Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits
Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker dat...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.20102 |
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| _version_ | 1846107962233323520 |
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| author | Hongyu Jin Michael C. Tross Ruijuan Tan Linsey Newton Ravi V. Mural Jinliang Yang Addie M. Thompson James C. Schnable |
| author_facet | Hongyu Jin Michael C. Tross Ruijuan Tan Linsey Newton Ravi V. Mural Jinliang Yang Addie M. Thompson James C. Schnable |
| author_sort | Hongyu Jin |
| collection | DOAJ |
| description | Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21st century. To address this challenge, we conducted linked yield trials of 752 public maize (Zea mays) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs. |
| format | Article |
| id | doaj-art-d16fba82d76a43e4af508220f6821181 |
| institution | Kabale University |
| issn | 2578-2703 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj-art-d16fba82d76a43e4af508220f68211812024-12-26T07:44:35ZengWileyPlant Phenome Journal2578-27032024-12-0171n/an/a10.1002/ppj2.20102Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traitsHongyu Jin0Michael C. Tross1Ruijuan Tan2Linsey Newton3Ravi V. Mural4Jinliang Yang5Addie M. Thompson6James C. Schnable7Center for Plant Science Innovation University of Nebraska‐Lincoln Lincoln Nebraska USACenter for Plant Science Innovation University of Nebraska‐Lincoln Lincoln Nebraska USAPlant Resilience Institute Michigan State University East Lansing Michigan USAPlant Resilience Institute Michigan State University East Lansing Michigan USACenter for Plant Science Innovation University of Nebraska‐Lincoln Lincoln Nebraska USACenter for Plant Science Innovation University of Nebraska‐Lincoln Lincoln Nebraska USAPlant Resilience Institute Michigan State University East Lansing Michigan USACenter for Plant Science Innovation University of Nebraska‐Lincoln Lincoln Nebraska USAAbstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21st century. To address this challenge, we conducted linked yield trials of 752 public maize (Zea mays) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.https://doi.org/10.1002/ppj2.20102 |
| spellingShingle | Hongyu Jin Michael C. Tross Ruijuan Tan Linsey Newton Ravi V. Mural Jinliang Yang Addie M. Thompson James C. Schnable Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits Plant Phenome Journal |
| title | Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits |
| title_full | Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits |
| title_fullStr | Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits |
| title_full_unstemmed | Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits |
| title_short | Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits |
| title_sort | imitating the breeder s eye predicting grain yield from measurements of non yield traits |
| url | https://doi.org/10.1002/ppj2.20102 |
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