The efficient and effective development of high-yielding, stress-resilient crop varieties is contingent on the ability to relate genotype to phenotype in the face of a changing climate. Recent innovations in DNA sequencing technologies have accelerated the development of rapid, low-cost genotyping approaches to simultaneously identify and score thousands of single-nucleotide polymorphism (SNP) markers across hundreds of individuals. In contrast, morphological and physiological traits of crop plants are still largely evaluated with laborious, expensive phenotyping approaches that have evolved minimally over the past decade and longer. Therefore, it remains a considerable challenge to phenotype large plant populations for a number of traits over the growing season, especially for life history traits that are greatly responsive to prolonged episodes of high temperature and drought. As a result, most, if not all, plant breeders and geneticists are significantly constrained by their lack of field-based high-throughput phenotyping (FB-HTP) tools to comprehensively elucidate the biology underlying natural trait variation as it relates to plant developmental phases, variable environmental conditions, and genetic improvement.
In collaboration with the University of Arizona and USDA-ARS, Arid Land Agricultural Research Center in Maricopa, Arizona, the Gore lab has developed and evaluated a FB-HTP platform to measure dynamic plant traits under relevant growing conditions. Through the use of this novel system, canopy height, temperature, and reflectance were measured at different times on multiple days in July and August on an upland (Gossypium hirsutum L.) cotton recombinant inbred line population grown under well-watered and water-limited conditions. We are currently using this highly dimensional data set to perform quantitative trait loci (QTL) mapping over the growing season. Our goal is to understand the pattern of QTL expression dynamics under varying environmental conditions and more specifically, what QTL may be important in mitigating the consequences of heat and drought stresses. Furthermore, research continues on the development of proximal sensing techniques and analysis of this type of data as it relates to resolving the genetic basis of important physiological, fiber quality, and agronomic traits.
Funding: Cotton Incorporated and NSF PGRP IOS-1238187.