Research Articles
Vol. 13 No. sp2 (2026): Recent Advances in Agriculture
Estimating the farmers’ acceptance of phygital services under the mobile agri support services (MASS) initiative: A difference-in-differences approach
Department of Agricultural Extension, Professor Jayashankar Telangana Agriculture University, Hyderabad 500 030, Telangana, India
Extension Education Institute, Professor Jayashankar Telangana Agriculture University, Hyderabad 500 030, Telangana, India
Evergreen Energy Enterprises IP Limited, Hyderabad 500 082, Telangana, India
Abstract
Effective agricultural transformation in India requires inclusive models that can connect small and marginal farmers to timely knowledge, tools and services. Despite growing digital infrastructure, last-mile delivery remains a major challenge due to socio-economic and infrastructural barriers. This study evaluates the mobile agri support services (MASS) intervention of a phygital model integrating physical and digital extension tools to improve farmer engagement and adoption of agri-technologies in selected aspirational districts of Telangana. Mobile agri support services is a unique 'phygital' (physical + digital) platform tested for a period of one year (2nd June 2023 to 31st July 2024). A quasi-experimental research design with treatment and control groups was adopted. A total of 1000 farmers were selected from three aspirational districts of Telangana: Kumuram Bheem Asifabad, Jayashankar Bhupalpally and Bhadradri Kothagudem. A difference-in-difference (DID) approach and fixed-effects regression measured changes in farmers’ attitudes before and after the intervention. Results showed that before the intervention, over 85 % of farmers in both groups exhibited a low attitude toward phygital services and had little awareness of digital agricultural platforms. After the MASS intervention, 80–90 % of beneficiary farmers shifted to the high-attitude category, whereas 88–92 % of farmers in the control group remained in the low-attitude category. The DID analysis indicated a net increase of 78.11 units in attitude scores, with the interaction effect statistically significant (p<0.01). Regression results further revealed that education, extension participation, media exposure, risk-taking ability and decision-making behaviour significantly influenced the adoption of phygital services. By combining mobile field demonstrations, expert interaction, soil testing, drone demonstrations and digital advisory platforms, the intervention effectively bridged the last-mile gap in agricultural extension. The findings highlight that phygital service delivery systems can serve as scalable and inclusive models for strengthening agricultural extension, improving technology adoption and supporting digital transformation in rural agriculture.
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