Machine learning analytics for predictive breeding

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Date
2020-01-01
Authors
Xu, Zhanyou
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William D Beavis
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Altmetrics
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Agronomy

The Department of Agronomy seeks to teach the study of the farm-field, its crops, and its science and management. It originally consisted of three sub-departments to do this: Soils, Farm-Crops, and Agricultural Engineering (which became its own department in 1907). Today, the department teaches crop sciences and breeding, soil sciences, meteorology, agroecology, and biotechnology.

History
The Department of Agronomy was formed in 1902. From 1917 to 1935 it was known as the Department of Farm Crops and Soils.

Dates of Existence
1902–present

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  • Department of Farm Crops and Soils (1917–1935)

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Agronomy
Abstract

Prediction accuracies of genomic selection methods are affected by the quality of the phenotypic and genotypic data and by the use of appropriate analytic models in the training sets. This research focuses on the impact of data quality for ordinal traits. Ordinal scores of traits are typical for various types of stress tolerance and resistance. Established spatial models developed for continuous quantitative traits were unknown whether they can effectively adjust the spatial autocorrelation for ordinal traits with sharp transitions patterns among groups of plots in experimental field trials. The effectiveness of the spatial adjustments was systematically compared with eight different spatial models using soybean iron deficiency chlorosis (IDC) as an example. After incorporation of the spatial pattern recognition to provide adjusted ordinal data, a comparison of prediction accuracies between algorithmic modeling and data modeling approaches were systematically conducted. The results revealed that genomic prediction accuracies could be dramatically improved by both machine learning models and geospatial spatial analyses. Overall, algorithmic modeling outperforms data modeling methods for the soybean IDC ordinal data type. Further, machine learning algorithms provide higher prediction accuracy than traditional statistical data models in terms of sensitivity, specificity, and overall accuracy.

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Sat Aug 01 00:00:00 UTC 2020