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Agronomy 2018, 8(4), 51; https://doi.org/10.3390/agronomy8040051

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

1
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
2
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
3
Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
4
School of Natural Resources and Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
Received: 26 February 2018 / Revised: 3 April 2018 / Accepted: 16 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Abstract

Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14–71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27–123%, 27–148%, and 65–165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14–33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season. View Full-Text
Keywords: genomic prediction; genotype by environment interaction; interaction models; canopy coverage; cross-validation schemes genomic prediction; genotype by environment interaction; interaction models; canopy coverage; cross-validation schemes
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jarquin, D.; Howard, R.; Xavier, A.; Das Choudhury, S. Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans. Agronomy 2018, 8, 51.

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