Profiling farmers’ personal, socio-economic and psychological attributes influencing the adoption of climate-smart agricultural technologies in Chhattisgarh State

Introduction

Climate change is widely recognized as a major environmental and societal challenge confronting agriculture globally. Interactions among climate variability, unsustainable land management, land degradation, and greenhouse gas fluxes have adversely affected agricultural systems, leading to declining productivity and increased threats to food security (Barros et al., 2014) [6] . These impacts are reflected in irregular rainfall, frequent droughts and floods, increased pest and disease incidence, and disrupted cropping calendars, resulting in higher production costs and reduced crop and livestock performance (Huho, 2012; Kangogo et al., 2020) [14,18] . In response to these challenges, Climate-Smart Agriculture (CSA) has emerged as a key approach to enhance agricultural productivity, resilience to climate change, and mitigation of greenhouse gas emissions (FAO, 2010; 2013). Despite the recognized potential of CSA technologies, their adoption across farming systems remains limited. Empirical studies identify several constraints influencing adoption, including institutional arrangements, limited technical skills and capacities, increased labour requirements, and the need for new knowledge and competencies (Lee, [25,2,45,46,29,39,44] . Additional barriers such as inadequate awareness, insufficient access to information, limited prior experience with new technologies, and farmers' perceptions and attitudes towards technological change further constrain adoption (Nyasimi et al., 2017; Senyolo et al., 2018; Ouédraogo et al., 2019; Khoza et al., 2021; Mashi et al., 2022) [33,43,34,21,28] . Adoption decisions are further shaped by financial and nonfinancial incentives and disincentives. High initial investment costs, perceived incompatibility with existing farming practices, and uncertainty regarding expected benefits discourage uptake, whereas trialability, social learning, peer influence, visibility of benefits, and institutional support through extension, training, and www.extensionjournal.com technical and financial assistance enhance adoption (Makate, [26,38,45,3,7,17,10,47] . Beyond these factors, farmers' behavioural and psychological attributes such as innovative proneness, risk orientation, and perceptions of costs and risks significantly influence adoption decisions (Barzola and Dentoni, 2020; Kangogo et al., 2021) [18] . Supported by theoretical frameworks including diffusion of innovations, the Theory of Planned Behavior, and utility-based approaches (Ajzen, 1991) [1] , the adoption of CSA technologies is understood as a function of personal, socio-economic, institutional, and psychological factors. Accordingly, the present study examines the personal, socio-economic, and psychological profile characteristics of farmers practicing climate-smart agricultural technologies in Chhattisgarh State.

Methodology

The present study adopted an ex-post facto research design. According to Kerlinger (1973) [20] , ex-post facto research is a form of systematic empirical enquiry in which the researcher does not have direct control over the independent variables, since their manifestations have already occurred or are inherently non-manipulable and continue to exist. The study was conducted in Chhattisgarh State, which is divided into three agro-climatic zones, namely Northern Hills, Chhattisgarh Plains, and Bastar Plateau. To ensure representation from all agro-climatic regions, districts were selected using proportional random sampling, wherein twenty per cent of the districts from each agro-climatic zone were selected. Accordingly, Surguja district was selected from the Northern Hills, Raipur, Rajnandgaon, Bemetara, and Dhamtari districts from the Chhattisgarh Plains, and Kanker district from the Bastar Plateau, making a total of six districts for the study. A multistage simple random sampling technique was employed for the selection of respondents. In the second stage, two blocks were randomly selected from each selected district, resulting in a total of twelve blocks. From each selected block, two villages were randomly selected, constituting a total of twenty-four villages. In the final stage, fifteen farmers were selected randomly from each village, resulting in a total sample size of 360 respondents (24 × 15 = 360). The study focused on farmers practicing climate-smart agricultural technologies. Data were collected using a structured interview schedule. The collected data were coded, tabulated, and analyzed using appropriate statistical tools such as frequency distribution and percentage analysis and the results were presented accordingly.

Results and Discussion

The profile characteristics of the farmer respondents comprised education, farming experience, farm size, family type, cropping pattern, occupation, livestock possession, family annual income, availability of irrigation facilities, social participation, extension contact, innovative proneness, risk orientation, level of farm mechanization, farmers' perception towards climate change, ownership and extent of utilization of information and communication technologies (ICTs), sources of information on Climate-Smart Agricultural Technologies, information needs related to Climate-Smart Agricultural Technologies, attitude of farmers towards Climate-Smart Agricultural Technologies and access to Weather-Based Agro-Advisory Services (WBAAS). Respondents were categorized into appropriate classes for each profile characteristic using the class-interval technique, and the resulting data were systematically analysed and discussed.

Education

Table 1 shows that 35.00 per cent of the respondents were educated up to middle school, followed by high school (21.11%) and primary education (16.67%). A small proportion were illiterate (7.50%), while only 6.11 per cent had attained graduation. Overall, 92.50 per cent of the respondents were literate, ranging from primary to college level. Education plays a vital role in enhancing access to information and is presumed to positively influence the adoption of Climate-Smart Agricultural Technologies. The above results are broadly supported by Chadachal (2017) [8] , Nayak (2018) [32] and Chandrashekhar (2019) [9] . [24] and Prasad (2016) [37] .

Farm size

Table 3 shows that 38.06 per cent of the respondents possessed semi-medium farm size, followed by medium (24.72%), small (19.72%) and marginal holdings (12.50%). Only 5.00 per cent of the respondents had large landholdings. The predominance of semi-medium and medium farms may be attributed to ancestral landholdings and agriculture as the primary occupation. Larger farm size is presumed to facilitate greater adoption of Climate-Smart Agricultural Technologies, which is in conformity with the findings of Chadachal (2017) [8] and Pabba et al. (2021) [35] www.extensionjournal.com Large (Above 10.0 ha) 18 5.00

Family type

Table 4 indicates that two third (66.94%) of the respondents belonged to nuclear family and one third of respondents belonged to joint family.

Cropping pattern

Regarding the cropping pattern, data presented in Table 5 indicate distinct zone-wise variations across the agroclimatic regions of Chhattisgarh. In the Chhattisgarh Plains, the predominant cropping pattern was Rice-Fallow, adopted by 25.00 per cent of the respondents. This was followed by Rice-Gram/Wheat/Sunflower (23.06%), Soybean + Arhar/Kodo/Urd/Moong/Til + Arhar (14.17%), Maize-Urd/Chickpea (9.17%) and Soybean-Gram/Wheat (8.06%). Vegetable-Vegetable systems accounted for 6.94 per cent, while Rice-Rice/Maize (5.83%) and Sugarcane cultivation (5.00%) were less common. The Soybean + Arhar system was the least adopted, followed by only 2.78 per cent of the respondents.

In the Bastar Plateau, Rice-Fallow was the most prevalent cropping pattern (20.00%), followed by Millets/Niger-Fallow (16.67%) and Rice-Maize (15.00%). Pulse-based systems such as Arhar + Moong/Urd-Fallow were practiced by 13.33 per cent of farmers. Maize-Gram/Mustard (11.67%) and Rice-Wheat/Gram (10.00%) reflected a balanced cereal-legume combination. Vegetable-Vegetable and Maize-Fallow systems were each adopted by 5.00 per cent, while Sugarcane was the least practiced crop (3.33%).

In the Northern Hills, Rice-Fallow emerged as the dominant cropping pattern (20.00%), followed by Millets/Niger-Fallow (16.67%) and Rice-Maize (15.00%). Both Arhar + Moong/Urd-Fallow and Rice-Wheat/Gram systems were practiced by 13.33 per cent of the respondents. Maize-Wheat/Mustard accounted for 10.00 per cent, while Fallow-Horse gram/Niger was followed by 13.33 per cent of farmers. Vegetable-Vegetable and Sugarcane cultivation were least prevalent, each adopted by 5.00 per cent of respondents.

Occupation

The respondents' occupation is their primary source of income and helps to meet their basic needs. All respondents in the study area depend on agriculture for their livelihood.

Distribution of respondents according to their occupation Table 6.1 indicates that 100 per cent of the respondents were engaged in agriculture and allied activities. In addition, www.extensionjournal.com 33.05 per cent combined agriculture with business, 14.16 per cent were involved in labour, 9.72 per cent pursued agriculture with other occupations, and only 2.50 per cent combined agriculture with service. These results confirm agriculture as the primary occupation of farm families, with other activities serving as supplementary income sources. Similar findings were reported by Joshi (2016) [16] , Parganiha (2016) [36] and Nayak (2018) [32] .

Livestock possession

With respect to livestock possession, Table 4.14 shows that 31.94 per cent of the respondents owned cows, followed by poultry (23.88%) and buffaloes (18.05%). Goat and sheep ownership was reported by 10.83 per cent and 5.27 per cent of the respondents, respectively, while only 3.61 per cent possessed pigs. Ownership of multiple livestock species reflects diversified income sources among farm households.

Family annual income

Annual income is defined as the sum of total earnings from all sources in a year. Because agriculture is the respondent's primary source of income, implementing new agricultural technologies necessitates significant financial resources. Farmers with limited income and resources were unable to afford the transaction costs required for adaptation measures, and they were sometimes unable to make effective use of the knowledge they do have. The data of the annual income of the respondents is given in table 8 that 35.00 per cent of the respondents had an annual income between Rs. 200001-300000 followed by 21.94 per cent, 19.17 per cent and 16.67 per cent with annual income Rs. 100001-200000, Rs. 300001-400000, and up to Rs. 100000 respectively, whereas only 7.22 per cent respondents had annual income More than 400000. The majority of the respondents belonged to the annual family income category of ₹2,00,001-3,00,000. This may be attributed to the predominance of semi-medium to medium farm sizes and agriculture as the primary occupation among respondents. Similar findings were reported by Laxman (2016) [24] and Prasad (2016) [37] .

Irrigation facilities

Irrigation facilities have a direct influence on agricultural production and productivity. Climate change is expected to affect agriculture primarily through its impacts on the water cycle, including increased evaporative demand and changes in rainfall volume and distribution, which in turn influence crop performance. Availability of irrigation is also closely linked with area coverage during the rabi season. Table 9.1 reveals that 73.06 per cent of the respondents had access to irrigation facilities, whereas 26.94 per cent did not have any irrigation facilities. Table 9.2 indicates that 70.28 per cent of the respondents used tubewells as the primary source of irrigation, followed by canals (30.00%), wells (10.55%), ponds (5.55%), rivers (2.50%) and other sources (1.94%). The predominance of tubewell irrigation reflects its importance in the study area. Similar findings were reported by Chandrashekhar (2019) [9] and Kapuria. www.extensionjournal.com

Social Participation

Human beings are social entities and function within organized social systems. In the present study, social participation refers to respondents' involvement in formal and informal organizations as members, office bearers, or both. Data presented in Table 10 show that the majority of respondents (55.83%) were members of two organizations, followed by membership in one organization (14.17%) and more than two organizations (13.61%). About 12.22 per cent of respondents served as office bearers, while 4.17 per cent had no membership in any organization. Regular participation, particularly in cooperative societies and Gram Sabha, may enhance awareness and information exchange related to Climate-Smart Agricultural Technologies. These findings are in agreement with Manjusha et al. (2019) and Sardhara et al. (2020) [42] , but are contrary to the observations of Atheequlla et al. (2021) [5] and Kumar (2023) [23] .

Extension contact

With regards to contact of respondents with extension personnel, the respondents were asked about their contact with six enlisted personnel (RAEO, Progressive farmers, RAEOs/RHEOs/VFOs, SMS/Agricultural scientist, Cooperative institutes, NGOs and Input dealers). Table 11 describes that the respondent had extension contact with Input dealers ranked 1 st followed by RAEOs/RHEOs/VFOs and Progressive farmers ranked 2 nd and 3 rd with extension contact. 45.55 per cent and 14.44 per cent reported occasional and regular contact, respectively. Most respondents (58.33%) had no contact with NGOs, whereas 33.05 per cent and 8.33 per cent reported occasional and regular contact. These findings are in agreement with Renuka (2020) [40] , Sardhara et al. (2020) [42] , and Muralikrishnan et al. (2021) [31] . [22] and Jamadar (2012) [15] . Engagement with extension services, including access to subsidies under agricultural development programmes, may further motivate farmers to participate in extension activities and acquire knowledge related to improved technologies and practices Innovative proneness. [32] and Chandrashekhar (2019) [9] .

Risk Orientation

Individual's risk-bearing ability is determined by their personal, psychological, and socio-economic qualities. The degree to which a respondent is oriented toward risks and uncertainty due to changing climatic circumstances, and have the capacity to face various hazards associated with farming, and other activities.

The data in table 13 is revealed that more than half (60.83%) of the respondents belonged to medium level of risk orientation followed by low (27.78%) and high (11.39%) level of risk orientation category. The majority of the respondents exhibited medium to low risk orientation, indicating a moderate willingness to adopt Climate-Smart Agricultural Technologies. A smaller proportion with high risk orientation may be attributed to larger farm size and greater farming experience. These findings are in conformity with the results reported by Parganiha (2016) [36] and Nayak (2018) [32] Farm mechanization level Table 14 indicates that under the prime mover category, 14.73 per cent of the respondents owned tractors, while only 1.39 per cent possessed power tillers. For tillage and seedbed preparation, the majority of respondents used desi ploughs (38.05%), followed by cultivators (16.11%) and rotavators (12.78%). About 11.94 per cent of the respondents owned drum seeders for sowing operations. For irrigation purposes, most respondents (80.83%) used pumps, whereas only 8.61 per cent owned engines. Knapsack sprayers were predominantly used for plant protection by 70.83 per cent of respondents, while 26.67 per cent and 6.11 per cent used power sprayers. Only 0.55 per cent of respondents owned combine harvesters for harvesting operations. These findings are in agreement with the results reported by Salini (2017) [41] and Aruna et al. (2024) [4] .

Perception of farmers towards Climate change

Farmers' perception is crucial in understanding the extent to which a region experiences direct or indirect agricultural impacts of climate change. Perception influences farmers' preparedness and willingness to adopt and implement new technologies, as successful adoption depends on their ability to perceive and respond favourably to changes in climate and the environment. In the present study, farmers' perception of climate change was measured using the scale developed by Eysenck and Crown (1949) [11] . Complete responses were obtained from all respondents, and their perception levels were computed accordingly. The classification of respondents based on perception level is presented in Table 15. The data in Table 15 reveal that a majority of the farmers (68.89%) exhibited a medium level of perception about climate change, followed by 18.06 per cent with a high level and 13.05 per cent with a low level of perception. The predominance of medium perception may be attributed to factors such as moderate levels of education, farming experience, social participation, extension contact, innovativeness, and risk-bearing ability, which contribute to a better understanding of climate-related issues. These findings are in agreement with the results reported by Laxman (2016) [24] , Chadachal (2017) [8] , and Chandrashekhar (2019) [9] .

Ownership and extent of utilization of ICTs by farmers

The overall extent of utilization of ICTs by respondents were determined and given in table 16 that the maximum number of respondents (60%) had medium extent of utilization followed by 21.39 per cent had low level and 18.61 per cent had high extent of utilization of ICTs. It can be concluded that respondents with high extent of utilization of ICTs can have more information about Climate Smart Agriculture Technologies (CSATs) and that will enhance adoption of Climate Smart Agriculture Technologies (CSATs). Kumar (2023) [23] also reported similar findings.

Source of information on Climate Smart Agriculture Technologies (CSATs)

The respondents' source of information covers both the information sources from which they obtain information and the respondents' perceptions of the credibility of these sources. also, It refers to the source used by respondents to obtain information about various aspects of farm technologies. Sources of information help respondents in obtaining information about many areas of agriculture and various agricultural schemes available to them, which in turn helps respondents in increasing their knowledge. Table 17 explains the distribution of respondents according to their overall utility of information source for Climate Smart Agriculture Technologies. The majority of the respondents (55.83%) belonged to medium category, followed by 40.56 per cent and 3.61 per cent of the respondents were from high and low level of category, respectively. [35]

Conclusion

The above results conclude that the majority of the respondents possessed basic to moderate educational status, with most educated up to the middle school level, and had www.extensionjournal.com medium farming experience (11-15 years). A considerable proportion of farmers belonged to the semi-medium farm size category, followed by medium, small, and marginal holdings, with most respondents living in nuclear family systems. Agriculture was the principal occupation of all respondents, with a sizeable proportion supplementing their income through business and allied activities. Most respondents reported a medium level of annual family income (₹2,00,001-3,00,000) and primarily depended on tubewells, canals, and wells as sources of irrigation. The findings further revealed that the majority of respondents exhibited medium levels of social participation, extension contact, innovative proneness, and risk orientation.

Acknowledgement

AcknowledgementI would like to extend my heartfelt gratitude to my esteemed major advisor, Dr. R.S. Sengar, for his trust, insightful discussions, invaluable advice, and unwavering support throughout the entire study. I am especially grateful for his patience and guidance during the writing process.

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