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Registro Completo |
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Biblioteca(s): |
Embrapa Acre; Embrapa Agropecuária Oeste; Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Meio-Norte; Embrapa Unidades Centrais. |
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Data corrente: |
18/02/2000 |
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Data da última atualização: |
22/05/2023 |
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Autoria: |
SÁ, C. P. de; PIMENTEL, F. A.; CABRAL, W. G.; SILVA, M. R. da; PINHEIRO, P. S. N.; BEZERRA, A. L. |
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Afiliação: |
CLAUDENOR PINHO DE SA, CPAF-Acre; FLAVIO ARAUJO PIMENTEL, CPAF-AC; WALDIRENE GOMES CABRAL, BOLSISTA CNPq; MARCOS ROCHA DA SILVA, PESACRE; PAULO SÉRGIO NRES PINHEIRO, BOLSISTA CNPq; ALEX LIRA BEZERRA, ESTAGIÁRIO EMBRAPA ACRE. |
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Título: |
Coeficientes técnicos e custos para exploração da pimenta longa. |
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Ano de publicação: |
1998 |
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Fonte/Imprenta: |
Rio Branco, AC: Embrapa CPAF-AC, 1998. |
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Páginas: |
2 p. |
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Série: |
(Embrapa CPAF-AC. Instruções técnicas, 8). |
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ISSN: |
0104-9038 |
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Idioma: |
Português |
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Conteúdo: |
A descoberta de populações nativas de pimenta longa (Piper hispidinervum) no Estado do Acre, apresentando teores de safrol acima de 90% em seu óleo essencial, criou uma grande perspectiva de sua exploração, principalmente, considerando os aspectos da geração de emprego e renda. Assim, torna-se necessário conhecer e discutir aspectos relacionados aos custos, como também seus coeficientes técnicos de produção. |
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Palavras-Chave: |
Aceites esenciales; Acre; Análisis de costo-beneficio; Análisis económico; Brasil; Cost beneficit analysis; Essenctial oils; Exploração; Insumos agrícolas; Piper hispidinervium; Rentabilidad; Safrol. |
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Thesagro: |
Análise de custo-benefício; Análise econômica; Custo; Custo-Benefício; Extração; Insumo; Óleo essencial; Pimenta; Pimenta Longa; Piper hispidinervum; Plantio; Processamento; Rentabilidade. |
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Thesaurus Nal: |
Amazonia; Cost benefit analysis; Economic analysis; Farm inputs; Piper; Piper longum; Profitability; Safrole. |
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Categoria do assunto: |
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F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/145919/1/4487.pdf
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Marc: |
LEADER 02007nam a2200601 a 4500
001 1495879
005 2023-05-22
008 1998 bl uuuu u0uu1 u #d
022 $a0104-9038
100 1 $aSÁ, C. P. de
245 $aCoeficientes técnicos e custos para exploração da pimenta longa.
260 $aRio Branco, AC: Embrapa CPAF-AC$c1998
300 $a2 p.
490 $a(Embrapa CPAF-AC. Instruções técnicas, 8).
520 $aA descoberta de populações nativas de pimenta longa (Piper hispidinervum) no Estado do Acre, apresentando teores de safrol acima de 90% em seu óleo essencial, criou uma grande perspectiva de sua exploração, principalmente, considerando os aspectos da geração de emprego e renda. Assim, torna-se necessário conhecer e discutir aspectos relacionados aos custos, como também seus coeficientes técnicos de produção.
650 $aAmazonia
650 $aCost benefit analysis
650 $aEconomic analysis
650 $aFarm inputs
650 $aPiper
650 $aPiper longum
650 $aProfitability
650 $aSafrole
650 $aAnálise de custo-benefício
650 $aAnálise econômica
650 $aCusto
650 $aCusto-Benefício
650 $aExtração
650 $aInsumo
650 $aÓleo essencial
650 $aPimenta
650 $aPimenta Longa
650 $aPiper hispidinervum
650 $aPlantio
650 $aProcessamento
650 $aRentabilidade
653 $aAceites esenciales
653 $aAcre
653 $aAnálisis de costo-beneficio
653 $aAnálisis económico
653 $aBrasil
653 $aCost beneficit analysis
653 $aEssenctial oils
653 $aExploração
653 $aInsumos agrícolas
653 $aPiper hispidinervium
653 $aRentabilidad
653 $aSafrol
700 1 $aPIMENTEL, F. A.
700 1 $aCABRAL, W. G.
700 1 $aSILVA, M. R. da
700 1 $aPINHEIRO, P. S. N.
700 1 $aBEZERRA, A. L.
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Registro original: |
Embrapa Acre (CPAF-AC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
05/07/2019 |
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Data da última atualização: |
30/10/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. |
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Afiliação: |
Leonardo Volpato, Universidade Federal de Viçosa; Rodrigo Silva Alves, Universidade Federal de Viçosa; Paulo Eduardo Teodoro, Universidade Federal de Mato Grosso do Sul; MARCOS DEON VILELA DE RESENDE, CNPF; Moysés Nascimento, Universidade Federal de Viçosa; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Willian Hytalo Ludke, Universidade Federal de Viçosa; Felipe Lopes da Silva, Universidade Federal de Viçosa; Aluízio Borém, Universidade Federal de Viçosa. |
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Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
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DOI: |
10.1371/journal.pone.0215315 |
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Idioma: |
Inglês |
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Conteúdo: |
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny. MenosAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided... Mostrar Tudo |
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Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
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Thesagro: |
Soja. |
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Thesaurus NAL: |
Agronomic traits; Prediction; Soybeans. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199239/1/2019-M.Deon-PO-Multi-trait.pdf
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Marc: |
LEADER 02789naa a2200373 a 4500
001 2110400
005 2019-10-30
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pone.0215315$2DOI
100 1 $aVOLPATO, L.
245 $aMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.$h[electronic resource]
260 $c2019
520 $aAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
650 $aAgronomic traits
650 $aPrediction
650 $aSoybeans
650 $aSoja
653 $aBayesian-inference
653 $aBreeding values
653 $aGenomic selection
653 $aInferência Bayesian
653 $aMixed models
653 $aModelo misto
653 $aSeed protein
653 $aSeleção genômica
700 1 $aALVES, R. S.
700 1 $aTEODORO, P. E.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aLUDKE, W. H.
700 1 $aSILVA, F. L. da
700 1 $aBORÉM, A.
773 $tPLoS ONE$gv. 14, n. 4, e0215315, Apr. 2019. 22 p.
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Registro original: |
Embrapa Florestas (CNPF) |
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