Mapping of integrated farming system diversity and predominant farming models in Cooch Behar, West Bengal

Introduction

Agriculture continues to be the primary source of livelihood for a majority of rural households in India, particularly in eastern states like West Bengal. However, increasing land fragmentation, climate variability, market uncertainties, and declining farm profitability have made farming a risky enterprise, especially for small and marginal farmers. In this context, enhancing livelihood security-which encompasses food security, income stability, employment generation, and resilience to shocks-has emerged as a central goal of agricultural development (Chambers & Conway, 1992) [2] . The Integrated Farming System (IFS) approach has been widely recognized as an effective strategy to address these challenges by promoting diversification and optimal use of farm resources. IFS involves the integration of multiple interrelated farm enterprises such as crops, livestock, fisheries, poultry, horticulture, and agroforestry in a mutually reinforcing manner (Behera & France, 2016) [1] . By recycling resources within the farm and reducing dependency on external inputs, IFS enhances productivity, income, employment opportunities, and environmental sustainability (Jayanthi et al., 2003) [4] . Several studies have demonstrated that greater farming system diversity contributes positively to livelihood security by spreading risk, ensuring year-round income, and improving nutritional security of farm households (Singh et al., 2012; Rautaray et al., 2019) [7,6] . Diversified farming systems are particularly suitable for smallholders, as they enable efficient utilization of limited land, labor, and capital resources while improving resilience against climatic and economic shocks (FAO, 2014) [3] . West Bengal, characterized by diverse agroecological situations, high population density, and predominance of small and marginal farmers, provides a conducive environment for the adoption of varied Integrated Farming System models. Farmers in the state practice a range of IFS combinations involving crops, livestock, fisheries, and allied enterprises based on resource availability, market access, and socio-economic conditions (Mondal et al., 2018) [5] . However, the nature, composition, and extent of diversity among these IFS models vary widely across regions and households. Although numerous studies have highlighted the benefits of IFS, systematic documentation and analysis of the different IFS models followed by farmers and their linkage with livelihood security remain limited, particularly in the context of West Bengal. Understanding the existing IFS models is essential for identifying successful combinations, scaling up best practices, and designing location-specific interventions. Therefore, the present study was undertaken to identify and analyze the different Integrated Farming System models practiced by farmers in West Bengal and to examine their relationship with the level of livelihood security.

Materials and Methods

The study was carried out in Cooch Behar district of West Bengal, specifically in the Cooch Behar-I, Cooch Behar-II, www.extensionjournal.com and Dinhata-II blocks. A multistage sampling design incorporating purposive and random sampling techniques was adopted for the selection of study areas and respondents. From the total of twelve blocks in the district, three blocks were selected through simple random sampling. Subsequently, two villages were randomly chosen from each selected block. Within each village, respondents were identified purposively based on insights obtained from a preliminary pilot survey, ensuring the inclusion of farmers practicing Integrated Farming Systems (IFS). In total, 175 IFS-adopting farmers from six villages across the three selected blocks constituted the sample for the study. Primary data were collected using a structured interview schedule administered through personal interviews. An open-ended questionnaire was constructed to get the data from the IFS farmers. They were asked to indicate the incorporated components at their farm. The components should be from agriculture and allied sectors like poultry, dairy, fishery etc. The collected data were subjected to statistical analysis using tools such as frequency, percentage, mean, standard deviation and coefficient of variation analysis. experience, and occupation. The findings revealed that a majority of the respondents were young farmers, followed by middle-aged and old-aged categories, with a mean age of 41.31 years. With respect to education, the mean score was 2.17, indicating that most of the respondents had attained college-level education or above, followed by higher secondary and primary education. Family size showed a mean value of 4.77, with respondents almost equally distributed between small and medium-sized families, while none belonged to the large family category. The dependency ratio recorded a mean score of 1.97, with most respondents falling under the low dependency category, followed by high and medium levels. Regarding family education status, the majority of respondents belonged to the medium category, followed by low and high levels, with a mean score of 2.74. Farming experience was predominantly high among the respondents, followed by low and medium levels, with a mean score of 2.02. Occupational distribution showed a mean score of 4.38, revealing that most farmers were engaged in farming along with animal husbandry and service, followed by farming with animal husbandry and business, and farming combined with animal husbandry alone. Socio-economic variables considered in the study included annual family income, annual family expenditure, landholding size, irrigation facilities, material possession, and animal possession. The mean score for annual family income was 2.51, with a majority of respondents belonging to the high-income category, followed by medium and low levels. The average landholding size was 4.15 acres, and most of the respondents were small farmers, followed by medium and marginal farmers. Annual family expenditure showed a mean score of 0.96, with a majority of respondents falling under the high expenditure category, followed by medium and low levels. In terms of material possession, the mean score was 19.02, and most respondents belonged to the medium category, followed by high and low levels. Similarly, animal possession was predominantly at a medium level among more than half of the respondents, followed by high and low levels. The irrigation status had a mean score of 2.86, with the majority of farmers categorized under medium irrigation status, followed by high and low levels.

Results and Discussion

The socio-psychological characteristics studied included credit-seeking behaviour, social participation, informationseeking behaviour, scientific orientation, risk orientation, economic orientation, innovativeness, and management efficiency. Credit-seeking behaviour recorded a mean score of 7.18, with most respondents exhibiting a medium level, followed by high and low levels. Information-seeking behaviour had a mean score of 18.84, with the majority of farmers falling under the medium category, followed by high and low levels. Social participation showed a mean score of 2.21, indicating that most respondents held membership with position in organizations, followed by those with membership in more than one organization, while a few had membership in a single organization. Scientific orientation had a mean score of 28.85, with the majority of respondents categorized under the medium level, followed by high and low levels. Risk orientation recorded a mean score of 28.91, with most respondents belonging to the medium category, followed by low and high levels.

Economic orientation showed a mean score of 29.52, with the majority of farmers falling under the medium category, followed by high and low levels. Innovativeness had a mean score of 1.99, indicating that most respondents belonged to the medium category, followed by low and high levels.

Management efficiency recorded a mean score of 60.87, with the majority of respondents exhibiting a medium level, followed by high and low levels. www.extensionjournal.com the results that majority of the farmers are taking dairy, fishery and poultry as the components of integrated farming system along with the agriculture. The possible reason behind it might be that, the high demand for milk products and meat of fish and chicken.

Conclusion

From the above results it is concluded that the Crop-based IFS models with fishery and dairy were the most commonly adopted, while more complex combinations with poultry, goat, duck, mushroom, rabbit, and apiculture were less frequent. The diversity of IFS practices reflects farmers' strategies to optimize resources, increase income, and enhance livelihood security. Promoting location-specific and diversified IFS models can further strengthen farm resilience and sustainable rural livelihoods.

References

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3. FAO. Building resilience for sustainable agriculture. Rome (Italy): Food and Agriculture Organization of the United Nations; 2014.
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6. Rautaray SK, Behera D, Swain P. Farming system diversification and livelihood security of smallholders. Journal of Farming Systems Research & Development. 2019;25(1):1-8.
7. Singh KM, Singh RKP, Kumar A. Livelihood security of farmers under different farming systems. Indian Journal of Extension Education. 2012;48(3&4):55-60.