Adapting Aqua-Capture Mulching for Water-Smart Maize Systems: Design, Implementation, Evaluation

Thesis Abstract

The study addresses the critical challenge of water scarcity in maize production by adapting Aqua-Capture Mulching (ACM) to enhance soil moisture retention, improve crop water use efficiency, and reduce irrigation demand under semi-arid conditions. This research posits that integrating ACM within maize production systems can modulate microclimate, reduce evapotranspiration, and sustain yields under limited water supply. The aim is to design, implement, and evaluate an ACM-based mulch system for water-smart maize, with objectives to (1) quantify soil moisture dynamics, (2) assess biometric and phenological responses of maize under ACM versus conventional mulch and bare soil, (3) evaluate grain yield and water productivity across irrigation regimes, (4) analyze economic feasibility and life-cycle environmental impacts, and (5) develop practical guidelines for farmers and extension services. The study hypothesizes that ACM will significantly improve soil moisture retention, increase water productivity by at least 15% under deficit irrigation, and maintain or enhance grain yield relative to standard mulching practices. A mixed-methods design was employed, combining a controlled field experiment with participatory on-farm trials across three agro-ecological zones in the study region. The experimental stage followed a randomized complete block design with four replicates per treatment ACM, conventional mulch (CM), and bare soil (BS), under three irrigation levels (full, 70% of full, and rainfed) across two maize cultivars. The population comprises maize fields under smallholder and commercial farming systems, with a target sample size of 360 plots for robust statistical power. Data collection instruments included soil moisture sensors (time-domain reflectometry and capacitance probes), tensiometers, portable chlorophyll meters, canopy temperature sensors, phenology scoring calendars, and grain yield measurement at harvest. Additional data were gathered on input costs, labor, energy use, and farmer perceptions through structured questionnaires and focus group discussions. Validity and reliability were ensured through sensor calibration, duplicate soil moisture readings, instrument inter-calibration, and pilot testing of survey instruments. Data analysis followed a multidisciplinary analytical framework (i) descriptive statistics to characterize environmental conditions and baseline performance; (ii) repeated-measures ANOVA and mixed-effects models to evaluate treatment effects on soil moisture trajectories, plant physiological indices, phenology, and yield across irrigation regimes; (iii) regression analysis to quantify relationships between soil moisture, leaf area index, and grain yield; (iv) water productivity calculations (kg m?3); (v) economic analysis using partial budget and break-even analysis; (vi) life-cycle environmental assessment focusing on irrigation water use, energy consumption, and carbon footprint per unit of yield. Model specification integrates a crop growth model augmented with an ACM-specific water balance module to simulate scenarios beyond the field trials. Theoretical grounding draws upon the theories of evapotranspiration optimization, agro-meteorological risk management, and the sustainability framework of production-ecology balance, with explicit reference to the Water Footprint and Diffusion of Innovations theories to explain adoption dynamics. Key expected findings include (a) ACM enhances soil moisture retention during critical growth stages, (b) maize under ACM exhibits improved transpiration efficiency and higher canopy photosynthesis during water deficit periods, (c) ACM yields are comparable to CM under full irrigation and superior under 70% irrigation, with a 12–18% increase in water productivity relative to BS, and (d) ACM demonstrates favorable economic viability with a positive net present value within a three-year horizon and a reduced irrigation energy demand of approximately 10–15%. The study contributes to knowledge by providing empirical evidence on a novel mulch technology integrated with water-smart irrigation practices, detailing its agronomic benefits, economic feasibility, and scalable adoption pathways. It advances methodological approaches by coupling field experiments with crop-water modeling and participatory validation to address real-world constraints. The main conclusion anticipated is that Aqua-Capture Mulching offers a viable pathway to enhance maize productivity under water scarcity while reducing irrigation intensity. Recommendations include policy support for cost-sharing of ACM materials, extension training for farmers, integration of ACM with climate-resilient maize hybrids, and further longitudinal studies to assess long-term soil health implications and regional scalability.