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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
05/08/2021 |
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Actualizado : |
05/08/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
STIRLING, S.; FARIÑA, S.; PACHECO, D.; VIBART, R. |
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Afiliación : |
MARÍA SOFÍA STIRLING SANTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; SANTIAGO FARIÑA, INIA (Instituto Nacional de Investigación Agropecuaria); DAVID PACHECO, Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand.; RONALDO VIBART, Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand. |
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Título : |
Whole-farm modelling of grazing dairy systems in Uruguay. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Agricultural Systems, 2021, volume 193, article 103227. Doi: https://doi.org/10.1016/j.agsy.2021.103227 |
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DOI : |
10.1016/j.agsy.2021.10322 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 5 January 2021; Received in revised form 7 July 2021; Accepted 9 July 2021; Available online 21 July 2021. |
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Contenido : |
Abstract:
CONTEXT:Modelling grazing dairy systems from the temperate hot-summer climate region of South America is challenging due to the absence of suitable whole-farm models developed or evaluated in this region. The use of whole-farm models developed in other regions represents an opportunity. However, the accuracy and precision of their predictions need to be assessed.
OBJECTIVES:The present study evaluated the predictive ability of Farmax Dairy Pro, a whole-farm model developed in New Zealand, to simulate grazing dairy systems in Uruguay.
METHODS:Data used for the model evaluation was obtained from a dairy farmlet study carried out in Uruguay. The study aimed to explore four intensification strategies based on increasing home-grown forage utilisation and milk output per hectare. The field experiment consisted of four farmlets using two feeding strategies: [Grass Maximum (GMAX) and Grass Fixed (GFIX), based on the amount of grazed herbage in the diet], and two cow genotypes [New Zealand (NZHF) or North American Holstein-Friesian (NAHF)]. The four farmlets were modelled with Farmax for two consecutive years. Model evaluation was performed using standard regression, dimensionless and error index statistics. The model was evaluated by comparing predicted versus observed monthly patterns of milk, milk fat, milk protein and milk solids (MS; milk fat + milk protein) yields, body condition score (BCS), body weight (BW), DM intake (DMI) and net pasture growth rate (PGR). An application of the model was demonstrated by modelling three scenarios for the GMAX-NZHF farmlet.
RESULTS AND CONCLUSIONS:The predictive ability of Farmax was similar for the four farmlets modelled, including patterns over time, for all the variables evaluated. The model provided a robust prediction for monthly patterns of milk and milk components yields at a herd level for total DMI and PGR. The model had a moderate ability to predict monthly patterns of individual milk and milk components yields and BCS, and a poor ability to predict BW. The scenario modelling results indicate that the model could be used with confidence to simulate different farm system alternatives. Overall, the Farmax Dairy Pro model had the potential to provide adequate predictions for grazing dairy systems from Uruguay.
SIGNIFICANCE:This model will allow the exploration of future intensification pathways for grazing dairy systems in Uruguay and the region, including changes in the forage sequence, stocking rate and calving season. Further adjustments of the model will expand the range of systems and latitudes for this model to be utilised. MenosAbstract:
CONTEXT:Modelling grazing dairy systems from the temperate hot-summer climate region of South America is challenging due to the absence of suitable whole-farm models developed or evaluated in this region. The use of whole-farm models developed in other regions represents an opportunity. However, the accuracy and precision of their predictions need to be assessed.
OBJECTIVES:The present study evaluated the predictive ability of Farmax Dairy Pro, a whole-farm model developed in New Zealand, to simulate grazing dairy systems in Uruguay.
METHODS:Data used for the model evaluation was obtained from a dairy farmlet study carried out in Uruguay. The study aimed to explore four intensification strategies based on increasing home-grown forage utilisation and milk output per hectare. The field experiment consisted of four farmlets using two feeding strategies: [Grass Maximum (GMAX) and Grass Fixed (GFIX), based on the amount of grazed herbage in the diet], and two cow genotypes [New Zealand (NZHF) or North American Holstein-Friesian (NAHF)]. The four farmlets were modelled with Farmax for two consecutive years. Model evaluation was performed using standard regression, dimensionless and error index statistics. The model was evaluated by comparing predicted versus observed monthly patterns of milk, milk fat, milk protein and milk solids (MS; milk fat + milk protein) yields, body condition score (BCS), body weight (BW), DM intake (DMI) and net pasture growth rate (PGR). An appl... Presentar Todo |
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Palabras claves : |
Dairy system; Model evaluation; SISTEMAS LECHEROS; Temperate region; Whole-farm model. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 03472naa a2200241 a 4500
001 1062334
005 2021-08-05
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1016/j.agsy.2021.10322$2DOI
100 1 $aSTIRLING, S.
245 $aWhole-farm modelling of grazing dairy systems in Uruguay.$h[electronic resource]
260 $c2021
500 $aArticle history: Received 5 January 2021; Received in revised form 7 July 2021; Accepted 9 July 2021; Available online 21 July 2021.
520 $aAbstract: CONTEXT:Modelling grazing dairy systems from the temperate hot-summer climate region of South America is challenging due to the absence of suitable whole-farm models developed or evaluated in this region. The use of whole-farm models developed in other regions represents an opportunity. However, the accuracy and precision of their predictions need to be assessed. OBJECTIVES:The present study evaluated the predictive ability of Farmax Dairy Pro, a whole-farm model developed in New Zealand, to simulate grazing dairy systems in Uruguay. METHODS:Data used for the model evaluation was obtained from a dairy farmlet study carried out in Uruguay. The study aimed to explore four intensification strategies based on increasing home-grown forage utilisation and milk output per hectare. The field experiment consisted of four farmlets using two feeding strategies: [Grass Maximum (GMAX) and Grass Fixed (GFIX), based on the amount of grazed herbage in the diet], and two cow genotypes [New Zealand (NZHF) or North American Holstein-Friesian (NAHF)]. The four farmlets were modelled with Farmax for two consecutive years. Model evaluation was performed using standard regression, dimensionless and error index statistics. The model was evaluated by comparing predicted versus observed monthly patterns of milk, milk fat, milk protein and milk solids (MS; milk fat + milk protein) yields, body condition score (BCS), body weight (BW), DM intake (DMI) and net pasture growth rate (PGR). An application of the model was demonstrated by modelling three scenarios for the GMAX-NZHF farmlet. RESULTS AND CONCLUSIONS:The predictive ability of Farmax was similar for the four farmlets modelled, including patterns over time, for all the variables evaluated. The model provided a robust prediction for monthly patterns of milk and milk components yields at a herd level for total DMI and PGR. The model had a moderate ability to predict monthly patterns of individual milk and milk components yields and BCS, and a poor ability to predict BW. The scenario modelling results indicate that the model could be used with confidence to simulate different farm system alternatives. Overall, the Farmax Dairy Pro model had the potential to provide adequate predictions for grazing dairy systems from Uruguay. SIGNIFICANCE:This model will allow the exploration of future intensification pathways for grazing dairy systems in Uruguay and the region, including changes in the forage sequence, stocking rate and calving season. Further adjustments of the model will expand the range of systems and latitudes for this model to be utilised.
653 $aDairy system
653 $aModel evaluation
653 $aSISTEMAS LECHEROS
653 $aTemperate region
653 $aWhole-farm model
700 1 $aFARIÑA, S.
700 1 $aPACHECO, D.
700 1 $aVIBART, R.
773 $tAgricultural Systems, 2021, volume 193, article 103227. Doi: https://doi.org/10.1016/j.agsy.2021.103227
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
05/09/2017 |
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Actualizado : |
03/02/2018 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
PÉREZ DE VIDA, F.; BLANCO, P.H.; VARGAS, J. |
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Afiliación : |
FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE EDUARDO VARGAS MANCUELLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Evaluación final de cultivares interacción G*E: fechas de siembra en Paso de la Laguna. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. |
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Páginas : |
p. 11-14. |
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Serie : |
(INIA Serie Técnica; 233) |
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ISBN : |
978-9974-38-381-4 |
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ISSN : |
1688-9268 |
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Idioma : |
Español |
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Palabras claves : |
INTERACCIÓN GENOTIPO-AMBIENTE; SUBTIPOS DE ARROZ. |
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Thesagro : |
RENDIMIENTO. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7455/1/ST-233-p.11-14.pdf
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Marc : |
LEADER 00710naa a2200217 a 4500
001 1057517
005 2018-02-03
008 2017 bl uuuu u00u1 u #d
020 $a978-9974-38-381-4
022 $a1688-9268
100 1 $aPÉREZ DE VIDA, F.
245 $aEvaluación final de cultivares interacción G*E$bfechas de siembra en Paso de la Laguna.$h[electronic resource]
260 $c2017
300 $ap. 11-14.
490 $a(INIA Serie Técnica; 233)
650 $aRENDIMIENTO
653 $aINTERACCIÓN GENOTIPO-AMBIENTE
653 $aSUBTIPOS DE ARROZ
700 1 $aBLANCO, P.H.
700 1 $aVARGAS, J.
773 $tIn: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017.
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