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Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.
Theor Appl Genet. 2020 Jul 01 [Online ahead of print]TA

Abstract

KEY MESSAGE

Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

Authors+Show Affiliations

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA. Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA. North Florida Research and Education Center, Plant Pathology Department, University of Florida, Quincy, FL, 32351, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA. Krr73@cornell.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32613265

Citation

Anche, Mahlet T., et al. "Temporal Covariance Structure of Multi-spectral Phenotypes and Their Predictive Ability for End-of-season Traits in Maize." TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 2020.
Anche MT, Kaczmar NS, Morales N, et al. Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. Theor Appl Genet. 2020.
Anche, M. T., Kaczmar, N. S., Morales, N., Clohessy, J. W., Ilut, D. C., Gore, M. A., & Robbins, K. R. (2020). Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik. https://doi.org/10.1007/s00122-020-03637-6
Anche MT, et al. Temporal Covariance Structure of Multi-spectral Phenotypes and Their Predictive Ability for End-of-season Traits in Maize. Theor Appl Genet. 2020 Jul 1; PubMed PMID: 32613265.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. AU - Anche,Mahlet T, AU - Kaczmar,Nicholas S, AU - Morales,Nicolas, AU - Clohessy,James W, AU - Ilut,Daniel C, AU - Gore,Michael A, AU - Robbins,Kelly R, Y1 - 2020/07/01/ PY - 2019/12/12/received PY - 2020/06/16/accepted PY - 2020/7/3/entrez JF - TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik JO - Theor. Appl. Genet. N2 - KEY MESSAGE: Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points. SN - 1432-2242 UR - https://www.unboundmedicine.com/medline/citation/32613265/Temporal_covariance_structure_of_multi-spectral_phenotypes_and_their_predictive_ability_for_end-of-season_traits_in_maize L2 - https://dx.doi.org/10.1007/s00122-020-03637-6 DB - PRIME DP - Unbound Medicine ER -
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