Asian Journal of Agricultural Extension, Economics & Sociology

Background: The advent of Smart Farming Technologies (SFTs) has revolutionised the agricultural sector in recent years. With the increasing penetration of smartphones across rural India, farmers now have greater access to extension services, daily market prices, market linkages, and other farming-related information. However, data relating specifically to SFT adoption in India remains limited. The only major secondary data source available is the 77th Round of the National Statistical Office (NSSO), which provides socio-economic and demographic information on farmers’ access to smartphone app-based digital extension services (in the Indian context, smartphone app-based digital extension services can be considered a proxy for the adoption of SFTs).

Aim: This study attempts to identify the socio-economic and demographic factors influencing farmers’ access to smart farming technologies, using smartphone app-based farming information services.

Study Design: The study uses the NSSO 77^th round, which is of Situational Assessment of Agricultural Households and Land and Livestock Holdings of Households in Rural 2019 (Ministry of Statistics & Programme Implementation, GOI, 2021). The study is based on 2,049 observations extracted from the NSSO database, filtered specifically to capture households utilizing digital extension services.

Methodology: The study uses a binary logit model with 0 and 1 categories for the dependent variable: 1 for farmers having access to smartphone app-based information and 0 for farmers having no access to smartphone app-based information.

Results: The marginal effect in the result shows that access to smartphone-based extension services is positively influenced by landholding size (0.0096), insurance coverage (0.105), education, female headship, and income (very negligible), while area under irrigation (−0.122) and belonging to socially disadvantaged groups (SC, OBC, others) reduce the likelihood of access. The Pseudo R-squared is 0.74, which suggests a strong model fit.

Conclusion: The results highlight that resource ownership, institutional access, and education significantly improve digital inclusion, whereas structural and social disparities continue to limit farmers’ access to smartphone-based extension services. This will be helpful for agriculturalists, policy makers and the government to frame farmer-centric policies for technology access and adoption.