Comparative Yield and Water-Use Efficiency Across Maize Varieties Under Drought

Thesis Abstract

Drought stress imposes substantial yield penalties and compromises water-use efficiency (WUE) in maize, undermining food security and farm profitability in rainfed systems. This study investigates comparative yield performance and WUE across diverse maize varieties under controlled drought and well-watered conditions to identify genotypes with superior drought tolerance and efficient water use. The aims are to quantify yield components, harvest index, and WUE across a panel of 12 genetically diverse maize varieties, and to elucidate physiological traits and trait combinations associated with enhanced drought resilience. Specific objectives include (i) estimating grain yield, kernel number, and aboveground biomass under drought and optimal irrigation; (ii) determining WUE using carbon isotope discrimination (?13C) and gravimetric methods; (iii) evaluating phenotypic traits such as stomatal conductance, leaf relative water content, root depth distribution, and stay-green characteristics; (iv) analyzing genotype-by-environment interactions across two planting seasons and two irrigation regimes; and (v) identifying marker-agnostic trait profiles linked to high yield and WUE through multivariate approaches. A randomized complete block design with split-plot arrangement will be employed across three sites representative of semiarid maize production, involving two irrigation treatments (well-watered and drought-stressed) and 12 maize varieties. Each site will include four replications, with plot dimensions of 5 m by 4 m and a 0.75 m inter-row spacing. Data will be collected on yield components (ears per plant, kernels per ear, thousand-kernel weight), total grain yield, aboveground biomass, harvest index, and WUE calculated as grain yield per unit evapotranspiration. Physiological and morphological measurements will include stomatal conductance (porometer), leaf water potential (Scholander-type pressure chamber), chlorophyll content (SPAD), canopy temperature (infrared thermography), root length density (minirhizotron imagery), and stay-green score. Carbon isotope discrimination (?13C) will be used as an integrated proxy for intrinsic WUE. Meteorological data (precipitation, temperature, solar radiation, VPD) will be collected at each site. Statistical analysis will include linear mixed-model analysis to partition variance components for genotype, environment, irrigation, and their interactions, using restricted maximum likelihood (REML). ANOVA will compare means across varieties and treatments. Regression and path analyses will identify direct and indirect effects of physiological traits on yield and WUE. Multivariate techniques, including principal component analysis (PCA) and cluster analysis, will delineate trait syndromes associated with high yield under drought and elevated WUE. Theoretical framing will integrate drought tolerance concepts from the C3–C4 efficiency framework and the transpiration efficiency paradigm, with references to the Stress Tolerance and Resource-Use Efficiency theories. Expectation is that certain varieties will exhibit favorable trait combinations—such as deep roots, high stay-green scores, moderated stomatal conductance, and favorable ?13C values—leading to high yields under drought and improved WUE without compromising biomass under well-watered conditions. Key anticipated findings include substantial genotype-by-environment interactions, with several varieties maintaining yields under drought via enhanced water extraction and efficient carbon assimilation, while others show marked yield declines. Variability in WUE across varieties is expected to be driven by stomatal behavior, leaf water status, and root phenotypes, with a subset of genotypes achieving high WUE without yield penalties. The study will contribute to knowledge by linking physiological trait networks to agronomic performance under drought, providing genotype-specific recommendations for breeding programs and management strategies to improve maize productivity under water-limited conditions. The results will support the development of selection indices that couple grain yield with WUE indicators and guide targeted breeding for drought-adaptive maize. Conclusions will emphasize the potential for identifying robust, high-yielding, water-efficient varieties suitable for climate-resilient maize production, with recommendations for deploying promising genotypes in drought-prone farming systems, refining irrigation scheduling, and prioritizing trait-assisted selection in breeding programs.