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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
29/10/2014 |
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Actualizado : |
30/09/2019 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
DURAN, H.; LÓPEZ-VILLALOBOS, N.; ALLES, G.; LA MANNA, A.; RAVAGNOLO, O. |
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Afiliación : |
HENRY DURAN OUDRI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; N. LÓPEZ-VILLALOBOS, Massey University (NZ); ALEJANDRO FRANCISCO LA MANNA ALONSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OLGA RAVAGNOLO GUMILA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Development and validation of a mechanistic whole dairy farm model to evaluate farming strategies under grazing conditions in Uruguay. |
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Complemento del título : |
Conference Proceeding. |
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Fecha de publicación : |
2009 |
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Fuente / Imprenta : |
In:18th World IMACS Congress and MODSIM International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings. Cairns, Australia 13-17 July 2009, p.512-518. |
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Descripción física : |
2-s2.0-80053020568 |
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ISBN : |
978-097584007-8 |
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Idioma : |
Inglés |
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Notas : |
Sponsors: CSIRO, Australian Mathematical Sciences Institute, Griffith University,eWater Cooperative Research Centre, Department of Sustainability and Environment. |
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Contenido : |
ABSTRACT.
A mechanistic, dynamic whole farm simulation model was developed to evaluate the effect of farming strategies on the productivity of dairy grazing systems. The model integrates local available information on pasture growth and quality and current knowledge on animal nutrition and metabolism. The pastoral component simulates the pasture rotation structure of the farm, with variable number and size of paddocks, to which the user must assign a pasture type from an available database. Each pasture type is represented by initial herbage mass (HM) and two vectors: monthly dry matter (DM) growth rate values and organic matter digestibility (OMD) values. The model is driven by pasture growth rate (PGR) on a monthly interval step. Several pasture production and management strategies can be defined as a per paddock basis. The cows are defined in terms of their potential for milk production (MPP), body condition score (BCS, scale 1-5), biotype Frame (body weight with BCS of 3), calving date, and contents of fat and protein in milk. These variables are used to characterize the average of up to six groups of adult cows which are defined by the user to represent the current situation of a dairy farm or a theoretical system. Average grazing DM intake (DMI) of each calving group of cows is estimated considering animal factors: Frame, MPP and days in milk (DIM); pasture factors: OMD, pre-grazing HM (pg-HM) and substitution rate (SR) of supplementary feed. The model is based on metabolisable energy (ME) and environmental thermo neutrality is assumed. Total ME intake (MEI) is partitioned among body functions following a defined priority: maintenance, pregnancy, milk production potential and body reserves (BR). One distinct feature of this model is that the approach used implies an active role of BR in defining the partition of MEI. If ME balance for potential milk is not achieved then BR are mobilized at a constant rate (κ) to give an absolute amount which is proportional to the current size of estimated mass of BR, whose initial level is set when inputting the initial BCS. Another feature of this model is that it can manage decisions taken at different system levels (pasture rotation structure, annual DM yield and seasonal distribution, reserves production and supplementation strategies, variables stocking rates, effects of animal size, BCS, milk potential, etc.), to quantitatively assess the impact of these decisions on cows and farm productivity. The model output was initially validated at the "cow biotype level" using published farmlet trials. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used as measures of fitness; models with values of RPE less than 10% and values of CCC greater than 0.90 were considered to have significant predictive power. Daily milk yield per cow, live weight and BCS change through the lactation were validated using a set of 12 monthly values for each trait, obtained from cows of contrasting body sizes (Heavy and Light).The RPE and CCC were 16% and 0.94 in Heavy, 20% and 0.87 in Light cows for milk yield; 3% and 0.72 in Heavy, 2% and 0.81 in Light cows for live weight; 6% and 0.18 in Heavy and 9% and -0.47 in Light cows for BCS change. Monthly intake of pasture per ha was validated using another independent set of 12 average monthly values for each of 5 farmlet stocking rates treatments (2.2; 2.7; 3.1; 3.7 and 4.3 cows/ha). RPE and CCC were: 13% and 0.77; 9% and 0.87; 12% and 0.93; 13% and 0.91; 16% and 0.88 respectively. The model was responsive to contrasting cow type and farming management. These results show that the model has acceptable predictive power and can be used to better understand actual farming systems and also to evaluate the expected productive impact of some technical changes introduced at the farm level. MenosABSTRACT.
A mechanistic, dynamic whole farm simulation model was developed to evaluate the effect of farming strategies on the productivity of dairy grazing systems. The model integrates local available information on pasture growth and quality and current knowledge on animal nutrition and metabolism. The pastoral component simulates the pasture rotation structure of the farm, with variable number and size of paddocks, to which the user must assign a pasture type from an available database. Each pasture type is represented by initial herbage mass (HM) and two vectors: monthly dry matter (DM) growth rate values and organic matter digestibility (OMD) values. The model is driven by pasture growth rate (PGR) on a monthly interval step. Several pasture production and management strategies can be defined as a per paddock basis. The cows are defined in terms of their potential for milk production (MPP), body condition score (BCS, scale 1-5), biotype Frame (body weight with BCS of 3), calving date, and contents of fat and protein in milk. These variables are used to characterize the average of up to six groups of adult cows which are defined by the user to represent the current situation of a dairy farm or a theoretical system. Average grazing DM intake (DMI) of each calving group of cows is estimated considering animal factors: Frame, MPP and days in milk (DIM); pasture factors: OMD, pre-grazing HM (pg-HM) and substitution rate (SR) of supplementary feed. The model is based on met... Presentar Todo |
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Thesagro : |
GANADO DE LECHE; MATERIA SECA; PASTURAS; PRODUCCION DE LECHE; SISTEMAS DE CULTIVO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04979nam a2200253 a 4500
001 1051369
005 2019-09-30
008 2009 bl uuuu u01u1 u #d
020 $a978-097584007-8
100 1 $aDURAN, H.
245 $aDevelopment and validation of a mechanistic whole dairy farm model to evaluate farming strategies under grazing conditions in Uruguay.$h[electronic resource]
260 $aIn:18th World IMACS Congress and MODSIM International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings. Cairns, Australia 13-17 July 2009, p.512-518.$c2009
300 $c2-s2.0-80053020568
500 $aSponsors: CSIRO, Australian Mathematical Sciences Institute, Griffith University,eWater Cooperative Research Centre, Department of Sustainability and Environment.
520 $aABSTRACT. A mechanistic, dynamic whole farm simulation model was developed to evaluate the effect of farming strategies on the productivity of dairy grazing systems. The model integrates local available information on pasture growth and quality and current knowledge on animal nutrition and metabolism. The pastoral component simulates the pasture rotation structure of the farm, with variable number and size of paddocks, to which the user must assign a pasture type from an available database. Each pasture type is represented by initial herbage mass (HM) and two vectors: monthly dry matter (DM) growth rate values and organic matter digestibility (OMD) values. The model is driven by pasture growth rate (PGR) on a monthly interval step. Several pasture production and management strategies can be defined as a per paddock basis. The cows are defined in terms of their potential for milk production (MPP), body condition score (BCS, scale 1-5), biotype Frame (body weight with BCS of 3), calving date, and contents of fat and protein in milk. These variables are used to characterize the average of up to six groups of adult cows which are defined by the user to represent the current situation of a dairy farm or a theoretical system. Average grazing DM intake (DMI) of each calving group of cows is estimated considering animal factors: Frame, MPP and days in milk (DIM); pasture factors: OMD, pre-grazing HM (pg-HM) and substitution rate (SR) of supplementary feed. The model is based on metabolisable energy (ME) and environmental thermo neutrality is assumed. Total ME intake (MEI) is partitioned among body functions following a defined priority: maintenance, pregnancy, milk production potential and body reserves (BR). One distinct feature of this model is that the approach used implies an active role of BR in defining the partition of MEI. If ME balance for potential milk is not achieved then BR are mobilized at a constant rate (κ) to give an absolute amount which is proportional to the current size of estimated mass of BR, whose initial level is set when inputting the initial BCS. Another feature of this model is that it can manage decisions taken at different system levels (pasture rotation structure, annual DM yield and seasonal distribution, reserves production and supplementation strategies, variables stocking rates, effects of animal size, BCS, milk potential, etc.), to quantitatively assess the impact of these decisions on cows and farm productivity. The model output was initially validated at the "cow biotype level" using published farmlet trials. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used as measures of fitness; models with values of RPE less than 10% and values of CCC greater than 0.90 were considered to have significant predictive power. Daily milk yield per cow, live weight and BCS change through the lactation were validated using a set of 12 monthly values for each trait, obtained from cows of contrasting body sizes (Heavy and Light).The RPE and CCC were 16% and 0.94 in Heavy, 20% and 0.87 in Light cows for milk yield; 3% and 0.72 in Heavy, 2% and 0.81 in Light cows for live weight; 6% and 0.18 in Heavy and 9% and -0.47 in Light cows for BCS change. Monthly intake of pasture per ha was validated using another independent set of 12 average monthly values for each of 5 farmlet stocking rates treatments (2.2; 2.7; 3.1; 3.7 and 4.3 cows/ha). RPE and CCC were: 13% and 0.77; 9% and 0.87; 12% and 0.93; 13% and 0.91; 16% and 0.88 respectively. The model was responsive to contrasting cow type and farming management. These results show that the model has acceptable predictive power and can be used to better understand actual farming systems and also to evaluate the expected productive impact of some technical changes introduced at the farm level.
650 $aGANADO DE LECHE
650 $aMATERIA SECA
650 $aPASTURAS
650 $aPRODUCCION DE LECHE
650 $aSISTEMAS DE CULTIVO
700 1 $aLÓPEZ-VILLALOBOS, N.
700 1 $aALLES, G.
700 1 $aLA MANNA, A.
700 1 $aRAVAGNOLO, O.
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| 84. |  | CIAPPESONI, G.; RAVAGNOLO, O.; GIMENO, D.; MONTOSSI, F.; DE BARBIERI, I. Estimation of genetic parameters and genetic trends for wool production and quality for the Uruguayan Merino. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 8., Belo Horizonte, Minas Gerais, Brazil, August 13-18, 2006, p. 05.04. Acknowledgements: This work would not have been possible without the support of ARU (Asociación Rural del Uruguay) and SCMAU (Association of the Uruguayan Merino Breeders of Uruguay).| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 86. | | MARTÍNEZ, G.; LÓPEZ, R.; MACEDO, F.; LEMA, O.M.; RAVAGNOLO, O. Fixed environmental effects and connectedness of the genetic evaluation of the Limousin breed in Uruguay. [Fixed environmental effects and connectedness of the genetic evaluation of the Limousin breed in Uruguay]. Agrociencia Uruguay, 2020, v. 24, n. 1, Epub 01-Jun-2020. Doi: http://dx.doi.org/10.31285/agro.24.141 Article history: Received 17 Oct 2019 // Accepted 02 Apr 2020 // Published 15 May 2020.
Martinez-Boggio G, López R, Macedo F, Lema M, Ravagnolo O. Fixed environmental effects and connectedness of the genetic evaluation of the Limousin...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 87. | | RAVAGNOLO, O.; BRITO, G.; AGUILAR, I.; CIAPPESONI, G.; LEMA, O.M. Genetic parameters for ultrasound live traits in pasture fed Angus cattle. Volume Session:Species breeding: Beef cattle breeding - Poster Sessions, 0617. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0617. Acknowledgements: The authors thank the Uruguayan Aberdeen Angus Breed Association, ARU and the University of Agronomy for their collaboration during the last years. This work could not have been possible without financing and technical...| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 88. | | CORREA, D.; LEMA, O.M.; RAVAGNOLO, O.; CLARIGET, J.M.; LUZARDO, S.; BRITO, G. Effects of differences in level of post-weaning nutrition and in sire expected progeny differences for ribeye area on retail cuts yield in Hereford steers. Animal Production Science, 2020, 61(2), p. 172-178. DOI: https://doi.org/10.1071/AN19604 Article history: Received 25 October 2019, accepted 25 September 2020, published online 15 October 2020. Acknowledgements: The authors would like to thank INIA Uruguay for the funding which made this study possible. We would also like to...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 89. | | AGUILAR, I.; FERNÁNDEZ, E.N.; BLASCO, A.; RAVAGNOLO, O.; LEGARRA, A. Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics, 1 July 2020, Volume 137, Issue 4, pp. 356-364. Doi: https://doi.org/10.1111/jbg.12470 Article history: Received: 22 August 2019 / Revised: 10 December 2019 / Accepted: 11 January 2020 / First published:20 February 2020
Corresponding author: Aguilar, I.; email:iaguilar@inia.org.uy
Funding information: FEDER; INRA;...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 97. | | CIAPPESONI, G.; GIMENO, D.; RAVAGNOLO, O.; DE BARBIERI, I.; MONTOSSI, F.; GRATTAROLA, M.; MEDEROS, A. Evaluación genética del núcleo fundacional: caracterización de los animales que se entregan. ln: INIA Tacuarembó. Unidad Experimental Glencoe. Proyecto Merino Fino del Uruguay: sexta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2005. 16 diciembre, Glencoe, Paysandú, 2005. Tacuarembó (Uruguay): INIA, 2005. p. 49-74 (INIA Serie Actividades de Difusión ; 439)| Biblioteca(s): INIA Tacuarembó. |
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| 100. | | CIAPPESONI, G.; GIMENO, D.; RAVAGNOLO, O.; DE BARBIERI, I.; AGUILAR, I.; MONTOSSI, F.; GRATTAROLA, M. Evaluación genética preliminar del Núcleo Fundacional Merino Fino: análisis combinado población Merino fino - generación 2003. ln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: quinta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2004. Glencoe, Paysandú, 10 diciembre, 2004. Tacuarembó (Uruguay): INIA, 2004. p. 69-83 (INIA Serie Actividades de Difusión ; 392)| Biblioteca(s): INIA Tacuarembó. |
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