A Crop Growth-Regulation Framework for Drought-Stress Adaptation

Thesis Abstract

Drought is a pervasive constraint on crop productivity, undermining yield stability and food security in rain-fed agroecosystems. The study addresses the gap in integrated frameworks that link physiological growth regulation, canopy dynamics, and resource use efficiency under episodic and chronic water deficit. The aim is to develop a Crop Growth-Regulation Framework for Drought-Stress Adaptation that synthesizes physiological processes, phenotypic traits, and management interventions into a parsimonious model capable of predicting growth trajectories and yield under variable moisture regimes. Specific objectives are to (1) identify key regulatory nodes governing leaf area expansion, photosynthetic acclimation, and root–shoot partitioning under drought; (2) quantify the relationships among soil water availability, stomatal conductance, hormonal signaling (abscisic acid and auxin dynamics), and assimilate distribution; (3) construct a mechanistic yet tractable framework that integrates growth regulation with water supply, nutrient uptake, and radiation use efficiency; (4) calibrate and validate the framework across two staple crops—maize and sorghum—under controlled drought simulators and field drought stress; and (5) generate scenario-based recommendations for cultivar selection, irrigation scheduling, and nutrient management. The theoretical lens combines the Water–Growth–Hormone Regulation Theory with a Dynamic Growth Allocation Model, drawing on the stress adaptation hypothesis and the sink–source balance concept to map causal pathways from soil moisture status to whole-plant growth outcomes. An empirical component will test hypotheses about the moderating roles of root depth, hydraulic redistribution, and stomatal sensitivity in determining yield under water limitation. The study adopts a mixed-methods research design consisting of (i) controlled-environment experiments with maize and sorghum subjected to well-watered and multiple drought regimes (terminal, intermittent, and progressive soil drying) to generate high-resolution physiological and morphological data, and (ii) field trials across two agroecological zones to evaluate model transferability. A multi-stage sampling strategy will recruit 60 genotypes (30 maize, 30 sorghum) for greenhouse trials and 20 genotypes (10 per crop) for field trials, with replication (n=4) per treatment. Data collection instruments include high-throughput phenotyping platforms for leaf area index, canopy temperature, and plant height; portable gas exchange systems for photosynthesis and stomatal conductance; soil moisture sensors for continuous water status; root imaging for depth and distribution; and HPLC-MS for quantifying abscisic acid and other hormones. Supplementary data will consist of plant biometrics, yield components, and nutrient uptake metrics. Validity and reliability will be ensured through calibration of instruments, standardized protocols, and cross-validation with duplicate measurements. Data analysis will employ structural equation modeling to test the regulatory pathways among soil moisture, hormonal signaling, and growth responses; mixed-effects ANOVA to compare treatments and genotypes; and nonlinear regression to parameterize growth curves. Model specification will articulate a process-based submodel for leaf area expansion, a carbon allocation submodel driven by sink strength and hormonal cues, and a soil–root water uptake module integrating soil water potential and root hydraulic conductance. Sensitivity analyses will identify pivotal parameters driving output, and bootstrapping will provide confidence intervals for model predictions. Expected findings include robust quantification of how drought-induced hormonal shifts modulate leaf area maintenance, photosynthetic acclimation, and root foraging behavior, leading to distinct growth trajectories and yield patterns across genotypes and environments. The framework is anticipated to demonstrate improved predictive accuracy for yield under drought when integrating growth regulation with water status and hormone dynamics, relative to conventional drought indices. The study contributes to knowledge by offering a unified model that bridges physiology, morphology, and management, enabling breeders and agronomists to identify traits and practices most influential for drought resilience. Practical implications encompass refined irrigation scheduling guided by predicted growth trajectories, targeted nutrient management to support osmotic adjustment and hormonal balance, and selection criteria emphasizing regulatory traits such as stomatal sensitivity and deep rooting. The conclusion points to the framework’s utility for policy-relevant scenario analysis under climate variability and recommends expanding validation to additional crops and agroecosystems, as well as integrating remote sensing data for scalable deployment.