Author
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Jaradat, Abdullah |
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Submitted to: Meeting Abstract
Publication Type: Abstract Only Publication Acceptance Date: 2/17/2018 Publication Date: N/A Citation: N/A Interpretive Summary: Technical Abstract: There is a growing need for adapted and more productive germplasm to expand oat production, optimize its yield, improve groat quality, and satisfy farmers and consumers demand, especially in the Upper Midwest of the US. Oat germplasm, representing different eco-geographical origins and breeding status, was characterized and evaluated using field- and laboratory-based forward phenomics. Whole plots were phenotyped at successive growth stages during three growing seasons using aerial and hand-held imagery and sensors. Digital and high-throughput data were captured and compiled on (1) color space descriptors of whole plots during key growth stages, (2) growing degree days to panicle emergence and maturity; (3) phenotypic and structural traits of panicles and spikelets; and (4) groat quality. A relational database was mined and statistically analyzed to (1) cluster the oat germplasm using unsupervised hierarchical clustering method; (2) identify traits with positive or negative, direct or indirect effect on the panicle phenome; (3) identify a minimum set of traits which can discriminate between structurally and agronomically different panicle phenotypes; (4) express groat weight per plant as a function of stochastic panicle architecture and (5) quantify the effect of panicle architecture traits that have implications for groat quality. A dynamic custom profiling procedure was instrumental in quantitatively assessing the importance of, and visually adjusting structural panicle traits to predict and optimize groat agronomic and quality traits. |
