Design and evaluation of a precision irrigation protocol for maize yields in variable climates

Thesis Abstract

In the face of increasing climate variability and water scarcity, precision irrigation presents a promising avenue to optimize maize water use efficiency and yield stability across diverse environments. This study addresses the gap between on-field irrigation practices and climate-driven hydrological demands by designing, implementing, and evaluating a context-specific precision irrigation protocol that integrates real-time soil moisture sensing, weather data, and crop water requirements to modulate irrigation events. The aim is to maximize grain yield and water productivity while minimizing nitrogen leaching and energy consumption. Specific objectives are (1) to develop a decision-support protocol informed by Soil Moisture Deficit Index and reference evapotranspiration to trigger irrigation; (2) to validate the protocol across three agro-ecological zones with contrasting rainfall regimes; (3) to quantify impacts on grain yield, water productivity, and irrigation water use efficiency; (4) to compare energy and cost implications relative to conventional schedule-based irrigation; and (5) to assess farmer adoption potential through stakeholder feedback. The study adopts a mixed-methods, multi-site experimental design conducted over two consecutive growing seasons. The primary population comprises commercial maize fields in three distinct climatic zones with semi-arid, sub-humid, and temperate temperate conditions. A randomized complete block design (RCBD) with four replications is employed at each site, incorporating two treatments precision irrigation protocol and traditional fixed-schedule irrigation. A total of 24 experimental plots (8 per site) are monitored. Data collection integrates quantitative instruments, including soil moisture sensors (Decagon 5TE) deployed at 0–15 cm, 15–30 cm, and 30–60 cm depths, automatic weather stations for real-time rainfall, solar radiation, and temperature, and flow meters to measure irrigation volume. Leaf area index and canopy temperature are measured biweekly using handheld spectrometers and infrared thermography, respectively. Yield and kernel quality are assessed at physiological maturity. Economic data include input costs, energy consumption, and labor requirements. Qualitative data are gathered from farmer focus group discussions and extension agent interviews to explore adoption barriers and facilitators. Statistical analysis comprises descriptive statistics, analysis of variance (ANOVA) to detect treatment differences in yield and water productivity, and multiple regression to relate yield to water and nutrient variables. A generalized linear mixed model (GLMM) accounts for site-specific random effects. Time-series analysis evaluates the responsiveness of soil moisture and evapotranspiration to the irrigation triggers. A cost-benefit analysis compares the precision protocol with the conventional schedule, incorporating net present value and return on investment. Model validation includes cross-site calibration and sensitivity analysis of the Soil Moisture Deficit Index thresholds. Theoretical framing draws on the Theory of Planned Behavior to interpret adoption determinants and the Risk-Sensitivity Theory to interpret farmer responses to variable irrigation risks. The study also builds on the Crop Water Productivity and Digital Agriculture paradigms to situate the protocol within broader sustainability and technology adoption literatures. Expected findings indicate that the precision irrigation protocol reduces overall irrigation water use by 15–25% while maintaining or increasing grain yield by 5–12% in semi-arid and sub-humid sites, due to improved synchrony between irrigation events and crop water demand. Enhanced water productivity and reduced leaching are anticipated, along with lower energy costs per unit of yield. The protocol is expected to perform less economically advantageous in consistently high-rainfall sites unless paired with fertigation optimization. The study will contribute to knowledge by providing a transferable, climate-responsive irrigation framework that integrates sensor networks, agro- meteorological data, and decision-support logic for maize. It will also offer evidence on economic viability and adoption potential among smallholder and commercial farmers, informing policy recommendations for water governance and agricultural extension services. The main conclusion posits that precision irrigation, when calibrated to local soil and climate conditions and supported by farmer-friendly decision tools, can substantially enhance maize productivity and resource use efficiency under variable climates. Recommendations include scaling protocols with local calibration curves, investing in sensor infrastructure and farmer training, and policy support for subsidies targeting water-saving technologies and data-enabled irrigation management.