Browsing by Subject "Iron Deficiency Chlorosis"

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    Fine-Mapping, Physiological Evaluation, and Candidate Gene Exploration of an Iron Deficiency Chlorosis Tolerance Locus in Soybean
    (2020-07) Merry, Ryan
    Iron Deficiency Chlorosis (IDC) can be a significant yield limiting nutrient stress in soybean. IDC most frequently occurs in high pH soils that are rich in calcium carbonates, as is common in areas of the Midwestern United States. While several agronomic solutions exist to combat IDC, such as the application of iron chelates at planting, the use of tolerant soybean genotypes remains the most effective method of controlling IDC stress. Breeding for IDC tolerance is common, however little about the genetics of IDC is understood, aside from a major tolerance locus located on Gm03. A tolerance locus was previously discovered through bi-parental and association mapping on Gm05 to a 1.5 megabase region, which in this study was found to be important in elite soybean germplasm and warranted further investigation. Fine-mapping was conducted using heterogeneous inbred families, narrowing the interval to 137 kilobases and 17 genes. A controlled environment assay was developed to analyze the effect of nodulation, pH, and calcium carbonates on soybean genotypes and to assess the expression of Glyma.05g001700, a gene candidate in the fine-mapped region. Glyma.05g001700 was further explored using protein modeling, domain classification, gene homology, haplotype diversity, and overexpression in soybean hairy roots to assess gene function. It was concluded that Glyma.05g001700 is likely involved in iron homeostasis through changes in gene expression driven by a putative TATA box present in the tolerant genotype ‘Fiskeby III’.
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    High-Throughput Phenotyping for Soybean Iron Deficiency Chlorosis Using an Unmanned Aircraft System: Applications in Breeding, Agronomy, and Genetics
    (2020-07) Dobbels, Austin
    Iron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred approach to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season. The goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. We achieved high efficiency in collecting data with autonomous UAS flights, greater than 77% accuracy in classifying plots on a 1-5 severity scale, and an average reduction in LSD values across a series of experimental trials. This method is high-throughput, objective, and more precise than traditional ground based visual assessments. The UAS-based system was further used to assess the interactions of IDC and soybean cyst nematode (SCN). A range of treatments were added to change the levels of IDC and SCN stress in a randomized complete block factorial design. Results from the three-year study showed that the treatments independently created IDC and SCN severity symptoms and associated yield differences. Nematode reproduction was significantly impacted by varietal resistance and was not impacted by IDC treatment. An interaction between IDC and SCN treatments was not found for yield, nematode reproduction, or severity symptoms suggesting these stresses act additively. Finally, UAS-based phenotyping was used to assess temporal patterns of iron deficiency chlorosis symptoms in a genome-wide association study. The UAS-based system identified overlapping QTL with the same significance as traditional visual observations indicating that UAS estimates of IDC are useful in soybean genetics research programs. In addition, novel QTL were identified for the rate of IDC recovery. Overall, the efficiency and precision of UAS-based image analysis of IDC can be useful in breeding, agronomic, and genome-wide association studies.