Informal social network attributes influencing climate-smart agricultural practices among farmers in Meghalaya

Introduction

Agriculture remains the backbone of global food and livelihood security and is expected to play a critical role in feeding the projected world population of nearly 9.7 billion by 2050. Global food demand is anticipated to increase by approximately 60-70 per cent over the coming decades, thereby exerting enormous pressure on agricultural systems, natural resources, and rural livelihoods (FAO, 2023) [7] . Simultaneously, Climate Change (CC) has emerged as one of the most serious global challenges affecting agricultural sustainability, environmental stability, and socio-economic development. According to the Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6), global surface temperature is projected to exceed 1.5°C during the twenty-first century unless substantial mitigation measures are implemented urgently (IPCC, 2023). Such climatic disruptions are expected to severely impact crop yields, water availability, biodiversity, and rural livelihoods, particularly in developing countries with limited adaptive capacity. India is considered one of the most climatevulnerable countries due to its high dependence on climatesensitive sectors such as agriculture. Recent climate assessments indicate that India continues to remain among the countries most affected by extreme weather events globally (Germanwatch, 2025) [9] . Irregular monsoon patterns, rising temperatures, prolonged dry spells, and frequent floods have increasingly affected agricultural productivity, soil health, water resources, and farm income (Jatav et al., 2024) [13] . Recent reports have highlighted that climate variability may reduce agricultural incomes by 15www.extensionjournal.com 25 per cent in vulnerable rainfed regions if effective adaptation strategies are not implemented (Government of India, 2024) [10] . Moreover, increasing incidences of pest outbreaks, soil degradation, and declining groundwater resources have intensified production risks and threatened food security in several states of the country. The northeastern region of India, particularly Meghalaya, is highly vulnerable to climate-induced agricultural risks due to its fragile hill ecosystems, predominantly rainfed agriculture, shifting cultivation practices, and socioeconomic limitations. Meghalaya receives one of the highest annual rainfall levels in the world; however, recent climatic irregularities, including erratic precipitation, landslides, soil erosion, flash floods, and rising temperatures, have adversely affected agricultural production and livelihood security (Lyngdoh et al., 2025) [15] . Agriculture contributes substantially to the state economy, and nearly 80 per cent of the rural population depends directly or indirectly on agriculture and allied activities for sustenance. Recent vulnerability assessments conducted in Meghalaya revealed that several districts and blocks fall under high to very high climate vulnerability categories due to poor infrastructure, low adaptive capacity, declining forest cover, and increasing climatic stresses (Lynrah et al., 2025) [16] . These climatic challenges have increased the urgency for resilient agricultural planning and localized adaptation strategies in Meghalaya (Wahlang et al., 2026) [20] . Climate Change not only affects agricultural production systems but also creates broader socio-economic consequences, including food insecurity, migration, livelihood instability, and water scarcity. Developing countries are disproportionately affected because of inadequate institutional support, poor access to technology, and limited financial resources for adaptation (Mekonnen & Hoekstra, 2023) [17] . In this context, Climate-Smart Agriculture (CSA) has emerged as a sustainable and strategic approach for addressing climaterelated agricultural challenges. CSA integrates sustainable agricultural practices that improve productivity and income, enhance resilience and adaptation to climate change, and contribute to mitigation by reducing greenhouse gas emissions (FAO, 2024) [8] . Climate-smart practices such as crop diversification, integrated farming systems, agroforestry, conservation agriculture, efficient water management, climate-resilient crop varieties, integrated nutrient management, and digital agro-advisory services are increasingly being promoted worldwide to strengthen agricultural resilience (Lipper et al., 2022) [14] . In India, several governmental and institutional initiatives, including the National Innovations in Climate Resilient Agriculture (NICRA), Paramparagat Krishi Vikas Yojana (PKVY), and digital agricultural extension platforms, have been introduced to encourage CSA adoption among farmers. However, the adoption rate of CSA practices among smallholder farmers remains uneven due to inadequate awareness, weak extension services, socio-economic barriers, poor access to information, and institutional constraints (Aryal et al., 2023) [1] . Farmer decision-making regarding climate adaptation and CSA adoption is often influenced by social interactions, knowledge exchange, trust networks, and institutional linkages. In recent years, Social Network Analysis (SNA) has gained increasing importance in understanding agricultural knowledge dissemination, farmer-to-farmer learning, and technology diffusion processes. SNA provides insights into how information flows among farmers, extension agencies, researchers, local institutions, and community organizations (Dympep et al., 2022) [6] . Informal Social Network Analysis (ISNA), in particular, examines informal relationships, communication patterns, and behavioural interactions within farming communities and organizations (Clifton & Webster, 2021) [4] . Such analyses help identify influential actors, information hubs, communication gaps, and collaborative structures that facilitate or hinder technology adoption and climate adaptation. Understanding these network dynamics is especially crucial in climate-vulnerable regions like Meghalaya, where indigenous knowledge systems, community interactions, and informal communication networks significantly influence farmers' adaptation behaviour (Springer & De Steiguer, 2022) [18] . Strengthening social networks and institutional collaborations can therefore accelerate dissemination of climate-resilient technologies, improve access to agricultural advisories, and enhance collective adaptive capacity among farming communities. Henceforth, this present study was undertaken to examine the ISN attributes and utilize key opinion leaders as bridge actors in these villages, also the study focussed extent of similarity of information seeking behaviour of respondents under adopted villages of CPGS-AS [CAU, Imphal], and ICAR RC for NEH Region.

Materials and Methods

Owing to the current execution of Mera Gaon Mera Gaurav programme by the two central institutes under Ministry of Agriculture and Farmers' Welfare, Govt. of India, namely (1) College of Post Graduate Studies -Agricultural Sciences (CAU, Imphal), and Indian Council of Agricultural Research-Research Centre for North East Hill Region at Umiam -793 103, Ri-bhoi district of the state Meghalaya, this state and district had been selected purposively for the scientific pursuit. As the Umsning block has contiguous agriculturally important villages which had been adopted under Mera Gaon Mera Gaurav (MGMG) by both the institutes of College of Post Graduate Studies in Agricultural Sciences (CAU, Imphal) and ICAR Research Complex for North East Hill Region were selected purposively in the scientific study. Out of the 21 adopted villages of Umsning Block under MGMG by the CPGS-AS, three most agriculturally important villages namely, (1) Lairkhla, (2) Khweng & (3) Palwi; as well as three utmost agriculturally significant villages out of 16 adopted villages of Umsning Block under MGMG by the ICAR RC for NEH Region namely (4) Thadnongiaw, (5) Kdonghulu & (6) Mawbri had been purposively selected for the scientific interrogatory. A key farmer who strictly followed CSAPs was identified in each of the identified villages, and implying the snowball sampling procedure, nine other farmers from the respective villages had been selected in order to constitute ten respondents from each village. Thereby, sixty respondents had been determined as sample of the study. Friedman Test was conducted to examine if there was any significant difference in information seeking and sharing of CSAPs by respondents in domains of Crop Improvement (CI), Agricultural Advisory Services (AAS), www.extensionjournal.com Natural Resource Management (NRM) and Plant Protection (PP) within the six MGMG adopted villages. The data collected were coded, tabulated and analysed in accordance with the objectives of the study using appropriate statistical tools. The software used for data analysis included the R, SPSS version 25.0 and MS Excel 2010.

Results and Discussion

The integration of Informal Social Networks with modern analytical software and visualization tools enables researchers to examine network characteristics such as centrality, density, cohesiveness, and tie strength among stakeholders involved in agricultural systems [6] , Valente (1996) [19] , Borgatti et al. (2018) [3] who observed that cohesive farmer networks enhance agricultural knowledge sharing and technology adoption. However, the results differ from those of Conley and Udry (2010) [5] , Isaac et al. (2007) [12] , and Bandiera and Rasul (2006) [2] , who reported significant variations in information flow and innovation diffusion across communities due to heterogeneous social structures and socio-economic differences. The nonsignificant Friedman test in the present study suggests relatively homogeneous information-seeking behaviour among the selected villages.

Conclusion

The study highlighted the importance of informal social networks in facilitating information exchange related to Climate-Smart Agricultural Practices (CSAPs) among farmers in Ri-Bhoi district of Meghalaya. The findings from Social Network Analysis all six villages using indicators such as degree centrality, eigenvector centrality, www.extensionjournal.com betweenness centrality revealed Khweng and Thadnongiaw villages emerged as strategically influential networks, while Kdongulu demonstrated the strongest cohesiveness and communication efficiency. The Friedman test indicated no significant differences in network characteristics across the six villages, suggesting similar information-seeking and sharing behaviour among respondents. Nonetheless, study emphasized the need to strengthen farmer-led networks and engage key opinion leaders and bridge actors to enhance dissemination, adoption, and sustainability of climate-smart agricultural practices in Meghalaya.

Acknowledgement

AcknowledgementThe cooperation and valuable participation of the farmers from the selected MGMG-adopted villages are gratefully acknowledged. The authors are also thankful to all field functionaries and respondents for their assistance during data collection.

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