Smart Irrigation Technologies: A Meta-Analytical and Structural Modeling Approach

Abstract

The emergence of smart irrigation systems supported by new technological capabilities such as the Internet of Things (IoT) has had transformative effects on water management. Fragmented and methodologically diverse empirical findings in researchers' studies makes it difficult to evaluate the effects of this technology. The aim of this scientific study is to present the multidimensional effects of smart irrigation technologies in a systematic and comparable framework. In the study, latent factor structures were identified and tested using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The findings reveal that smart irrigation technologies have a strong and positive effect on agricultural performance (r = 0.853). Subgroup analyses show that machine learning-based systems have a higher impact when compared to traditional smart irrigation systems. The study offers important policy implications regarding agricultural productivity gains and environmental sustainability through water management with smart irrigation technologies.