| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
21/06/2016 |
|
Data da última atualização: |
21/06/2016 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
OLIVEIRA, H. R.; SILVA, F. F.; SIQUEIRA, O. H. G. B. D.; SOUZA, N. O.; JUNQUEIRA, V. S.; RESENDE, M. D. V. de; BORQUIS, R. R. A.; RODRIGUES, M. T. |
|
Afiliação: |
H. R. Oliveira, UFV; F. F. Silva, UFV; O. H. G. B. D. Siqueira, UFV; N. O. Souza, UFV; V. S. Junqueira, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; R. R. A. Borquis, UNESP; M. T. Rodrigues, UFV. |
|
Título: |
Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models. |
|
Ano de publicação: |
2016 |
|
Fonte/Imprenta: |
Journal of Animal Science, v. 94 n. 5, p. 1865-1874, May 2016. |
|
DOI: |
10.2527/jas2015-0150 |
|
Idioma: |
Inglês |
|
Conteúdo: |
We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from −0.58 to 0.03, −0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats. MenosWe proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged fro... Mostrar Tudo |
|
Palavras-Chave: |
Ali and Schaeffer function; B-splines; Deviance information criterion; Legendre polynomials; Posterior model probabilities; Wilmink function. |
|
Thesagro: |
Cabra leiteira; Leite; Método estatístico. |
|
Thesaurus Nal: |
Dairy goats; Milk yield; Statistical analysis. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02889naa a2200361 a 4500
001 2047576
005 2016-06-21
008 2016 bl uuuu u00u1 u #d
024 7 $a10.2527/jas2015-0150$2DOI
100 1 $aOLIVEIRA, H. R.
245 $aCombining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models.$h[electronic resource]
260 $c2016
520 $aWe proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from −0.58 to 0.03, −0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.
650 $aDairy goats
650 $aMilk yield
650 $aStatistical analysis
650 $aCabra leiteira
650 $aLeite
650 $aMétodo estatístico
653 $aAli and Schaeffer function
653 $aB-splines
653 $aDeviance information criterion
653 $aLegendre polynomials
653 $aPosterior model probabilities
653 $aWilmink function
700 1 $aSILVA, F. F.
700 1 $aSIQUEIRA, O. H. G. B. D.
700 1 $aSOUZA, N. O.
700 1 $aJUNQUEIRA, V. S.
700 1 $aRESENDE, M. D. V. de
700 1 $aBORQUIS, R. R. A.
700 1 $aRODRIGUES, M. T.
773 $tJournal of Animal Science$gv. 94 n. 5, p. 1865-1874, May 2016.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Arroz e Feijão. |
|
Data corrente: |
25/06/2014 |
|
Data da última atualização: |
26/06/2014 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
CALINGACION, M.; LABORTE, A.; RESURRECCION, A.; CONCEPCION, J. C.; DAYGON, V. D.; MUMM, R.; REINKE, R.; DIPTI, S.; BASSINELLO, P. Z.; MANFUL, J.; SOPHANY, S.; LARA, K. C.; BAO, J.; XIE, L.; LOAIZA, K.; EL-HISSEWY, A.; GAYIN, J.; SHARMA, N.; RAJESWARI, S.; MANONMANI, S.; RANI, N. S.; KOTA, S.; INDRASARI, S. D.; HABIBI, F.; HOSSEINI, M.; TAVASOLI, F.; SUZUKI, K.; UMEMOTO, T.; BOUALAPHANH, C.; LEE, H. H.; HUNG, Y. P.; RAMLI, A.; RAMLI, A.; AUNG, P. P.; AHMAD, R.; WATTOO, J. I.; BANDONILL, E.; ROMERO, M.; BRITES, C. M.; HAFEEL, R.; LUR, H.-S.; CHEAUPUN, K.; JONGDEE, S.; BLANCO, P.; BRYANT, R.; LANG, N. T.; HALL, R. D.; FITZGERALD, M. |
|
Afiliação: |
MARIAFE CALINGACION, IRRI; ALICE LABORTE, IRRI; ADORACION RESURRECCION, IRRI; JEANAFLOR CRYSTAL CONCEPCION, IRRI; VENEA DARA DAYGON, IRRI; ROLAND MUMM, PLANT RESEARCH INTERNATIONAL, Wageningen; RUSSELL REINKE, INTERNATIONAL NETWORK FOR QUALITY RICE; SHARIFA DIPTI, INTERNATIONAL NETWORK FOR QUALITY RICE; PRISCILA ZACZUK BASSINELLO, CNPAF; JOHN MANFUL, INTERNATIONAL NETWORK FOR QUALITY RICE; SAKHAN SOPHANY, CAMBODIAN AGRICULTURAL RESEARCH AND DEVELOPMENT INSTITUTE; KARLA CORDERO LARA, INIA; JINSONG BAO, INSTITUTE OF NUCLEAR AGRICULTURAL SCIENCES; LIHONG XIE, CHINA NATIONAL RICE RESEARCH INSTITUTE; KATERINE LOAIZA, CIAT; AHMAD EL-HISSEWY, RICE RESEARCH & TRAINING CENTER, Egypt; JOSEPH GAYIN, CSIR; NEERJA SHARMA, AGRICULTURAL UNIVERSITY LUDHIANA, India; SIVAKAMI RAJESWARI, TAMIL NADU AGRICULTURAL UNIVERSITY COIMBATORE, India; SWAMINATHAN MANONMANI, TAMIL NADU AGRICULTURAL UNIVERSITY COIMBATORE, India; N. SHOBHA RANI, DIRECTORATE OF RICE RESEARCH, India; SUNEETHA KOTA, DIRECTORATE OF RICE RESEARCH, India; SITI DEWI INDRASARI, DIRECTORATE OF RICE RESEARCH, India; FATEMEH HABIBI, RICE RESEARCH INSTITUTE OF IRAN; MARYAM HOSSEINI, RICE RESEARCH INSTITUTE OF IRAN; FATEMEH TAVASOLI, RICE RESEARCH INSTITUTE OF IRAN; KEITARO SUZUKI, NARO, Japan; TAKAYUKI UMEMOTO, NARO, Japan; CHANTHKONE BOUALAPHANH, RICE AND CASH CROP RESEARCH INSTITUTE - NAFRI; HUEI HONG LEE, UNIVERSITI PUTRA MALAYSIA; YIU PANG HUNG, UNIVERSITI PUTRA MALAYSIA; ASFALIZA RAMLI, PUSAT PENYELIDIKAN PADI DAN TANAMAN INDUSTRI, Malaysia; ASFALIZA RAMLI, MARDI, Malaysia; PA PA AUNG, PLANT BIOTECHNOLOGY CENTER, Myanmar; RAUF AHMAD, NATIONAL AGRICULTURAL RESEARCH CENTRE, Pakistan; JAVED IQBAL WATTOO, NATIONAL INSTITUTE FOR BIOTECHNOLOGY AND GENETIC ENGINEERING, Pakistan; EVELYN BANDONILL, PHILIPPINE RICE RESEARCH INSTITUTE, Philippines; MARISSA ROMERO, PHILIPPINE RICE RESEARCH INSTITUTE, Philippines; CARLA MOITA BRITES, INSTITUTO NACIONAL DE INVESTIGAÇÃO AGRÁRIA E VETERINÁRIA, Portugal; ROSHNI HAFEEL, RICE RESEARCH STATION, Sri Lanka; HUU-SHENG LUR, NATIONAL TAIWAN UNIVERSITY, Taiwan; KUNYA CHEAUPUN, PATHUMTHANI RICE RESEARCH CENTRE, Thailand; SUPANEE JONGDEE, KHON KAEN RICE RESEARCH CENTER, Thailand; PEDRO BLANCO, INIA, Uruguay; ROLFE BRYANT, USDA-ARS, Arkansas; NGUYEN THI LANG, GENETIC & PLANT BREEDING DIVISION, Viet Nam; ROBERT D. HALL, PLANT RESEARCH INTERNATIONAL, Wageningen; MELISSA FITZGERALD, IRRI. |
|
Título: |
Diversity of global rice markets and the science required for consumer-targeted rice breeding. |
|
Ano de publicação: |
2014 |
|
Fonte/Imprenta: |
Plos One, San Francisco, v. 9, n. 1, p. 1-12, Jan. 2014. |
|
DOI: |
10.1371/journal.pone.0085106 |
|
Idioma: |
Inglês |
|
Conteúdo: |
With the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a ?one size fits all? crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global MenosWith the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a ?one size fits all? crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine impor... Mostrar Tudo |
|
Palavras-Chave: |
Melhoramento genético. |
|
Thesagro: |
Arroz; Consumo; Oryza sativa. |
|
Categoria do assunto: |
Q Alimentos e Nutrição Humana |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/104026/1/plosone.pdf
|
|
Marc: |
LEADER 03571naa a2200745 a 4500
001 1988936
005 2014-06-26
008 2014 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pone.0085106$2DOI
100 1 $aCALINGACION, M.
245 $aDiversity of global rice markets and the science required for consumer-targeted rice breeding.$h[electronic resource]
260 $c2014
520 $aWith the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a ?one size fits all? crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global
650 $aArroz
650 $aConsumo
650 $aOryza sativa
653 $aMelhoramento genético
700 1 $aLABORTE, A.
700 1 $aRESURRECCION, A.
700 1 $aCONCEPCION, J. C.
700 1 $aDAYGON, V. D.
700 1 $aMUMM, R.
700 1 $aREINKE, R.
700 1 $aDIPTI, S.
700 1 $aBASSINELLO, P. Z.
700 1 $aMANFUL, J.
700 1 $aSOPHANY, S.
700 1 $aLARA, K. C.
700 1 $aBAO, J.
700 1 $aXIE, L.
700 1 $aLOAIZA, K.
700 1 $aEL-HISSEWY, A.
700 1 $aGAYIN, J.
700 1 $aSHARMA, N.
700 1 $aRAJESWARI, S.
700 1 $aMANONMANI, S.
700 1 $aRANI, N. S.
700 1 $aKOTA, S.
700 1 $aINDRASARI, S. D.
700 1 $aHABIBI, F.
700 1 $aHOSSEINI, M.
700 1 $aTAVASOLI, F.
700 1 $aSUZUKI, K.
700 1 $aUMEMOTO, T.
700 1 $aBOUALAPHANH, C.
700 1 $aLEE, H. H.
700 1 $aHUNG, Y. P.
700 1 $aRAMLI, A.
700 1 $aRAMLI, A.
700 1 $aAUNG, P. P.
700 1 $aAHMAD, R.
700 1 $aWATTOO, J. I.
700 1 $aBANDONILL, E.
700 1 $aROMERO, M.
700 1 $aBRITES, C. M.
700 1 $aHAFEEL, R.
700 1 $aLUR, H.-S.
700 1 $aCHEAUPUN, K.
700 1 $aJONGDEE, S.
700 1 $aBLANCO, P.
700 1 $aBRYANT, R.
700 1 $aLANG, N. T.
700 1 $aHALL, R. D.
700 1 $aFITZGERALD, M.
773 $tPlos One, San Francisco$gv. 9, n. 1, p. 1-12, Jan. 2014.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
