Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-22T10:38:48.120Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of remote monitoring units for estimating body weight and supplement intake of grazing cattle

Published online by Cambridge University Press:  03 March 2020

G. Simanungkalit Open the ORCID record for G. Simanungkalit [Opens in a new window] ,
R. S. Hegarty ,
F. C. Cowley Open the ORCID record for F. C. Cowley [Opens in a new window]  and
M. J. McPhee Open the ORCID record for M. J. McPhee [Opens in a new window]
G. Simanungkalit*
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale, New South Wales2351, Australia
R. S. Hegarty
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale, New South Wales2351, Australia
F. C. Cowley
Affiliation:
School of Environmental and Rural Science, University of New England, Armidale, New South Wales2351, Australia
M. J. McPhee
Affiliation:
NSW Department of Primary Industries, Livestock Industries Centre, University of New England, Armidale, New South Wales2351, Australia
*
†E-mail: [email protected]
Get access

Abstract

Automated weighing systems to monitor BW and supplement intake (SI) of individual grazing cattle are being developed to better understand the seasonal nutrition and performance of grazing livestock. This study established (1) the accuracy and repeatability of a commercial walk-over weighing (WoW) system for estimating BW and (2) the accuracy of an automatic supplement weighing (ASW) unit for estimating SI based on measuring time spent at the unit. The WoW and ASW units monitored BW and SI of 112 cattle consisting of 55 cows and 57 calves grazed on a 32.5 ha paddock for 41 days, with an average of 258 BW records collected per day. Static BWs were recorded at each mustering event (n = 7) and were compared to repeated measurements collected by the WoW on the day of each mustering event. Body weight was overestimated by the WoW, with the predicted BW of calves and cows averaging 10 and 21 kg heavier, respectively, than actual, and root MS prediction errors (RMSPE) of 5.1% and 5.5% of the static BW, respectively. For both calves and cows, 38% of the MS prediction errors (MSPE) was mean bias (MB) error and 9% of MSPE was slope bias error. The concordance correlation coefficient (CCC; 0.90 v. 0.80) and modelling efficiency (MEF; 0.78 v. 0.62) of WoW BW for calves were higher than for cows, indicating that the predicted values were deviating from a 1 : 1 relationship and in particular as weight increases. A rolling average across five or more consecutive BW measures improved the accuracy of the WoW BW estimates. Regarding estimates of SI, the aggregated time the herd spent at the ASW unit was strongly associated with total SI (R2 = 0.92; P < 0.001). Further, positive linear relationships (P < 0.001) existed between cumulative weighted time spent at the ASW unit (min) and concentration of fenbendazole (FBZ) used as an intake marker and its derivatives (oxfendazole and oxfendazole sulfone) in the plasma of individual cows, with R2 of 0.54, 0.73 and 0.75, respectively. Although the WoW overestimated static BW, the low bias in the slope indicated that a linear regression model could be developed to adjust the WoW BW to reduce the MB and improve the estimate of WoW BW. The significant positive relationship between time spent at the ASW unit and individual blood FBZ concentration identified the suitability of the ASW unit for estimating SI by grazing cattle.

Keywords

individual grazing cattlesupplementationwalk-over weighingautomatic supplement weighingfenbendazole
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s332 - s340
Copyright
© The Animal Consortium 2020

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

Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. John Wiley & Sons and Akademie-Verlag, Berlin, German Democratic Republic.Google Scholar
Brown, DJ, Savage, DB, Hinch, GN and Semple, SJ 2012. Mob-based walk-over weights: similar to the average of individual static weights? Animal Production Science 52, 613–618.CrossRefGoogle Scholar
Brown, DJ, Savage, DB and Hinch, GN 2014. Repeatability and frequency of in-paddock sheep walk-over weights: implications for individual animal management. Animal Production Science 54, 207–213.CrossRefGoogle Scholar
Cockwill, CL, McAllister, TA, Olson, ME, Milligan, DN, Ralston, BJ, Huisma, C and Hand, RK 2000. Individual intake of mineral and molasses supplements by cows, heifers and calves. Canadian Journal of Animal Science 80, 681–690.CrossRefGoogle Scholar
Derner, JD, Reeves, JL, Mortenson, MC, West, M, Irisarri, JG and Durante, M 2016. Estimating overnight weight loss of corralled yearling steers in semiarid rangeland. Rangelands 38, 101–104.CrossRefGoogle Scholar
Dickinson, RA, Morton, JM, Beggs, DS, Anderson, GA, Pyman, MF, Mansell, PD and Blackwood, CB 2013. An automated walk-over weighing system as a tool for measuring liveweight change in lactating dairy cows. Journal of Dairy Science 96, 4477–4486.CrossRefGoogle ScholarPubMed
Dixon, RM, Smith, DR, Porch, I and Petherick, JC 2001. Effects of experience on voluntary intake of supplements by cattle. Australian Journal of Experimental Agriculture 41, 581–592.CrossRefGoogle Scholar
Earley, AV, Sowell, BF and Bowman, JGP 1999. Liquid supplementation of grazing cows and calves. Animal Feed Science and Technology 80, 281–296.CrossRefGoogle Scholar
Fishpool, FJ, Kahn, LP, Tucker, DJ, Nolan, JV and Leng, RA 2012. Fenbendazole as a method for measuring supplement intake in grazing sheep. Animal Production Science 52, 1142–1152.CrossRefGoogle Scholar
Fonseca, MA, Tedeschi, LO, Valadares Filho, SC, De Paula, NF, Silva, LD and Sathler, DFT 2017. Evaluation of equations to estimate body composition in beef cattle using live, linear and standing-rib cut measurements. Animal Production Science 57, 378–390.CrossRefGoogle Scholar
Fuentes-Pila, J, DeLorenzo, MA, Beede, DK, Staples, CR and Holter, JB 1996. Evaluation of equations based on animal factors to predict intake of lactating Holstein cows1. Journal of Dairy Science 79, 1562–1571.CrossRefGoogle Scholar
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, 1687–1693.CrossRefGoogle Scholar
González-García, E, Alhamada, M, Pradel, J, Douls, S, Parisot, S, Bocquier, F, Menassol, JB, Llach, I and González, LA 2018. A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. Computers and Electronics in Agriculture 153, 226–238.CrossRefGoogle Scholar
Lanusse, CE, Sallovitz, JM, Bruni, SFS and Alvarez, LI 2018. Antinematodal drugs. In Veterinary pharmacology and therapeutics (Ed. Riviere, JE and Papich, MG), pp. 1035–1080. John Wiley & Sons Inc., Hoboken, NJ, USA.Google Scholar
McDowell, LR 1996. Feeding minerals to cattle on pasture. Animal Feed Science and Technology 60, 247–271.CrossRefGoogle Scholar
Neave, HW, Weary, DM and Von Keyserlingk, MAG 2018. Individual variability in feeding behaviour of domesticated ruminants. Animal, 12(suppl. 2), s419–s430.CrossRefGoogle ScholarPubMed
Oliveira, BR, Ribas, MN, Machado, FS, Lima, JAM, Cavalcanti, LFL, Chizzotti, ML and Coelho, SG 2018. Validation of a system for monitoring individual feeding and drinking behaviour and intake in young cattle. Animal 12, 634–639.CrossRefGoogle ScholarPubMed
Pszczola, M, Szalanski, M, Rzewuska, K and Strabel, T 2018. Improving repeatability of cows’ body weight recorded by an automated milking system. Livestock Science 214, 149–152.CrossRefGoogle Scholar
R Core Team 2019. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Reuter, RR, Moffet, CA, Horn, GW, Zimmerman, S and Billars, M 2017. TECHNICAL NOTE: daily variation in intake of a salt-limited supplement by grazing steers. The Professional Animal Scientist 33, 372–377.CrossRefGoogle Scholar
Sanyal, PK 1993. The uptake of fenbendazole by cattle and buffalo following long-term low-level administration in urea-molasses blocks: further studies on block formulations. Veterinary Research Communications 17, 325–331.CrossRefGoogle ScholarPubMed
Simanungkalit, G, Hegarty, RS, Cowley, FC and McPhee, MJ 2019. Evaluation of remote monitoring units for estimating body weight and supplement intake of grazing cattle. Proceedings of the 9th ModNut workshop. Advances in Animal Biosciences Journal 10, 309.Google Scholar
Sowell, BF, Bowman, JGP, Grings, EE and MacNeil, MD 2003. Liquid supplement and forage intake by range beef cows. Journal of Animal Science 81, 294–303.CrossRefGoogle ScholarPubMed
Taylor, K, Appuhamy, JRN, Dijkstra, J and Kebreab, E 2018. Development of mathematical models to predict calcium, magnesium and selenium excretion from lactating Holstein cows. Animal Production Science 58, 489–498.CrossRefGoogle Scholar
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.CrossRefGoogle Scholar
Wishart, H, Morgan-Davies, C, Stott, A, Wilson, R and Waterhouse, T 2017. Liveweight loss associated with handling and weighing of grazing sheep. Small Ruminant Research 153, 163–170.CrossRefGoogle Scholar
Wyffels, SA, Williams, AR, Parsons, CT, Dafoe, JM, Boss, DL, DelCurto, T, Davis, NG and Bowman, JGP 2018. The influence of age and environmental conditions on supplement intake and behavior of winter grazing beef cattle on mixed-grass rangelands. Translational Animal Science 2, S89–S92.CrossRefGoogle ScholarPubMed