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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
02/06/2017 |
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Actualizado : |
02/06/2017 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
MONTOSSI, F.; LUZARDO, S.; CUADRO, R.; BRITO, G.; SAN JULIÁN, R.; SILVEIRA, C.; DEL CAMPO, M. |
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Afiliación : |
FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO WALTER BRITO DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO SAN JULIAN SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA INES SILVEIRA ROJAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCIA DEL CAMPO GIGENA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Could restricted grain supplementation modify fatty acid composition in beef meat under grazing conditions? |
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Fecha de publicación : |
2010 |
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Fuente / Imprenta : |
In:International Congress of Meat Science and Technology (ICoMST), 56o., Corea, 2010. |
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Idioma : |
Inglés |
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Notas : |
Abstracts session B: Meat and health. |
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Contenido : |
Restricted grain supplementation effects on animal performance, carcass weight, meat quality and fatty acids profile were investigated on finnishing steers under grazing conditions, focused on their influences on human health. During 194 days (from June to December 2008), 24 Uruguayan Hereford steers were assigned to different treatments (T) considering herbage allowance (HA) and level of grain (ground sorghum) supplementation (G) according to the liveweight (LW) of the animals.
Treatments were a combination of pastures (P) and G levels, where T1 (P at 4% HA of LW); T2 (P at 2% HA of LW + G at 0.8% of LW); T3 (P at 2% HA of LW + G at 1.6% of LW) were applied. It was proven that increasing levels of G supplementation improved animal peformance and carcass weight, having minor influences on meat quality traits (pH, meat colour, tenderness). Intramuscular fat was not affected by T. The concentrations of linolenic (18:3 n-3) followed the pattern of T1=T2>T3. In the
case of linoleic acid (18:2 n-6), T2 had higher concentrations than T1 and T3. The long chain arachidonic (20:4 n-6), eicosapentaenoic-EPA (20:5 n-3) and docosapentaenoic-DPA (22:5 n-3) fatty acids were significant lower for T3 in comparison with T2. Human health recommendations for PUFA:SFA and ?6:?3 ratios are over 0.45 and below 4.0, respectively. The PUFA:SFA ration fell into the range of 0.22 to 0.36, while ?6:?3 ratio was always below 0.4. However, T2 had better PUFA:SFA
ratio than the rest of the treatments, while T1 produced the best ?6:?3 ratio. It is highlighted the potential utilization of restricted amounts of grain supplementation G in beef finishing system under grazing conditions for increasing productivity as well as promoting healthy meat. This proposal could have productive and economical benefits for livestock farmers in extensive regions of Uruguay and for the beef industry. MenosRestricted grain supplementation effects on animal performance, carcass weight, meat quality and fatty acids profile were investigated on finnishing steers under grazing conditions, focused on their influences on human health. During 194 days (from June to December 2008), 24 Uruguayan Hereford steers were assigned to different treatments (T) considering herbage allowance (HA) and level of grain (ground sorghum) supplementation (G) according to the liveweight (LW) of the animals.
Treatments were a combination of pastures (P) and G levels, where T1 (P at 4% HA of LW); T2 (P at 2% HA of LW + G at 0.8% of LW); T3 (P at 2% HA of LW + G at 1.6% of LW) were applied. It was proven that increasing levels of G supplementation improved animal peformance and carcass weight, having minor influences on meat quality traits (pH, meat colour, tenderness). Intramuscular fat was not affected by T. The concentrations of linolenic (18:3 n-3) followed the pattern of T1=T2>T3. In the
case of linoleic acid (18:2 n-6), T2 had higher concentrations than T1 and T3. The long chain arachidonic (20:4 n-6), eicosapentaenoic-EPA (20:5 n-3) and docosapentaenoic-DPA (22:5 n-3) fatty acids were significant lower for T3 in comparison with T2. Human health recommendations for PUFA:SFA and ?6:?3 ratios are over 0.45 and below 4.0, respectively. The PUFA:SFA ration fell into the range of 0.22 to 0.36, while ?6:?3 ratio was always below 0.4. However, T2 had better PUFA:SFA
ratio than the rest of the treatments, wh... Presentar Todo |
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Palabras claves : |
BEEF; FATTY ACID COMPOSITION; GRAIN; MEAT QUALITY; PASTURE. |
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Thesagro : |
CALIDAD DE CARNE; PASTURAS. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/6797/1/Could-restricted.pdf
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Marc : |
LEADER 02718nam a2200277 a 4500
001 1057236
005 2017-06-02
008 2010 bl uuuu u01u1 u #d
100 1 $aMONTOSSI, F.
245 $aCould restricted grain supplementation modify fatty acid composition in beef meat under grazing conditions?$h[electronic resource]
260 $aIn:International Congress of Meat Science and Technology (ICoMST), 56o., Corea$c2010
500 $aAbstracts session B: Meat and health.
520 $aRestricted grain supplementation effects on animal performance, carcass weight, meat quality and fatty acids profile were investigated on finnishing steers under grazing conditions, focused on their influences on human health. During 194 days (from June to December 2008), 24 Uruguayan Hereford steers were assigned to different treatments (T) considering herbage allowance (HA) and level of grain (ground sorghum) supplementation (G) according to the liveweight (LW) of the animals. Treatments were a combination of pastures (P) and G levels, where T1 (P at 4% HA of LW); T2 (P at 2% HA of LW + G at 0.8% of LW); T3 (P at 2% HA of LW + G at 1.6% of LW) were applied. It was proven that increasing levels of G supplementation improved animal peformance and carcass weight, having minor influences on meat quality traits (pH, meat colour, tenderness). Intramuscular fat was not affected by T. The concentrations of linolenic (18:3 n-3) followed the pattern of T1=T2>T3. In the case of linoleic acid (18:2 n-6), T2 had higher concentrations than T1 and T3. The long chain arachidonic (20:4 n-6), eicosapentaenoic-EPA (20:5 n-3) and docosapentaenoic-DPA (22:5 n-3) fatty acids were significant lower for T3 in comparison with T2. Human health recommendations for PUFA:SFA and ?6:?3 ratios are over 0.45 and below 4.0, respectively. The PUFA:SFA ration fell into the range of 0.22 to 0.36, while ?6:?3 ratio was always below 0.4. However, T2 had better PUFA:SFA ratio than the rest of the treatments, while T1 produced the best ?6:?3 ratio. It is highlighted the potential utilization of restricted amounts of grain supplementation G in beef finishing system under grazing conditions for increasing productivity as well as promoting healthy meat. This proposal could have productive and economical benefits for livestock farmers in extensive regions of Uruguay and for the beef industry.
650 $aCALIDAD DE CARNE
650 $aPASTURAS
653 $aBEEF
653 $aFATTY ACID COMPOSITION
653 $aGRAIN
653 $aMEAT QUALITY
653 $aPASTURE
700 1 $aLUZARDO, S.
700 1 $aCUADRO, R.
700 1 $aBRITO, G.
700 1 $aSAN JULIÁN, R.
700 1 $aSILVEIRA, C.
700 1 $aDEL CAMPO, M.
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Registro original : |
INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
10/08/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
BHATTA, M.; GUTIERREZ, L.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GÓMEZ-GUERRERO, B.; PARDO, M.F.; LANARO, V.; SAYAS, M.; CASTRO, A.J. |
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Afiliación : |
MADHAV BHATTA, Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LUCIA GUTIERREZ, Agronomy, University of Wisconsin-Madison, 1575 Linden Dr., WI, 53706, USA.; LORENA CAMMAROTA, Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay./Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; FERNANDA CARDOZO, Maltería Uruguay S.A. Ruta 55, Km26, Ombúes de Lavalle, Uruguay.; SILVIA ELISA GERMAN FAEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BLANCA GÓMEZ-GUERRERO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MARÍA FERNANDA PARDO, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; VALERIA LANARO, LATU Foundation, Av Italia 6201, Montevideo 11500, Uruguay.; MERCEDES SAYAS, Maltería Oriental S.A., Camino Abrevadero 5525, Montevideo 12400, Uruguay.; ARIEL J. CASTRO, Ariel J. Castro ?Department of plant production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km363, Paysandú 60000, Uruguay,. |
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Título : |
Multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.). |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
G3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968 |
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DOI : |
10.1534/g3.119.400968 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript. |
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Contenido : |
Abstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles. MenosAbstract:
Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for ... Presentar Todo |
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Palabras claves : |
GENOMIC PREDICTION; GENPRED; GRAIN QUALITY; GRAIN YIELD; MALTING QUALITY; MULTI-ENVIRONMENT; MULTI-TRAIT; SHARED DATA RESOURCES. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16688/1/G3-Bethesda-2020.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056970/pdf/1113.pdf
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Marc : |
LEADER 04092naa a2200349 a 4500
001 1061265
005 2022-09-05
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1534/g3.119.400968$2DOI
100 1 $aBHATTA, M.
245 $aMulti-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L.).$h[electronic resource]
260 $c2020
500 $aArticle history: Received July 26, 2019/Accepted January 22, 2020/Published online March 5, 2020. This work was funded in part by the following grants from ANII (FSA-1-2013-12977), CSIC (CSIC_I+D_ 1131 and CSIC_Movilidad_ 1131). The work was also funded by the Cereals Breeding and Quantitative Genetics group at the University of Wisconsin - Madison. We would like to acknowledge Dr. Juan Diaz at INIA, who developed the double haploid population and also contributed to the planning of the study. Malteria Oriental S.A. (MOSA) contributed with the experiments in their experimental areas and with some of the lab work. Malteria Uruguay S.A. (MUSA) contributed to the experiments in their experimental areas. We would also like to acknowledge: USDA-ARS small grains genotyping lab at Fargo, North Dakota for genotyping service; the Center for High Throughput Computing (CHTC) service at the University of Wisconsin-Madison for providing the high-performance computing resources; and Dr. Bettina Lado for sharing the R scripts. We would like to thank two anonymous reviewers and editors who provided constructive suggestions to this manuscript.
520 $aAbstract: Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.
653 $aGENOMIC PREDICTION
653 $aGENPRED
653 $aGRAIN QUALITY
653 $aGRAIN YIELD
653 $aMALTING QUALITY
653 $aMULTI-ENVIRONMENT
653 $aMULTI-TRAIT
653 $aSHARED DATA RESOURCES
700 1 $aGUTIERREZ, L.
700 1 $aCAMMAROTA, L.
700 1 $aCARDOZO, F.
700 1 $aGERMAN, S.
700 1 $aGÓMEZ-GUERRERO, B.
700 1 $aPARDO, M.F.
700 1 $aLANARO, V.
700 1 $aSAYAS, M.
700 1 $aCASTRO, A.J.
773 $tG3: Genes, Genomes, Genetics, March 1, 2020 vol. 10 no. 3 1113-1124. Open Acces. Doi: https://doi.org/10.1534/g3.119.400968
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