Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials
The development of improved cultivars requires establishing multi-environment trials (METs) to evaluate their performance under a wide range of environmental conditions. However, the high phenotyping costs often limit the capacity to evaluate genotypes in all the target environments. Our main object...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/175311 |
| _version_ | 1855538484596441088 |
|---|---|
| author | Lubanga, N. Ifie, B.E. Persa, R. Dieng, I. Rabbi, I.Y. Jarquin, D. |
| author_browse | Dieng, I. Ifie, B.E. Jarquin, D. Lubanga, N. Persa, R. Rabbi, I.Y. |
| author_facet | Lubanga, N. Ifie, B.E. Persa, R. Dieng, I. Rabbi, I.Y. Jarquin, D. |
| author_sort | Lubanga, N. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The development of improved cultivars requires establishing multi-environment trials (METs) to evaluate their performance under a wide range of environmental conditions. However, the high phenotyping costs often limit the capacity to evaluate genotypes in all the target environments. Our main objective was to explore the potential of implementing sparse testing in cassava breeding programs to reduce the cost of phenotyping in METs. The population used in this study consisted of 435 cassava genotypes evaluated in five environments in Nigeria for dry matter (dm) and fresh root yield (fyld). Sparse testing designs were developed based on non-overlapping (NOL), completely overlapping (OL), and intermediates between NOL and OL genotypes. Three prediction models were assessed (one based on phenotypes only, while two had genomic data). All the three models had a higher predictive ability and a lower mean square error (MSE) when a large training set was used. Predictive ability increased and MSE reduced when genotype-by-environment interaction (G × E) was modeled for the same training set sizes and allocations. Predictive ability decreased while MSE increased with the increasing OL genotypes across the environments, suggesting that only a few OL genotypes may be required to set up METs for model training. Sparse testing using a model incorporating G × E could be implemented to reduce cost of phenotyping in cassava METs. If data were available, integrating crop growth models (CGMs) with genomic prediction holds the potential to improve predictive ability. The training population used for sparse testing could be optimized to determine the optimal size and distribution of genotypes to increase the predictive ability and reduce cost under a fixed budget. |
| format | Journal Article |
| id | CGSpace175311 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1753112025-11-11T10:45:35Z Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials Lubanga, N. Ifie, B.E. Persa, R. Dieng, I. Rabbi, I.Y. Jarquin, D. cassava manihot esculenta breeding genotypes The development of improved cultivars requires establishing multi-environment trials (METs) to evaluate their performance under a wide range of environmental conditions. However, the high phenotyping costs often limit the capacity to evaluate genotypes in all the target environments. Our main objective was to explore the potential of implementing sparse testing in cassava breeding programs to reduce the cost of phenotyping in METs. The population used in this study consisted of 435 cassava genotypes evaluated in five environments in Nigeria for dry matter (dm) and fresh root yield (fyld). Sparse testing designs were developed based on non-overlapping (NOL), completely overlapping (OL), and intermediates between NOL and OL genotypes. Three prediction models were assessed (one based on phenotypes only, while two had genomic data). All the three models had a higher predictive ability and a lower mean square error (MSE) when a large training set was used. Predictive ability increased and MSE reduced when genotype-by-environment interaction (G × E) was modeled for the same training set sizes and allocations. Predictive ability decreased while MSE increased with the increasing OL genotypes across the environments, suggesting that only a few OL genotypes may be required to set up METs for model training. Sparse testing using a model incorporating G × E could be implemented to reduce cost of phenotyping in cassava METs. If data were available, integrating crop growth models (CGMs) with genomic prediction holds the potential to improve predictive ability. The training population used for sparse testing could be optimized to determine the optimal size and distribution of genotypes to increase the predictive ability and reduce cost under a fixed budget. 2025-03 2025-06-25T14:42:07Z 2025-06-25T14:42:07Z Journal Article https://hdl.handle.net/10568/175311 en Open Access application/pdf Lubanga, N., Ifie, B.E., Persa, R., Dieng, I., Rabbi, I.Y. & Jarquin, D. (2025). Sparse testing designs for optimizing resource allocation in multi‐environment cassava breeding trials. The Plant Genome, 18(1): e20558, 1-13. |
| spellingShingle | cassava manihot esculenta breeding genotypes Lubanga, N. Ifie, B.E. Persa, R. Dieng, I. Rabbi, I.Y. Jarquin, D. Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title | Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title_full | Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title_fullStr | Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title_full_unstemmed | Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title_short | Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials |
| title_sort | sparse testing designs for optimizing resource allocation in multi environment cassava breeding trials |
| topic | cassava manihot esculenta breeding genotypes |
| url | https://hdl.handle.net/10568/175311 |
| work_keys_str_mv | AT lubangan sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials AT ifiebe sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials AT persar sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials AT diengi sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials AT rabbiiy sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials AT jarquind sparsetestingdesignsforoptimizingresourceallocationinmultienvironmentcassavabreedingtrials |