Browsing by Author "Adeyemo, Emmanuel"
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Item Combating Fusarium Head Blight Resistance in Wheat with Genomic Selection and Computer Vision Technology(2022-01) Adeyemo, EmmanuelFusarium head blight (FHB), primarily caused by Fusarium graminarum, Schwabe, is a devastating fungal disease that limits wheat production globally and can significantly reduce yield and grain quality. At the University of Minnesota, screening for FHB begins at the F5 stage and continues annually until the line is released as a cultivar. Before implementing genomic selection at the F5 stage in 2016, we evaluated ~ 3,000 F5 lines annually in field nurseries. The use of genomic selection allowed the prediction of untested lines with a training population of 500 lines selected by pedigree information. The first study showed that a set of 200 lines selected by genomic relationship led to predictive abilities of up to 0.49, whereas a larger, randomly selected subset of 500 F5 lines had a maximum predictive ability of 0.34. While the addition of parents also led to increased predictive abilities, the increments were not significant in most cases. The second study examined the merit of incorporating available germplasm into our existing genomic selection pipeline. We observed that training populations that contained historical data were less useful while those that included a subset of 200 F5 lines selected by genomic relationship, were more effective for predicting FHB. The third study demonstrated the use of computer vision to estimate the percentage of kernels damaged by Fusarium. We utilized 85 samples containing five check cultivars with varying levels of FHB susceptibility and maturity and achieved an accuracy of 90%. Additional studies should be done to assess the utilization of this technology among our experimental lines.Item Predicting Genetic Variance from Genomewide Marker Effects in Maize(2018-06) Adeyemo, EmmanuelPredicting the genetic variance (VG) in a biparental population has been difficult. An effective procedure for predicting VG would likely require modeling of progeny segregation within each cross. Our objective was to determine whether the population mean, VG, and mean of the top 10% of progeny in a cross can be predicted effectively from genomewide marker effects. Eight maize (Zea mays L.) crosses that differed in predicted mean and VG were evaluated for plant and ear height, and growing degree days to silking across three locations in Minnesota in 2017. Each cross was represented by 120 to 144 random F3 lines. Correlations between the observed and predicted means of each breeding population were significant (P = 0.05) for all three traits (0.91 for plant height, 0.83 for ear height, and 0.80 for silking date). However, correlations between the observed and predicted VG were nonsignificant, ranging from -0.24 to 0.14 for the three traits. Correlations between the observed and predicted mean of the top 10% of progeny in each cross were significant for plant height (0.72) but not for ear height (0.56) and silking date (0.37). These results for predicting the mean of the top 10% of progeny reflected the ability to predict the mean but not VG. We concluded that while the means of breeding populations can be predicted effectively from genomewide marker effects, predicting the VG of a cross remains difficult.