Farm diversification a key livelihood strategy for employment generation and socio-economic improvement for the farmers in west Jaintia hills, Meghalaya

Introduction

Agriculture is the primary source of livelihood for about 58% of India's population (M. Satheesh, 2025) [7] . Farming systems in India are strategically utilized, according to the locations where they are most suitable. Most Indian farmers practice subsistence farming, producing mainly for household consumption with little or no surplus for the market. In Meghalaya, approximately 83% of the tribal population is engaged in agriculture, predominantly practicing shifting cultivation and terrace (bun) farming (Solomon et. al, 2005) However, these traditional practices often result in soil erosion, land degradation, loss of biodiversity, deforestation, and increased carbon emissions, thereby contributing to ecological imbalance and climate change (Manikant Kumar, 2023) [9] . In addition, the increasing population pressure, fragmented landholdings, and climatic uncertainties have made traditional farming systems less sustainable. Farm diversification has emerged as a key strategy to enhance income, reduce risks, and improve livelihood security. Meraner, Heijman, Kuhlman, and Fingre (2015) [10] define "diversification" as reallocating farm resources from traditional activities to generate alternative income sources. Agricultural diversification indicates the changes in crop-mix, enterprises-mix and activity-mix at household level (Chand, 1999) [1] . It not only enhances income but also contributes to food security, employment generation, and resilience against economic and environmental shocks. The NE offers a conductive environment for diverse farming systems, including upland paddy, vegetables, and livestock. In Meghalaya, the state's favorable climate and natural resources offer significant potential for producing a variety of fruits, flowers, and medicinal plants, while also supporting agriculture and livestock rearing to ensure food security. Backyard piggery and poultry farming are widely practiced in the state and serve as crucial supplementary income sources, particularly for economically vulnerable households. These practices are especially significant in drought-prone, hilly, and remote areas where conventional agriculture alone may not be sufficient to sustain livelihoods. Leveraging these resources for sustainable practices is essential for their economic stability. In this context, the present study was undertaken to analyze the socio-economic characteristics of livestock farmers and examine the role of farm diversification in improving their livelihood systems and socio-economic status in West Jaintia Hills district of Meghalaya.

Materials and Methods

The study was conducted in Laskein and Thadlaskein blocks of West Jaintia Hills district of Meghalaya. A total of 120 respondents (60 from each block), having 2-3 livestock units along with backyard poultry and agricultural land, were selected using purposive sampling. Data were collected through personal interviews with the help of a structured schedule. Both independent and dependent variables were included in the study. Independent variables comprised socioeconomic characteristics such as age, gender, family size, education, landholding, herd size, farming experience, social participation, mass media exposure, extension contact, and income. The dependent variables included factors affecting farm diversification, its effect on livelihood, and its impact on socio-economic status. To assess factors affecting farm diversification, a total of 20 potential factors were considered. These included age, number of working household members, education level, gender, family size, farming experience, skill development, herd strength, marital status, access to extension services, availability of market information, resource ownership, communication effectiveness, relationship with rural institutions, reliability of electricity supply, environmental changes, participation in training programmes, access to institutional credit, improved marketing facilities, and overall agricultural experience. For evaluating the effect of farm diversification on livelihood, 15 indicators were developed covering economic, social, environmental, and technological dimensions. These included income enhancement, food security, risk mitigation, resource-use efficiency, value addition, sustainable practices, community engagement, women empowerment, and technology adoption. Similarly, to assess the impact on socio-economic status, 15 indicators were identified, including employment generation, income diversification, consumption patterns, resilience to shocks, market participation, decision-making ability, poverty reduction, and rural development. Data were collected during April, July, and October 2024 and analyzed using descriptive statistics and correlation coefficients to examine relationships between variables.

Results

Socio-Economic Profile of Respondents

The majority of respondents belonged to the middle age group (56.66%) and were predominantly male (58.33%). Most respondents were married (91.66%) and lived in nuclear families (97.50%) with medium family size (70.00%). Educational attainment was generally at the middle school level (33.33%). Landholding and herd size were mostly of medium category, indicating moderate resource availability. A significant proportion of respondents had medium levels of farming experience, social participation, mass media exposure, and extension contact. Income levels were also predominantly moderate, reflecting the subsistence-oriented nature of farming systems in the region.

Factors Affecting Farm Diversification

The data in Fig. 1 revealed that identified institutional and economic factors as the most influential determinants of farm diversification. Access to institutional credit emerged as the most critical factor, with nearly all respondents acknowledging its importance. Other major influencing factors included farming experience, availability of market information, improved marketing facilities, and resource ownership. In contrast, demographic variables such as marital status and education were perceived as less influential. The data in table 1 showed that the mean scores of factors affecting farm diversification were 50.63 (Laskein), 45.30 (Thadlaskein), and 47.97 (pooled), with standard deviations of 4.35, 12.56, and 9.74, respectively. Most respondents fall under the medium category in all cases and the difference between the two blocks was highly significant (Z = 3.10**).

Effect of Farm Diversification on Livelihood System:

The data in Fig. 2 revealed that Farm diversification was found to have a positive effect on the livelihood systems of respondents. A large proportion reported increased household income and improved access to nutritious food. Diversification also created opportunities for supplementary income through agritourism and value-added enterprises. It helped farmers mitigate risks associated with price fluctuations and adverse climatic conditions, while promoting efficient resource utilization and sustainable agricultural practices. Social benefits were also evident, including increased awareness, improved communication, enhanced political participation, and greater community involvement. Importantly, diversification contributed to women empowerment by providing opportunities beyond traditional roles. The data in Table 2 showed that the mean effect of farm diversification on livelihood systems was 40.22 (Laskein), 37.97 (Thadlaskein), and 39.09 (pooled), with standard deviations of 2.75, 6.60, and 5.16, respectively. Most respondents fell in the medium category across all groups.

The difference between the two blocks was statistically significant (Z = 2.43*).

Impact on Socio-Economic Status

The data in fig. 3 The data in table 3 show that the mean impact of farm diversification was 40.28 (Laskein), 37.57 (Thadlaskein), and 38.93 (pooled). Most respondents fell under the medium category in all cases. The difference between the two blocks was highly significant (Z = 2.77**).

Discussion

The study revealed that most farmers were middle-aged, reflecting a balance of experience and active involvement in agriculture. Similar findings were observed by O.T. Olukunle, 2021 [12] and Nanda et al., 2022 [11] . Male dominance was evident due to their role as household heads, while most respondents were married and lived in medium-sized nuclear families, consistent with the finding of Gummagolmath et al., 2020 [4] . Farmers generally had medium levels of education, landholding, herd size, and farming experience, indicating semi-subsistence systems influenced by land fragmentation and backyard livestock practices which is similar with the findings of Ume et al., 2020 [14] and Kumar et al., 2018 [9] . Moderate levels of social participation, mass media exposure, extension contact, and income suggest limited but improving access to information, infrastructure, and institutional support. Similar findings were reported by Nongtdu et al.,2012 [3] . Farm diversification was influenced by socio-personal, economic, institutional, and environmental factors, including age, family size, resource ownership, credit access, extension services, and market facilities, consistent with Kassie et al., 2017 [6] . Diversification positively affected livelihoods by enhancing income, food security, and resilience while promoting sustainable practices and skill development, consistent with Heumesser & Kray, 2019 [5] . It also improved socio-economic status through employment generation, better consumption patterns, and reduced vulnerability to shocks, contributing to rural development and poverty reduction, similar with the findings of Dev et al., 2017 [2] .

Correlation Analysis Between Independent and Dependent Variables

Laskein Block Correlation analysis revealed that family size (r = 0.257), *landholding (r = 0.251), and years of experience (r = 0.254) were positively and significantly associated with farm diversification, indicating that labour availability, resource base, and farming knowledge enhance diversification. Extension contact (r = 0.334)** showed a strong positive influence on impact, highlighting the role of institutional support. In contrast, total annual income (r = -0.250)* and income from livestock and poultry (r = -0.254)* were negatively correlated with the effect of diversification, suggesting that households with higher or alternative income sources rely less on diversification. These findings are in line with Gummagolmath et al., 2020 [4] .

Thadlaskein Block

In Thadlaskein Block, age (r = 0.268)* was positively and significantly related to the impact of diversification, indicating that middle-aged farmers benefit more due to experience and stability. However, landholding (r = -0.297)* and mass media exposure (r = -0.293)* showed negative significant relationships, implying that smaller farmers diversify more intensively while media exposure may shift focus away from agriculture. Further, total annual income (r = -0.428), livestock income (r = -0.429, -0.460), and monthly income (r = -0.415) were negatively correlated, suggesting that higher-income households depend less on farm diversification. Similar trends were reported by O.T. Olukunle,2021 [12] .

Pooled Data

The pooled analysis showed that *age (r = 0.239), *family size (r = 0.250), *landholding (r = 0.268), *herd size (r = 0.299), experience (r = 0.251)**, and extension contact (r = 0.256) were positively and significantly associated with farm diversification. Additionally, total annual income (r = 0.264), *livestock income (r = 0.264), and monthly income (r = 0.261) also showed positive relationships, indicating that better economic status enhances the capacity to adopt and benefit from diversification. These findings agree with the findings of Mandavkar et al., 2020 and Akriti et al., 2023.

Conclusion

References

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