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Registros recuperados : 231 | |
83. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | DESTEFANI, C. R.; FERREIRA, R. de P.; BARIONI JUNIOR, W.; FREITAS, A. R. de. Procedimentos GLM e MIXED do SAS para análise de dados de produção de matéria seca de alfafa. In: SIMPÓSIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA PECUÁRIA SUDESTE, 2., 2007, São Carlos, SP. Anais... São Carlos: Embrapa Pecuária Sudeste, 2007. (Embrapa Pecuária Sudeste. Documentos, 76). Biblioteca(s): Embrapa Pecuária Sudeste. |
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84. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | BOTREL, M. de A.; ALVIM, M. J.; FERREIRA, R. de P.; XAVIER, D. F. Potencial forrageiro de gramíneas em condições de baixas temperaturas e altitude elevada. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 37, n. 3, p. 393-398, mar. 2002 Título em inglês: Forage potential of grass species under conditions of low temperature and high altitude. Biblioteca(s): Embrapa Unidades Centrais. |
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87. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | BERNARDI, A. C. de C.; RASSINI, J. B.; FERREIRA, R. de P.; MOREIRA, A. Produção de matéria seca, teores no solo e extração de potássio pela alfafa em função de doses e freqüência da adubação potássica. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 31., 2007, Gramado. Conquistas e desafios da Ciência do Solo brasileira: anais. Gramado: SBCS, 2007. 4 p. 1 CD-ROM. Resumos na seguinte fonte: In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 31., 2007, Gramado. Conquistas e desafios da Ciência do Solo brasileira: resumos. Gramado: SBCS, 2007. p. 126 Biblioteca(s): Embrapa Pecuária Sudeste. |
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88. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | BERNARDI, A. C. de C.; CARDOSO, R. D.; MOTA, E. P. da; FERREIRA, R. de P. Produção, estado nutricional e qualidade da alfafa sob pastejo e ocorrência de plantas daninhas em resposta à calagem, gessagem e adubação potássica. Boletim de Indústria Animal, v. 70, n. 1, p. 67-74, 2013. Biblioteca(s): Embrapa Pecuária Sudeste. |
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94. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | SHIMOYA, A.; FERREIRA, R. de P.; PEREIRA, A. V.; CRUZ, C. D.; CARNEIRO, P. C. S. Comportamento morfo-agronomico de genotipos de capim-elefante. Revista Ceres, Vicosa, v.48, n.276, p.141-158, 2001. Biblioteca(s): Embrapa Gado de Leite. |
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96. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | FERREIRA, R. de P.; BOTREL, M. de A.; PEREIRA, A. V.; CRUZ, C. D. Avaliação de cultivares de alfafa e estimativas de repetibilidade de caracteres forrageiro. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 34, n. 6, p. 995-1002, jun. 1999 Título em inglês: Evaluation of alfalfa cultivars and estimates of repeatability coefficient of forage traits. Biblioteca(s): Embrapa Unidades Centrais. |
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Registros recuperados : 231 | |
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Registro Completo
Biblioteca(s): |
Embrapa Pecuária Sudeste. |
Data corrente: |
21/05/2019 |
Data da última atualização: |
13/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
SANTOS, I. G. dos; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P. |
Afiliação: |
Iara Gonçalves dos Santos, UFV; Cosme Damião Cruz, UFV; Moysés Nascimento, UFV; REINALDO DE PAULA FERREIRA, CPPSE. |
Título: |
Selection index as a priori information for using artificial neural networks to classify alfalfa genotypes. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 2, gmr18221, 2019. |
DOI: |
doi.org/10.4238/gmr18221 |
Idioma: |
Inglês |
Conteúdo: |
The efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs. MenosThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear,... Mostrar Tudo |
Palavras-Chave: |
Computational intelligence; Tai index. |
Thesagro: |
Medicago Sativa. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/197607/1/gmr18221-selection-index-priori-information-using.pdf
|
Marc: |
LEADER 02470naa a2200205 a 4500 001 2109207 005 2023-03-13 008 2019 bl uuuu u00u1 u #d 024 7 $adoi.org/10.4238/gmr18221$2DOI 100 1 $aSANTOS, I. G. dos 245 $aSelection index as a priori information for using artificial neural networks to classify alfalfa genotypes.$h[electronic resource] 260 $c2019 520 $aThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs. 650 $aMedicago Sativa 653 $aComputational intelligence 653 $aTai index 700 1 $aCRUZ, C. D. 700 1 $aNASCIMENTO, M. 700 1 $aFERREIRA, R. de P. 773 $tGenetics and Molecular Research$gv. 18, n. 2, gmr18221, 2019.
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Embrapa Pecuária Sudeste (CPPSE) |
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