AMMI-Bayesian perspective in the selection of pre-cultivars of carioca beans in Agreste-Sertão of Pernambuco, Brazil.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
22 03 2023
Historique:
received: 07 12 2022
accepted: 16 03 2023
entrez: 23 3 2023
pubmed: 24 3 2023
medline: 25 3 2023
Statut: epublish

Résumé

The productivity of beans is greatly influenced by the different edaphoclimatic conditions in the Agreste-Sertão region, requiring the identification of adapted and stable genotypes to minimize the effects of the interaction between genotypes per environments (GxE). The objective of this work was to analyze the adaptability and stability of carioca bean pre-cultivars in three municipalities in the Agreste-Sertão of Pernambuco using the AMMI model in its Bayesian version BAMMI and compare the results with the frequentist approach. According to the results, the BAMMI analysis showed better predictive capacity, as well as better performance in the study of adaptability and stability. The cultivar BRS Notável stood out in terms of main effect and stability. Adaptability of genotypes to specific locations was also observed, enabling the use of the positive effect of the GxE interaction, which was more evident with the BAMMI model. From this work, the flexibility of BAMMI model to deal with data resulting from multi-environmental experiments can be seen, overcoming limitations of the standard analysis of the AMMI model.

Identifiants

pubmed: 36949093
doi: 10.1038/s41598-023-31768-5
pii: 10.1038/s41598-023-31768-5
pmc: PMC10033516
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4700

Informations de copyright

© 2023. The Author(s).

Références

PLoS One. 2020 Jul 30;15(7):e0236571
pubmed: 32730284
Front Plant Sci. 2020 Aug 05;11:1168
pubmed: 32849723
Theor Appl Genet. 1992 Jun;84(1-2):161-72
pubmed: 24203043
Nat Hum Behav. 2020 Jun;4(6):561-563
pubmed: 31988442
PLoS One. 2021 Aug 30;16(8):e0256882
pubmed: 34460844
PLoS One. 2015 Jul 09;10(7):e0131414
pubmed: 26158452

Auteurs

Gérsia Gonçalves de Melo (GG)

Universidade Federal Rural de Pernambuco, Recife, PE, Brazil.

Luciano Antonio de Oliveira (LA)

Universidade Federal da Grande Dourados, Dourados, MS, Brazil.

Carlos Pereira da Silva (CP)

Universidade Federal de Lavras, Lavras, MG, Brazil.

Alessandra Querino da Silva (AQ)

Universidade Federal da Grande Dourados, Dourados, MS, Brazil.

Maxwel Rodrigues Nascimento (MR)

Universidade Estadual Do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil. maxwel.rn88@gmail.com.

Ranoel José de Sousa Gonçalves (RJ)

Universidade Federal de Campina Grande, Sumé, PB, Brazil.

Paulo Ricardo Dos Santos (PR)

Instituto Federal do Amapá, Porto Grande, AP, Brazil.

Antônio Félix da Costa (AF)

Instituto Agronômico de Pernambuco, Recife, PE, Brazil.

Damião Ranieri Queiroz (DR)

Universidade Federal Rural de Pernambuco, Recife, PE, Brazil.

José Wilson da Silva (JW)

Universidade Federal Rural de Pernambuco, Recife, PE, Brazil.

Articles similaires

Populus Soil Microbiology Soil Microbiota Fungi

Perceptions of the neighbourhood food environment and food insecurity of families with children during the Covid-19 pandemic.

Irene Carolina Sousa Justiniano, Matheus Santos Cordeiro, Hillary Nascimento Coletro et al.
1.00
Humans COVID-19 Food Insecurity Cross-Sectional Studies Female
Humans COVID-19 Brazil Resilience, Psychological Cross-Sectional Studies
Animals Lung India Sheep Transcriptome

Classifications MeSH