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Registro Completo |
Biblioteca(s): |
Embrapa Meio-Norte. |
Data corrente: |
28/02/2020 |
Data da última atualização: |
12/12/2023 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
PIRES, P. R. S.; BARRETO, A. L. H.; LOPES, M. T. do R.; MEIRELLES, R. N. |
Afiliação: |
PAOLA RAMOS SIMÕES PIRES, ESTUDANTE UFPI; ANA LUCIA HORTA BARRETO, CPAMN; MARIA TERESA DO REGO LOPES, CPAMN; RAFAEL NARCISO MEIRELLES, BOLSISTA PÓS-DOUTORADO DA EMBRAPA MEIO-NORTE. |
Título: |
Caracterização do mel de Apis mellifera da microrregião de Campo Maior, PI. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA MEIO-NORTE, 3., 2017, Teresina, PI. Anais da III Jornada Científica da Embrapa Meio-Norte. Teresina: Embrapa Meio-Norte, 2017. |
Páginas: |
p. 36. |
Idioma: |
Português |
Conteúdo: |
O objetivo deste trabalho foi analisar as características físico-químicas e melissopalinológicas de sete amostras de méis silvestres produzidas por Apis mellifera, coletadas entre abril e julho de 2017, na microrregião de Campo Maior, PI, compreendendo os municípios de São Miguel do Tapuio e Campo Maior. |
Palavras-Chave: |
Melissopalinologia. |
Thesagro: |
Abelha; Flora; Qualidade. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/211234/1/Terceira-Jornada-Cientifica-00036.pdf
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Marc: |
LEADER 01026nam a2200205 a 4500 001 2120604 005 2023-12-12 008 2017 bl uuuu u00u1 u #d 100 1 $aPIRES, P. R. S. 245 $aCaracterização do mel de Apis mellifera da microrregião de Campo Maior, PI.$h[electronic resource] 260 $aIn: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA MEIO-NORTE, 3., 2017, Teresina, PI. Anais da III Jornada Científica da Embrapa Meio-Norte. Teresina: Embrapa Meio-Norte$c2017 300 $ap. 36. 520 $aO objetivo deste trabalho foi analisar as características físico-químicas e melissopalinológicas de sete amostras de méis silvestres produzidas por Apis mellifera, coletadas entre abril e julho de 2017, na microrregião de Campo Maior, PI, compreendendo os municípios de São Miguel do Tapuio e Campo Maior. 650 $aAbelha 650 $aFlora 650 $aQualidade 653 $aMelissopalinologia 700 1 $aBARRETO, A. L. H. 700 1 $aLOPES, M. T. do R. 700 1 $aMEIRELLES, R. N.
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Registro original: |
Embrapa Meio-Norte (CPAMN) |
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Registro Completo
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
20/08/2020 |
Data da última atualização: |
04/11/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
OLIVEIRA, I. C. M.; GUILHEN, J. H. S.; RIBEIRO, P. C. de O.; GEZAN, S. A.; SCHAFFERT, R. E.; SIMEONE, M. L. F.; DAMASCENO, C. M. B.; CARNEIRO, J. E. de S.; CARNEIRO, P. C. S.; PARRELLA, R. A. da C.; PASTINA, M. M. |
Afiliação: |
Isadora Cristina Martins Oliveira; José Henrique Soler Guilhen; Pedro César de Oliveira Ribeiro, Universidade Federal de Viçosa; Salvador Alejandro Gezan, VSN International; ROBERT EUGENE SCHAFFERT, CNPMS; MARIA LUCIA FERREIRA SIMEONE, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; José Eustáquio de Souza Carneiro, Universidade Federal de Viçosa; Pedro Crescêncio Souza Carneiro, Universidade Federal de Viçosa; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARIA MARTA PASTINA, CNPMS. |
Título: |
Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Field Crops Research, v. 257, 107929, 2020. |
Idioma: |
Inglês |
Conteúdo: |
Biomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environmental factors affecting G × E, such as minimumtemperature, precipitation, solar radiation, crop growth cycle and altitude. MenosBiomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environment... Mostrar Tudo |
Thesagro: |
Bioenergia; Melhoramento Genético Vegetal; Sorghum Bicolor. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/215435/1/Genotype-environment.pdf
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Marc: |
LEADER 02518naa a2200277 a 4500 001 2124452 005 2020-11-04 008 2020 bl uuuu u00u1 u #d 100 1 $aOLIVEIRA, I. C. M. 245 $aGenotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates.$h[electronic resource] 260 $c2020 520 $aBiomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years,using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G× E between environments clustered in a given factor. For example: precipitation, minimum temperature andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally,FA models can be successfully used to identify the main environmental factors affecting G × E, such as minimumtemperature, precipitation, solar radiation, crop growth cycle and altitude. 650 $aBioenergia 650 $aMelhoramento Genético Vegetal 650 $aSorghum Bicolor 700 1 $aGUILHEN, J. H. S. 700 1 $aRIBEIRO, P. C. de O. 700 1 $aGEZAN, S. A. 700 1 $aSCHAFFERT, R. E. 700 1 $aSIMEONE, M. L. F. 700 1 $aDAMASCENO, C. M. B. 700 1 $aCARNEIRO, J. E. de S. 700 1 $aCARNEIRO, P. C. S. 700 1 $aPARRELLA, R. A. da C. 700 1 $aPASTINA, M. M. 773 $tField Crops Research$gv. 257, 107929, 2020.
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