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| Registros recuperados : 24 | |
| 3. |  | FERREIRA, R. de P.; SANTOS, I. G. dos; CARNEIRO, V. Q.; CRUZ, C. D. Adaptability and stability of alfalfa genotypes using fuzzy controller. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 54., 2017, Foz do Iguaçu, PR. Proceedings... Foz do Iguaçu, PR: SBZ, 2017. p. 430.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 5. | | SANTOS, I. G.; NUNES, E. A.; SOUZA, P. B. de; PREVIERO, C. A. Diversidade florística do estrato arbustivo-arbóreo em quintais agroflorestais do reassentamento Mariana, TO. Pesquisa Florestal Brasileira, Colombo, v. 37, n. 92, p. 513-524, out./dez. 2017.| Biblioteca(s): Embrapa Florestas. |
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| 8. | | SANTOS, R. M.; VOLTOLINI, T. V.; ANGELOTTI, F.; MOURA, M. S. B. de; SANTOS, I. G. dos. Aptidão climática do capim-búffel. In: CONGRESSO NORDESTINO DE PRODUÇÃO ANIMAL, 6.; SIMPÓSIO NORDESTINO DE ALIMENTAÇÃO DE RUMINANTES, 7.; FÓRUM DE COORDENADORES DE PÓS GRADUAÇÃO EM PRODUÇÃO ANIMAL DO NORDESTE, 1.; FÓRUM DE AGROECOLOGIA RO RIO GRANDE DO NORTE, 1., 2010, Mossoró. Anais... Mossoró: Sociedade Nordestina de Producao Animal; UFERSA, 2010. 1 CD-ROM.| Biblioteca(s): Embrapa Semiárido. |
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| 9. | | BORGES, L. de A. B.; SANTOS, I. G. dos; FONTOURA, P. R.; FERNANDES, P. M.; MADARI, B. E. Avaliação da produtividade de colmos em uma cronossequência de canaviais cultivados em sistema orgânico. In: SEMINÁRIO JOVENS TALENTOS, 7., 2013, Santo Antônio de Goiás. Coletânea dos resumos apresentados. Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2013. p. 100. (Embrapa Arroz e Feijão. Documentos, 292). Pôster - pós-graduação.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 11. | | SANTOS, I. G. dos; CARNEIRO, V. Q.; SANT'ANNA, I. de C.; CRUZ, C. D.; CARVALHO, C. G. P.; BORBA FILHO, A. L.; ALVES, A. D. Factor analysis and GGE biplot for environmental andgenotypic evaluation in sunflower trials. Functional Plant Breeding Journal, v. 1, n. 2, p. 29-40, 2019.| Biblioteca(s): Embrapa Soja. |
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| 12. | | SANTOS, I. G.; TEODORO, P. E.; FARIAS, F. C.; FARIAS, F. J. C.; CARVALHO, L. P. de; RODRIGUES, J. I. S.; CRUZ, C. D. Genetic diversity among cotton cultivars in two environments in the State of Mato Grosso. Genetics and Molecular Research, v. 16, n. 2, gmr16029628, 2017.| Biblioteca(s): Embrapa Algodão. |
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| 13. | | RIBEIRO, G. H. DA S.; DIAS, L. E. R.; FONTOURA, P. R.; SANTOS, I. G. DOS; BARRETO, C. C.; REIS JUNIOR, F. B. dos; MENDES, I. de C. Atividade enzimática de solos do cerrado sob cultivo de cana-de-açúcar em diferentes sistemas de manejo. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 33., 2011, Uberlândia. Solos nos biomas brasileiros: sustentabilidade e mudanças climáticas. Uberlândia: Sociedade Brasileira de Ciência do Solo, 2011. 1 CD-ROM.| Biblioteca(s): Embrapa Cerrados. |
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| 14. | | ALVES, R. T.; REIN, T. A.; FONTOURA, P. R.; CAMPOS, P. F.; MALAQUIAS, J. V.; FERNANDES, P. M.; TRISTÃO, V. L.; SANTOS, I. G. Avaliação do controle biológico da cigarrinha-da-raiz da cana-de-açúcar, Mahanarva Fimbriolata (Stal. 1854) no Cerrado do Brasil. In: CONGRESSO BRASILEIRO DE ENTOMOLOGIA, 23., 2010, Natal. Anais... Natal: Sociedade Brasileira de Entomologia, 2010. 1 CD-ROM. 1 CD-ROM.| Biblioteca(s): Embrapa Cerrados. |
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| 15. | | BORGES, L. de A. B.; SANTOS, I. G. dos; SOARES, R. A. B.; FERNANDES, P. M.; MADARI, B. E.; SILVA, M. A. S. da. Carbono e densidade do solo em uma cronosequência de canaviais cultivado em sistema orgânico. In: ENCONTRO BRASILEIRO DE SUBSTÂNCIAS HÚMICAS, 10., 2013, Santo Antônio de Goiás. Matéria orgânica e qualidade ambiental: anais. Brasília, DF: Embrapa, 2013. p. 149-152.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 16. | | 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. A novel approach to determine tropical persistence on alfalfa germplasm. Agronomy Journal, v. 114, p. 3225-3233, 2022.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 17. | | COSTA, W. G. da; SANTOS, I. G. dos; SILVA JÚNIOR, A. C. da; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P.; VILELA, D. Potential of dry matter yield from alfalfa germplasm in composing base populations. Crop Breeding and Applied Biotechnology, v. 21, n. 2, e36702132, 2021.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 18. | | SANTOS, B. R. C. dos; VOLTOLINI, T. V.; NOGUEIRA, D. M.; SANTOS, E. F. dos; SANTOS, I. G. dos; OLIVEIRA, R. G. de. Vegetação espontânea e áreas de solo descoberto em pastagens de capim-buffel sob diferentes ofertas de biomassa no período seco na região Semiárida de Pernambuco. In: CONGRESSO NORDESTINO DE PRODUÇÃO ANIMAL, 6.; SIMPÓSIO NORDESTINO DE ALIMENTAÇÃO DE RUMINANTES, 7.; FÓRUM DE COORDENADORES DE PÓS GRADUAÇÃO EM PRODUÇÃO ANIMAL DO NORDESTE, 1.; FÓRUM DE AGROECOLOGIA RO RIO GRANDE DO NORTE, 1., 2010, Mossoró. Anais... Mossoró: Sociedade Nordestina de Producao Animal; UFERSA, 2010. 1 CD-ROM.| Biblioteca(s): Embrapa Semiárido. |
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| 19. | | SANTOS, I. G. dos; ROCHA, J. R. do A. S. de C.; VIGNA, B. B. Z.; CRUZ, C. D.; FERREIRA, R. de P.; HORACIO BASIGALUP, D.; MARCHINI, R. M. S. Exploring the diversity of alfalfa within Brazil for tropical production. Euphytica, v. 216, n. 72, p. 1-15. apr. 2020.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 20. | | OLIVEIRA, R. G. de; VOLTOLINI, T. V.; SANTOS, B. R. C. dos; SILVA, M. R. C. da; SANTOS, I. G. dos; DAMASCENO, M. G.; SANTOS, E. F. dos; ROSAM P. R. da. Avaliação do desempenho produtivo de ovinos SRD em pastagens de capim-búffel, sob duas variedades e três ofertas de biomassa no Semiárido de Pernambuco. In: CONGRESSO NORDESTINO DE PRODUÇÃO ANIMAL, 6.; SIMPÓSIO NORDESTINO DE ALIMENTAÇÃO DE RUMINANTES, 7.; FÓRUM DE COORDENADORES DE PÓS GRADUAÇÃO EM PRODUÇÃO ANIMAL DO NORDESTE, 1.; FÓRUM DE AGROECOLOGIA RO RIO GRANDE DO NORTE, 1., 2010, Mossoró. Anais... Mossoró: Sociedade Nordestina de Producao Animal; UFERSA, 2010. 1 CD-ROM.| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 24 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
21/05/2019 |
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Data da última atualização: |
13/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SANTOS, I. G. dos; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P. |
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Afiliação: |
Iara Gonçalves dos Santos, UFV; Cosme Damião Cruz, UFV; Moysés Nascimento, UFV; REINALDO DE PAULA FERREIRA, CPPSE. |
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Título: |
Selection index as a priori information for using artificial neural networks to classify alfalfa genotypes. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 2, gmr18221, 2019. |
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DOI: |
doi.org/10.4238/gmr18221 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Computational intelligence; Tai index. |
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Thesagro: |
Medicago Sativa. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/197607/1/gmr18221-selection-index-priori-information-using.pdf
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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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