Sparse testing designs for optimizing resource allocation in multi‐environment cassava breeding trials
Abstract 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 ma...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20558 |
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| author | Nelson Lubanga Beatrice E. Ifie Reyna Persa Ibnou Dieng Ismail Yusuf Rabbi Diego Jarquin |
| author_facet | Nelson Lubanga Beatrice E. Ifie Reyna Persa Ibnou Dieng Ismail Yusuf Rabbi Diego Jarquin |
| author_sort | Nelson Lubanga |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-0ebbbcb667ba4829bba04a650bd72e5f |
| institution | OA Journals |
| issn | 1940-3372 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
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| series | The Plant Genome |
| spelling | doaj-art-0ebbbcb667ba4829bba04a650bd72e5f2025-08-20T01:50:06ZengWileyThe Plant Genome1940-33722025-03-01181n/an/a10.1002/tpg2.20558Sparse testing designs for optimizing resource allocation in multi‐environment cassava breeding trialsNelson Lubanga0Beatrice E. Ifie1Reyna Persa2Ibnou Dieng3Ismail Yusuf Rabbi4Diego Jarquin5Insitute of Biological, Environmental and Rural Sciences Aberystwyth University Aberystwyth UKInsitute of Biological, Environmental and Rural Sciences Aberystwyth University Aberystwyth UKAgronomy Department University of Florida Gainesville Florida USAInternational Institute of Tropical Agriculture Ibadan NigeriaInternational Institute of Tropical Agriculture Ibadan NigeriaAgronomy Department University of Florida Gainesville Florida USAAbstract 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.https://doi.org/10.1002/tpg2.20558 |
| spellingShingle | Nelson Lubanga Beatrice E. Ifie Reyna Persa Ibnou Dieng Ismail Yusuf Rabbi Diego Jarquin Sparse testing designs for optimizing resource allocation in multi‐environment cassava breeding trials The Plant Genome |
| 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 |
| url | https://doi.org/10.1002/tpg2.20558 |
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