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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
08/09/2020 |
Actualizado : |
08/09/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
LÓPEZ-VALIENTE, S.; MAREZCA, S.; RODRÍGUEZ, A. M.; LONG, N. M.; QUINTANS, G.; PALLADINO, R. A. |
Afiliación : |
SEBASTIÁN LÓPEZ-VALIENTE, Instituto Nacional de Tecnología Agropecuaria. Cuenca del Salado Experimental Station, Rauch, Argentina; SEBASTIÁN MAREZCA, Instituto Nacional de Tecnología Agropecuaria. Cuenca del Salado Experimental Station, Rauch, Argentina; ALEJANDROO M. RODRÍGUEZ, Instituto Nacional de Tecnología Agropecuaria. Cuenca del Salado Experimental Station, Rauch, Argentina; NATHAN M. LONG, Department of Animal and Veterinary Sciences, Clemson University, Clemson, USA.; GRACIELA QUINTANS ILARIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAFAEL ALEJANDRO PALLADINO, PROLACT-CONICET, Universidad Nacional de Lomas de Zamora, Argentina. |
Título : |
Effect of protein restriction during mid-to late gestation of beef cows on female offspring fertility, lactation performance and calves development. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
EC Veterinary Science, November 2019, v. 4 (10), p. 1-12. Open Access. Doi: 10.31080/ecve.2019.04.00186 |
DOI : |
10.31080/ecve.2019.04.00186 |
Idioma : |
Inglés |
Notas : |
Article history: Received: October 24, 2019; Published: November 13, 2019. |
Contenido : |
Cow calf operation in Argentina are managed under extensive grazing condition and the quality of forages is often poor during second half of gestation. Protein restriction in bovine gestation affects production traits in progeny. The present work investigated the effects of two levels of crude protein (CP) provided to mature dams during late gestation on subsequent heifer?s growth postweaning, reproductive performance, milk production and grand offspring evolution. At 121 d prepartum, 68 multiparous Angus cows were randomly assigned to low protein (LP, 6% CP) or high protein (HP, 12% CP) at 12 pens per treatment group. Following calving, all cow/calf pairs were housed together until weaning, then female progeny were removed and maintained on pasture as a single group.
At puberty (P = 0.01) and pregnancy determination (P = 0.05) the HP heifers were heavier than LP heifers. The LM area was greater at 20 mo of age in HP compared to LP heifers (P = 0.01) and the HP heifers had greater 12th rib fat thickness at 30 and 35 mo of age (P = 0.02). Serum IGF-1 concentration was greater in HP heifers compared to LP heifers (P = 0.05). No dam nutrition effects were found on offspring age at puberty (P = 0.98), final pregnancy rate (P = 0.28) or milk yield (P = 0.76) but heifers born to LP dams had greater milk protein percentage (P = 0.04) and tended to produce increased milk fat percentage (P = 0.08) compared with HP heifers.
The LP grand offspring grew faster from birth until weaning compared with HP grand offspring (P < 0.01) with reduced insulin concentrations (P = 0.03) and tended to have increased glucose concentrations than HP calves (P = 0.09). Protein supplementation during late gestation does not affect reproductive performance of the offspring heifers but did impact their BW evolution, milk quality and grand offspring performance. Implications: The use of diet with low amount of protein which the female fetus is exposed in utero can affect her subsequent development and ability to nourish subsequent generations. MenosCow calf operation in Argentina are managed under extensive grazing condition and the quality of forages is often poor during second half of gestation. Protein restriction in bovine gestation affects production traits in progeny. The present work investigated the effects of two levels of crude protein (CP) provided to mature dams during late gestation on subsequent heifer?s growth postweaning, reproductive performance, milk production and grand offspring evolution. At 121 d prepartum, 68 multiparous Angus cows were randomly assigned to low protein (LP, 6% CP) or high protein (HP, 12% CP) at 12 pens per treatment group. Following calving, all cow/calf pairs were housed together until weaning, then female progeny were removed and maintained on pasture as a single group.
At puberty (P = 0.01) and pregnancy determination (P = 0.05) the HP heifers were heavier than LP heifers. The LM area was greater at 20 mo of age in HP compared to LP heifers (P = 0.01) and the HP heifers had greater 12th rib fat thickness at 30 and 35 mo of age (P = 0.02). Serum IGF-1 concentration was greater in HP heifers compared to LP heifers (P = 0.05). No dam nutrition effects were found on offspring age at puberty (P = 0.98), final pregnancy rate (P = 0.28) or milk yield (P = 0.76) but heifers born to LP dams had greater milk protein percentage (P = 0.04) and tended to produce increased milk fat percentage (P = 0.08) compared with HP heifers.
The LP grand offspring grew faster from birth until weaning c... Presentar Todo |
Palabras claves : |
FETAL PROGRAMMING; MILK YIELD; OFFSPRING PUBERTY; PROGENY GROWTH; PROGRAMACIÓN FETAL. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14623/1/ECVE-04-00186.pdf
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Marc : |
LEADER 03003naa a2200265 a 4500 001 1061299 005 2020-09-08 008 2019 bl uuuu u00u1 u #d 024 7 $a10.31080/ecve.2019.04.00186$2DOI 100 1 $aLÓPEZ-VALIENTE, S. 245 $aEffect of protein restriction during mid-to late gestation of beef cows on female offspring fertility, lactation performance and calves development.$h[electronic resource] 260 $c2019 500 $aArticle history: Received: October 24, 2019; Published: November 13, 2019. 520 $aCow calf operation in Argentina are managed under extensive grazing condition and the quality of forages is often poor during second half of gestation. Protein restriction in bovine gestation affects production traits in progeny. The present work investigated the effects of two levels of crude protein (CP) provided to mature dams during late gestation on subsequent heifer?s growth postweaning, reproductive performance, milk production and grand offspring evolution. At 121 d prepartum, 68 multiparous Angus cows were randomly assigned to low protein (LP, 6% CP) or high protein (HP, 12% CP) at 12 pens per treatment group. Following calving, all cow/calf pairs were housed together until weaning, then female progeny were removed and maintained on pasture as a single group. At puberty (P = 0.01) and pregnancy determination (P = 0.05) the HP heifers were heavier than LP heifers. The LM area was greater at 20 mo of age in HP compared to LP heifers (P = 0.01) and the HP heifers had greater 12th rib fat thickness at 30 and 35 mo of age (P = 0.02). Serum IGF-1 concentration was greater in HP heifers compared to LP heifers (P = 0.05). No dam nutrition effects were found on offspring age at puberty (P = 0.98), final pregnancy rate (P = 0.28) or milk yield (P = 0.76) but heifers born to LP dams had greater milk protein percentage (P = 0.04) and tended to produce increased milk fat percentage (P = 0.08) compared with HP heifers. The LP grand offspring grew faster from birth until weaning compared with HP grand offspring (P < 0.01) with reduced insulin concentrations (P = 0.03) and tended to have increased glucose concentrations than HP calves (P = 0.09). Protein supplementation during late gestation does not affect reproductive performance of the offspring heifers but did impact their BW evolution, milk quality and grand offspring performance. Implications: The use of diet with low amount of protein which the female fetus is exposed in utero can affect her subsequent development and ability to nourish subsequent generations. 653 $aFETAL PROGRAMMING 653 $aMILK YIELD 653 $aOFFSPRING PUBERTY 653 $aPROGENY GROWTH 653 $aPROGRAMACIÓN FETAL 700 1 $aMAREZCA, S. 700 1 $aRODRÍGUEZ, A. M. 700 1 $aLONG, N. M. 700 1 $aQUINTANS, G. 700 1 $aPALLADINO, R. A. 773 $tEC Veterinary Science, November 2019$gv. 4 (10), p. 1-12. Open Access. Doi: 10.31080/ecve.2019.04.00186
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Registro original : |
INIA Treinta y Tres (TT) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
27/09/2022 |
Actualizado : |
27/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
ZARBÁ, L.; PIQUER-RODRÍGUEZ, M.; BOILLAT, S.; LEVERS, C.; GASPARRI, I.; AIDE, T. M.; ÁLVAREZ-BERRÍOS, N. L.; ANDERSON, L. O.; ARAOZ, E.; ARIMA, E.; BATISTELLA, M.; CALDERÓN-LOOR, M.; ECHEVERRÍA, C.; GONZALEZ-ROGLICH, M.; JOBBÁGY, E. G.; MATHEZ-STIEFEL, S.-L.; RAMIREZ-REYES, C-; PACHECHO, A.; VALLEJOS, M.; YOUNG, K. R.; GRAU, R. |
Afiliación : |
LUCÍA ZARBÁ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.; MARÍA PIQUER-RODRÍGUEZ, Instituto Ecología Regional (IER), Univ. Nacional de Tucumán (UNT). Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; Lateinamerika-Institut, Freie Universität Berlin, Germany; Geography Department, Humbold, Germany; SÉBASTIEN BOILLAT, Institute of Geography, University of Bern, Bern, Switzerland; CHRISTIAN LEVERS, Depart. Environmental Geography, Inst. for Environmental Studies, Vrije Univ. Amsterdam, Netherlands; Inst. for Resources, Environment and Sustainability, Univ. of British Columbia, Vancouver, BC, Canada; School of Public Policy and Global Affairs, Univ.; IGNACIO GASPARRI, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; T. MITCHELL AIDE, Department of Biology, University of Puerto Rico-Rio Piedras, Puerto Rico; NORA L. ÁLVAREZ-BERRÍOS, USDA Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; LIANA O. ANDERSON, National Center for Monitoring and Early Warning of Natural Disasters-CEMADEN, Ministry of Science, Technology and Innovation-MCTI, Brazil; EZEQUIEL ARAOZ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; EUGENIO ARIMA, Department of Geography and the Environment, University of Texas at Austin, United States; MATEUS BATISTELLA, Brazilian Agricultural Research Corporation (Embrapa Agricultural Informatics) State University of Campinas (Unicamp), Brazil; MARCO CALDERÓN-LOOR, Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Australia;Grupo de Investigación de Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas (UDLA), Quito, Ecuador; CRISTIAN ECHEVERRÍA, Landscape Ecology Laboratory, Facultad de Ciencias Forestales, Universidad de Concepción, Chile; Millennium Nucleus Center for the Socioeconomic Impact of Environmental Policies (CESIEP), Santiago de Chile, Chile; MARIANO GONZALEZ-ROGLICH, Wildlife Conservation Society, Buenos Aires, Argentina; ESTEBAN G. JOBBÁGY, Grupo de Estudios Ambientales, IMASL-CONICET and Universidad Nacional de San Luis, San Luis, Argentina; South American Institute for Resilience and Sustainability Studies (SARAS), Maldonado, Uruguay; SARAH-LAN MATHEZ-STIEFEL, Centre for Development and Environment, University of Bern, Switzerland; Wyss Academy for Nature at the University of Bern, Switzerland; CARLOS RAMIREZ-REYES, Quantitative Ecology & Spatial Technologies Laboratory, Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, United States; ANDREA PACHECO, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany; MARÍA VALLEJOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires, Argentina; KENNETH R. YOUNG, Department of Geography and the Environment, University of Texas at Austin, United States; RICARDO GRAU, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina. |
Título : |
Mapping and characterizing social-ecological land systems of South America. |
Fecha de publicación : |
2022 |
Fuente / Imprenta : |
Ecology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227 |
ISSN : |
1708-3087 |
DOI : |
10.5751/ES-13066-270227 |
Idioma : |
Inglés |
Notas : |
Article: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt --
LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme |
Contenido : |
ABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s). MenosABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide ran... Presentar Todo |
Palabras claves : |
Automatization; Hierarchical clustering; Multidisciplinary data; Participatory mapping; Social-ecological mapping. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16772/1/ES-2021-13066.pdf
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Marc : |
LEADER 03737naa a2200457 a 4500 001 1063581 005 2022-09-27 008 2022 bl uuuu u00u1 u #d 022 $a1708-3087 024 7 $a10.5751/ES-13066-270227$2DOI 100 1 $aZARBÁ, L. 245 $aMapping and characterizing social-ecological land systems of South America.$h[electronic resource] 260 $c2022 500 $aArticle: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt -- LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme 520 $aABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s). 653 $aAutomatization 653 $aHierarchical clustering 653 $aMultidisciplinary data 653 $aParticipatory mapping 653 $aSocial-ecological mapping 700 1 $aPIQUER-RODRÍGUEZ, M. 700 1 $aBOILLAT, S. 700 1 $aLEVERS, C. 700 1 $aGASPARRI, I. 700 1 $aAIDE, T. M. 700 1 $aÁLVAREZ-BERRÍOS, N. L. 700 1 $aANDERSON, L. O. 700 1 $aARAOZ, E. 700 1 $aARIMA, E. 700 1 $aBATISTELLA, M. 700 1 $aCALDERÓN-LOOR, M. 700 1 $aECHEVERRÍA, C. 700 1 $aGONZALEZ-ROGLICH, M. 700 1 $aJOBBÁGY, E. G. 700 1 $aMATHEZ-STIEFEL, S.-L. 700 1 $aRAMIREZ-REYES, C- 700 1 $aPACHECHO, A. 700 1 $aVALLEJOS, M. 700 1 $aYOUNG, K. R. 700 1 $aGRAU, R. 773 $tEcology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227
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