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Relationship between metabolic status and behavior in dairy cows in week 4 of lactation

Published online by Cambridge University Press:  26 July 2018

R. J. van Hoeij Open the ORCID record for R. J. van Hoeij [Opens in a new window] ,
A. Kok ,
R. M. Bruckmaier ,
M. J. Haskell ,
B. Kemp  and
A. T. M. van Knegsel Open the ORCID record for A. T. M. van Knegsel [Opens in a new window]
R. J. van Hoeij
Affiliation:
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University, PO Box 338, 6700 AHWageningen, The Netherlands
A. Kok
Affiliation:
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University, PO Box 338, 6700 AHWageningen, The Netherlands Animal Production Systems Group, Department of Animal Sciences, Wageningen University, PO Box 338, 6700 AHWageningen, The Netherlands
R. M. Bruckmaier
Affiliation:
Veterinary Physiology, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109a, CH-3001 Bern, Switzerland
M. J. Haskell
Affiliation:
Scotland’s Rural College (SRUC), West Mains Road, Edinburgh EH9 3JG, UK
B. Kemp
Affiliation:
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University, PO Box 338, 6700 AHWageningen, The Netherlands
A. T. M. van Knegsel*
Affiliation:
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University, PO Box 338, 6700 AHWageningen, The Netherlands
*
†E-mail: ariette.vanknegsel@wur.nl
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Abstract

Blood metabolite and hormone concentrations are indicative of metabolic status, but blood sampling and analysis is invasive and time-consuming. Monitoring behavior can be done automatically, and behaviors may also be used as indicators of metabolic status. The aim of this study was to analyze the relationships between metabolic status and feeding behavior, lying behavior, motion index and steps of dairy cows in week 4 postpartum. Behavioral data from 81 Holstein-Friesian cows were collected using computerized feeders and accelerometers, and blood samples were collected for analysis of free-fatty acid (FFA), β-hydroxybutyrate (BHB), glucose, insulin, IGF-1 and growth hormone (GH) concentrations. First, cluster analysis was performed to categorize cows as having poor, average, good or very good metabolic status based on their plasma FFA, BHB, glucose, insulin, IGF-1 and GH concentration. Subsequently, the performance and behavior of cows in clusters with poor, average and good metabolic status were compared using GLM. Cows with a poor or average metabolic status tended to have greater fat-and-protein-corrected milk yield than cows with good metabolic status. Furthermore, cows with a poor metabolic status had a lower energy balance and dry matter intake (DMI) than cows with an average or good metabolic status and had a lower number of meals than cows with good metabolic status. Daily number of visits to the feeder and lying time tended to be positively related with metabolic status. Feeding rate (kg/min), daily meal time (min/day), number of lying bouts per day, steps and motion index were not related with metabolic status. In conclusion, better metabolic status in dairy cows in early lactation was associated with a greater DMI, increased feeding activity and a tendency to more time spent lying, compared with poor metabolic status. These results suggest that compromised metabolic status is reflected in altered cow’s behavior in week 4 of lactation.

Keywords

dry period lengthcontinuous milkingfeeding behaviorlactogenic hormonessensor technology
Type
Research Article
Information
animal , Volume 13 , Issue 3 , March 2019 , pp. 640 - 648
Copyright
© The Animal Consortium 2018 

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References

Adewuyi, AA, Roelofs, JB, Gruys, E, Toussaint, MJM and van Eerdenburg, FJCM 2006. Relationship of plasma nonestrified fatty acids and walking activity in postpartum dairy cows. Journal of Dairy Science 89, 2977–2979.Google Scholar
Allen, MS, Bradford, BJ and Oba, M 2009. Board-invited review: the hepatic oxidation theory of the control of feed intake and its application to ruminants. Journal of Animal Science 87, 3317–3334.Google Scholar
Bertulat, S, Isaka, N, de Prado, A, Lopez, A, Hetreau, T and Heuwieser, W 2017. Effect of a single injection of cabergoline at dry off on udder characteristics in high-yielding dairy cows. Journal of Dairy Science 100, 3220–3232.Google Scholar
Butler, S, Marr, A, Pelton, S, Radcliff, R, Lucy, M and Butler, W 2003. Insulin restores GH responsiveness during lactation-induced negative energy balance in dairy cattle: effects on expression of IGF-I and GH receptor 1A. Journal of Endocrinology 176, 205–217.Google Scholar
Chen, J, Gross, JJ, van Dorland, HA, Remmelink, GJ, Bruckmaier, RM, Kemp, B and van Knegsel, ATM 2015. Effects of dry period length and dietary energy source on metabolic status and hepatic gene expression of dairy cows in early lactation. Journal of Dairy Science 98, 1033–1045.Google Scholar
Collard, BL, Boettcher, PJ, Dekkers, JCM, Petitclerc, D and Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 83, 2683–2690.Google Scholar
Centraal Veevoederbureau 2016. Tabellenboek Veevoeding. Voedernormen Rundvee, Schapen en Geiten, en voederwaarden voermiddelen herkauwers (in Dutch). The Hague, The Netherlands. Google Scholar
Drackley, JK 1999. Biology of dairy cows during the transition period: the final frontier? Journal of Dairy Science 82, 2259–2273.Google Scholar
Edwards, JL and Tozer, PR 2004. Using activity and milk yield as predictors of fresh cow disorders. Journal of Dairy Science 87, 524–531.Google Scholar
Fenwick, MA, Llewellyn, S, Fitzpatrick, R, Kenny, DA, Murphy, JJ, Patton, J and Wathes, DC 2008. Negative energy balance in dairy cows is associated with specific changes in IGF-binding protein expression in the oviduct. Reproduction 135, 63–75.Google Scholar
Fourichon, C, Seegers, H, Bareille, N and Beaudeau, F 1999. Effects of disease on milk production in the dairy cow: a review. Preventive Veterinary Medicine 41, 1–35.Google Scholar
Graber M, Kohler S, Muller A, Burgermeister K, Kaufman T, Bruckmaier RM and Van Dorland HA 2012. Identification of plasma and hepatic parameters related to metabolic robustness in dairy cows. Journal of Animal Physiology and Animal Nutrition 96, 75–84. Google Scholar
González, 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
Huzzey, JM, Von Keyserlingk, MAG and Weary, DM 2005. Changes in feeding, drinking and standing behavior in dairy cows during the transition period. Journal of Dairy Science 88, 2454–2461.Google Scholar
Ingvartsen, KL, Dewhurst, RJ and Friggens, NC 2003. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livestock Production Science 83, 277–308.Google Scholar
Kok, A, van Knegsel, ATM, van Middelaar, CE, Hogeveen, H, Kemp, B and de Boer, IJM 2015. Technical note: Validation of sensor-recorded lying bouts in lactating dairy cows using a 2-sensor approach. Journal of Dairy Science 98, 7911–7916.Google Scholar
Kok, A, Van Hoeij, RJ, Tolkamp, BJ, Haskell, MJ, Van Knegsel, ATM, De Boer, IJM and Bokkers, EA 2016. Behavioural adaptation to a short or no dry period in dairy cows. Applied Animal Behaviour Science 186, 7–15.Google Scholar
Lean, IJ, Farver, TB, Troutt, HF, Bruss, ML, Galland, JC, Baldwin, RL, Holmberg, CA and Weaver, LD 1992. Time series cross-correlation analysis of postparturient relationships among serum metabolites and yield variables in Holstein cows. Journal of Dairy Science 75, 1891–1900.Google Scholar
Maselyne, J, Pastell, M, Thomsen, PT, Thorup, VM, Hänninen, L, Vangeyte, J, Van Nuffel, A and Munksgaard, L 2017. Daily lying time, motion index and step frequency in dairy cows change throughout lactation. Research in Veterinary Science 110, 1–3.Google Scholar
Munksgaard, L., Jensen, M.B., Pedersen, L.J., Hansen, S.W. and Matthews, L. 2005. Quantifying behavioural priorities – effects of time constraints on behaviour of dairy cows, Bos taurus . Applied Animal Behavior Science 92, 3–14.Google Scholar
Rastani, RR, Grummer, RR, Bertics, SJ, Gümen, A, Wiltbank, MC, Mashek, DG and Schwab, MC 2005. Reducing dry period length to simplify feeding transition cows: milk production, energy balance, and metabolic profiles. Journal of Dairy Science 88, 1004–1014.Google Scholar
Reith, S, Brandt, H and Hoy, S 2014. Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period. Livestock Science 170, 219–227.Google Scholar
Rutten, CJ, Kamphuis, C, Hogeveen, H, Huijps, K, Nielen, M and Steeneveld, W 2017. Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows. Computers and Electronics in Agriculture 132, 108–118.Google Scholar
Soriani, N, Trevisi, E and Calamari, L 2012. Relationships between rumination time, metabolic conditions, and health status in dairy cows during the transition period. Journal of Animal Science 90, 4544–4554.Google Scholar
Stangaferro, ML, Wijma, R, Caixeta, LS, Al-Abri, MA and Giordano, JO 2016. Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders. Journal of Dairy Science 99, 7395–7410.Google Scholar
Tolkamp, BJ, Friggens, NC, Emmans, GC, Kyriazakis, I and Oldham, JD 2002. Meal patterns of dairy cows consuming mixed foods with a high or a low ratio of concentrate to grass silage. Animal Science 74, 369–382.Google Scholar
Urton, G, von Keyserlingk, MAG and Weary, DM 2005. Feeding behavior identifies dairy cows at risk for metritis. Journal of Dairy Science 88, 2843–2849.Google Scholar
Van Es, AJH 1975. Feed evaluation for dairy cows. Livestock Production Science 40, 95–107.Google Scholar
Van Hoeij, RJ, Dijkstra, J, Bruckmaier, RM, Gross, JJ, TJGM, Lam, Remmelink, GJ, Kemp, B and Van Knegsel, ATM 2017. The effect of dry period length and postpartum level of concentrate on milk production, energy balance, and plasma metabolites of dairy cows across the dry period and in early lactation. Journal of Dairy Science 100, 5863–5879.Google Scholar
Van Knegsel, ATM, Hammon, HM, Bernabucci, U, Bertoni, G, Bruckmaier, RM, Goselink, RMA, Gross, JJ, Kuhla, B, Metges, CC, Parmentier, HK, Trevisi, E, Tröscher, A and Van Vuuren, AM 2014b. Metabolic adaptation during early lactation: key to cow health, longevity and a sustainably dairy production chain. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 9, 1–15.Google Scholar
Van Knegsel, ATM, Remmelink, GJ, Jorjong, S, Fievez, V and Kemp, B 2014a. Effect of dry period length and dietary energy source on energy balance, milk yield, and milk composition of dairy cows. Journal of Dairy Science 97, 1499–1512.Google Scholar
Van Winden, SCL, Jorritsma, R, Müller, KE and Noordhuizen, JPTM 2003. Feed intake, milk yield, and metabolic parameters prior to left displaced abomasum in dairy cows. Journal of Dairy Science 86, 1465–1471.Google Scholar
Vicari, T, van den Borne, JJGC, Gerrits, WJJ, Zbinden, Y and Blum, JW 2008. Postprandial blood hormone and metabolite concentrations influenced by feeding frequency and feeding level in veal calves. Domestic Animal Endocrinology 34, 74–88.Google Scholar
Waterman, R, Schwalm, JW and Schultz, LH 1972. Nicotinic acid treatment of bovine ketosis I. Effects on circulatory metabolites and interrelationships. Journal of Dairy Science 55, 1447–1453.Google Scholar
Yeates, MP, Tolkamp, BJ, Allcroft, DJ and Kyriazakis, I 2001. The use of mixed distribution models to determine bout criteria for analysis of animal behaviour. Journal of Theoretical Biology 213, 413–425.Google Scholar