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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
27/02/2023 |
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Data da última atualização: |
27/02/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, I. G. DOS; PEIXOTO, M. A.; CRUZ, C. D.; FERREIRA, R. de P.; NASCIMENTO, M.; ROSADO, R. D. S.; SANT ANNA, I. DE C. |
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Afiliação: |
IARA GONÇALVES DOS SANTOS, Federal Univ. of Viçosa; MARCO ANTÔNIO PEIXOTO, Federal Univ. of Viçosa; COSME DAMIÃO CRUZ, Federal Univ. of Viçosa; REINALDO DE PAULA FERREIRA, CPPSE; MOYSÉS NASCIMENTO, Federal Univ. of Viçosa; RENATO DOMICIANO SILVA ROSADO, Federal Univ. of Viçosa; ISABELA DE CASTRO SANT ANNA, Agronomic Institute of Campinas. |
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Título: |
A novel approach to determine tropical persistence on alfalfa germplasm. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Agronomy Journal, v. 114, p. 3225-3233, 2022. |
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DOI: |
https://doi.org/10.1002/agj2.21147 |
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Idioma: |
Inglês |
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Conteúdo: |
Persistence plays a key role in alfalfa (Medicago sativa L. ssp. sativa) cultivation in tropical areas, but it is still a restriction for breeding programs. The objectives of this study were to identify persistent alfalfa accessions evaluated under tropical conditions, and to propose a method for selecting persistent accessions based on random regression (RR) models using artificial neural networks (ANN). Dry matter yield (DMY) of 77 alfalfa accessions from 24 cuts was measured to evaluate persistence using different RR models. A persistence method was proposed based on the trajectory curves of the accessions. The fitted curves showed a great amplitude regarding DMY over time, which suggest high persistence variability. The three-step method for accessing persistence presented in this study included RR modeling to obtain trends of persistence, k-means to define different persistence clusters, and ANN to perform persistence classification in an automated way. When new accessions are evaluated by an alfalfa breeding program, they will be classified according to their genetic values scores using the same ANN previously fitted. The ANN will greatly enhance the decision-making process. |
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Palavras-Chave: |
Alfalfa germplasm. |
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Thesagro: |
Alfafa; Medicago Sativa. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01906naa a2200241 a 4500
001 2151959
005 2023-02-27
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/agj2.21147$2DOI
100 1 $aSANTOS, I. G. DOS
245 $aA novel approach to determine tropical persistence on alfalfa germplasm.$h[electronic resource]
260 $c2022
520 $aPersistence plays a key role in alfalfa (Medicago sativa L. ssp. sativa) cultivation in tropical areas, but it is still a restriction for breeding programs. The objectives of this study were to identify persistent alfalfa accessions evaluated under tropical conditions, and to propose a method for selecting persistent accessions based on random regression (RR) models using artificial neural networks (ANN). Dry matter yield (DMY) of 77 alfalfa accessions from 24 cuts was measured to evaluate persistence using different RR models. A persistence method was proposed based on the trajectory curves of the accessions. The fitted curves showed a great amplitude regarding DMY over time, which suggest high persistence variability. The three-step method for accessing persistence presented in this study included RR modeling to obtain trends of persistence, k-means to define different persistence clusters, and ANN to perform persistence classification in an automated way. When new accessions are evaluated by an alfalfa breeding program, they will be classified according to their genetic values scores using the same ANN previously fitted. The ANN will greatly enhance the decision-making process.
650 $aAlfafa
650 $aMedicago Sativa
653 $aAlfalfa germplasm
700 1 $aPEIXOTO, M. A.
700 1 $aCRUZ, C. D.
700 1 $aFERREIRA, R. de P.
700 1 $aNASCIMENTO, M.
700 1 $aROSADO, R. D. S.
700 1 $aSANT ANNA, I. DE C.
773 $tAgronomy Journal$gv. 114, p. 3225-3233, 2022.
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