Integrated Design, Implementation, and Evaluation of a Precision Irrigation System for Maize

Thesis Abstract

The study addresses the persistent inefficiencies in water use and uneven maize yields in rainfed and irrigated farming systems, where conventional irrigation practices produce suboptimal water productivity and elevated production costs under increasing climate variability. The aim is to develop, implement, and evaluate a farmer-centered precision irrigation system that integrates sensor-driven moisture monitoring, remote sensing inputs, site-specific irrigation scheduling, and an automated actuating framework to optimize water application for maize. Specific objectives include (1) designing an integrated hardware-software architecture for moisture sensing, telemetry, and irrigation control; (2) calibrating and validating soil water content models using pedotransfer functions and empirical field data; (3) assessing the agronomic performance of the precision system in terms of grain yield, water productivity, and nitrogen-use efficiency; (4) evaluating techno-economic feasibility, energy consumption, and maintenance requirements; and (5) identifying adoption determinants and user acceptance among smallholder and commercial maize producers. A mixed-methods approach is employed. The research adopts a two-stage design an experimental field trial followed by a longitudinal implementation study. The population comprises maize-growing farms in three agro-ecological zones with contrasting rainfall patterns. A stratified random sample of 60 plots (20 per zone) is selected, with a split-plot arrangement comparing precision irrigation against conventional irrigation over two growing seasons. Data collection instruments include soil moisture sensors (EC-5 and TDR probes), calibrated capacitance probes, weather stations, and drone-derived vegetative indices (NDVI, NWI). Additional instruments encompass on-farm surveys and semi-structured interviews with 24 farm managers or owners to capture adoption determinants. Instrument validity is established through pilot testing and content validity checks with agronomists and irrigation engineers; reliability is evaluated using test-retest procedures and intra-class correlation for sensor-derived measures. Quantitative data are analyzed using a combination of regression analysis and ANOVA to quantify treatment effects on grain yield, evapotranspiration, water productivity (kg m?3), and nitrogen-use efficiency, with a repeated-measures framework to account for multi-season data. Soil moisture and yield models are developed using multiple linear regression and nonlinear soil-plant-atmosphere models, calibrated with observed field data. A dynamical system model integrates sensor inputs, irrigation scheduling rules, and pump actuation to assess system performance under varying climate scenarios. Cost-benefit analysis, including net present value, internal rate of return, and payback period, evaluates economic viability. Sensitivity analyses test robustness to input uncertainties. Qualitative data from farmer interviews are analyzed thematically, guided by the Technology Acceptance Model and the Diffusion of Innovations framework to elucidate perceived relative advantage, compatibility, complexity, and trialability. Expected findings indicate that the precision irrigation system reduces consumptive water use by 22–35% and increases maize yield by 6–12% under comparable agronomic practices, while improving water productivity by 0.8–1.5 kg m?3. The system is anticipated to deliver a 10–25% reduction in irrigation energy use, with higher gains in resource-poor settings where irrigation scheduling previously relied on fixed intervals. Economically, the technology demonstrates a favorable cost-benefit profile with an estimated payback period under five years for commercial farms and under three years for medium-scale operations, assuming scalable sensor costs and maintenance. Theoretical contributions include operationalizing an integrated design framework that links agro-hydrological modeling, cyber-physical control, and user-centered adoption theory within maize production. The study advances knowledge on how sensor-driven, site-specific irrigation can be harmonized with decision-support tools to optimize agronomic and economic outcomes in diverse agro-ecologies. Conclusions indicate that a well-integrated precision irrigation system can substantially improve water use efficiency and maize productivity while remaining economically viable for a broad range of producers. Recommendations emphasize modular system deployment, open data interfaces, capacity-building programs for farmers, and policy incentives to promote adoption. Further research suggestions include multi-season scalability assessments, integration with nutrient management in split-application schemes, and exploration of remote sensing fusion for real-time stress detection.