Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea
Files
(Published version)
Date
2022
Authors
Zhao, H.
Pandey, B.R.
Khansefid, M.
Khahrood, H.V.
Sudheesh, S.
Joshi, S.
Kant, S.
Kaur, S.
Rosewarne, G.M.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Frontiers in Plant Science, 2022; 13:923381-1-923381-11
Statement of Responsibility
Huanhuan Zhao, Babu R. Pandey, Majid Khansefid, Hossein V. Khahrood, Shimna Sudheesh, Sameer Joshi, Surya Kant, Sukhjiwan Kaur, and Garry M. Rosewarne
Conference Name
Abstract
Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines’ advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial highthroughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31–0.71) and narrow-sense heritability (0.13–0.71) were observed, as the estimated additive and nonadditive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0–S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6–50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2022 Zhao, Pandey, Khansefid, Khahrood, Sudheesh, Joshi, Kant, Kaur and Rosewarne. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.