|
|
Registro Completo |
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
03/09/1997 |
Data da última atualização: |
13/07/2018 |
Autoria: |
VIANA, P. A.; SILVA, A. E. da. |
Afiliação: |
PAULO AFONSO VIANA, CNPMS. |
Título: |
Melhoramento da população de milho CMS 14 C para resistência a lagarta-do-cartucho, Spodoptera frugiperda. |
Ano de publicação: |
1994 |
Fonte/Imprenta: |
In: EMBRAPA. Centro de Nacional de Pesquisa de Milho e Sorgo. Relatório técnico anual do Centro Nacional de Pesquisa de Milho e Sorgo: período 1992-1993. Sete Lagoas, 1994. v. 6, p. 138. |
Idioma: |
Português |
Palavras-Chave: |
Lagarta do cartucho; Maize; Pest; Resistance. |
Thesagro: |
Milho; Praga; Resistência; Spodoptera Frugiperda; Zea Mays. |
Thesaurus Nal: |
breeding. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/41333/1/Melhoramento-populacao.pdf
|
Marc: |
LEADER 00827naa a2200241 a 4500 001 1476208 005 2018-07-13 008 1994 bl uuuu u00u1 u #d 100 1 $aVIANA, P. A. 245 $aMelhoramento da população de milho CMS 14 C para resistência a lagarta-do-cartucho, Spodoptera frugiperda.$h[electronic resource] 260 $c1994 650 $abreeding 650 $aMilho 650 $aPraga 650 $aResistência 650 $aSpodoptera Frugiperda 650 $aZea Mays 653 $aLagarta do cartucho 653 $aMaize 653 $aPest 653 $aResistance 700 1 $aSILVA, A. E. da 773 $tIn: EMBRAPA. Centro de Nacional de Pesquisa de Milho e Sorgo. Relatório técnico anual do Centro Nacional de Pesquisa de Milho e Sorgo: período 1992-1993. Sete Lagoas, 1994.$gv. 6, p. 138.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Mandioca e Fruticultura. Para informações adicionais entre em contato com cnpmf.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
Data corrente: |
11/05/2020 |
Data da última atualização: |
15/07/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
CARMO, C. D. do; SOUSA, M. B. e; PEREIRA, J. dos S.; CEBALLOS, H.; OLIVEIRA, E. J. de. |
Afiliação: |
CÁTIA DIAS DO CARMO; MASSAINE BANDEIRA E SOUSA; JOCILENE DOS SANTOS PEREIRA; HERNÁN CEBALLOS; EDER JORGE DE OLIVEIRA, CNPMF. |
Título: |
Identification of waxy cassava genotypes using fourier-transform near-infrared spectroscopy. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Crop Science, 1-13, 2020. |
ISSN: |
0011-183X |
Idioma: |
Inglês |
Conteúdo: |
High?throughput phenotyping tools that allow the early and accurate evaluation of important agronomic traits have gained space in current breeding programs. The aim of this study was to evaluate the potential of Fourier?transform near?infrared spectroscopy (FT?NIRS) to identify cassava (Manihot esculenta Crantz) clones with waxy starch (i.e., amylose?free) by screening leaves rather than roots, and to validate prediction models for classifying these phenotypes. We analyzed the spectra of 162 waxy and 180 nonwaxy genotypes from five different growing environments. The mean FT?NIRS spectra and principal component analysis (PCA) were used to investigate the potential for grouping the data. For classification, five supervised pattern recognition techniques were tested: Bayesian generalized linear model (BGLM), high?dimensional discriminant analysis (HDDA), partial least squares?discriminant analysis (PLS?DA), parallel random forest (PRANDF), and support vector machines with linear kernel (SVM). The mean spectra and the PCA did not allow discrimination of the genotypes based on starch classification. The SVM and BGLM showed the highest classification accuracy in cross?validation (.86?.87), with higher concordance rates (.88?.83), sensitivity (.87?.85) and specificity (.88). The BGLM and SVM models also obtained better indices in the external validation, with high accuracy (.85) and correct classification of 93% of the waxy genotypes. Thus, performing early selection of root characteristics based on the indirect selection of variables extracted from leaf spectra is a good potential strategy for more efficient breeding of the waxy phenotype. MenosHigh?throughput phenotyping tools that allow the early and accurate evaluation of important agronomic traits have gained space in current breeding programs. The aim of this study was to evaluate the potential of Fourier?transform near?infrared spectroscopy (FT?NIRS) to identify cassava (Manihot esculenta Crantz) clones with waxy starch (i.e., amylose?free) by screening leaves rather than roots, and to validate prediction models for classifying these phenotypes. We analyzed the spectra of 162 waxy and 180 nonwaxy genotypes from five different growing environments. The mean FT?NIRS spectra and principal component analysis (PCA) were used to investigate the potential for grouping the data. For classification, five supervised pattern recognition techniques were tested: Bayesian generalized linear model (BGLM), high?dimensional discriminant analysis (HDDA), partial least squares?discriminant analysis (PLS?DA), parallel random forest (PRANDF), and support vector machines with linear kernel (SVM). The mean spectra and the PCA did not allow discrimination of the genotypes based on starch classification. The SVM and BGLM showed the highest classification accuracy in cross?validation (.86?.87), with higher concordance rates (.88?.83), sensitivity (.87?.85) and specificity (.88). The BGLM and SVM models also obtained better indices in the external validation, with high accuracy (.85) and correct classification of 93% of the waxy genotypes. Thus, performing early selection of root chara... Mostrar Tudo |
Thesagro: |
Mandioca. |
Thesaurus NAL: |
Cassava. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02233naa a2200205 a 4500 001 2122196 005 2020-07-15 008 2020 bl uuuu u00u1 u #d 022 $a0011-183X 100 1 $aCARMO, C. D. do 245 $aIdentification of waxy cassava genotypes using fourier-transform near-infrared spectroscopy.$h[electronic resource] 260 $c2020 520 $aHigh?throughput phenotyping tools that allow the early and accurate evaluation of important agronomic traits have gained space in current breeding programs. The aim of this study was to evaluate the potential of Fourier?transform near?infrared spectroscopy (FT?NIRS) to identify cassava (Manihot esculenta Crantz) clones with waxy starch (i.e., amylose?free) by screening leaves rather than roots, and to validate prediction models for classifying these phenotypes. We analyzed the spectra of 162 waxy and 180 nonwaxy genotypes from five different growing environments. The mean FT?NIRS spectra and principal component analysis (PCA) were used to investigate the potential for grouping the data. For classification, five supervised pattern recognition techniques were tested: Bayesian generalized linear model (BGLM), high?dimensional discriminant analysis (HDDA), partial least squares?discriminant analysis (PLS?DA), parallel random forest (PRANDF), and support vector machines with linear kernel (SVM). The mean spectra and the PCA did not allow discrimination of the genotypes based on starch classification. The SVM and BGLM showed the highest classification accuracy in cross?validation (.86?.87), with higher concordance rates (.88?.83), sensitivity (.87?.85) and specificity (.88). The BGLM and SVM models also obtained better indices in the external validation, with high accuracy (.85) and correct classification of 93% of the waxy genotypes. Thus, performing early selection of root characteristics based on the indirect selection of variables extracted from leaf spectra is a good potential strategy for more efficient breeding of the waxy phenotype. 650 $aCassava 650 $aMandioca 700 1 $aSOUSA, M. B. e 700 1 $aPEREIRA, J. dos S. 700 1 $aCEBALLOS, H. 700 1 $aOLIVEIRA, E. J. de 773 $tCrop Science, 1-13, 2020.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Expressão de busca inválida. Verifique!!! |
|
|