A Framework for Optimizing Water Use Efficiency in Precision Crop Irrigation

Thesis Abstract

In the context of escalating water scarcity and increasing pressure on agricultural productivity, optimizing water use efficiency (WUE) in crop irrigation has become a critical priority for sustainable farm management. This study aims to develop a comprehensive framework that enhances water use efficiency through the integration of precision irrigation technologies and decision-support systems. The primary objectives are to identify key factors influencing WUE, evaluate current precision irrigation practices, formulate a model for optimal water application, and validate the framework within a representative agro-ecological zone. Employing a mixed-methods research design, the study synthesizes quantitative data collection through a cross-sectional survey of 150 farmers practicing precision irrigation across maize and tomato crops, selected via stratified random sampling. Data collection instruments include structured questionnaires assessing irrigation practices, sensor data logs, and satellite imagery analysis for soil and crop health metrics. Qualitative data are gathered through semi-structured interviews with agronomists and extension officers to contextualize technical and managerial aspects influencing WUE. The validity and reliability of survey instruments are established through pilot testing, Cronbach’s alpha (?=0.82), and expert review. Data analysis involves multiple regression analysis to quantify the influence of variables such as soil moisture levels, crop water requirements, and sensor accuracy on irrigation efficiency. Thematic analysis is applied to qualitative data to elucidate contextual barriers and enabling factors. To develop the proposed framework, the study integrates the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), testing their relevance in predicting the adoption of precision irrigation practices. The framework development incorporates a systems thinking approach and is operationalized through a multi-criteria decision analysis (MCDA) to derive optimal irrigation schedules under varying environmental and crop conditions. Expected findings include a significant correlation between sensor-based irrigation scheduling and enhanced WUE, with identified barriers being limited technology accessibility and user awareness. The model is anticipated to demonstrate improved water savings—estimated at 20-30%—without compromising crop yields, as validated through field trials over two cropping seasons involving 50 experimental plots. The study is bound to contribute novel insights into the operationalization of integrated decision-support systems for precision irrigation, filling the existing gaps in spatial and temporal water management literature. This research advances the understanding of how technological integration and behavioral factors influence water use efficiency, offering a transferable framework applicable to diverse cropping systems and regions facing water constraints. The main conclusion emphasizes the importance of adopting tailored, sensor-driven irrigation strategies underpinned by robust decision-support frameworks for sustainable water management. Policy recommendations advocate for increased investment in precision agricultural technologies, farmer training programs, and water governance reforms. Future research directions include refining the model for smallholder farms and exploring climate variability impacts to further enhance the resilience of irrigation water management systems.