Background and ObjectivesNear-infrared spectroscopy (NIRS) and small-scale testing are used to evaluate wheat germplasm is a cost-effective strategy to reduce thousands of wheat lines to hundreds and then to tens of lines. Identification of high-quality germplasm among thousands of lines is dependent on the accuracy of the tests that are used and how well these data correlate with the large-scale tests that are used to confirm the end-use quality before commercial release of a new wheat variety. In this study, NIR-based testing was investigated to determine the effectiveness of this high-throughput, nondestructive technology.FindingsTo demonstrate the effectiveness of NIRS and small-scale testing as a selection strategy, interpretive population statistics evaluating three consecutive generations (F4, F5, and F6) from a wheat breeding program were compared. The F4 (early stage) generation (2019) was predicted for milling yield with a proportion of 13.9% of lines above 74% (w/w) milling yield. From those lines that progressed to F5 (mid-generation in 2020), the proportion increased to 26.2% of lines above 74% (w/w) milling yield. Selected lines from F5 were progressed to F6 (advanced generation in 2021), where 62.4% of lines were above 74% (w/w) milling yield.ConclusionsEach time the set progressed through the selection strategy, the portion of lines above 74% (w/w) milling yield increased.Significance and NoveltyThis study demonstrates the value and efficiency of high-throughput selection strategies using nondestructive NIRS at the F4 (early-stage generation) stage and small-scale testing at the F5 stage of a wheat breeding program. Near-infrared spectroscopy (NIR)-based testing was investigated to determine its effectiveness as a high-throughput, nondestructive technology in wheat breeding selection. Application of NIR at the F4 generation and small-scale testing at the F5 generation reduced the proportion of low-quality lines at F6.
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Univ Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, BrazilUniv Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, Brazil
Corredo, Lucas P.
Wei, Marcelo C. F.
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Univ Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, BrazilUniv Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, Brazil
Wei, Marcelo C. F.
Ferraz, Marcos N.
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Univ Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, Brazil
Smart Agri, Piracicaba, SP, BrazilUniv Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, Brazil
Ferraz, Marcos N.
Molin, Jose P.
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Univ Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, BrazilUniv Sao Paulo, Luiz de Queiroz Coll Agr, Biosyst Engn Dept, Precis Agr Lab, Sao Paulo, Brazil
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Sun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R ChinaSun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R China
Liu, Tao
Pelowski, Matthew
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Univ Vienna, Fac Psychol, Dept Basic Psychol Res & Res Methods, Vienna, AustriaSun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R China
Pelowski, Matthew
Pang, Changle
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China Agr Univ, Dept Vehicle & Transportat Engn, Beijing 100094, Peoples R ChinaSun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R China
Pang, Changle
Zhou, Yuanji
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Sun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R ChinaSun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R China
Zhou, Yuanji
Cai, Jianfeng
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Sun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R ChinaSun Yat Sen Univ, Dept Psychol, Guangzhou 510275, Guangdong, Peoples R China
机构:
Tufts Univ, Human Robot Interact Lab, 200 Boston Ave, Medford, MA 02155 USATufts Univ, Human Robot Interact Lab, 200 Boston Ave, Medford, MA 02155 USA
Canning, Cody
Scheutz, Matthias
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Tufts Univ, Human Robot Interact Lab, 200 Boston Ave, Medford, MA 02155 USATufts Univ, Human Robot Interact Lab, 200 Boston Ave, Medford, MA 02155 USA