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Registro Completo |
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Biblioteca(s): |
Embrapa Agropecuária Oeste; Embrapa Algodão; Embrapa Clima Temperado; Embrapa Trigo; Embrapa Unidades Centrais. |
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Data corrente: |
29/03/1993 |
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Data da última atualização: |
06/06/2013 |
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Autoria: |
FERNANDES, J. M.; FERNANDEZ, M. R.; KOCHHANN, R. A.; SELLES, F.; ZENTNER, R. P. (ed.). |
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Título: |
Manual de manejo conservacionista do solo para os estados do Rio Grande do Sul, Santa Catarina e Paraná. |
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Ano de publicação: |
1991 |
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Fonte/Imprenta: |
Passo Fundo: EMBRAPA-CNPT; Swift Current: CIDA, 1991. |
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Páginas: |
69 p. |
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Série: |
(EMBRAPA-CNPT. Documentos, 1). |
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ISSN: |
0101-6644 |
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Idioma: |
Português |
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Conteúdo: |
O solo no sistema de manejo conservacionista; Rotacao de culturas e culturas alternativas de inverno no sistema de manejo conservacionista; Controle de plantas invasoras no sistema de manejo conservacionista; Relacao entre insetos-pragas e manejo do solo; Doencas das culturas sob manejo conservacionista; Semeadoras para uso em manejo aonservacionista; Aspectos economicos no sistema de manejo conservacionista. |
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Palavras-Chave: |
Alternativas; Aspectos; Caracteristica; Controle; Culturas; Doencas; Economicos; Erva-daninha; Fertilizacao; Fisicas; Insetos; Invasoras; Manejo conservacionista do solo; Paraná; Plantas; Pragas; Quimicas; Rio Grande do Sul; Rotacao; Santa Catarina; Semeadora; Sementes; Sistema; Solos. |
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Thesagro: |
Calagem; Conservação; Conservação do Solo; Manejo; Produção; Sistema de Produção; Solo. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/84173/1/CNPT-DOC.-1-91.pdf
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Marc: |
LEADER 01842nam a2200565 a 4500
001 1815636
005 2013-06-06
008 1991 bl uuuu 00u1 u #d
022 $a0101-6644
100 1 $aFERNANDES, J. M.
245 $aManual de manejo conservacionista do solo para os estados do Rio Grande do Sul, Santa Catarina e Paraná.
260 $aPasso Fundo: EMBRAPA-CNPT; Swift Current: CIDA$c1991
300 $a69 p.
490 $a(EMBRAPA-CNPT. Documentos, 1).
520 $aO solo no sistema de manejo conservacionista; Rotacao de culturas e culturas alternativas de inverno no sistema de manejo conservacionista; Controle de plantas invasoras no sistema de manejo conservacionista; Relacao entre insetos-pragas e manejo do solo; Doencas das culturas sob manejo conservacionista; Semeadoras para uso em manejo aonservacionista; Aspectos economicos no sistema de manejo conservacionista.
650 $aCalagem
650 $aConservação
650 $aConservação do Solo
650 $aManejo
650 $aProdução
650 $aSistema de Produção
650 $aSolo
653 $aAlternativas
653 $aAspectos
653 $aCaracteristica
653 $aControle
653 $aCulturas
653 $aDoencas
653 $aEconomicos
653 $aErva-daninha
653 $aFertilizacao
653 $aFisicas
653 $aInsetos
653 $aInvasoras
653 $aManejo conservacionista do solo
653 $aParaná
653 $aPlantas
653 $aPragas
653 $aQuimicas
653 $aRio Grande do Sul
653 $aRotacao
653 $aSanta Catarina
653 $aSemeadora
653 $aSementes
653 $aSistema
653 $aSolos
700 1 $aFERNANDEZ, M. R.
700 1 $aKOCHHANN, R. A.
700 1 $aSELLES, F.
700 1 $aZENTNER, R. P.
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Registro original: |
Embrapa Trigo (CNPT) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
04/01/2023 |
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Data da última atualização: |
04/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
SANTANA, T. E. Z.; SILVA, J. C. F.; SILVA, L. O. C. da; ALVARENGA, A. B.; MENEZES, G. R. de O.; TORRES JUNIOR, R. A. de A.; DUARTE, M. de S.; SILVA, F. F. e. |
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Afiliação: |
TALITA ESTEFANI ZUNINO SANTANA, UNIVERSIDADE FEDERAL DE VIÇOSA; JOSE CLEYDSON F. SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIZ OTAVIO CAMPOS DA SILVA, CNPGC; AMANDA BOTELHO ALVARENGA, PURDUE UNIVERSITY; GILBERTO ROMEIRO DE OLIVEIRA MENEZE, CNPGC; ROBERTO AUGUSTO DE A TORRES JUNIOR, CNPGC; MARCIO DE SOUZA DUARTE, UNIVERSITY GUELPH; FABYANO FONSECA E SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Livestock Science, v. 260, article 104935, 2022. |
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ISSN: |
1871-1413 |
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DOI: |
https://doi.org/10.1016/j.livsci.2022.104935 |
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Idioma: |
Inglês |
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Conteúdo: |
Stayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle. MenosStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number ... Mostrar Tudo |
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Thesagro: |
Gado de Corte; Gado Nelore; Touro. |
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Thesaurus NAL: |
Beef cattle; Bulls; Daughters; Genome; Genomics; Nellore. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02992naa a2200337 a 4500
001 2150623
005 2023-01-04
008 2022 bl uuuu u00u1 u #d
022 $a1871-1413
024 7 $ahttps://doi.org/10.1016/j.livsci.2022.104935$2DOI
100 1 $aSANTANA, T. E. Z.
245 $aGenome-enabled classification of stayability in Nellore cattle under a machine learning framework.$h[electronic resource]
260 $c2022
520 $aStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle.
650 $aBeef cattle
650 $aBulls
650 $aDaughters
650 $aGenome
650 $aGenomics
650 $aNellore
650 $aGado de Corte
650 $aGado Nelore
650 $aTouro
700 1 $aSILVA, J. C. F.
700 1 $aSILVA, L. O. C. da
700 1 $aALVARENGA, A. B.
700 1 $aMENEZES, G. R. de O.
700 1 $aTORRES JUNIOR, R. A. de A.
700 1 $aDUARTE, M. de S.
700 1 $aSILVA, F. F. e
773 $tLivestock Science$gv. 260, article 104935, 2022.
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Embrapa Gado de Corte (CNPGC) |
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