Socio-economic and psychological profile of sugarcane growers towards carbon sequestration practices

Introduction

Climate change is increasingly recognized as a critical global concern, largely driven by the accumulation of greenhouse gases such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) in the atmosphere (IPCC, 2021). Agriculture occupies a unique position in this scenario, serving both as a contributor to greenhouse gas emissions and as a means of mitigating them through sustainable resource management practices (Bhaskaran & Nair, 2014; Anand et al., 2025). In India, an estimated 500-550 million tonnes of crop residues are produced annually, with nearly 90-140 million tonnes disposed of through open-field burning, leading to the emission of harmful pollutants and deterioration of environmental quality (NAAS, 2014) [11,6,1,17] . Adoption of improved residue management and sustainable land-use practices can reduce emissions, enhance soil health, and support carbon credit opportunities (Gopinath et al., 2025) [9] . Among various mitigation approaches, carbon sequestration, which involves storing atmospheric carbon in soil and plant biomass, has emerged as an important strategy for improving soil productivity, strengthening ecosystem resilience, and promoting environmental sustainability www.extensionjournal.com (IPCC, 2021) [11] . Sugarcane cultivation possesses considerable potential for both biomass and soil carbon sequestration. However, the influence of farmers' personal, socio-economic, communication, and psychological characteristics on their engagement in carbon sequestration practices remains underexplored. This knowledge gap limits the formulation of effective extension and policy interventions for promoting such practices. Therefore, an understanding of the profile characteristics of sugarcane growers is essential for developing targeted strategies to enhance the adoption of carbon sequestration techniques.

Objective: To study the profile of sugarcane growers.

Methodology

The study was conducted in the South Gujarat region of Gujarat, India, under the jurisdiction of Navsari Agricultural University. South Gujarat is a major sugarcane-growing region characterized by high rainfall, diversified cropping systems, and a well-established sugar industry, making it suitable for the present investigation. An ex post facto research design (Anand et al., 2024; Patel et al., 2025) was adopted, as the variables under study had already occurred and were beyond the researcher's control (Kerlinger, 1976). [2, 19, 14] The study employed a multistage purposive random sampling technique. Bharuch, Surat, and Tapi districts were purposively selected due to their larger area under sugarcane cultivation. Subsequently, two talukas from each district and three villages from each taluka were selected randomly. In the final stage, 15 sugarcane growers were selected at random from each village, constituting a total sample of 270 respondents. The sample size was estimated using the finite population formula suggested by Arkin and Colton (1963) [3] , assuming a 95 per cent confidence level and a 5 per cent margin of error. The calculated sample size was 277; however, considering practical constraints, the final sample was fixed at 270 respondents. Primary data were collected during the agricultural year 2024-26 through personal interviews using a pre-tested structured interview schedule. The collected www.extensionjournal.com data were coded, tabulated, and analyzed using appropriate statistical tools to achieve the objectives of the study.

Result and Discussion

1. Profile of sugarcane growers 1.1 Age (X 1 )

Age refers to the chronological age of respondents in completed years at the time of investigation and is an important personal characteristic influencing farming experience, decision-making, risk-taking ability, and adoption of improved agricultural practices. The data presented in [13,25,18,20] .

Education (X2)

Education refers to the extent of formal schooling completed by the respondents at the time of investigation and is an important factor influencing their knowledge, comprehension, and decision-making ability. [13,25,12] .

Farming Experience (x3)

Farming experience refers to the cumulative years of engagement in agricultural activities, reflecting the practical knowledge and skills acquired through farming. It was measured by aggregating respondents' years of experience in sugarcane cultivation, trash mulching, FYM/press mud application, green manuring, and drip irrigation. Since respondents could practice multiple activities simultaneously, the cumulative experience score could exceed their actual age. [12,7] .

Landholding (X4)

Landholding refers to the total area of agricultural land owned and cultivated by the respondents, including irrigated, rainfed, plantation/garden, wasteland, agroforestry, and other land-use categories, expressed in acres. The data presented in Table 5 indicate considerable variation in landholding across different land-use categories. A majority of respondents possessed marginal holdings under rainfed (75.93%), plantation/garden (89.63%), wasteland (96.30%), agroforestry (94.81%), and other land categories (93.70%). In contrast, irrigated land was relatively better distributed, with the highest proportion of respondents falling under the small (31.48%) and semimedium (28.89%) categories. Considering total operational landholding, the largest proportion of respondents belonged to the medium category (42.59%), followed by semimedium (33.70%), small (16.30%), large (4.81%), and marginal (2.59%) categories. The findings suggest that although holdings under individual land-use categories were generally small, respondents possessed relatively larger aggregate operational holdings. The predominance of medium and semi-medium farmers indicates a favourable resource base for adopting improved agricultural practices, including carbon sequestration measures, as larger operational holdings often provide greater flexibility for implementing conservation-oriented interventions. The low proportion of marginal farmers in terms of total landholding further reflects the commercial nature of sugarcane cultivation in the study area.

Area Under Sugarcane Cultivation (X5)

Area under sugarcane cultivation refers to the total area cultivated with sugarcane by a respondent during the reference cropping season, including both self-owned and leased-in land, measured in acres and classified into marginal, small, semi-medium, medium, and large categories. The data presented in Table 6 reveal that the largest proportion of respondents (34.07%) belonged to the semi-medium category in terms of total area under sugarcane cultivation, followed closely by the medium (33.33%), small (17.41%), marginal (12.59%), and large (2.59%) categories. Together, semi-medium and medium farmers accounted for more than two-thirds (67.40%) of the respondents. With respect to self-owned sugarcane area, the highest proportion of respondents (36.67%) also fell under the semi-medium category, whereas leased-in sugarcane area was predominantly concentrated in the marginal category (77.78%). [24,10,4] .

Annual Income (X7)

Annual income refers to the total income earned by the respondents from all sources during a year and serves as an indicator of their economic status and capacity to invest in agricultural activities. [15,16] .

Dependency Ratio (X8)

Dependency ratio refers to the proportion of dependent family members to working members in a household and indicates the economic burden on earners, influencing household decision-making and resource allocation. [23] .

Extension Contact (X9)

Extension contact refers to the frequency and extent of respondents' interaction with extension personnel and agencies, reflecting their access to agricultural information, technical guidance, and advisory services. [13] .

Social Participation (X10):

Social participation refers to the extent of respondents' involvement in formal and informal organizations and reflects their level of social interaction, information sharing, and participation in collective decision-making processes.

The data presented in Table 11 reveal that the highest proportion of respondents (36.67%) belonged to the low social participation category, followed by medium (31.85%), high (14.81%), very low (9.26%), and very high (7.41%) categories. Overall, 77.78 per cent of the respondents fell within the very low to medium social participation categories. The predominance of low to medium social participation indicates limited involvement of respondents in formal and informal organizations. Such limited participation may reduce opportunities for information exchange, collective learning, and exposure to new agricultural innovations. Consequently, the dissemination and adoption of practices such as carbon sequestration may be slower among farmers with lower levels of social engagement. The findings are in accordance with those reported by Hebsale Mallappa and Pathak (2023) [10] .

Innovative Proneness (X11):

Innovative proneness refers to the tendency of respondents to accept and adopt new ideas, technologies, and practices ahead of others. It reflects an individual's willingness to experiment with innovations, openness to change, and readiness to take calculated risks in pursuit of improved agricultural productivity and sustainability. Farmers with higher innovative proneness are generally more likely to seek new information and adopt emerging agricultural technologies. This may be due to their preference for proven practices and cautious approach towards innovations that involve uncertainty or risk. However, the presence of a sizeable proportion of farmers in the high and very high categories (36.29%) suggests the existence of a progressive segment that may serve as early adopters and facilitate the diffusion of emerging technologies, including carbon sequestration and climate-smart agricultural practices. These findings are consistent with those reported by Hebsale Mallappa and Pathak (2023) [10] .

Management Orientation (X12)

Management orientation refers to the ability of respondents to effectively plan, organize, coordinate, and utilize farm resources for achieving desired agricultural outcomes. It reflects their managerial competence in making informed decisions and optimizing the use of available resources. The data presented in Table 13 indicate that the highest proportion of respondents (33.33%) belonged to the high management orientation category, followed by very low (28.52%), medium (16.67%), low (16.30%), and very high (5.19%) categories. Overall, 55.19 per cent of the respondents exhibited medium to high levels of management orientation. The findings suggest that a substantial proportion of sugarcane growers possessed the ability to effectively plan and manage farm resources, which may facilitate informed decision-making and efficient farm operations. However, the considerable proportion of respondents in the very low management orientation category indicates that managerial competencies were not uniformly distributed across the farming community. Farmers with higher management orientation are generally better positioned to evaluate and adopt resource-conserving innovations, including carbon sequestration and climate-smart agricultural practices. The findings conform with those reported by Suresh et al. (2018) and Pawar and Devendrappa (2022) [24,25] .

Decision-Making Ability (X13)

Decision-making ability refers to the capacity of respondents to make timely, appropriate, and effective decisions related to farm management and agricultural activities. It reflects their ability to evaluate alternatives, solve problems, and select suitable courses of action under varying farm situations. The data presented in Table 14 reveal that the highest proportion of respondents (37.04%) belonged to the high decision-making ability category, followed by medium (30.00%), very high (15.93%), low (11.48%), and very low (5.56%) categories. Overall, 82.97 per cent of the respondents exhibited medium to very high levels of decision-making ability.

The predominance of medium to high decision-making ability indicates that most respondents were capable of making informed and timely farm management decisions. Such ability may help farmers evaluate alternatives, allocate resources efficiently, and respond effectively to changing agricultural conditions. Farmers with better decision-making skills are generally more likely to assess the benefits of innovative practices and adopt improved technologies, including carbon sequestration and climate-smart agricultural interventions. The findings conform with those reported by Rathod et al. (2012) [21] .

Scientific Orientation (X14)

Scientific orientation refers to the extent to which respondents exhibit a rational outlook, rely on scientific principles, and prefer evidence-based agricultural practices. It reflects their tendency to understand cause-and-effect relationships, evaluate technologies objectively, and adopt improved innovations, including carbon farming and other climate-smart agricultural practices. www.extensionjournal.com The data presented in Table 15 indicate that the highest proportion of respondents (50.37%) belonged to the high scientific orientation category, followed by very high (18.52%), medium (17.41%), and low (13.70%) categories. None of the respondents fell under the very low scientific orientation category. Overall, more than two-thirds (68.89%) of the respondents exhibited high to very high scientific orientation. The predominance of high scientific orientation suggests that most sugarcane growers possessed a favourable outlook towards scientific farming and evidence-based agricultural practices. This may be attributed to their exposure to modern cultivation practices, extension services, and the commercial nature of sugarcane farming in the region. Farmers with higher scientific orientation are generally more receptive to improved technologies and better equipped to understand the benefits of innovations such as carbon sequestration and climate-smart agricultural practices. The findings are in line with those reported by Pawar and Devendrappa (2022) [20] .

Risk Preference (X15)

Risk preference was operationalized as the degree to which respondents are willing to take risks in adopting new agricultural practices and technologies. It plays a crucial role in influencing innovation adoption, especially for practices with uncertain outcomes such as carbon farming interventions. The data presented in Table 16 reveal that the majority of respondents (55.93%) belonged to the low risk preference category, followed by medium (29.26%), very low (7.04%), high (6.30%), and very high (1.48%) categories. Overall, 92.23 per cent of the respondents fell within the very low to medium risk preference categories. The predominance of low-risk preference indicates that most respondents were cautious in their approach to agricultural decision-making and were less inclined to undertake practices involving uncertainty. Such risk-averse behaviour may limit the adoption of relatively new and unfamiliar innovations, including carbon sequestration and climate-smart agricultural practices. The small proportion of respondents with high-risk preferences suggests that only a limited segment of farmers is willing to experiment with novel technologies and management practices. The findings conform with those reported by Bhairagond et al. (2024) [5] .

Climate Change Perception (X16)

Climate change perception was operationalized as the extent to which respondents recognize and understand changes in climate patterns and their impact on agriculture. It influences farmers' adaptive behaviour and willingness to adopt climate-resilient practices. [22] .

1.17 Ecological Conservation Perception (X17) Table 5.17 indicates that an overwhelming majority of the respondents (85.93%) belonged to the low ecological conservation perception category, followed by 7.04 per cent in the very low, 4.07 per cent in the high, 1.85 per cent in the medium, and only 1.11 per cent in the very high category. The findings suggest that although a substantial proportion of farmers have adopted certain climate-resilient practices, the overall level of application remains moderate. This may be attributed to constraints such as limited technical knowledge, resource and financial limitations, and uncertainty regarding the benefits of such practices. The relatively smaller proportion of high and very high adopters highlights the need for strengthened extension support, capacity-building initiatives, and enabling policy measures to promote wider adoption of climate-resilient farming practices. The findings conform with those reported by Fadina and Barjolle (2018) [8] .

Conclusion

The study revealed that most sugarcane growers were middle-aged to older farmers with moderate educational attainment, considerable farming experience, and small to medium landholdings. Although sugarcane cultivation formed a major component of their farming system, the majority of respondents had low annual income and limited livestock resources, indicating moderate economic capacity.

The findings further showed that most farmers possessed low to medium levels of extension contact, social participation, and innovative proneness. However, higher levels of scientific orientation, management orientation, and decision-making ability suggest their potential to adopt improved agricultural technologies when provided with adequate support. The predominance of low-risk preference indicates reluctance toward adopting unfamiliar practices without assurance of benefits. Regarding climate-related aspects, most respondents exhibited a medium level of climate change perception, reflecting awareness of climatic changes and their impacts on agriculture. However, ecological conservation perception remained largely low, and the application of climate-resilient practices was predominantly confined to low and medium levels. Overall, the study concludes that sugarcane growers possess the necessary cognitive and managerial attributes to adapt to climate challenges, but are constrained by limited resources, low risk-taking behaviour, and inadequate extension support.

Acknowledgement

AcknowledgementsThe authors sincerely thank the members of the Research Advisory Committee and the Department of Agricultural www.extensionjournal.com Extension and Communication, Navsari Agricultural University, Navsari, for their valuable guidance and support. We also gratefully acknowledge the financial assistance provided under the Scheme of Developing High Quality Research (SHODH), Government of Gujarat, and extend our appreciation to the farmers of the selected villages and the local administration for their cooperation during the study.• Conflict of Interest: The authors declare that they have no conflict of interest.

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