| Autor: |
Yi Yu, Qin Cheng, Fei Wang, Yulei Zhu, Xiaoguang Shang, Ashley Jones, Haohua He, Youhong Song |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Plant Phenomics, Vol 5 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2643-6515 |
| DOI: |
10.34133/plantphenomics.0099 |
| Popis: |
The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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