Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials
In cowpea breeding, multi‐environment trials are conducted to select lines with high yield. The occurrence of genetic and/or statistical imbalance is common in these experiments, in addition to the possibility of (co)variance between genetic and non‐genetic effects. We explore the restricted maximum...
| Autores principales: | , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2023
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/175723 |
| _version_ | 1855520065906016256 |
|---|---|
| author | Araújo, Maurício dos Santos Chaves, Saulo Fabrício da Silva Damasceno‐Silva, Kaesel Jackson Dias, Luiz Antônio dos Santos de Rocha, Maurisrael de Moura |
| author_browse | Araújo, Maurício dos Santos Chaves, Saulo Fabrício da Silva Damasceno‐Silva, Kaesel Jackson Dias, Luiz Antônio dos Santos de Rocha, Maurisrael de Moura |
| author_facet | Araújo, Maurício dos Santos Chaves, Saulo Fabrício da Silva Damasceno‐Silva, Kaesel Jackson Dias, Luiz Antônio dos Santos de Rocha, Maurisrael de Moura |
| author_sort | Araújo, Maurício dos Santos |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In cowpea breeding, multi‐environment trials are conducted to select lines with high yield. The occurrence of genetic and/or statistical imbalance is common in these experiments, in addition to the possibility of (co)variance between genetic and non‐genetic effects. We explore the restricted maximum likelihood/best linear unbiased prediction features to select the model with the most appropriated covariance structure and compare the results with the traditional model (homogenous variances and no covariances). Then, 17 inbred lines and three cultivars were evaluated in six experiments during two crop years in the semiarid zone of Northeast Brazil. The trait evaluated was the 100‐grain weight. We selected the best model considering the Akaike Information Criterion. The model with diagonal structure for the residual effects and heterogeneous compound symmetry for the genetic effects had the best fit. The predicted genetic gain of lines selected in this model was 1.18% higher compared to the traditional model. Modeling different (co)variance structures for genetic and non‐genetic effects is an efficient approach in selecting superior genotypes in multi‐environment trials in cowpea breeding. |
| format | Journal Article |
| id | CGSpace175723 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1757232025-12-08T09:54:28Z Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials Araújo, Maurício dos Santos Chaves, Saulo Fabrício da Silva Damasceno‐Silva, Kaesel Jackson Dias, Luiz Antônio dos Santos de Rocha, Maurisrael de Moura cowpeas biofortification yield factors In cowpea breeding, multi‐environment trials are conducted to select lines with high yield. The occurrence of genetic and/or statistical imbalance is common in these experiments, in addition to the possibility of (co)variance between genetic and non‐genetic effects. We explore the restricted maximum likelihood/best linear unbiased prediction features to select the model with the most appropriated covariance structure and compare the results with the traditional model (homogenous variances and no covariances). Then, 17 inbred lines and three cultivars were evaluated in six experiments during two crop years in the semiarid zone of Northeast Brazil. The trait evaluated was the 100‐grain weight. We selected the best model considering the Akaike Information Criterion. The model with diagonal structure for the residual effects and heterogeneous compound symmetry for the genetic effects had the best fit. The predicted genetic gain of lines selected in this model was 1.18% higher compared to the traditional model. Modeling different (co)variance structures for genetic and non‐genetic effects is an efficient approach in selecting superior genotypes in multi‐environment trials in cowpea breeding. 2023-05 2025-07-22T16:57:39Z 2025-07-22T16:57:39Z Journal Article https://hdl.handle.net/10568/175723 en Limited Access Wiley Araújo, Maurício dos Santos; Chaves, Saulo Fabrício da Silva; Damasceno‐Silva, Kaesel Jackson; Dias, Luiz Antônio dos Santos; and de Rocha, Maurisrael de Moura. 2023. Modeling covariance structures for genetic and non-genetic effects in cowpea multi-environment trials. Agronomy Journal 115(3): 1248-1256. https://doi.org/10.1002/agj2.21321 |
| spellingShingle | cowpeas biofortification yield factors Araújo, Maurício dos Santos Chaves, Saulo Fabrício da Silva Damasceno‐Silva, Kaesel Jackson Dias, Luiz Antônio dos Santos de Rocha, Maurisrael de Moura Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title | Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title_full | Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title_fullStr | Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title_full_unstemmed | Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title_short | Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials |
| title_sort | modeling covariance structures for genetic and non genetic effects in cowpea multi environment trials |
| topic | cowpeas biofortification yield factors |
| url | https://hdl.handle.net/10568/175723 |
| work_keys_str_mv | AT araujomauriciodossantos modelingcovariancestructuresforgeneticandnongeneticeffectsincowpeamultienvironmenttrials AT chavessaulofabriciodasilva modelingcovariancestructuresforgeneticandnongeneticeffectsincowpeamultienvironmenttrials AT damascenosilvakaeseljackson modelingcovariancestructuresforgeneticandnongeneticeffectsincowpeamultienvironmenttrials AT diasluizantoniodossantos modelingcovariancestructuresforgeneticandnongeneticeffectsincowpeamultienvironmenttrials AT derochamaurisraeldemoura modelingcovariancestructuresforgeneticandnongeneticeffectsincowpeamultienvironmenttrials |