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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
21/02/2014 |
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Actualizado : |
10/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
ASTESSIANO, A.L.; PÉREZ-CLARIGET, R.; QUINTANS, G.; SOCA, P.; CARRIQUIRY, M. |
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Afiliación : |
GRACIELA QUINTANS ILARIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Effects of a short-term increase in the nutritional plane before the mating period on metabolic and endocrine parameters, hepatic gene expression and reproduction in primaparous beef cows on grazing conditions. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
Journal of Animal Physiology and Animal Nutrition, 2012, v.96, no.3, p.535-544. |
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ISSN : |
1439-0396 |
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DOI : |
10.1111/j.1439-0396.2011.01178.x |
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Idioma : |
Inglés |
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Notas : |
Article history: received: 23 February 2011; accepted: 5 May 2011. |
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Contenido : |
Abstract:
Sixty-four spring-calved primiparous crossbred cows paired by calving date and body condition score (BCS) at calving were used to study the effect of a short-term increase in the nutritional plane before the mating period on cow and calf performance, changes in metabolic and endocrine parameters and hepatic gene expression. At 48 ± 10 days postpartum (onset of nutritional treatment = day 0), cows were assigned to two treatments during 23 days: control (grazing of native pastures; NP; n = 31) and increased nutritional plane (NP improved with Lotus subbiflorous cv Rincon; IP; n = 33). Cow body weight (BW), BCS and total
protein and albumin concentrations increased while urea and non-esterified fatty acids levels decreased from the beginning of the nutritional treatment in both groups, indicating the animal positive energy balance as forage growth and availability of pastures increased during spring. In addition, cow BW and BCS, as well as calf average daily gain and BW, were greater in IP than in NP cows groups. Insulin concentrations were less in IP than in NP (1.37 vs. 2.25 ± 0.26 lU/ml) because insulin increased owing to nutritional treatment only in NP cows. Hepatic insulin receptor mRNA at day 23 tended to be 1.5-fold greater, while insulin growth factor binding protein-3 mRNA expression was 1.7-fold greater in NP than in IP cows. Reproductive responses were not affected by nutritional treatment, but days to initiation of ovarian ciclicity (108 ± 10 days) were positively correlated with insulin concentrations.
Grazing of improved NP for 23 days before the mating period did not improve cow reproductive performance but modified metabolic, endocrine and gene expression parameters, in agreement with greater nutrient and energy partitioning towards milk production, reflected in better calf performance. MenosAbstract:
Sixty-four spring-calved primiparous crossbred cows paired by calving date and body condition score (BCS) at calving were used to study the effect of a short-term increase in the nutritional plane before the mating period on cow and calf performance, changes in metabolic and endocrine parameters and hepatic gene expression. At 48 ± 10 days postpartum (onset of nutritional treatment = day 0), cows were assigned to two treatments during 23 days: control (grazing of native pastures; NP; n = 31) and increased nutritional plane (NP improved with Lotus subbiflorous cv Rincon; IP; n = 33). Cow body weight (BW), BCS and total
protein and albumin concentrations increased while urea and non-esterified fatty acids levels decreased from the beginning of the nutritional treatment in both groups, indicating the animal positive energy balance as forage growth and availability of pastures increased during spring. In addition, cow BW and BCS, as well as calf average daily gain and BW, were greater in IP than in NP cows groups. Insulin concentrations were less in IP than in NP (1.37 vs. 2.25 ± 0.26 lU/ml) because insulin increased owing to nutritional treatment only in NP cows. Hepatic insulin receptor mRNA at day 23 tended to be 1.5-fold greater, while insulin growth factor binding protein-3 mRNA expression was 1.7-fold greater in NP than in IP cows. Reproductive responses were not affected by nutritional treatment, but days to initiation of ovarian ciclicity (108 ± 10 days) were p... Presentar Todo |
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Palabras claves : |
CATTLE; HORMONES; METABOLITES; MRNA; PASTURES. |
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Thesagro : |
BOVINOS; REPRODUCCION; SUPLEMENTACION; URUGUAY. |
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Asunto categoría : |
L51 Fisiología Animal - Nutrición |
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Marc : |
LEADER 02874naa a2200313 a 4500
001 1032760
005 2019-10-10
008 2012 bl uuuu u00u1 u #d
022 $a1439-0396
024 7 $a10.1111/j.1439-0396.2011.01178.x$2DOI
100 1 $aASTESSIANO, A.L.
245 $aEffects of a short-term increase in the nutritional plane before the mating period on metabolic and endocrine parameters, hepatic gene expression and reproduction in primaparous beef cows on grazing conditions.
260 $c2012
500 $aArticle history: received: 23 February 2011; accepted: 5 May 2011.
520 $aAbstract: Sixty-four spring-calved primiparous crossbred cows paired by calving date and body condition score (BCS) at calving were used to study the effect of a short-term increase in the nutritional plane before the mating period on cow and calf performance, changes in metabolic and endocrine parameters and hepatic gene expression. At 48 ± 10 days postpartum (onset of nutritional treatment = day 0), cows were assigned to two treatments during 23 days: control (grazing of native pastures; NP; n = 31) and increased nutritional plane (NP improved with Lotus subbiflorous cv Rincon; IP; n = 33). Cow body weight (BW), BCS and total protein and albumin concentrations increased while urea and non-esterified fatty acids levels decreased from the beginning of the nutritional treatment in both groups, indicating the animal positive energy balance as forage growth and availability of pastures increased during spring. In addition, cow BW and BCS, as well as calf average daily gain and BW, were greater in IP than in NP cows groups. Insulin concentrations were less in IP than in NP (1.37 vs. 2.25 ± 0.26 lU/ml) because insulin increased owing to nutritional treatment only in NP cows. Hepatic insulin receptor mRNA at day 23 tended to be 1.5-fold greater, while insulin growth factor binding protein-3 mRNA expression was 1.7-fold greater in NP than in IP cows. Reproductive responses were not affected by nutritional treatment, but days to initiation of ovarian ciclicity (108 ± 10 days) were positively correlated with insulin concentrations. Grazing of improved NP for 23 days before the mating period did not improve cow reproductive performance but modified metabolic, endocrine and gene expression parameters, in agreement with greater nutrient and energy partitioning towards milk production, reflected in better calf performance.
650 $aBOVINOS
650 $aREPRODUCCION
650 $aSUPLEMENTACION
650 $aURUGUAY
653 $aCATTLE
653 $aHORMONES
653 $aMETABOLITES
653 $aMRNA
653 $aPASTURES
700 1 $aPÉREZ-CLARIGET, R.
700 1 $aQUINTANS, G.
700 1 $aSOCA, P.
700 1 $aCARRIQUIRY, M.
773 $tJournal of Animal Physiology and Animal Nutrition, 2012$gv.96, no.3, p.535-544.
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INIA Treinta y Tres (TT) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/10/2020 |
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Actualizado : |
09/04/2021 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
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Afiliación : |
FLORENCIA HASTINGS, School of Agronomy Universidad de la República, Montevideo, Uruguay; Directorate of Natural Resources, Ministry of Agriculture, Livestock and Fisheries, Montevideo, Uruguay; IGNACIO FUENTES, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; MARIO PÉREZ-BIDEGAIN, School of Agronomy, Universidad de la República, Montevideo, Uruguay; RAFAEL NAVAS NÚÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ÁNGELA GORGOGLIONE, School of Engineering, Universidad de la República, Montevideo, Uruguay. |
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Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52 |
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ISBN : |
e-ISBN: 978-3-030-58811-3 |
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DOI : |
10.1007/978-3-030-58811-3_52 |
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Idioma : |
Inglés |
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Notas : |
Article history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH.
20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529.
Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy |
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Contenido : |
Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG. |
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Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 02413nam a2200229 a 4500
001 1061424
005 2021-04-09
008 2020 bl uuuu u0uu1 u #d
024 7 $a10.1007/978-3-030-58811-3_52$2DOI
100 1 $aHASTINGS, F.
245 $aLand-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)$h[electronic resource]
260 $aIn: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52$c1007
500 $aArticle history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH. 20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529. Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy
520 $aLand-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG.
653 $aAgricultural region
653 $aGoogle earth engine
653 $aLand-cover map
653 $aSupervised classification
700 1 $aFUENTES, I.
700 1 $aPÉREZ-BIDEGAIN, M.
700 1 $aNAVAS, R.
700 1 $aGORGOGLIONE, A.
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