A Sustainable Decision-Making Framework for Precision Irrigation Management

Thesis Abstract

Optimized water use in agriculture has become imperative in the face of increasing water scarcity, climate variability, and the need for sustainable resource management; however, current irrigation practices often lack a comprehensive decision-making framework that integrates ecological, economic, and social considerations to promote long-term sustainability. This study aims to develop a robust, evidence-based decision-making framework for precision irrigation management that enhances water efficiency, crop yield, and environmental sustainability. The specific objectives include (1) identifying key factors influencing efficient irrigation practices; (2) analyzing existing decision models and frameworks in precision irrigation; (3) designing an integrated, sustainable decision model based on multi-criteria analysis; and (4) validating the framework through empirical application in operational farms. The research adopted a mixed-methods approach, combining qualitative and quantitative techniques within a pragmatic research design. The quantitative component involved a survey of 150 farmers and irrigation managers across a representative sample of commercial and smallholder farms in a semi-arid agricultural region. The qualitative phase comprised focus group discussions and semi-structured interviews with 30 stakeholders, including agronomists, policy makers, and extension officers. Data collection instruments included structured questionnaires, interview guides, and observational checklists, all validated through content validity indices and test-retest reliability measures. Quantitative data were analyzed using statistical techniques such as principal component analysis (PCA) to identify key influencing factors, followed by multiple regression analysis to examine relationships between variables and water use efficiency. Qualitative data were subjected to thematic analysis to extract contextual insights, which informed the conceptualization of the decision framework. The core of the study involves developing a multi-criteria decision analysis (MCDA) model, integrated within the framework through Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The model incorporates ecological variables (e.g., soil moisture, water quality), economic factors (crop profitability, input costs), and social considerations (farmer preferences, community impacts). Validation was undertaken by applying the framework in five farms, where water savings, crop yield, and environmental indicators were monitored over one growing season. The anticipated findings include improved decision consistency among users, quantified water savings of approximately 20%, and increased crop yields by 15% relative to traditional methods. The study also expects to uncover significant relationships between socio-economic factors and adoption of sustainable irrigation practices. The expected contribution to knowledge lies in providing a comprehensive, adaptable decision-making model that balances ecological, economic, and social dimensions for sustainable irrigation management. It advances theoretical understanding by integrating multi-criteria decision analysis with sustainability principles within a practical agricultural context, thereby filling identified gaps in the literature regarding holistic, user-centric decision frameworks. The study’s findings are expected to facilitate policymakers and practitioners in developing targeted interventions for water conservation, technology adoption, and sustainable farming practices. In conclusion, this research demonstrates that implementing an integrated decision framework significantly enhances water use efficiency and sustainability in irrigation practices, with broad implications for resource management in water-scarce regions. Recommendations include scaling the framework through training programs, policy adjustments to incentivize sustainable practices, and continuous monitoring at regional levels. Future research should explore the integration of remote sensing data and machine learning techniques to further refine decision support systems and adapt to evolving environmental conditions. The development of this framework provides a strategic tool to bridge the gap between technological innovation and sustainable resource governance, ultimately contributing to resilient agricultural systems amid global climate challenges.