Susana R. Milla‐Lewis, Beatriz Tome Gouveia, Kevin Kenworthy, Jing Zhang, Ambika Chandra, Grady L. Miller, Esdras M. Carbajal, Brian Schwartz, Paul Raymer, Marta Pudzianowska, James H. Beard, J. Bryan Unruh
{"title":"Maximizing genetic gains across agronomic and consumer preference traits in St. Augustinegrass breeding","authors":"Susana R. Milla‐Lewis, Beatriz Tome Gouveia, Kevin Kenworthy, Jing Zhang, Ambika Chandra, Grady L. Miller, Esdras M. Carbajal, Brian Schwartz, Paul Raymer, Marta Pudzianowska, James H. Beard, J. Bryan Unruh","doi":"10.1002/csc2.21374","DOIUrl":null,"url":null,"abstract":"Combining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [<jats:italic>Stenotaphrum secundatum</jats:italic> (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crop Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/csc2.21374","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Combining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.
期刊介绍:
Articles in Crop Science are of interest to researchers, policy makers, educators, and practitioners. The scope of articles in Crop Science includes crop breeding and genetics; crop physiology and metabolism; crop ecology, production, and management; seed physiology, production, and technology; turfgrass science; forage and grazing land ecology and management; genomics, molecular genetics, and biotechnology; germplasm collections and their use; and biomedical, health beneficial, and nutritionally enhanced plants. Crop Science publishes thematic collections of articles across its scope and includes topical Review and Interpretation, and Perspectives articles.