Optimization of sparse phenotyping strategy in multi-environmental trials in maize
The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our rese...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179243 |
| _version_ | 1855513561624739840 |
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| author | Mothukuri, Srinivasa Reddy Beyene, Yoseph Gültas, Mehmet Burgueño, Juan Griebel, Stefanie |
| author_browse | Beyene, Yoseph Burgueño, Juan Griebel, Stefanie Gültas, Mehmet Mothukuri, Srinivasa Reddy |
| author_facet | Mothukuri, Srinivasa Reddy Beyene, Yoseph Gültas, Mehmet Burgueño, Juan Griebel, Stefanie |
| author_sort | Mothukuri, Srinivasa Reddy |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing sets (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, and balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs. |
| format | Journal Article |
| id | CGSpace179243 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1792432025-12-24T02:00:32Z Optimization of sparse phenotyping strategy in multi-environmental trials in maize Mothukuri, Srinivasa Reddy Beyene, Yoseph Gültas, Mehmet Burgueño, Juan Griebel, Stefanie maize phenotyping experimentation genetic gain genotypes The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing sets (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, and balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs. 2025-03 2025-12-23T17:15:29Z 2025-12-23T17:15:29Z Journal Article https://hdl.handle.net/10568/179243 en Open Access application/pdf Springer Mothukuri, S. R., Beyene, Y., Gültas, M., Burgueño, J., & Griebel, S. (2025). Optimization of sparse phenotyping strategy in multi-environmental trials in maize. Theoretical and Applied Genetics, 138(3), 62. https://doi.org/10.1007/s00122-025-04825-y |
| spellingShingle | maize phenotyping experimentation genetic gain genotypes Mothukuri, Srinivasa Reddy Beyene, Yoseph Gültas, Mehmet Burgueño, Juan Griebel, Stefanie Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title | Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title_full | Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title_fullStr | Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title_full_unstemmed | Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title_short | Optimization of sparse phenotyping strategy in multi-environmental trials in maize |
| title_sort | optimization of sparse phenotyping strategy in multi environmental trials in maize |
| topic | maize phenotyping experimentation genetic gain genotypes |
| url | https://hdl.handle.net/10568/179243 |
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