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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
16/09/1998 |
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Data da última atualização: |
13/07/2018 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BAHIA FILHO, A. F. C.; MAGNAVACA, R.; SCHAFFERT, R. E.; ALVES, V. M. C. |
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Afiliação: |
EMBRAPA/CNPMS; ROBERT EUGENE SCHAFFERT, CNPMS; VERA MARIA CARVALHO ALVES, CNPMS. |
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Título: |
Identification, utilization, and economic impact of maize germplasm tolerant to low levels of phosphorus and toxic levels of exchangeable aluminum in Brazilian soils. |
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Ano de publicação: |
1997 |
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Fonte/Imprenta: |
In: INTERNATIONAL SYMPOSIUM ON PLANT SOIL INTERACTIONS AT LOW pH, 4., 1996, Belo Horizonte. Proceedings... Campinas: SBCS, 1997. p. 59-70. |
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Idioma: |
Inglês |
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Conteúdo: |
A historical review of research and development related to the identification, utilization and economic impact of maize germplasm adapted to acid soils a EMBRAPA/CNPMS is presented. Methods to identify tolerant germplasm and to select segregation materials, through field experiments in acid and fertile soils and screening in nutrient solution are discussed. Results of research including inheritance for aluminum tolerance and development of high yielding tolerant hybrds are presented. Also, results of phosphorus efficiency related to root development of high yielding tolerant hybrids are presented. Also, results of phosphorus efficiency related to root development, internal accumulation of P and kinetics of absorption of P and N are discussed. The experience accumulated in this research program has shown that adaptation to acid soils can be associated, in the same genotype with high yield potential and yield stability, as a result of tolerance to Al toxicity and improved P use efficiency. This program has generated several commercial cultivars which have been successfully utilized in a large area of Brazil. The knowledge and experimental germplasm generated are also valuable resources which are being used to support basic studies of mechanisms of tolerance to aluminum and nutrient use efficiency. A better understanding of these mechanisms will help the development of better strategies for selection and improved cultivars in the future. |
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Palavras-Chave: |
Aluminium; Efficiency; Eficiencia; Maize; Tolerance; Tolerancia. |
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Thesagro: |
Alumínio; Fósforo; Germoplasma; Milho; Solo Ácido; Zea Mays. |
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Thesaurus Nal: |
acid soils; germplasm; phosphorus. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02486nam a2200325 a 4500
001 1480559
005 2018-07-13
008 1997 bl uuuu u00u1 u #d
100 1 $aBAHIA FILHO, A. F. C.
245 $aIdentification, utilization, and economic impact of maize germplasm tolerant to low levels of phosphorus and toxic levels of exchangeable aluminum in Brazilian soils.$h[electronic resource]
260 $aIn: INTERNATIONAL SYMPOSIUM ON PLANT SOIL INTERACTIONS AT LOW pH, 4., 1996, Belo Horizonte. Proceedings... Campinas: SBCS, 1997. p. 59-70.$c1997
520 $aA historical review of research and development related to the identification, utilization and economic impact of maize germplasm adapted to acid soils a EMBRAPA/CNPMS is presented. Methods to identify tolerant germplasm and to select segregation materials, through field experiments in acid and fertile soils and screening in nutrient solution are discussed. Results of research including inheritance for aluminum tolerance and development of high yielding tolerant hybrds are presented. Also, results of phosphorus efficiency related to root development of high yielding tolerant hybrids are presented. Also, results of phosphorus efficiency related to root development, internal accumulation of P and kinetics of absorption of P and N are discussed. The experience accumulated in this research program has shown that adaptation to acid soils can be associated, in the same genotype with high yield potential and yield stability, as a result of tolerance to Al toxicity and improved P use efficiency. This program has generated several commercial cultivars which have been successfully utilized in a large area of Brazil. The knowledge and experimental germplasm generated are also valuable resources which are being used to support basic studies of mechanisms of tolerance to aluminum and nutrient use efficiency. A better understanding of these mechanisms will help the development of better strategies for selection and improved cultivars in the future.
650 $aacid soils
650 $agermplasm
650 $aphosphorus
650 $aAlumínio
650 $aFósforo
650 $aGermoplasma
650 $aMilho
650 $aSolo Ácido
650 $aZea Mays
653 $aAluminium
653 $aEfficiency
653 $aEficiencia
653 $aMaize
653 $aTolerance
653 $aTolerancia
700 1 $aMAGNAVACA, R.
700 1 $aSCHAFFERT, R. E.
700 1 $aALVES, V. M. C.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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Biblioteca |
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Origem |
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Cutter |
Registro |
Volume |
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URL |
Voltar
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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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Embrapa Pecuária Sudeste (CPPSE) |
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