Adoption and Barriers to Data‑Driven Irrigation for Sustainable Agriculture

This study investigates the adoption, perceived effectiveness, and barriers to data-driven irrigation in sustainable agriculture, drawing on a multi-country, 115-respondent survey involving farmers, engineers, researchers, and other practitioners. Responses were collected primarily from Azerbaijan, along with participants from Pakistan, India, Nigeria, Germany, Afghanistan, Sri Lanka, Trinidad and Tobago, and other countries, reflecting diverse agronomic and climatic contexts. Current irrigation practices include drip (33), sprinkler (29), flood irrigation (21), rainwater harvesting (20), and groundwater pumping (20). Although respondents widely rate data-driven techniques as highly effective (71 “very effective”), adoption remains uneven: precision irrigation (32), data analytics (30), and drone or satellite monitoring (27) are the most considered options, whereas “none of the above” appears 36 times. Major barriers include inadequate infrastructure (53), lack of technical expertise (43), high initial cost (28), and insufficient institutional support (29). Key challenges in water management are water scarcity (41) and climate-related extremes (40). The findings highlight a strong perception–adoption gap consistent across countries, shaped by infrastructural and capacity limitations. The study offers policy insights relevant to both local and global sustainability transitions, underscoring the need for infrastructure modernization, targeted training programs, and financial mechanisms to accelerate the uptake of water-efficient technologies.