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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
25/10/2023 |
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Data da última atualização: |
25/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
COSTA-NETO, G.; MATTA, D. H. da; FERNANDES, I. K.; STONE, L. F.; HEINEMANN, A. B. |
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Afiliação: |
GERMANO COSTA-NETO, CORNELL UNIVERSITY, Ithaca-NY; DAVID HENRIQUES DA MATTA, UNIVERSIDADE FEDERAL DE GOIÁS; IGOR KUIVJOGI FERNANDES, UNIVERSIDADE FEDERAL DE GOIÁS; LUIS FERNANDO STONE, CNPAF; ALEXANDRE BRYAN HEINEMANN, CNPAF. |
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Título: |
Environmental clusters defining breeding zones for tropical irrigated rice in Brazil. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Agronomy Journal, 2023. |
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ISSN: |
1435-0645 |
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DOI: |
https://doi.org/10.1002/agj2.21481 |
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Idioma: |
Inglês |
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Notas: |
Early view. |
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Conteúdo: |
Geographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region of IR, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields. MenosGeographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region... Mostrar Tudo |
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Thesagro: |
Arroz Irrigado; Genótipo; Meio Ambiente; Melhoramento Genético Vegetal; Oryza Sativa; Sistema de Produção. |
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Thesaurus Nal: |
Breeding; Climate models; Environmental factors; Genotype; Genotype-environment interaction; Rice; Tropical agriculture. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02650naa a2200361 a 4500
001 2157493
005 2023-10-25
008 2023 bl uuuu u00u1 u #d
022 $a1435-0645
024 7 $ahttps://doi.org/10.1002/agj2.21481$2DOI
100 1 $aCOSTA-NETO, G.
245 $aEnvironmental clusters defining breeding zones for tropical irrigated rice in Brazil.$h[electronic resource]
260 $c2023
500 $aEarly view.
520 $aGeographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region of IR, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields.
650 $aBreeding
650 $aClimate models
650 $aEnvironmental factors
650 $aGenotype
650 $aGenotype-environment interaction
650 $aRice
650 $aTropical agriculture
650 $aArroz Irrigado
650 $aGenótipo
650 $aMeio Ambiente
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aSistema de Produção
700 1 $aMATTA, D. H. da
700 1 $aFERNANDES, I. K.
700 1 $aSTONE, L. F.
700 1 $aHEINEMANN, A. B.
773 $tAgronomy Journal, 2023.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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| Registros recuperados : 16 | |
| 3. |  | COSTA NETO, G. M. F.; CASTRO, A. P. de; HEINEMANN, A. B.; DUARTE, J. B. Adaptação genotípica regionalizada por regressão fatorial e covariáveis geográficas. In: SIMPÓSIO INTERNACIONAL DE GENÉTICA E MELHORAMENTO, 7., 2016, Viçosa, MG. Desafios biométricos no melhoramento genético: anais. Viçosa, MG: UFV, 2016. p. 27.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 7. | | ANTOLIN, L. A. S.; COSTA NETO, G. M. F.; BORGES, M. G.; HEINEMANN, A. B. Irrigation efficiency simulation for common bean during dry season, in the municipality of Goiânia, Goiás. In: INOVAGRI INTERNATIONAL MEETING, 3., 2015, Fortaleza. Anais... Fortaleza: INI, 2015.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 8. | | COSTA NETO, G. M. F.; CASTRO, A. P. de; HEINEMANN, A. B.; DUARTE, J. B. Integrando modelos mistos, variáveis ambientais e regressão PLS no estudo dos efeitos G+GE em ensaios de VCU em arroz de terras altas. In: SEMINÁRIO JOVENS TALENTOS, 11., 2017, Santo Antônio de Goiás. Coletânea dos resumos apresentados. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2017. p. 29. (Embrapa Arroz e Feijão. Documentos, 316).| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 13. | | COSTA NETO, G. M. F.; DUARTE, J. B.; HEINEMANN, A. B.; CASTRO, A. P. de; ANTOLIN, L. A. S. Simulação de interação "genótipo x ambiente" para arroz de terras altas em cenários de aumento da temperatura do ar. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 15. | | CROSSA, J.; MONTESINOS-LÓPEZ, O. A.; PÉREZ-RODRÍGUEZ, P.; COSTA-NETO, G.; FRITSCHE-NETO, R.; ORTIZ, R.; MARTINI, J. W. R.; LILLEMO, M.; MONTESINOS-LÓPEZ, A.; JARQUIN, D.; BRESEGHELLO, F.; CUEVAS, J.; RINCENT, R. Genome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction. In: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022. p. 245-283. (Methods in Molecular Biology).| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 16. | | ARAÚJO, M. S.; CHAVES, S. F. S.; DIAS, L. A. S.; FERREIRA, F. M.; PEREIRA, G. R.; BEZERRA, A. R. G.; ALVES, R. S.; HEINEMANN, A. B.; BRESEGHELLO, F.; CARNEIRO, P. C. S.; KRAUSE, M. D.; COSTA-NETO, G.; DIAS, K. O. G. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. Theoretical and Applied Genetics, v. 137, 80, Mar. 2024.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 16 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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