Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield
Genotype-by-environment (GEI) analysis guides the recommendation of best-performing crop genotypes and production environments. The objective of this study was to determine the extent of GEI on seed yield in tepary bean for genotype recommendation and cultivation in drought-prone environments. Forty...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2023
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/132656 |
| _version_ | 1855521884470247424 |
|---|---|
| author | Mwale, Saul Eric Shimelis, Hussein Nkhata, Wilson Sefasi, Abel Fandika, Isaac Mashilo, Jacob |
| author_browse | Fandika, Isaac Mashilo, Jacob Mwale, Saul Eric Nkhata, Wilson Sefasi, Abel Shimelis, Hussein |
| author_facet | Mwale, Saul Eric Shimelis, Hussein Nkhata, Wilson Sefasi, Abel Fandika, Isaac Mashilo, Jacob |
| author_sort | Mwale, Saul Eric |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genotype-by-environment (GEI) analysis guides the recommendation of best-performing crop genotypes and production environments. The objective of this study was to determine the extent of GEI on seed yield in tepary bean for genotype recommendation and cultivation in drought-prone environments. Forty-five genetically diverse tepary bean genotypes were evaluated under non-stressed and drought-stressed conditions for two seasons using a 9 × 5 alpha lattice design with three replications in four testing environments. Data were collected on seed yield (SY) and days to physiological maturity (DTM) and computed using a combined analysis of variance, the additive main effect and multiplicative interaction (AMMI), the best linear unbiased predictors (BLUPs), the yield stability index (YSI), the weighted average of absolute scores (WAASB) index, the multi-trait stability index (MTSI), and a superiority measure. AMMI analysis revealed a significant (p < 0.001) GEI, accounting for 13.82% of the total variation. Genotype performance was variable across the test environments, allowing the selection of best-suited candidates for the target production environment. The environment accounted for a substantial yield variation of 52.62%. The first and second interaction principal component axes accounted for 94.8 and 4.7% of the total variation in the AMMI-2 model, respectively, of surmountable variation due to GEI. The AMMI 2 model family was sufficient to guide the selection of high-yielding and stable genotypes. Based on best linear unbiased predictors (BLUPs), yield stability index (YSI), superiority measure (Pi), and broad adaptation, the following tepary bean genotypes were identified as high-yielding and suited for drought-prone environments: G40138, G40148, G40140, G40135, and G40158. The selected tepary bean genotypes are recommended for cultivation and breeding in Malawi or other related agroecologies. |
| format | Journal Article |
| id | CGSpace132656 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1326562025-12-08T10:29:22Z Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield Mwale, Saul Eric Shimelis, Hussein Nkhata, Wilson Sefasi, Abel Fandika, Isaac Mashilo, Jacob genotypes drought genotype-environment interaction phaseolus acutifolius yield stability additive main effect and multiplicative interaction; best linear unbiased predictors Genotype-by-environment (GEI) analysis guides the recommendation of best-performing crop genotypes and production environments. The objective of this study was to determine the extent of GEI on seed yield in tepary bean for genotype recommendation and cultivation in drought-prone environments. Forty-five genetically diverse tepary bean genotypes were evaluated under non-stressed and drought-stressed conditions for two seasons using a 9 × 5 alpha lattice design with three replications in four testing environments. Data were collected on seed yield (SY) and days to physiological maturity (DTM) and computed using a combined analysis of variance, the additive main effect and multiplicative interaction (AMMI), the best linear unbiased predictors (BLUPs), the yield stability index (YSI), the weighted average of absolute scores (WAASB) index, the multi-trait stability index (MTSI), and a superiority measure. AMMI analysis revealed a significant (p < 0.001) GEI, accounting for 13.82% of the total variation. Genotype performance was variable across the test environments, allowing the selection of best-suited candidates for the target production environment. The environment accounted for a substantial yield variation of 52.62%. The first and second interaction principal component axes accounted for 94.8 and 4.7% of the total variation in the AMMI-2 model, respectively, of surmountable variation due to GEI. The AMMI 2 model family was sufficient to guide the selection of high-yielding and stable genotypes. Based on best linear unbiased predictors (BLUPs), yield stability index (YSI), superiority measure (Pi), and broad adaptation, the following tepary bean genotypes were identified as high-yielding and suited for drought-prone environments: G40138, G40148, G40140, G40135, and G40158. The selected tepary bean genotypes are recommended for cultivation and breeding in Malawi or other related agroecologies. 2023-01-01 2023-11-02T07:47:30Z 2023-11-02T07:47:30Z Journal Article https://hdl.handle.net/10568/132656 en Open Access application/pdf MDPI Mwale, S.E.; Shimelis, H.; Nkhata, W.; Sefasi, A.; Fandika, I.; Mashilo, J. (2023) Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield. Agronomy 13(1): 12. ISSN: 2073-4395 |
| spellingShingle | genotypes drought genotype-environment interaction phaseolus acutifolius yield stability additive main effect and multiplicative interaction; best linear unbiased predictors Mwale, Saul Eric Shimelis, Hussein Nkhata, Wilson Sefasi, Abel Fandika, Isaac Mashilo, Jacob Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title | Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title_full | Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title_fullStr | Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title_full_unstemmed | Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title_short | Genotype-by-Environment Interaction in tepary bean (Phaseolus acutifolius A. Gray) for seed yield |
| title_sort | genotype by environment interaction in tepary bean phaseolus acutifolius a gray for seed yield |
| topic | genotypes drought genotype-environment interaction phaseolus acutifolius yield stability additive main effect and multiplicative interaction; best linear unbiased predictors |
| url | https://hdl.handle.net/10568/132656 |
| work_keys_str_mv | AT mwalesauleric genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield AT shimelishussein genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield AT nkhatawilson genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield AT sefasiabel genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield AT fandikaisaac genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield AT mashilojacob genotypebyenvironmentinteractioninteparybeanphaseolusacutifoliusagrayforseedyield |