Browsing by Author "Oakes, Joseph Carroll"

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    Development of high-throughput phenotyping methods and evaluation of morphological and physiological characteristics of peanut in a sub-humid environment
    Sarkar, Sayantan (Virginia Tech, 2021-01-05)
    Peanut (Arachis hypogaea L.) is an important food crop in the USA and worldwide with high net returns but yield in excess of 4500 kg ha-1 is needed to offset the production costs. Because yield is limited by biotic and abiotic stresses, cultivars with stress tolerance are needed to optimize yield. The U.S. peanut mini-core germplasm collection is a valuable resource that breeders can use to improve stress tolerance in peanut. Phenotyping for plant height, leaf area, and leaf wilting have been used as proxies for the desired tolerance traits. However, proximal data collection, i.e. measurements are taken on individual plants or in the proximity, is slow. Remote data collection and machine learning techniques for analysis offer a high-throughput phenotyping (HTP) alternative to manual measurements that could help breeding for stress tolerance. The objectives of this study were to 1) develop HTP methods using aerial remote sensing; 2) evaluate the mini-core collection in SE Virginia; and 3) perform a detailed physiological analysis on a sub-set of 28 accessions from the mini-core collection under drought stress, i.e. the sub-set was selected based on contrasting differences under drought in three states, Virginia, Texas, and Oklahoma. To address these objectives, replicated experiments were performed in the field at the Tidewater Agricultural Research and Extension Center in Suffolk, VA, in 2017, 2018, and 2019, under rainfed, irrigated, and controlled conditions using rainout shelters to induce drought. Proximal data collection involved physiological, morphological, and yield measurements. Remote data collection was performed aerially and included collection of red-green-blue (RGB) images and canopy reflectance in the visible, near infra-red, and infra-red spectra. This information was used to estimate plant characteristics related to growth and drought tolerance. Under objective 1), we developed HTP for plant height with 85-95% accuracy, LAI with 85-88% accuracy, and wilting with 91-99% accuracy; this was done with significant reduction of time as compared to proximal data collection. Under objectives 2) and 3), we determined that shorter genotypes were more drought tolerant than taller genotypes; and identified CC650 less wilted and with increased carbon assimilation, electron transport, quantum efficiency, and yield than other accessions.
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    Genetic Variability of Growth and Development in Response to Nitrogen in Two Soft Winter Wheat Populations
    Hoyt, Cameron Michael (Virginia Tech, 2022-07-11)
    The use of nitrogen (N) fertilizers is both costly to farmers and contributes to environmental degradation. N applied to wheat accounts for 18% of N applied to farmland globally, making it a prime target for reducing and optimizing N application. Chapter I is a review on nitrogen use efficiency (NUE) in wheat, with emphasis on breeding efforts and genetic resources available to increase NUE. The concept of effective use of nitrogen (EUN) as yield per unit N applied as a measure of N use, is also introduced. Chapter II is a study using two bi-parental double haploid families to evaluate genetic variability of both the genetic main effects (intercept) and linear response to N (slope) and determine the feasibility of selection for EUN in wheat. Using cross validation, a genomic prediction accuracy of 0.68 for intercept and 0.50 for slope was found, indicating that EUN is under genetic control and can be selected for. The prospect of breeding for EUN under limited resources, i.e., using fewer N rates and fewer experimental plots, is also explored. It was found that two different N treatments can be used to produce accurate predictions of intercept and slope as high as 0.98 and 0.95, respectively. Chapter III uses the same population described in chapter II to further investigate feasibility of selection for EUN using a normalized difference vegetation index (NDVI) obtained from multi-spectral aerial images gathered throughout the growing season. Cumulative photosynthesis across the growing season was estimated by integration across the NDVI curve, and compared to grain yield estimates to determine the efficacy of aerial imaging to identify high EUN lines. NDVI values and the area under the NDVI curve were able to predict yield and had the strongest ability to predict yield in moderate to low N treatments, with R2 values as high as 0.81 and 0.78 respectively.