Influence of Environmental Temperature on Dairy Herd Performance and Behaviour

Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, S. Harapanahalli, J. Walsh

Abstract:

The objective of this study was to determine the effects of environmental stressors on the performance of lactating dairy cows and discuss some future trends. There exists a relationship between the meteorological data and milk yield prediction accuracy in pasture-based dairy systems. New precision technologies are available and are being developed to improve the sustainability of the dairy industry. Some of these technologies focus on welfare of individual animals on dairy farms. These technologies allow the automatic identification of animal behaviour and health events, greatly increasing overall herd health and yield while reducing animal health inspection demands and long-term animal healthcare costs. The data set consisted of records from 489 dairy cows at two dairy farms and temperature measured from the nearest meteorological weather station in 2018. The effects of temperature on milk production and behaviour of animals were analyzed. The statistical results indicate different effects of temperature on milk yield and behaviour. The “comfort zone” for animals is in the range 10 °C to 20 °C. Dairy cows out of this zone had to decrease or increase their metabolic heat production, and it affected their milk production and behaviour.

Keywords: Behaviour, milk yield, temperature, precision technologies.

References:

[1] D. L. Hill, E. Wall, “Weather influences feed intake and feed efficiency in a temperate climate” J. Dairy Sci., vol. 100, no. 3., pp. 2240–2257, Mar 2017.
[2] M. Rhoads, R. Rhoads, M. VanBaale, R. Collier, S. Sanders, W. Weber, B. Crooker, L. Baumgard, “ Effects of heat stress and plane of nutrition on lactating Holstein cows: I. Production, metabolism, and aspects of circulating somatotropin”, J. Dairy Sci., vol. 92, no. 5., pp. 1986–1997, Jan 2010.
[3] V. Robinson, “Effects of cold stress on the submission and conception rates of New Zealand dairy cows”, a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Agricultural Science at Massey University, Palmerston North, New Zealand, 2015, http://hdl.handle.net/10179/7528.
[4] N. Lacetera, U. Bernabucci, D. Scalia, B. Ronchi, G. Kuzminsky, A. Nardone,” Lymphocyte functions in dairy cows in hot environment”, Int. J. Biometeorol. vol. 50, no. 2, pp. 105–110, Jul 2005.
[5] L. Calamari, P. Morera, P. Bani, A. Minuti, L. Basiricò, A. Vitali, U. Bernabucci, “Effect of hot season on blood parameters, fecal fermentative parameters, and occurrence of Clostridium tyrobutyricum spores in feces of lactating dairy cows,” J. Dairy Sci., vol. 101, no. 5., pp. 4437–4447, May 2018.
[6] M. Gauly, H. Bollwein, G. Breves, K. Brügemann, S. Dänicke, G. Daş, et al.,” Future consequences and challenges for dairy cow production systems arising from climate change in Central Europe – a review”, Animal, vol. 7, no. 5., pp. 843–59, May 2013.
[7] F. Zhang, J. Upton, L. Shalloo, P. Shine, M. D. Murphy, “ Effect of introducing weather parameters on the accuracy of milk production forecast models,” Inf. Process. Agric., vol. 7, no. 1., pp. 120 – 138, Mar. 2020.
[8] N. Silanikove, “Effects of heat stress on the welfare of extensively managed domestic ruminants”, Livest. Prod. Sci., vol. 67, no. 1-2., pp. 1-18, Dec 2000.
[9] M. Bohlouli, S. Alijani, S. Naderi, T. Yin, S. König, “Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions” J. Dairy Sci., vol. 102, no.1., pp. 488–502, Jan 2019.
[10] C. Arndt, J. M. Powell, M. J. Aguerre, P. M. Crump, M. A. Wattiaux,” Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens”, J. Dairy Sci., vol. 98, no. 6., pp. 3938-3950, Jun 2015.
[11] K. DiGiacomo, L. Marett, W. Wales, B. Hayes, F. Dunshea, B. Leury, “ Thermoregulatory differences in lactating dairy cattle classed as efficient or inefficient based on residual feed intake”, Anim. Prod. Sci., vol. 54, no. 10, pp. 1877-1881, Jun 2014.
[12] T. T. T. Nguyen, P. J. Bowman, M. Haile-Mariam, J. E. Pryce, B. J. Hayes, “ Genomic selection for tolerance to heat stress in Australian dairy cattle” , J. Dairy Sci., vol.99, no. 4., pp. 2849–2862, Apr 2016.
[13] L. N. Grinter, M. R. Campler, J. H. C. Costa, “Technical note: Validation of a behavior-monitoring collar’s precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows”, J. Dairy Sci., vol. 102, no. 4., pp. 3487–3494, Feb 2019.
[14] J. Werner, C. Umstatter, L. Leso, E. Kennedy, A. Geoghegan, L. Shalloo, M. Schick, B. O'Brien, “Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows,” Animal, vol. 13, no. 9., pp. 2070–2079, Feb 2019.
[15] S. Angrecka, P. Herbut, “Conditions for cold stress development in dairy cattle kept in free stall barn during severe frosts”, Czech J. Anim. Sci., vol. 60, no.2., pp. 81-87, Feb 2015.
[16] E. I. Kaufman, V. H. Asselstine, S. J. LeBlanc, T. F. Duffield, T. J. DeVries, “Association of rumination time and health status with milk yield and composition in early-lactation dairy cows” J. Dairy Sci., vol. 101, no.1., pp. 462–471, Jan 2018.
[17] T. Garnett, M. C. Appleby, A. Balmford, I. J. Bateman, T. G. Benton, P. Bloomer, et al.,” Sustainable intensification in agriculture: Premises and policies”, Science, vol. 341, no.6141., pp. 33-34, Jul 2013.
[18] C. S. Cardoso, M. Jose Hotzel, D. M. Weary, J. A. Robbins, M. A. G. von Keyserlingk,” Imagining the ideal dairy farm”, J. Dairy Sci., vol. 99, no.2., pp. 1663–1671, Jan 2016.
[19] J. A. Perez-Mendez, D. Roibaz, A. Wall, “The influence of weather conditions on dairy production,”Agric. Eco., vol. 50, no. 2, pp. 165–175, Mar 2019.
[20] M. C. Ferris, A. Christensen, and S. R. Wangen, “Symposium review: Dairy Brain—Informing decisions on dairy farms using data analytics, J. Dairy Sci., vol. 103, no. 4., pp. 3874–3881, April 2020.
[21] N. O’ Mahony, S. Campbell, A. Carvalho, L. Krpalkova, D. Riordan, J. Walsh, “3D Vision for Precision Dairy Farming,” IFAC-PapersOnLine, vol. 52, no. 30., pp. 312 – 317, 2019.