Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-13T03:38:41.296Z Has data issue: false hasContentIssue false
  • English
  • Français

Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator

Published online by Cambridge University Press:  21 September 2015

I. Halachmi ,
Y. Ben Meir ,
J. Miron  and
E. Maltz
I. Halachmi*
Affiliation:
The Volcani Centre, The Institute of Agricultural Engineering, PO Box 6, Bet Dagan 50250, Israel
Y. Ben Meir
Affiliation:
The Volcani Centre, The Institute of Agricultural Engineering, PO Box 6, Bet Dagan 50250, Israel The Volcani Centre, Animal Science Institute – Agricultural Research Organization (A.R.O.), PO Box 6, Bet Dagan 50250, Israel
J. Miron
Affiliation:
The Volcani Centre, Animal Science Institute – Agricultural Research Organization (A.R.O.), PO Box 6, Bet Dagan 50250, Israel
E. Maltz
Affiliation:
The Volcani Centre, The Institute of Agricultural Engineering, PO Box 6, Bet Dagan 50250, Israel
*
† E-mail: halachmi@volcani.agri.gov.il
Get access

Abstract

Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow level, eating velocity seemed to be correlated with feed intake (R 2=0.93 to 0.94). The eating velocity coefficient varied among individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI) model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by 1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost feeding-behavior sensors on commercial level.

Keywords

individual coweating speed sensorprecision livestock farming
Type
Research Article
Information
animal , Volume 10 , Issue 9 , September 2016 , pp. 1501 - 1506
Copyright
© The Animal Consortium 2015 

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

Arnerdal, S 2005. Predictions for voluntary dry matter intake in dairy cows. Theses and Dissertations, Swedish University of Agricultural Sciences.Google Scholar
Bach, A, Iglesias, C and Busto, I 2004. Technical note: a computerized system for monitoring feeding behavior and individual feed intake of dairy cattle. Journal of Dairy Science 87, 4207–4209.Google Scholar
Buza, MH, Holden, LA, White, RA and Ishler, VA 2014. Evaluating the effect of ration composition on income over feed cost and milk yield. Journal of Dairy Science 97, 3073–3080.Google Scholar
Calan, A 1997. Calan Broadbent Feeding System, American Calan. Retrieved September 9, 2015, from http://americancalan.com.Google Scholar
Chapinal, N, Veira, DM, Weary, DM and von Keyserlingk, MA 2007. Technical note: validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of Dairy Science 90, 5732–5736.Google Scholar
Clément, P, Guatteo, R, Delaby, L, Rouillé, B, Chanvallon, A, Philipot, JM and Bareille, N 2014. Short communication: added value of rumination time for the prediction of dry matter intake in lactating dairy cows. Journal of Dairy Science 97, 6531–6535.Google Scholar
DeVries, TJ, von Keyserlingk, MA, Weary, DM and Beauchemin, KA 2003. Technical note: validation of a system for monitoring feeding behavior of dairy cows. Journal of Dairy Science 86, 3571–3574.Google Scholar
Ferris, CP, Keady, TWJ, Gordon, FJ and Kilpatrick, DJ 2006. Comparison of a Calan gate and a conventional feed barrier system for dairy cows: feed intake and cow behaviour. Irish Journal of Agricultural and Food Research 45, 149–156.Google Scholar
Fox, D, Sniffen, C, O’connor, J, Russell, J and Van Soest, P 1992. A net carbohydrate and protein system for evaluating cattle diets: III. Cattle requirements and diet adequacy. Journal of Animal Science 70, 3578–3596.Google Scholar
Fox, DG, Tedeschi, LO, Tylutki, TP, Russell, JB, Van Amburgh, ME, Chase, LE, Pell, AN and Overton, TR 2004. The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology 112, 29–78.Google Scholar
Gonzalez, LA, Tolkamp, BJ, Coffey, MP, Ferret, A and Kyriazakis, I 2008. Changes in feeding behavior as possible indicators for the automatic monitoring of health disorders in dairy cows. Journal of Dairy Science 91, 1017–1028.Google Scholar
Grant, RJ, Albright, JL 2001. Effect of animal grouping on feeding behavior and intake of dairy cattle. Journal of Dairy Science 84, 156–163.Google Scholar
Halachmi, I 2004. Designing the automatic milking farm in a hot climate Journal of Dairy Science 87, 764–775.Google Scholar
Halachmi, I 2015. Precision livestock farming applications. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Halachmi, I, Børsting, CF, Maltz, E, Edan, Y and Weisbjerg, MR 2011. Feed intake of Holstein, Danish Red, and Jersey cows in automatic milking systems. Livestock Science 138, 56–61.Google Scholar
Halachmi, I, Edan, Y, Maltz, E, Peiper, UM, Brukental, I and Moalem, U 1996. A controlled automatic fodder consumption system and method for feeding livestock using same. In Patent PCT 119109.Google Scholar
Halachmi, I, Edan, Y, Maltz, E, Peiper, UM, Brukental, I and Moalem, U 1998. A real-time control system for individual dairy cow food intake. Computers and Electronics in Agriculture 20, 131–144.Google Scholar
Halachmi, I, Edan, Y, Moallem, U and Maltz, E 2004. Predicting feed intake of the individual dairy cow. Journal of Dairy Science 87, 2254–2267.Google Scholar
Halachmi, I, Ofir, S and Miron, J 2005. Comparing two concentrate allowances in an automatic milking system. Animal Science 80, 339–344.Google Scholar
Halachmi, I, Tello, AS, Fernández, AP, Hertem, Tv, Sibony, V, Weyl-Feinstein, S, Verbrugge, A, Bonneau, M and Neilson, R 2015. 8.5. Discussion: rumen sensing, feed intake & precise feeding. In Precision livestock farming applications (ed. Ilan Halachmi), pp. 319–322. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Huisma, C 2002. U.S. Patent No. 6,427,627. (Ed. USPaT Office). Washington, DC.Google Scholar
Hvelplund, T and Nørgard, NR 2003. Kvægets ernæring og fysiologi. Bind 1 – Næringsstofomsætning og fodervurdering. In DJF rapport Husdyrbrug p. 642 pp. DJF rapport (ISSN 1397-9892; nr. 53) (Husdyrbrug). Danmarks JordbrugsForskning, Tjele, Denmark.Google Scholar
Ingvartsen, KL 1994. Models of voluntary food intake in cattle. Livestock Production Science 39, 19–38.Google Scholar
Kertz, AF, Reutzel, LF and Thomson, GM 1991. Dry matter intake from parturition to midlactation. Journal of Dairy Science 74, 2290–2295.Google Scholar
Kjos, NP 2002. Faglig rapport. System for beregning av fôropptak hos drøvtyggere. Sluttrapport til Norges forskningsråd, Prosjekt 114264/110 periode 1997–2001, Institutt for husdyrfag, NLH, Norway.Google Scholar
Krawczel, PD, Klaiber, LM, Thibeau, SS and Dann, HM 2012. Technical note: data loggers are a valid method for assessing the feeding behavior of dairy cows using the Calan Broadbent Feeding System. Journal of Dairy Science 95, 4452–4456.Google Scholar
Lindgren, E, Murphy, M and Andersson, T 2001. Värdering av Foder. Lantmännen Foderutveckling AB, Nötfor. Almqvist & Wiksell, Uppsala.Google Scholar
Livshin, N, Maltz, E and Edan, Y 1995. Regularity of dairy cow feeding behavior with computer-controlled feeders. Journal of Dairy Science 78, 296–304.Google Scholar
Madsen, J, Weisbjerg, MR and Hvelplund, T 2010. Concentrate composition for Automatic Milking Systems – effect on milking frequency. Livestock Science 127, 45–50.Google Scholar
Maltz, E, Barbosa, LF, Bueno, P, Scagion, L, Kaniyamattam, K, Greco, LF, De Vries, A and Santos, JE 2013. Effect of feeding according to energy balance on performance, nutrient excretion, and feeding behavior of early lactation dairy cows. Journal of Dairy Science 96, 5249–5266.Google Scholar
Mendes, EDM, Carstens, GE, Tedeschi, LO, Pinchak, WE and Friend, TH 2011. Validation of a system for monitoring feeding behavior in beef cattle. Journal of Animal Science 89, 2904–2910.Google Scholar
National Research Council (NRC) 1989. Nutrient Requirements of Dairy Cattle, 6th revised edition. National Research Council, National Academy Press, Washington, DC. p. 381.Google Scholar
National Research Council (NRC) 2007. Nutrient requirements of small ruminants: sheep, goats, cervids and new world camelids. The National Academic Press, Washington, DC.Google Scholar
Rayburn, EB and Fox, DG 1993. Variation in neutral detergent fiber intake of Holstein cows1. Journal of Dairy Science 76, 544–554.Google Scholar
Roseler, DK, Fox, DG, Chase, LE, Pell, AN and Stone, WC 1997. Development and evaluation of equations for prediction of feed intake for lactating Holstein dairy cows. Journal of Dairy Science 80, 878–893.Google Scholar
Schwartzkopf-Genswein, KS, Huisma, C and McAllister, TA 1999. Validation of a radio frequency identification system for monitoring the feeding patterns of feedlot cattle. Livestock Production Science 60, 27–31.Google Scholar
Shelley, AN 2013. Monitoring dairy cow feed intake using machine vision. Theses and Dissertations-Electrical and Computer Engineering, Paper 24, University of Kentucky, Lexington, Kentucky, USA. Retrieved September 9, 2015, from http://uknowledge.uky.edu/ece_etds/24.Google Scholar
Urton, G, von Keyserlingk, MA and Weary, DM 2005. Feeding behavior identifies dairy cows at risk for metritis. Journal of Dairy Science 88, 2843–2849.Google Scholar
Vandehaar, MJ 1998. Efficiency of nutrient use and relationship to profitability on dairy farms. Journal of Dairy Science 81, 272–282.Google Scholar
Volden, H 2001. Utvikling av et mekanistisk system for vurdering av fôr til drøvtyggere, AAT-modellen. Fôropptak og fôrmiddelvurdering hos drøvtyggere. Fagseminar 18–19. September 2001. Qaulity Hotel Halvorsbole, Jevnaker, Norway, 30pp. (in Norwegian).Google Scholar
Wang, Z, Nkrumah, JD, Li, C, Basarab, JA, Goonewardene, LA, Okine, EK, Crews, DH Jr. and Moore, SS 2006. Test duration for growth, feed intake, and feed efficiency in beef cattle using the GrowSafe System. Journal of Animal Science 84, 2289–2298.Google Scholar
Weiss, WP 1991. Estimating dry matter intake. Proceedings of Ohio Dairy Nutrition Conference, Ohio State University Ext., Wooster, OH, 9pp.Google Scholar