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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
13/01/2020 |
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Data da última atualização: |
06/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, D. A.; COSTA, C. N.; SILVA, A. A.; SILVA, H. T.; LOPES, P. S.; SILVA, F. F.; VERONEZE, R.; THOMPSON, G.; AGUILAR, I.; CARVALHEIRA, J. |
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Afiliação: |
CLAUDIO NAPOLIS COSTA, CNPGL. |
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Título: |
Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 3, p. 305-315, 2020. |
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DOI: |
https://doi.org/10.1111/jbg.12459 |
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Idioma: |
Inglês |
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Conteúdo: |
Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder. |
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Palavras-Chave: |
Autoregression; Legendre polynomials; Random regression. |
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Thesaurus Nal: |
Dairy cattle. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02289naa a2200289 a 4500
001 2118639
005 2024-02-06
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1111/jbg.12459$2DOI
100 1 $aSILVA, D. A.
245 $aAutoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle.$h[electronic resource]
260 $c2020
520 $aAutoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.
650 $aDairy cattle
653 $aAutoregression
653 $aLegendre polynomials
653 $aRandom regression
700 1 $aCOSTA, C. N.
700 1 $aSILVA, A. A.
700 1 $aSILVA, H. T.
700 1 $aLOPES, P. S.
700 1 $aSILVA, F. F.
700 1 $aVERONEZE, R.
700 1 $aTHOMPSON, G.
700 1 $aAGUILAR, I.
700 1 $aCARVALHEIRA, J.
773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 3, p. 305-315, 2020.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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| Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
25/02/2013 |
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Data da última atualização: |
10/11/2022 |
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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: |
STARK, S. C.; LEITOLD, V.; WU, J. L.; HUNTER, M. O.; CASTILHO, C. V. de; COSTA, F. R. C.; MCMAHON, S. M.; PARKER, G. G.; SHIMABUKURO, M. T.; LEFSKY, M. A.; KELLER, M.; ALVES, L. F.; SCHIETTI, J.; SHIMABUKURO, Y. E.; BRANDÃO, D. O.; WOODCOCK, T. K.; HIGUCHI, N.; CAMARGO, P. B. de; OLIVEIRA, R. C. de; SALESKA, S. R. |
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Afiliação: |
Scott C. Stark; Veronika Leitold; Jin L. Wu; Maria O. Hunter; Carolina V. de Castilho; Flávia R. C. Costa; Sean M. McMahon; Geoffrey G. Parker; Mônica Takako Shimabukuro; Michael A. Lefsky; Michael Keller; Luciana F. Alves; Juliana Schietti; Yosio Edemir Shimabukuro; Diego O. Brandão; Tara K. Woodcock; Niro Higuchi; Plinio B. de Camargo; RAIMUNDO COSME DE OLIVEIRA JUNIOR, CPATU; Scott R. Saleska. |
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Título: |
Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Ecology Letters, v. 15, n. 12, p. 1406-1414, dez. 2012. |
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DOI: |
10.1111/j.1461-0248.2012.01864.x |
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Idioma: |
Inglês |
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Conteúdo: |
Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) ? remotely estimated from LiDAR ? control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function. |
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Palavras-Chave: |
Biomass growth; Carbon balance; Gap fraction; Leaf area profiles; Remote sensing of canopy structure. |
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Thesaurus NAL: |
lidar. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02301naa a2200433 a 4500
001 1950777
005 2022-11-10
008 2012 bl uuuu u00u1 u #d
024 7 $a10.1111/j.1461-0248.2012.01864.x$2DOI
100 1 $aSTARK, S. C.
245 $aAmazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment.$h[electronic resource]
260 $c2012
520 $aTropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) ? remotely estimated from LiDAR ? control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.
650 $alidar
653 $aBiomass growth
653 $aCarbon balance
653 $aGap fraction
653 $aLeaf area profiles
653 $aRemote sensing of canopy structure
700 1 $aLEITOLD, V.
700 1 $aWU, J. L.
700 1 $aHUNTER, M. O.
700 1 $aCASTILHO, C. V. de
700 1 $aCOSTA, F. R. C.
700 1 $aMCMAHON, S. M.
700 1 $aPARKER, G. G.
700 1 $aSHIMABUKURO, M. T.
700 1 $aLEFSKY, M. A.
700 1 $aKELLER, M.
700 1 $aALVES, L. F.
700 1 $aSCHIETTI, J.
700 1 $aSHIMABUKURO, Y. E.
700 1 $aBRANDÃO, D. O.
700 1 $aWOODCOCK, T. K.
700 1 $aHIGUCHI, N.
700 1 $aCAMARGO, P. B. de
700 1 $aOLIVEIRA, R. C. de
700 1 $aSALESKA, S. R.
773 $tEcology Letters$gv. 15, n. 12, p. 1406-1414, dez. 2012.
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Embrapa Amazônia Oriental (CPATU) |
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