A Crop Yield Optimization Framework via Integrated Phenotyping and Modeling

Thesis Abstract

This study addresses the persistent gap between phenotypic indicators and realized yield in field-grown crops, aiming to develop a robust framework that integrates high-throughput phenotyping with predictive crop modeling to optimize yield under variable environmental and management conditions. The objective is to (i) identify key phenotypic traits with the strongest predictive power for yield across diverse environments, (ii) develop and validate a coupled learning and mechanistic model that links phenotypic signals to sink-source dynamics, resource use efficiency, and abiotic stress responses, and (iii) evaluate the framework’s performance for scenario-based recommendations on management practices, cultivar selection, and irrigation scheduling. The research employs a mixed-methods design combining empirical data collection with computational modeling. Field experiments will be conducted over three growing seasons in a representative temperate maize system, involving 480 plots arranged in a randomized complete block design with four replications. The population consists of three commercially relevant hybrids and two elite inbred lines, subjected to four water regimes and three nitrogen levels, yielding a factorial treatment structure. Phenotypic data will be gathered using high-throughput imaging (hyperspectral, chlorophyll fluorescence, thermal infrared) and proximal sensors to quantify traits such as leaf area index, normalized difference vegetation index, canopy temperature, and nutrient status at key growth stages. Yield components will be measured at harvest to provide ground-truth against model outputs. Data collection instruments include handheld spectrometers, drone-based multispectral cameras, and automated phenotyping platforms, with standardized calibration procedures to ensure cross-site comparability. The analytical framework integrates (a) multivariate regression and machine learning approaches (random forest, gradient boosting) for trait selection and structure discovery, (b) structural equation modeling to elucidate causal pathways among phenotypes, resource use, and yield, and (c) a mechanistic crop model that embodies sink-source dynamics, photosynthetic capacity, and abiotic stress tolerance, calibrated using Bayesian inference to quantify uncertainty. Model specification will incorporate growth-stage specific parameters and genotype-by-environment interactions, with cross-validation across environments and years. Hypothesis tests will assess the incremental predictive value of integrated phenotypic signals over traditional yield forecasts, and sensitivity analyses will identify critical parameters driving model robustness. The study anticipates that integrated phenotyping will enhance yield prediction accuracy by 15–25% relative to baseline models, with improved decision support for irrigation timing, nitrogen management, and cultivar choice under climate variability. The expected contribution to knowledge includes (i) a transferable, scalable framework that marries high-resolution phenotypic data with a dual-structure modeling approach to yield optimization, (ii) empirical evidence on the relative importance and interaction of phenotypic traits across environments, and (iii) an adaptive decision-support tool enabling timely management recommendations. The findings will advance theoretical understanding of trait-based yield optimization and provide practical guidelines for breeders, agronomists, and farmers seeking to maximize yield stability and resource-use efficiency. The study concludes that a tightly integrated phenotyping-modeling framework offers superior predictive capability and actionable insights for optimizing crop yield under diverse environmental and management conditions, with recommendations for extending the framework to other grain crops and incorporating genomic information to further enhance predictive power.