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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Crop Germplasm Research » Research » Research Project #441631

Research Project: Exploring the Genetics of Drought Resistance with Field-based Phenomics and Biophysical Process-based Modeling

Location: Crop Germplasm Research

Project Number: 3091-21000-041-016-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Feb 1, 2022
End Date: Jan 31, 2024

Plants depend on the ability to effectively maintain internal water balance to provide the hydration needed for critical physiological processes such as photosynthesis. Due to its fundamental importance, the biophysical underpinnings of plant water dynamics have been extensively studied in woody perennial species; however, characterization of intraspecific variation for hydraulic traits and understanding of their genetic controls lags in crop species research. These knowledge gaps are largely due to the painstaking care and significant time required for some of the most critical measurements (e.g., xylem pressure, hydraulic conductivity), limiting our capacity to breed for drought resistance traits in crop plants. Understanding the physiological and molecular regulatory mechanisms behind plant water dynamics, and their trade-offs, is crucial to continued varietal crop improvement in the face of changing climates and increasing environmental pressures. To address this challenge, an interdisciplinary approach is needed that links environmental fluctuations with variation in plant growth, development, and function to explore the fitness landscape associated with drought resistance. In this proposal, we propose the use of biophysical process models (BPMs), which provide a quantitative framework that explicitly accounts for regulatory feedback and feed forward loops in complex biological systems. In this specific application, BPMs will be used to explore plant-water relations to illuminate how they are genetically controlled in response to environmental conditions and perturbations. Use of BPMs will be critical in revealing the relationships and potential trade-offs to growth and reproduction when water is a limiting resource, something that genomic approaches, on their own, have struggled to capture. Our BPM of choice, the Terrestrial Regional Ecosystem Exchange Simulator (TREES), couples plant hydraulic traits to carbon assimilation, making it an excellent tool to leverage in modeling intraspecific diversity of drought resistance under fluctuating environments.

Cotton (Gossypium hirsutum L.) is a woody perennial tree species typically cultivated as an annual row crop. As such, cotton offers a unique opportunity to exploit the hydraulic knowledge of perennials to investigate the effects of differential acclimating behaviors on the productivity of fast-growing annuals. Cotton is also a critical fiber crop impacted by climate change and reduced water supply. Through the novel integration of biophysical modeling and -omics data presented here, our goal is to identify the genetic mechanisms controlling plant water dynamics in cotton while simultaneously developing a BPM capable of making predictions about future plant performance under water deficit conditions across genotypes. To accomplish this goal we propose the following aims: Aim 1: Characterize temporal transcriptome-phenome associations for a cotton calibration panel to identify key biophysical linkages affecting water dynamics under limiting conditions to instruct BPM improvements. 1A: Determine the phenotypic and transcriptomic responses to water limitation in cotton using fine-resolution spatiotemporal analyses under controlled conditions. 1B: Examine the phenotypic and transcriptomic responses to water limitation in a contrasting subset of the calibration panel under field conditions. Aim 2: Adapt TREES to simulate genotype-specific acclimation responses to water limitation in cotton and develop a novel approach for genotype-specific parameter estimation via model inversion. 2A: Modify TREES to capture genotypic variation in cotton growth under water limitation by leveraging data collected in Aim 1. 2B: Validate the cotton-adapted BPM using drought trials in an untested environment. 2C: Estimate genotype-specific model parameters using model inversion and phenomic data. Aim 3: Reveal the genetic basis of drought resistance in cotton, leveraging traditional and innovative model-derived phenotypes associated with stress-adaptive traits. 3A: Utilize field-based, high-throughput phenotyping to measure key traits identified in Aim 2. 3B: Utilize extracted model parameters as novel, latent space phenotypes to map stress-adaptive traits.