Asian Journal of Agricultural Extension, Economics & Sociology

Agritech startup solutions have emerged as an important determinant of digital transformation in agriculture by improving farm decision-making, resource-use efficiency, and market connectivity. However, their adoption among small and medium-scale farmers remains uneven, particularly in developing regions where awareness, training, trust, and digital literacy vary considerably. This study examined farmers' adoption behaviour toward agritech startup solutions and assessed the influence of training and awareness programmes on adoption in Rajasthan, India.

An explanatory sequential mixed-methods research design was adopted. Primary quantitative data were collected from 360 farmers selected from Hanumangarh, Sri Ganganagar, and Bikaner districts using a structured questionnaire, followed by qualitative interviews, focus group discussions, and key informant interviews to support interpretation of the quantitative findings. Descriptive statistics, reliability analysis, exploratory factor analysis, analysis of variance, multiple linear regression, binary logistic regression, and structural equation modelling were employed for data analysis.

The analysis indicated that 187 farmers (51.9%) had adopted at least one agritech startup solution, whereas 173 (48.1%) remained non-adopters. Multiple linear regression explained only a small proportion of variance in adoption intensity (R² = 0.036, F(7,352) = 1.874, p = .073), and binary logistic regression showed a similarly limited explanatory power (Nagelkerke R² = 0.046, model χ² = 12.740, p = .079). Awareness was the only variable to reach statistical significance in the logistic model (p = .016), while Training Quality approached significance in the linear model (p = .050); Trust, Perceived Usefulness, Social Influence, Perceived Risk, and Perceived Ease of Use were not significant predictors of adoption. Farmers indicated the highest awareness of weather–pest advisory services, followed by FinTech applications and AI-based advisory systems, while awareness of soil IoT sensors remained comparatively lower. Reliability analysis suggested acceptable internal consistency across all latent constructs (Cronbach's α = 0.78–0.91). The qualitative findings indicated that practical demonstrations, continuous extension support, and locally relevant training were described by farmers as important to their willingness to adopt digital agricultural technologies, although this qualitative emphasis was not fully supported by the quantitative results.

The study concludes that awareness building emerged as the most consistently supported factor associated with agritech adoption in the study area, while training, trust, usefulness, and social influence — although theoretically relevant and qualitatively salient — were not statistically supported as significant determinants in this sample. The proposed Training–Trust–Utility–Network (TTUN) framework is offered as a conceptual proposition for future testing rather than an empirically supported model, and requires validation with a larger and/or differently sampled dataset before broader application.