Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures
A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/12/12/2343 |
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| author | Sintayehu D. Daba Alecia M. Kiszonas Rebecca J. McGee |
| author_facet | Sintayehu D. Daba Alecia M. Kiszonas Rebecca J. McGee |
| author_sort | Sintayehu D. Daba |
| collection | DOAJ |
| description | A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012–2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models. Predictive assessment using cross-validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that performs well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI’s yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80–87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production. |
| first_indexed | 2024-03-11T02:00:53Z |
| format | Article |
| id | doaj.art-4bf361a9cf424fd3b7ce53d1e13e46cf |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-11T02:00:53Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-4bf361a9cf424fd3b7ce53d1e13e46cf2023-11-18T12:10:48ZengMDPI AGPlants2223-77472023-06-011212234310.3390/plants12122343Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP ProceduresSintayehu D. Daba0Alecia M. Kiszonas1Rebecca J. McGee2USDA-ARS Western Wheat & Pulse Quality Laboratory, Pullman, WA 99164, USAUSDA-ARS Western Wheat & Pulse Quality Laboratory, Pullman, WA 99164, USAUSDA-ARS Grain Legume Genetics and Physiology Research Unit, Pullman, WA 99164, USAA large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012–2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models. Predictive assessment using cross-validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that performs well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI’s yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80–87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production.https://www.mdpi.com/2223-7747/12/12/2343stabilityWAASBpredictive assessmentpostdictive assessmentcross-validation |
| spellingShingle | Sintayehu D. Daba Alecia M. Kiszonas Rebecca J. McGee Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures Plants stability WAASB predictive assessment postdictive assessment cross-validation |
| title | Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures |
| title_full | Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures |
| title_fullStr | Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures |
| title_full_unstemmed | Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures |
| title_short | Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures |
| title_sort | selecting high performing and stable pea genotypes in multi environmental trial met applying ammi gge biplot and blup procedures |
| topic | stability WAASB predictive assessment postdictive assessment cross-validation |
| url | https://www.mdpi.com/2223-7747/12/12/2343 |
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