 0.00 1.00 0.72 0.30 –0.51 0.7358 finpro 0.00 1.00 0.69 0.46 –0.83 0.7567 finsupgov 0.00 1.00 0.44 0.50 0.25 0.7640 farassstahol 0.00 1.00 0.51 0.50 –0.05 0.7644 skitecsupfar 0.00 1.00 0.32 0.47 0.77 0.7717 adstfa 0.00 1.00 0.46 0.50 0.15 0.7351 Source: Author’s estimation using primary and secondary data. 45 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 and institutional support and CAS-related variables are given in Table 3, Table 4, Table 5, Table 6 and Table 7, respectively. The KMO values of most variables (except, a few) were observed more than 0.5 and the overall KMO value was reported more than 0.72. Also, the Chi2 value was found statistically significant at a 1% significance level in each category. Thus, estimates infer that all fac- tors have consistency for the application of CFA. In the category of climate change-related variables, the first 3 factors were found retained factors that contribute 87.83% variation among the five different climatic factors (Table 3). The variation in an individual factor loaded onto 3 retained factors (i.e., factor1, factor2 …, factor3) and the uniqueness of each factor in farm income/hectare detect the category of latent variables. If the value of a factor loaded is less than 0.40, then the factor cannot be used in the statistical explanation [57,75]. The estimates reveal that cvaaea, cvaamaxtem, cvaamintem, cvaapre, and cvaarf have high loaded on factor1. Hence, actual annual average evapotranspiration, annual average maximum temperature, annual average minimum temperature, annual average precipitation and annual actual rainfall appeared as latent variables in the category of climate change-related variables. For the social-economic related variables, CFA results infer that the first 3 factors seemed retained variables. These first 3 factors have a 60.10% variation in the 8 so- cial-economic related variables (Table 4). As per the fac- tor loading and uniqueness, agrees, famsizres, typfamres, edulevres and nlf have highly loaded on factor1. There- fore, the farmer’s age, family size, type of family, educa- tion level and number of livestock seemed latent variables in the category of social-economic variables. In the category of agricultural inputs related-variables, the first 3 factors were detected as retained factors (Table 5). The first 3 factors have a 72.5% variation among the 7 variables in this category of variables. The results also suggested that gross cropped area, irrigated area, crop diversification and fertilizer application were found latent variables in the category of agricultural input-related variables. Appropriate technology and its other components have highly loaded on factor1 (Table 6). Thus, these variables were found latent variables in the category of techno- logical change and appropriate technology related vari- ables. While, the cost of technology has highly loaded on factor2. Cost of technology, therefore, was also found as a hidden variable for factor2. As the eigenvalue and proportion share of individual factors, the first 3 factors were reported retained fac- tors and these variables have 65.10% variation among the 5 factors in the category of institutional support and CAS-related variables (Table 7). The estimates infer that government financial support, farmers’ association with different stakeholders, skilled and technical support for farmers and CAS have highly loaded on factor1. Hence, these variables were observed as latent variables in this category of variables. Table 3. Proportion of factors, factors loading and unique variances in climatic factors. Eigenvalue and proportion of factors Number of obs. 240 Number of params 15 Retained factors 3 Chi2 1271.20* Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Eigenvalue 2.8221 1.4432 1.0043 0.47505 0.24555 0.0099 Difference 1.3789 0.4389 0.52925 0.2295 0.23569 . Proportion 0.4703 0.2405 0.1674 0.0792 0.0409 0.0016 Cumulative 0.4703 0.7109 0.8783 0.9574 0.9984 1 Factor loadings (pattern matrix) and unique variances Variable Factor1 Factor2 Factor3 Uniqueness KMO Value fiph 0.0757 0.0298 0.9952 0.0030 0.3234 cvaaea 0.6750 0.6250 0.0487 0.1514 0.6960 cvaamaxtem 0.9470 –0.2733 –0.0280 0.0278 0.4087 cvaamintem 0.7376 –0.6019 0.0009 0.0937 0.3263 cvaapre 0.4589 0.7785 –0.0921 0.1750 0.2578 cvaarf 0.8422 –0.0933 –0.0476 0.2796 0.3985 Source: Author’s estimation using primary and secondary data. *: show that Chi2 value is statistically significant at 1% significance level. 46 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 Table 4. Proportion of factors, factors loading and unique variances in social-economic related variables. Eigenvalue and proportion of factors Number of obs. 240 Number of params 24 Retained factors 3 Chi2 708.33* Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8 Factor9 Eigenvalue 3.092 1.261 1.057 0.945 0.921 0.761 0.589 0.254 0.122 Difference 1.832 0.204 0.112 0.024 0.160 0.172 0.335 0.131 . Proportion 0.344 0.140 0.117 0.105 0.102 0.0845 0.065 0.028 0.014 Cumulative 0.344 0.484 0.601 0.706 0.808 0.893 0.958 0.986 1.000 Factor loadings (pattern matrix) and unique variances Variable Factor1 Factor2 Factor3 Uniqueness KMO Value fiph –0.056 0.4837 –0.270 0.6901 0.5759 genres 0.2998 0.4328 –0.203 0.6816 0.6023 ageres 0.5178 –0.185 –0.510 0.4378 0.7304 famsizres 0.7837 0.3793 0.1331 0.2242 0.6237 typfamres 0.7836 –0.155 0.0827 0.3553 0.7476 edulevres –0.823 0.2842 0.0977 0.2332 0.6942 maioccres 0.285 –0.278 0.7126 0.3336 0.7002 annincfam –0.314 0.5749 0.3421 0.4543 0.6714 nlf –0.804 –0.385 –0.155 0.1806 0.6258 Source: Author’s estimation using primary. *: show that Chi2 value is statistically significant at 1% significance level. Table 5. Proportion of factors, factors loading and unique variances in in agricultural inputs related variables. Number of obs. 240 Number of params 21 Retained factors 3 Chi2 1529.94* Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8 Eigenvalue 3.070 1.722 1.004 0.866 0.695 0.350 0.288 0.005 Difference 1.348 0.718 0.138 0.170 0.346 0.061 0.284 . Proportion 0.384 0.215 0.126 0.108 0.087 0.044 0.036 0.001 Cumulative 0.384 0.599 0.725 0.833 0.920 0.963 0.999 1.000 Factor loadings (pattern matrix) and unique variances Variable Factor1 Factor2 Factor3 Uniqueness KMO Value fiph –0.092 0.915 0.091 0.147 0.5051 tagla 0.966 0.101 –0.015 0.056 0.4980 irrare 0.913 0.102 –0.007 0.157 0.4669 nonirrare 0.851 0.059 –0.005 0.272 0.4284 cdi 0.427 –0.080 0.177 0.780 0.9448 ualph –0.019 –0.082 0.981 0.030 0.1017 ficcph –0.077 0.917 0.011 0.153 0.5035 faph 0.618 –0.088 –0.036 0.609 0.8100 Source: Author’s estimation using primary data. *: show that Chi2 value is statistically significant at 1% significance level. 47 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 4. Discussion on Findings The results based on CFA indicate that climatic factors, social-economic variables, CAS and institutional support have an important contribution to the agricultural sector. However, the roles of different categories of variables were found to differ in the cultivation. Social-economic factors such as the farmer’s age, family size, type of fam- ily, education level and the number of livestock can be applied as CAS in the cultivation. Gross cropped area, irrigated area, crop diversification and application of fertilizer are reported as vital agricultural inputs. These variables may be worked as CAS in the cultivation. The government’s financial, skilled and technical support by agricultural development agencies may be helpful for the farmer to increase their intention toward CAS. Thus, most social-economic and institutional support-related variables work as CAS in the cultivation. Evapotranspiration, maximum temperature, minimum temperature, precipitation and rainfall were reported as hidden variables in the group of climatic factors (Table 3). As these variables cannot be controlled by farmers, thus, climatic factors can be considered exogenous vari- Table 6. Proportion of factors, factors loading and unique variances in technological development and appropriate tech- nology related variables. Number of obs. 240 Number of params 11 Retained factors 2 Chi2 4607.75* Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Eigenvalue 2.451 1.159 0.996 0.918 0.477 0 Difference 1.292 0.163 0.077 0.441 0.477 . Proportion 0.408 0.193 0.166 0.153 0.080 0 Cumulative 0.408 0.602 0.768 0.921 1.000 1 Factor loadings (pattern matrix) and unique variances Variable Factor1 Factor2 Uniqueness KMO Value fiph 0.1880 –0.4966 0.7180 0.8867 cotepeha –0.1853 0.4202 0.7891 0.6819 ecoviatec 0.6622 –0.4557 0.3538 0.1695 socviatec 0.4683 0.7181 0.2651 0.1013 envviatec 0.8589 0.0939 0.2535 0.2394 apptec 0.9927 0.0565 0.0114 0.3225 Source: Author’s estimation using primary data. *: show that Chi2 value is statistically significant at 1% significance level. Table 7. Proportion of factors, factors loading and unique variances in institutional support and CAS related variables. Number of obs. 240 Number of params 15 Retained factors 3 Chi2 101.57* Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Eigenvalue 1.586 1.310 1.009 0.917 0.677 0.500 Difference 0.277 0.300 0.092 0.240 0.177 . Proportion 0.264 0.218 0.168 0.153 0.113 0.083 Cumulative 0.264 0.483 0.651 0.804 0.917 1.000 Factor loadings (pattern matrix) and unique variances Variable Factor1 Factor2 Factor3 Uniqueness KMO Value fiph 0.214 0.3195 0.8651 0.1037 0.4627 finpro –0.2867 0.7604 0.1311 0.3224 0.4488 finsupgov 0.7983 0.1583 –0.2062 0.2951 0.5042 farassstahol 0.4452 –0.1179 0.0245 0.7873 0.6882 skitecsupfar 0.6013 0.5667 –0.2841 0.2365 0.4713 adstfa 0.5111 –0.5186 0.3463 0.3499 0.5606 Source: Author’s estimation using primary data. *: show that Chi2 value is statistically significant at 1% significance level. 48 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 ables in the agricultural production system. Here, it can be argued that climatic factors work as crucial inputs during various phases, i.e., sowing, growing and harvesting time of a crop [1,9,16,20]. The plant growth of a crop can toler- ate the impact of certain climatic factors up to a certain extent. Subsequently, there is expected a non-linear and hilly-shaped relationship between land productivity and climatic factors [14]. Thus, the production of most crops may decline due to high variability in climatic factors. Accordingly, farm income per hectare may be declined due to changes in climatic factors. The mentioned results are consistent with previous studies like Singh et al. [5]; Angom et al. [14]; Singh and Issac [35]; Singh et al. [75] have also reported the negative impact of climatic factors in the agricultural and its associated activities in Gujarat. Therefore, at present agricultural production activities are in a vulnerable position due to climate change. Therefore, there is indispensable to apply CAS to mitigate the nega- tive impact of climate change in the agricultural sector. Farmer ’s age, family size, education level, annual income and livestock seemed as latent variables in the category of the social-economic profile of farmers (Table 4). Hence, the estimates demonstrated that productivity and production of crops may be increased as the social- economic status of farmers improves. Previous studies also reported significant implications of mentioned vari- ables in farming activities [5,12,19,68,73]. Age, family size and education level of farmers have a substantial role in farming activities. Educated farmers have more skills to apply various CAS, inputs and farm practices in the culti- vation to increase yield. Farm management practices and productivity are to be improved as the involvement of experienced farmers increase in the cultivation. The result of the study also found a significant role of farmers’ age in cultivation. Most family members of the farming com- munity generally engaged in agricultural production ac- tivities. Hence, land productivity increases up to a certain level as the family size of farmers increases. Otherwise, land productivity may be declined due to the applicabil- ity of the law of diminishing returns in the agricultural sector [1,16]. Farmers can use various inputs like new seeds, fertilizer, pesticides and technology as their annual in- come increases. Moreover, literate and experienced farm- ers use different techniques of cultivation to increase farm income [16]. Availability of physical assets (i.e., number of livestock) also helps farmers to apply various CAS. The mentioned findings are similar to existing studies like Singh et al. [5]; Mitra et al. [12]; Basu [19]; Dhanya and Ramachandran [68]; Singh [73]. Briefly, the farmer’s family size, education level, annual income, family type and live- stock seemed useful to mitigate the climate change impact on farming activities. Gross cropped area, agricultural labour, irrigated area, crop diversification and fertilizer were noticed as latent variables in the group of agricultural inputs (Table 6). Therefore, these variables appeared as significant inputs for the agricultural sector. Earlier studies like Ashraf and Singh [5]; Kumar [9]; Kumar et al. [16]; Chandio et al. [22]; Ashraf and Singh [45]; Singh [73] have also noticed a posi- tive impact of mentioned inputs in the agricultural sector. Thereafter, income from cash crops farming also showed a positive impact on farm income. Income from cash crops farming is necessary to increase the economic capacity of the farmers for purchasing new technologies, new va- rieties of seed, irrigation resources, green fertilizers and other inputs in cultivation. Hence, income from cash crop farming may be beneficial for farmers to apply a climate adaptation approach to avoid the risk of climate change in the agricultural sector. Cash crop farming may be fa- vorable to creating an agri-entrepreneurial ecosystem. Farmers, therefore, should grow cash crops to get a better return and increase CAS. All components of appropriate technology appeared as latent variables in the group of technological development and appropriate technology-associated variables (Table 7). The estimates can be defensible that the application of agricultural technologies is helpful to increase productiv- ity and production of crops [6,13,44,46,50]. Cropping patterns and crop diversification also improve as the use of tech- nology increases in cultivation. Moreover, the application of appropriate technology helps to save water, and human resources, germination of seeds, seed fertility and increase plant growth. The use of appropriate technology may also reduce the fertilizer and pesticides, and waste materials in the cultivation. Land management practices also improve as the application of technology increases in the agricul- tural sector. Furthermore, the use of appropriate tech- nology enhances soil quality and fertility, water and air quality, and other ecosystem services [5]. Subsequently, the use of appropriate technology would increase sustainable agricultural development. The CFA results of this study proposed that appropriate technology and its dimensions have a positive impact on the farming activities. The men- tioned findings are consistent with previous studies [5,45]. Financial support from the government, farmer’s as- sociation with stakeholders, skill and technical support from technology developers and adaptation practices seemed as latent variables among the institutional support and CAS-associated variables. The abovementioned find- ings are consistent with Naidu et al. [48]. The government should provide subsidies on seeds, fertilizer, pesticides, electric engines and irrigation sprinkler machines to the 49 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 farmers. The government also announced the minimum support price (MSP) of food-grain and cash crops before their sowing time. MSP will motivate the farmers to grow a crop that provides them with better benefits. Moreover, credit accessibility for farmers would also increase their purchasing power to buy different inputs (i.e., seed, ferti- lizer, pesticides, etc.) during the sowing time of crops. Ap- propriate credit accessibility for the farming community would also enhance the production and productivity of crops. Kumar et al. [16] also found that credit accessibility was a vital driver to increasing agricultural productivity in Indian states. Agricultural development agencies, agricul- tural cooperative societies, technology developers’ agen- cies, and research institutions and agricultural universities should provide training and organize various programs for farmers to increase their awareness of new varieties of seeds, fertilizer, technology and scientific methods of cultivation. Therefore, institutional support has a positive involvement to increase farm income [5,47]. It is consist- ently accepted that climate change is highly responsible to reduce the farm income and productivity of food-grain and crops in India [1,8-10,15,22,23,25-29,32,36,59]. Therefore, CAS may be effective to mitigate the climate change impact in the Indian agricultural sector. Previous studies like Singh et al. [5]; Mitra et al. [12]; Angom et al. [14]; Dhanya et al. [68]; Singh [73] have also observed a positive and significant role of CAS in the Indian agricultural sector. 5. Conclusions and Recommendations The prime aim of this study was to examine the im- plication of climatic factors, social-economic variables, agricultural inputs, technological advancement and ap- propriate technology and institutional support and climate adaptation strategy in the agricultural sector of Gujarat using CFA. The CFA was run on 31 factors which were divided into five different categories. Farm income per hectare was treated as a dependent variable in every group of variables. Subsequently, this study could find latent variables in each category of a variable. These variables can be used as a CAS by farmers to mitigate the adverse impact of climate change. The results based on CFA, demonstrate that the coeffi- cient of variation in annual actual evapotranspiration, an- nual average maximum temperature, annual average mini- mum temperature, annual actual precipitation and annual actual rainfall have a significant influence on farm income per hectare. In the category of social-economic variables, the farmer’s age, family size, type of family, education level and a number of livestock have a vital contribution to increasing farm income per hectare. Gross cropped area, irrigated area, crop diversification and fertilizer application seemed important agricultural inputs. Farm income was also significantly linked with technological development and appropriate technology. The govern- ment financial support, farmer’s association with different stakeholders, skilled and technical support for farmers and CAS were also found vital determinants to increasing farm income. The study suggests policy recommendations such as improving soil and seed quality, adopting green fertilizers, implementing appropriate farm management practices, and utilizing irrigation methods to mitigate the negative impact of climate change on agriculture. Further, the study recommends that future researchers consider the factors that have minimal negative effects on the environment and ecosystem services. Additionally, future studies could investigate the categorization of appropriate technologies and develop universally accepted indicators to determine which technologies are most suitable for the agricultural sector. Finally, replicating this study with larger samples from different states in India could yield more robust find- ings. Author Contributions The proposal for this article was prepared by Dr. Ajay K. Singh and Shah Nawaz Ashraf. Dr. Shah Nawaz Ashraf collected primary data from the selected respondents through field surveys. Dr. Sandeep Kumar Sharma col- lected the secondary data from various sources. Dr. Ajay Kumar Singh completed the formulation of empirical models and statistical inferences. Review of literature was undertaken by Dr. Sandeep Kumar Sharma and Shah Nawaz Ashraf. The final draft of this article was written by all authors jointly. Funding No funding received. Acknowledgments The authors are thankful to the local farmers for giv- ing their valuable time and support to provide the desired feedback during field survey. The authors are also thankful to the anonymous reviewers for giving their valuable time and suggestions to increase the quality of this manuscript. The authors also give their gratitude to the editorial team of the journal for providing their valuable feedback and comments to maintain the strength of this article. Data Availability The data presented in this study are available on re- quest from the corresponding author. 50 Research on World Agricultural Economy | Volume 04 | Issue 01 | March 2023 Conflict of Interest The authors declare no conflict of interest. References [1] Kumar, A., Sharma, P., Ambrammal, S.K., 2015. Climatic effects on sugarcane productivity in India: A stochastic production function application. Interna- tional Journal of Economics and Business Research. 10(2), 179-203. [2] Abeysingha, N.S., Singh, M., Islam, A., et al., 2016. Climate change impacts on irrigated rice and wheat production in Gomti River basin of India: A case study. SpringerPlus. 5(125), 1-20. [3] Malhi, G.S., Kaur, M., Kaushik, P., 2021. Impact of climate change on agriculture and its mitigation strategies. Sustainability. 13(1318), 1-21. 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