Perception and adoption of drip irrigation technology among beneficiaries of the Tamil Nadu Irrigated Agriculture Modernization Project: A Structural Equation Modelling approach
DOI:
https://doi.org/10.14719/pst.5795Keywords:
drip irrigation, perceived usefulness (PU), precision farming, sem analysis, technology acceptanceAbstract
The article examines the beneficiaries’ perceptions of Drip Irrigation Adoption under the Tamil Nadu Irrigated Agriculture Modernization Project using a quantitative research approach and Structural Equation Modelling (SEM). A survey was conducted with 559 respondents from different districts of Tamil Nadu using the Technology Acceptance Model (TAM). Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude Toward Use (ATU) and Behavioural Intension (BI) were some of the key constructs in the study. The study utilized a structured questionnaire for data collection, which was then analyzed using percentage analysis and SEM to explore the relationships among the constructs. The results showed that the respondents were predominately older, more experienced farmers with small to marginal landholdings. Educational levels among the respondents were diverse but skewed toward middle and secondary schooling, with (47.39%) having received this level of education. Incomes were predominantly in the lower-middle-class range, with respondents showing moderate interest in scientific practices and a high degree of openness to innovation. The study established that Perceived Usefulness and Perceived Ease of Use were crucial factors influencing farmers' attitudes toward drip irrigation, affecting their behavioural intention to adopt the technology. This study concludes that perceived benefits and ease of use are critical drivers in encouraging the adoption of water-saving technologies such as drip irrigation among farmers. Future research could include longitudinal studies on whether drip irrigation is eventually adopted and impacts farm productivity and water conservation. It may be possible to extend the model to include external variables, such as social influence, economic incentives and policy support, to better understand adoption dynamics.
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