Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-19T21:30:47.167Z Has data issue: false hasContentIssue false

An assessment of Walk-over-Weighing to estimate short-term individual forage intake in sheep

Published online by Cambridge University Press:  26 October 2017

E. González-García*
Affiliation:
INRA UMR868, Systèmes d’Elevage Méditerranées et Tropicaux (SELMET), F-34060 Montpellier, France
P. de Oliveira Golini
Affiliation:
Faculdade de Zootecnia e Engenharia de Alimentos- FZEA/USP, Universidade de São Paulo (USP), Campus da USP Fernando Costa, Pirassununga, São Paulo, SP 13635-900, Brazil
P. Hassoun
Affiliation:
INRA UMR868, Systèmes d’Elevage Méditerranées et Tropicaux (SELMET), F-34060 Montpellier, France
F. Bocquier
Affiliation:
INRA UMR868, Systèmes d’Elevage Méditerranées et Tropicaux (SELMET), F-34060 Montpellier, France Montpellier SupAgro, Sciences Animales, F-34060 Montpellier, France
D. Hazard
Affiliation:
INRA UR631, Génétique, Physiologie et Systèmes d’Elevage (GenPhySE), Chemin de Borde Rouge, Auzeville, F-31326 Castanet-Tolosan Cedex, France
L. A. González
Affiliation:
Centre for Carbon Water and Food, School of Life and Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Camden, NSW 2570, Australia
A. B. Ingham
Affiliation:
CSIRO Agriculture and Food, St Lucia, QLD 4067, Australia
G. J. Bishop-Hurley
Affiliation:
CSIRO Agriculture and Food, St Lucia, QLD 4067, Australia
P. L. Greenwood
Affiliation:
NSW Department of Primary Industries, Beef Industry Centre, University of New England, Armidale, NSW 2351, Australia CSIRO Agriculture and Food, Armidale, NSW 2350, Australia
Get access

Abstract

The main limitation for determining feed efficiency of freely grazing ruminants is measurement of daily individual feed intake. This paper describes an investigation that assessed a method for estimating intake of forage based on changes in BW of ewes. A total of 24 dry and non-pregnant Romane ewes (12 hoggets, HOG; mean±SD 51.8±2.8 kg BW; body condition score (BCS) 2.6±0.2; and 12 adults, ADU; 60.4±8.5 kg BW; BCS 2.7±0.8) were selected for the study and moved from their rangeland system to a confined pen with controlled conditions and equipped with individual automatic feeders. The experiment lasted for 28 days (21 days adaptation and 7 days feed intake measurement). Ewes were fed hay and trained to use the electronic feeders (one feeding station per ewe) in which actual daily intake (H intake24) was measured. The pens were designed to maximize movement of trained ewes through an automated Walk-over-Weighing device, by using water and mineral salts as attractants. Total individual intake of hay measured in the automatic feeder at each meal (H intake) was compared with indirect estimates of feed intake determined using differences in the BW of the ewes (∆BW) before and 1 h following morning and afternoon feeding at fixed times. The BW, BCS, H intake, H intake24, as well as plasma non-esterified fatty acids (NEFA), glucose and insulin profiles were determined. The BW was higher in ADU v. HOG but BCS was not affected by parity. The H intake24 was affected by day of experiment as a consequence of reduced availability and intake of water on one day. Plasma glucose, NEFA and insulin were not affected by parity or day of experiment. The H Intake was and ∆BW tended to be higher in the morning in HOG, whereas H intake was and ∆BW tended to be higher in ADU at the afternoon meal. Irrespective of parity or feeding time, there was very strong correlation (r 2=0.93) between H intake and ∆BW. This relationship confirms that our indirect method of estimating individual forage intake was reliable within the strictly controlled conditions of the present experiment. The method appears suitable for use in short-term intensive group feeding situations, and has potential to be further developed for longer-term forage intake studies, with a view to developing a method for freely grazing ruminants.

Type
Research Article
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barlow, R, Ellis, KJ, Williamson, PJ, Costigan, P, Stephenson, PD, Rose, G and Mears, PT 1988. Dry-matter intake of Hereford and first-cross cows measured by controlled release of chromic oxide on three pasture systems. The Journal of Agricultural Science 110, 217231.CrossRefGoogle Scholar
Bompa, J, Ricard, E and Bodin, L 2013. Automatisms for phenotyping data gathering in animal farms. In EAAP 64th Annual Meeting, Nantes, France.Google Scholar
Brown, DJ, Savage, DB, Hinch, GN and Hatcher, S 2015. Monitoring liveweight in sheep is a valuable management strategy: a review of available technologies. Animal Production Science 55, 427436.CrossRefGoogle Scholar
Coates, DB and Penning, P 2000. Measuring animal performance. In Field and laboratory methods for grassland and animal production research (ed. L t’Mannetje and R Jones), pp. 353402. CABI Publishing, Wallinford, UK.CrossRefGoogle Scholar
Dove, H and Mayes, RW 2006. Protocol for the analysis of n-alkanes and other plant wax compounds and for their use as markers for quantifying the nutrient supply of large mammalian herbivores. Nature Protocols 1, 16801697.CrossRefGoogle ScholarPubMed
González, LA, Bishop-Hurley, G, Henry, D and Charmley, E 2014. Wireless sensor networks to study, monitor and manage cattle in grazing systems. Animal Production Science 54, 16871693.CrossRefGoogle Scholar
González-García, E, Gozzo de Figuereido, V, Foulquie, D, Jousserand, E, Autran, P, Camous, S, Tesniere, A, Bocquier, F and Jouven, M 2014. Circannual body reserve dynamics and metabolic profile changes in Romane ewes grazing on rangelands. Domestic Animal Endocrinology 46, 3748.CrossRefGoogle ScholarPubMed
González-García, E, Tesniere, A, Camous, S, Bocquier, F, Barillet, F and Hassoun, P 2015. The effects of parity, litter size, physiological state, and milking frequency on the metabolic profile of Lacaune dairy ewes. Domestic Animal Endocrinology 50, 3244.CrossRefGoogle ScholarPubMed
Greenwood, PL, Bishop-Hurley, GJ, González, LA and Ingham, AB 2016. Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture. Animal Production Science 56, 12991311.CrossRefGoogle Scholar
Greenwood, PL, Paull, DR, McNally, J, Kalinowsky, T, Ebert, D, Little, B, Smith, DV, Rahman, A, Valencia, P, Ingham, AB and Bishop-Hurley, GJ 2017. Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattle. Crop and Pasture Science http://dx.doi.org/: 10.1071/CP16383.CrossRefGoogle Scholar
Greenwood, PL, Valencia, P, Overs, L, Paull, DR and Purvis, IW 2014. New ways of measuring intake, efficiency and behaviour of grazing livestock. Animal Production Science 54, 17961804.CrossRefGoogle Scholar
Hassoun, P, Viudes, G, Autran, P, Bastianelli, D and Bocquier, F 2013. A method for estimating dry forage intake by sheep using polyethylene glycol as a faecal marker measured with NIRS. Animal 7, 12801288.CrossRefGoogle ScholarPubMed
Jarrige, R, Demarquilly, C, Dulphy, JP, Hoden, A, Robelin, J, Beranger, C, Geay, Y, Journet, M, Malterre, C, Micol, D and Petit, M 1986. The INRA “Fill Unit” system for predicting the voluntary intake of forage-based diets in ruminants: a review. Journal of Animal Science 63, 17371758.CrossRefGoogle Scholar
Penning, PD 2004. Herbage intake handbook. British Grassland Society, Reading, UK.Google Scholar
Russel, AJF, Doney, JM and Gunn, RG 1969. Subjective assessment of body fat in live sheep. Journal of Agricultural Science 72, 451454.CrossRefGoogle Scholar
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225247.CrossRefGoogle Scholar