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Registro completo
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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 : |
22/05/2023 |
Actualizado : |
22/05/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
Circulación / Nivel : |
Nacional - -- |
Autor : |
FASSANA , C.N.; HOFFMAN , E.M.; BERGER, A.; ERNST, O. |
Afiliación : |
CÉSAR NICOLÁS FASSANA, Universidad de la República, Facultad de Agronomía, Departamento de Producción Vegetal, Paysandú, Uruguay; ESTEBAN MARTÍN HOFFMAN, Universidad de la República, Facultad de Agronomía, Departamento de Producción Vegetal, Paysandú, Uruguay; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OSWALDO ERNST, Universidad de la República, Facultad de Agronomía, Departamento de Producción Vegetal, Paysandú, Uruguay. |
Título : |
Nitrogen nutrition index at GS 3.3 is an effective tool to adjust nitrogen required to reach attainable wheat yield. [El índice de nutrición nitrogenada en GS 3.3 es una herramienta eficaz para ajustar el nitrógeno necesario para lograr el rendimiento de trigo alcanzable]. [O índice de nutrição de nitrogênio no GS 3.3 é uma ferramenta eficaz para ajustar o nitrogênio necessário para alcançar a produtividade de trigo atingível]. |
Complemento del título : |
Plant production. |
Fecha de publicación : |
2022 |
Fuente / Imprenta : |
Agrociencia Uruguay, 2022, Vol.26, number 2, e924. https://doi.org/10.31285/AGRO.26.924 -- OPEN ACCESS. |
ISSN : |
2730-5066 |
DOI : |
10.31285/AGRO.26.924 |
Idioma : |
Inglés |
Notas : |
Article history: Received 8 Jul 2021; Accepted 21 Jun 2022; Published 30 Aug 2022. -- Correspondence: Nicolás Fassana, fassana@fagro.edu.uy -- Editor: José A. Terra,
Instituto Nacional de Investigación Agropecuaria (INIA), Treinta y Tres, Uruguay. -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/ ) |
Contenido : |
ABSTRACT.- Current nitrogen (N) fertilization schedule for spring wheat was developed under a dominant crop-pasture rotation. After the year 2002, this cropping system was converted to continuous annual cropping systems under no-till, reducing soil N supply capacity progressively. Additionally, highest grain yield of new varieties ncreased N demand. The required additional N fertilizer can be adjusted by monitoring nutritional status of the crop. .-.-.-.-.-.-.-.-. RESUMEN.- El esquema actual de fertilización con nitrógeno (N) para el trigo de primavera se desarrolló bajo una rotación dominante de cultivo-pastura. Después de 2002, este sistema se convirtió en un sistema de cultivo anual continuo con labranza cero, reduciendo progresivamente la capacidad de suministro de N del suelo. Además, el mayor rendimiento en grano de las nuevas variedades aumentó la demanda de N. El fertilizante nitrogenado adicional requerido se puede ajustar monitoreando el estado nutricional del cultivo. .-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
RESUMO.- O esquema atual de fertilização com nitrogênio (N) para o trigo de primavera foi desenvolvido sob uma rotação dominante de cultivo e pastagem. A partir de 2002, esse sistema passou a ser um sistema de cultivo anual contínuo com plantio direto, reduzindo progressivamente a capacidade de suprimento de N do solo. Além disso, o maior rendimento de grãos das novas variedades aumentou a demanda por N. O fertilizante de nitrogênio adicional necessário pode ser ajustado monitorando o estado nutricional da cultura. Copyright (c) 2022 Agrociencia Uruguay MenosABSTRACT.- Current nitrogen (N) fertilization schedule for spring wheat was developed under a dominant crop-pasture rotation. After the year 2002, this cropping system was converted to continuous annual cropping systems under no-till, reducing soil N supply capacity progressively. Additionally, highest grain yield of new varieties ncreased N demand. The required additional N fertilizer can be adjusted by monitoring nutritional status of the crop. .-.-.-.-.-.-.-.-. RESUMEN.- El esquema actual de fertilización con nitrógeno (N) para el trigo de primavera se desarrolló bajo una rotación dominante de cultivo-pastura. Después de 2002, este sistema se convirtió en un sistema de cultivo anual continuo con labranza cero, reduciendo progresivamente la capacidad de suministro de N del suelo. Además, el mayor rendimiento en grano de las nuevas variedades aumentó la demanda de N. El fertilizante nitrogenado adicional requerido se puede ajustar monitoreando el estado nutricional del cultivo. .-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
RESUMO.- O esquema atual de fertilização com nitrogênio (N) para o trigo de primavera foi desenvolvido sob uma rotação dominante de cultivo e pastagem. A partir de 2002, esse sistema passou a ser um sistema de cultivo anual contínuo com plantio direto, reduzindo progressivamente a capacidade de suprimento de N do solo. Além disso, o maior rendimento de grãos das novas variedades aumentou a demanda por N. O fertilizante de nitrogênio adicional necessário pode ser ajustad... Presentar Todo |
Palabras claves : |
Diagnosis; Diagnóstico; Nutrição do trigo; Nutrición de trigo; Sincronizar oferta/demanda; Synchronize supply/demand; Wheat nutrition. |
Thesagro : |
TRITICUM AESTIVUM. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17163/1/2730-5066-924.pdf
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Marc : |
LEADER 03239naa a2200289 a 4500 001 1064151 005 2023-05-22 008 2022 bl uuuu u00u1 u #d 022 $a2730-5066 024 7 $a10.31285/AGRO.26.924$2DOI 100 1 $aFASSANA , C.N. 245 $aNitrogen nutrition index at GS 3.3 is an effective tool to adjust nitrogen required to reach attainable wheat yield. [El índice de nutrición nitrogenada en GS 3.3 es una herramienta eficaz para ajustar el nitrógeno necesario para lograr el rendimiento de trigo alcanzable]. [O índice de nutrição de nitrogênio no GS 3.3 é uma ferramenta eficaz para ajustar o nitrogênio necessário para alcançar a produtividade de trigo atingível].$h[electronic resource] 260 $c2022 500 $aArticle history: Received 8 Jul 2021; Accepted 21 Jun 2022; Published 30 Aug 2022. -- Correspondence: Nicolás Fassana, fassana@fagro.edu.uy -- Editor: José A. Terra, Instituto Nacional de Investigación Agropecuaria (INIA), Treinta y Tres, Uruguay. -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/ ) 520 $aABSTRACT.- Current nitrogen (N) fertilization schedule for spring wheat was developed under a dominant crop-pasture rotation. After the year 2002, this cropping system was converted to continuous annual cropping systems under no-till, reducing soil N supply capacity progressively. Additionally, highest grain yield of new varieties ncreased N demand. The required additional N fertilizer can be adjusted by monitoring nutritional status of the crop. .-.-.-.-.-.-.-.-. RESUMEN.- El esquema actual de fertilización con nitrógeno (N) para el trigo de primavera se desarrolló bajo una rotación dominante de cultivo-pastura. Después de 2002, este sistema se convirtió en un sistema de cultivo anual continuo con labranza cero, reduciendo progresivamente la capacidad de suministro de N del suelo. Además, el mayor rendimiento en grano de las nuevas variedades aumentó la demanda de N. El fertilizante nitrogenado adicional requerido se puede ajustar monitoreando el estado nutricional del cultivo. .-.-.-.-.-.-.-.-.-.-.-.-.-.-.- RESUMO.- O esquema atual de fertilização com nitrogênio (N) para o trigo de primavera foi desenvolvido sob uma rotação dominante de cultivo e pastagem. A partir de 2002, esse sistema passou a ser um sistema de cultivo anual contínuo com plantio direto, reduzindo progressivamente a capacidade de suprimento de N do solo. Além disso, o maior rendimento de grãos das novas variedades aumentou a demanda por N. O fertilizante de nitrogênio adicional necessário pode ser ajustado monitorando o estado nutricional da cultura. Copyright (c) 2022 Agrociencia Uruguay 650 $aTRITICUM AESTIVUM 653 $aDiagnosis 653 $aDiagnóstico 653 $aNutrição do trigo 653 $aNutrición de trigo 653 $aSincronizar oferta/demanda 653 $aSynchronize supply/demand 653 $aWheat nutrition 700 1 $aHOFFMAN , E.M. 700 1 $aBERGER, A. 700 1 $aERNST, O. 773 $tAgrociencia Uruguay, 2022, Vol.26, number 2, e924. https://doi.org/10.31285/AGRO.26.924 -- OPEN ACCESS.
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