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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
27/11/2020 |
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Actualizado : |
22/02/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
HERD, R.M.; ARTHUR, P.F.; HEGARTY, R.S.; BIRD-GARDINER, T.; DONOGHUE, K.A.; VELAZCO, J.I. |
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Afiliación : |
NSW Department of Primary Industries, Livestock Industry Centre. Environmental and Rural Science, University of New England, Australia.; NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute; Environmental and Rural Science, University of New England.; NSW Department of Primary Industries, Agricultural Research Centre. Australia; NSW Department of Primary Industries, Agricultural Research Centre. Australia; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Predicting metabolisable energy intake by free-ranging cattle using multiple short-term breath samples and applied to a pasture case-study. (Article in Press) |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Animal Production Science, 4 Nov. 2020, 61(4), p. 381-389 Doi: https://doi.org/10.1071/AN20162 |
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DOI : |
10.1071/AN20162 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 3 April 2020, accepted 11 October 2020, published online 4 November 2020. |
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Contenido : |
Context. Research into improving feed efficiency by ruminant animals grazing pastures has historically been restrained by an inability to measure feed intake by large numbers of individual animals. Recent advances in portable breath measurement technology could be useful for this purpose but methodologies need to be developed.
Aims. To evaluate predictive models for metabolisable energy intake (MEI) by free-ranging cattle using multiple short-term breath samples and then apply these to predict MEI by free-ranging cattle in a historic grazing experiment with cattle genetically divergent for residual feed intake (feed efficiency).
Methods. Predictive models for MEI were developed using bodyweight (BW) data, and carbon dioxide production rate (CPR) and methane production rate (MPR) from multiple short-term breath measurements, from an experiment with long-fed Angus steers on a grain-based diet, and an experiment with short-fed Angus heifers on a roughage diet.
Heat production was calculated using CPR and MPR. Energy retained (ER) in body tissue gain by steers was calculated from BW, ADG, initial and final subcutaneous fat depths, and for both groups using feeding-standards equations.
Key results. Metabolic mid-test BW (MBW) explained 49 and 47% of the variation in MEI in the steer and heifer experiment, respectively, and for the steers adding ADG and then subcutaneous fat gain resulted in the models accounting for 60 and then 65% of the variation in MEI. In the steer experiment, MBW with CPR explained 57% of the variation in MEI, and including MPR did not account for any additional variation. In the heifer experiment, MBW with CPR explained 50%, and with MPR accounted for 52% of the variation in MEI. Heat production plus ER explained 60, 35 and 85% of the variation in MEI in the steer and the heifer experiments, and in the pooled data from both experiments, respectively.
Conclusions. Multiple short-term breath measurements, together simple BW data, can be used to predict MEI by free-ranging cattle in studies in which animals do not have feed-intake or ADG recorded.
Implications. This methodology can be used for research into improving feed efficiency by farm animals grazing pastures. MenosContext. Research into improving feed efficiency by ruminant animals grazing pastures has historically been restrained by an inability to measure feed intake by large numbers of individual animals. Recent advances in portable breath measurement technology could be useful for this purpose but methodologies need to be developed.
Aims. To evaluate predictive models for metabolisable energy intake (MEI) by free-ranging cattle using multiple short-term breath samples and then apply these to predict MEI by free-ranging cattle in a historic grazing experiment with cattle genetically divergent for residual feed intake (feed efficiency).
Methods. Predictive models for MEI were developed using bodyweight (BW) data, and carbon dioxide production rate (CPR) and methane production rate (MPR) from multiple short-term breath measurements, from an experiment with long-fed Angus steers on a grain-based diet, and an experiment with short-fed Angus heifers on a roughage diet.
Heat production was calculated using CPR and MPR. Energy retained (ER) in body tissue gain by steers was calculated from BW, ADG, initial and final subcutaneous fat depths, and for both groups using feeding-standards equations.
Key results. Metabolic mid-test BW (MBW) explained 49 and 47% of the variation in MEI in the steer and heifer experiment, respectively, and for the steers adding ADG and then subcutaneous fat gain resulted in the models accounting for 60 and then 65% of the variation in MEI. In the steer experiment... Presentar Todo |
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Palabras claves : |
AVERAGE DAILY GAIN; CARBON DIOXIDE; FEED EFFICIENCY; FEED INTAKE; GRAZING; METABOLISABLE ENERGY INTAKE; METHANE; METHANE PRODUCTION RATE; OXYGEN; PASTURE. |
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Asunto categoría : |
L02 Alimentación animal |
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Marc : |
LEADER 03329naa a2200325 a 4500
001 1061522
005 2021-02-22
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1071/AN20162$2DOI
100 1 $aHERD, R.M.
245 $aPredicting metabolisable energy intake by free-ranging cattle using multiple short-term breath samples and applied to a pasture case-study. (Article in Press)$h[electronic resource]
260 $c2020
500 $aArticle history: Received 3 April 2020, accepted 11 October 2020, published online 4 November 2020.
520 $aContext. Research into improving feed efficiency by ruminant animals grazing pastures has historically been restrained by an inability to measure feed intake by large numbers of individual animals. Recent advances in portable breath measurement technology could be useful for this purpose but methodologies need to be developed. Aims. To evaluate predictive models for metabolisable energy intake (MEI) by free-ranging cattle using multiple short-term breath samples and then apply these to predict MEI by free-ranging cattle in a historic grazing experiment with cattle genetically divergent for residual feed intake (feed efficiency). Methods. Predictive models for MEI were developed using bodyweight (BW) data, and carbon dioxide production rate (CPR) and methane production rate (MPR) from multiple short-term breath measurements, from an experiment with long-fed Angus steers on a grain-based diet, and an experiment with short-fed Angus heifers on a roughage diet. Heat production was calculated using CPR and MPR. Energy retained (ER) in body tissue gain by steers was calculated from BW, ADG, initial and final subcutaneous fat depths, and for both groups using feeding-standards equations. Key results. Metabolic mid-test BW (MBW) explained 49 and 47% of the variation in MEI in the steer and heifer experiment, respectively, and for the steers adding ADG and then subcutaneous fat gain resulted in the models accounting for 60 and then 65% of the variation in MEI. In the steer experiment, MBW with CPR explained 57% of the variation in MEI, and including MPR did not account for any additional variation. In the heifer experiment, MBW with CPR explained 50%, and with MPR accounted for 52% of the variation in MEI. Heat production plus ER explained 60, 35 and 85% of the variation in MEI in the steer and the heifer experiments, and in the pooled data from both experiments, respectively. Conclusions. Multiple short-term breath measurements, together simple BW data, can be used to predict MEI by free-ranging cattle in studies in which animals do not have feed-intake or ADG recorded. Implications. This methodology can be used for research into improving feed efficiency by farm animals grazing pastures.
653 $aAVERAGE DAILY GAIN
653 $aCARBON DIOXIDE
653 $aFEED EFFICIENCY
653 $aFEED INTAKE
653 $aGRAZING
653 $aMETABOLISABLE ENERGY INTAKE
653 $aMETHANE
653 $aMETHANE PRODUCTION RATE
653 $aOXYGEN
653 $aPASTURE
700 1 $aARTHUR, P.F.
700 1 $aHEGARTY, R.S.
700 1 $aBIRD-GARDINER, T.
700 1 $aDONOGHUE, K.A.
700 1 $aVELAZCO, J.I.
773 $tAnimal Production Science, 4 Nov. 2020, 61(4), p. 381-389 Doi: https://doi.org/10.1071/AN20162
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Registro original : |
INIA Treinta y Tres (TT) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
14/06/2017 |
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Tipo de producción científica : |
Documentos |
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Autor : |
MINISTERIO DE AGRICULTURA Y PESCA. CENTRO DE INVESTIGACIONES AGRICOLAS "ALBERTO BOERGER" |
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Título : |
Sistema de certificación de semillas. |
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Fecha de publicación : |
1965 |
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Fuente / Imprenta : |
Colonia (Uruguay): CIIAB, 1965. |
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Páginas : |
11 p. |
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Serie : |
(CIAAB Miscelánea; 1) |
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Idioma : |
Español |
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Notas : |
Material proporcionado por los técnicos del Programa de Semillas de Centro de Investigaciones Agrícolas, Ing.Agr. Elsa Manzini de Zamuz, Cadmo Rosell y éctor Zamuz.
Los standards fueron adecuados por el Dr. Russell Bradley, técnico de FAO que asesoró dicho Programa durante el período 1962-63. |
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Contenido : |
ÍNDICE.
1 - Sistema de certificación.
2 - Variedades.
3 - Categorías de semillas.
6 - Requisitos y standards.
9 - Etiquetas y sellos.
10 - Categorías especiales.
11 - Semilla retenida. |
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Thesagro : |
CERTIFICACION DE SEMILLAS; NORMAS; URUGUAY; VARIEDADES. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5983/1/CIAAB-Miscelanea-1-1965.pdf
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Marc : |
LEADER 01029nam a2200193 a 4500
001 1043515
005 2017-06-14
008 1965 bl uuuu u0uu1 u #d
100 1 $aMINISTERIO DE AGRICULTURA Y PESCA. CENTRO DE INVESTIGACIONES AGRICOLAS "ALBERTO BOERGER"
245 $aSistema de certificación de semillas.
260 $aColonia (Uruguay): CIIAB$c1965
300 $a11 p.
490 $a(CIAAB Miscelánea; 1)
500 $aMaterial proporcionado por los técnicos del Programa de Semillas de Centro de Investigaciones Agrícolas, Ing.Agr. Elsa Manzini de Zamuz, Cadmo Rosell y éctor Zamuz. Los standards fueron adecuados por el Dr. Russell Bradley, técnico de FAO que asesoró dicho Programa durante el período 1962-63.
520 $aÍNDICE. 1 - Sistema de certificación. 2 - Variedades. 3 - Categorías de semillas. 6 - Requisitos y standards. 9 - Etiquetas y sellos. 10 - Categorías especiales. 11 - Semilla retenida.
650 $aCERTIFICACION DE SEMILLAS
650 $aNORMAS
650 $aURUGUAY
650 $aVARIEDADES
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