Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.
Autor: | DeSalvio AJ; Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, Texas, USA., Adak A; Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA., Murray SC; Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA., Jarquín D; Department of Agronomy, University of Florida, Gainesville, Florida, USA., Winans ND; Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA., Crozier D; Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA., Rooney WL; Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA. |
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Jazyk: | angličtina |
Zdroj: | The plant genome [Plant Genome] 2024 Jun; Vol. 17 (2), pp. e20454. Date of Electronic Publication: 2024 May 07. |
DOI: | 10.1002/tpg2.20454 |
Abstrakt: | For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F (© 2024 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.) |
Databáze: | MEDLINE |
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