Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta)
The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-enviro...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Acceso en línea: | https://hdl.handle.net/10568/125920 |
| _version_ | 1855521531839381504 |
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| author | Bakare, M.A. Kayondo, S.I. Aghogho, C.I. Wolfe, M. Parkes, Elizabeth Y. Kulakow, Peter A. Egesi, Chiedozie N. Jannink, Jean-Luc Rabbi, I.Y. |
| author_browse | Aghogho, C.I. Bakare, M.A. Egesi, Chiedozie N. Jannink, Jean-Luc Kayondo, S.I. Kulakow, Peter A. Parkes, Elizabeth Y. Rabbi, I.Y. Wolfe, M. |
| author_facet | Bakare, M.A. Kayondo, S.I. Aghogho, C.I. Wolfe, M. Parkes, Elizabeth Y. Kulakow, Peter A. Egesi, Chiedozie N. Jannink, Jean-Luc Rabbi, I.Y. |
| author_sort | Bakare, M.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons
(2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression
(PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling. |
| format | Journal Article |
| id | CGSpace125920 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1259202025-12-08T10:29:22Z Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) Bakare, M.A. Kayondo, S.I. Aghogho, C.I. Wolfe, M. Parkes, Elizabeth Y. Kulakow, Peter A. Egesi, Chiedozie N. Jannink, Jean-Luc Rabbi, I.Y. cassava genotypes genotype environment interaction food security nigeria The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling. 2022-09-21 2022-12-13T15:26:48Z 2022-12-13T15:26:48Z Journal Article https://hdl.handle.net/10568/125920 en Open Access application/pdf Frontiers Media Bakare, M.A., Kayondo, S.I., Aghogho, C.I., Wolfe, M., Parkes, E., Kulakow, P., ... & Rabbi, I.Y. (2022). Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta). Frontiers in Plant Science, 13: 978248, 1-18. |
| spellingShingle | cassava genotypes genotype environment interaction food security nigeria Bakare, M.A. Kayondo, S.I. Aghogho, C.I. Wolfe, M. Parkes, Elizabeth Y. Kulakow, Peter A. Egesi, Chiedozie N. Jannink, Jean-Luc Rabbi, I.Y. Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title | Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title_full | Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title_fullStr | Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title_full_unstemmed | Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title_short | Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta) |
| title_sort | parsimonious genotype by environment interaction covariance models for cassava manihot esculenta |
| topic | cassava genotypes genotype environment interaction food security nigeria |
| url | https://hdl.handle.net/10568/125920 |
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