Coastal agriculture in India, where it serves as an important economic pillar, operates under escalating climate variability, fragmented landholdings, and risks in marketing and socio-economic conditions. Integrated farming systems (IFS) provide a system-based approach for smallholder farmers to improve resource-use efficiency, diversify income streams, and become more resilient to climatic conditions and other forms of risk. This study outlines the current status and determinants of IFS adoption in coastal Odisha using primary data gathered from 208 farm respondents selected through a multi-stage random sampling design across three coastal districts during 2020–2023. Principal Component Analysis included nine socio-economic variables that were used to describe five latent components that explained 86.67% of the cumulative variance. Probit regression analysis (McFadden’s pseudo-R2 = 0.712; 95.95% correctly classified) indicates that access to institutional credit (marginal effect = 0.286), education (0.174), market connectivity (0.213), labour availability, and family type significantly increase the adoption probability (p < 0.05), whereas age and operational landholding exert negative effects. Economic comparison revealed significant differences in gross income among farming systems for marginal farmers (F = 124.63, p < 0.01) and medium farmers (F = 9.89, p < 0.01), and in variable costs for marginal farmers (F = 247.57, p < 0.01) and small farmers (F = 15.56, p < 0.01). Tukey’s post-hoc analysis further confirmed relative economic differentiation among farming system models. The Crop–Livestock–Pisciculture–Resource Generating (vermicompost and FYM)–Mushroom model, integrated for additional income generation and resource recycling, demonstrated the highest economic performance, whereas Crop–Livestock systems were more suitable for smaller holdings. The findings underscore the need for targeted credit mechanisms, specially designed location specific extension services, and promoting region-specific IFS models aligned with agro-ecological conditions and farm-size categories for enhancing livelihood resilience and aligning with broader sustainability targets.
Pattanaik et al. (Wed,) studied this question.