Upcycling rice yield trial data using a weather-driven crop growth model
Abstract Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-023-05145-x |
| _version_ | 1797774011821719552 |
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| author | Hiroyuki Shimono Akira Abe Chyon Hae Kim Chikashi Sato Hiroyoshi Iwata |
| author_facet | Hiroyuki Shimono Akira Abe Chyon Hae Kim Chikashi Sato Hiroyoshi Iwata |
| author_sort | Hiroyuki Shimono |
| collection | DOAJ |
| description | Abstract Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y p) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate. |
| first_indexed | 2024-03-12T22:14:45Z |
| format | Article |
| id | doaj.art-3dd0f6ef628f4b7b8868cbd56ec9ba76 |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-12T22:14:45Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-3dd0f6ef628f4b7b8868cbd56ec9ba762023-07-23T11:22:52ZengNature PortfolioCommunications Biology2399-36422023-07-016111110.1038/s42003-023-05145-xUpcycling rice yield trial data using a weather-driven crop growth modelHiroyuki Shimono0Akira Abe1Chyon Hae Kim2Chikashi Sato3Hiroyoshi Iwata4Faculty of Agriculture, Iwate UniversityIwate Biotechnology Research CenterFaculty of Science and Engineering, Iwate UniversityIfuu RinrinLaboratory of Biometry and Bioinformatics, University of TokyoAbstract Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y p) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate.https://doi.org/10.1038/s42003-023-05145-x |
| spellingShingle | Hiroyuki Shimono Akira Abe Chyon Hae Kim Chikashi Sato Hiroyoshi Iwata Upcycling rice yield trial data using a weather-driven crop growth model Communications Biology |
| title | Upcycling rice yield trial data using a weather-driven crop growth model |
| title_full | Upcycling rice yield trial data using a weather-driven crop growth model |
| title_fullStr | Upcycling rice yield trial data using a weather-driven crop growth model |
| title_full_unstemmed | Upcycling rice yield trial data using a weather-driven crop growth model |
| title_short | Upcycling rice yield trial data using a weather-driven crop growth model |
| title_sort | upcycling rice yield trial data using a weather driven crop growth model |
| url | https://doi.org/10.1038/s42003-023-05145-x |
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