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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
29/10/2014 |
Actualizado : |
30/09/2019 |
Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
Autor : |
DURAN, H.; LÓPEZ-VILLALOBOS, N.; ALLES, G.; LA MANNA, A.; RAVAGNOLO, O. |
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. |
Título : |
Development and validation of a mechanistic whole dairy farm model to evaluate farming strategies under grazing conditions in Uruguay. |
Complemento del título : |
Conference Proceeding. |
Fecha de publicación : |
2009 |
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. |
Descripción física : |
2-s2.0-80053020568 |
ISBN : |
978-097584007-8 |
Idioma : |
Inglés |
Notas : |
Sponsors: CSIRO, Australian Mathematical Sciences Institute, Griffith University,eWater Cooperative Research Centre, Department of Sustainability and Environment. |
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 |
Thesagro : |
GANADO DE LECHE; MATERIA SECA; PASTURAS; PRODUCCION DE LECHE; SISTEMAS DE CULTIVO. |
Asunto categoría : |
-- |
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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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
18/06/2015 |
Actualizado : |
18/06/2015 |
Tipo de producción científica : |
Informes Agroclimáticos |
Autor : |
GIMENEZ, A.; CASTAÑO, J.; FUREST, J.; AUNCHAYNA, R. |
Afiliación : |
AGUSTIN EDUARDO GIMENEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE PEDRO CASTAÑO SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE MARIA FUREST CROCCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROSSINA MARIANA AUNCHAYNA REILLY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informe Agroclimático 2010 - Situación a Agosto. |
Fecha de publicación : |
2010 |
Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2010. |
Páginas : |
4 p. |
Idioma : |
Español |
Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
Asunto categoría : |
P40 Meteorología y climatología |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4682/1/Inf.Agr.-agosto-2010.pdf
http://www.inia.uy/Publicaciones/Paginas/publicacion-2148.aspx
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Marc : |
LEADER 02040nam a2200781 a 4500 001 1052834 005 2015-06-18 008 2010 bl uuuu 0 #d 100 1 $aGIMENEZ, A. 245 $aInforme Agroclimático 2010 - Situación a Agosto.$h[electronic resource] 260 $aMontevideo (Uruguay): INIA$c2010 300 $a4 p. 650 $aAGROCLIMATOLOGIA 650 $aCAMBIO CLIMATICO 650 $aCLIMA 650 $aCLIMATOLOGIA 650 $aESTACIONES METEOROLOGICAS 650 $aESTRES HIDRICO 650 $aEVAPORACION 650 $aEVAPOTRANSPIRACION 650 $aHUMEDAD 650 $aHUMEDAD RELATIVA 650 $aLLUVIA 650 $aMETEOROLOGIA 650 $aPERSPECTIVAS 650 $aPLUVIOMETROS 650 $aPRONOSTICO DEL TIEMPO 650 $aSENSORES 650 $aSISTEMAS 650 $aSISTEMAS DE INFORMACION 650 $aSUELO 650 $aTEMPERATURA 650 $aTERMOMETROS 653 $aAGROCLIMA 653 $aAGROCLIMATOLOGÍA 653 $aBOLETIN AGROCLIMÁTICO 653 $aCARACTERIZACIÓN AGROCLIMÁTICA 653 $aDIRECCION VIENTO 653 $aESTACIONES AGROMETEOROLOGICAS 653 $aESTACIONES AUTOMATICAS 653 $aESTACIONES INIA 653 $aESTADO DEL TIEMPO 653 $aESTRÉS HÍDRICO 653 $aGRAFICAS AGROCLIMATICOS 653 $aGRAS 653 $aHELIOFANOGRAFO 653 $aINFORMACION SATELITAL 653 $aINUNDACIONES 653 $aLLUVIAS DIARIAS 653 $aMAXIMA 653 $aMEDIA 653 $aMINIMA 653 $aPANEL SOLAR 653 $aPERSPECTIVAS CLIMATICAS 653 $aPLUVIOMETRO 653 $aPRECIPITACION NACIONAL 653 $aPREVENCION HELADAS 653 $aPRONOSTICO 653 $aSENSOR 653 $aSIMETRICO 653 $aTANQUE A 653 $aTERMOCUPLAS 653 $aTERMOHIDROGRAFO 653 $aVARIABLES AGROCLIMATICAS 653 $aVELETA 700 1 $aCASTAÑO, J. 700 1 $aFUREST, J. 700 1 $aAUNCHAYNA, R.
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