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A generalised additive model to characterise dairy cows’ responses to heat stress

Published online by Cambridge University Press:  31 July 2019

S. Benni Open the ORCID record for S. Benni [Opens in a new window] ,
M. Pastell Open the ORCID record for M. Pastell [Opens in a new window] ,
F. Bonora Open the ORCID record for F. Bonora [Opens in a new window] ,
P. Tassinari Open the ORCID record for P. Tassinari [Opens in a new window]  and
D. Torreggiani Open the ORCID record for D. Torreggiani [Opens in a new window]
S. Benni*
Affiliation:
Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 48, 40127 Bologna, Italy
M. Pastell
Affiliation:
Natural Resources Institute Finland (Luke), Production Systems Unit, Latokartanonkaari 9, PO Box 2 FI-00791 Helsinki, Finland
F. Bonora
Affiliation:
Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 48, 40127 Bologna, Italy
P. Tassinari
Affiliation:
Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 48, 40127 Bologna, Italy
D. Torreggiani
Affiliation:
Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 48, 40127 Bologna, Italy
*
†E-mail: [email protected]
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Abstract

Heat stress is one of the most critical issues jeopardising animal welfare and productivity during the warm season in dairy cattle farms. The global trend of increase in average and peak temperatures is making the problem more and more serious. Many devices have been introduced in livestock farms to monitor and control temperature-humidity index, as well as animal behaviour and production parameters. The consequent availability of collected databases has increasingly enhanced the research aimed to understand the consequences of heat stress in cattle, in relation to genetic, reproductive, productive and behavioural features. Moreover, these investigations laid the foundations for the development, calibration, validation and test of numerical models quantifying the individual responses to heat stress conditions. In this work, a generalised additive model with mixed effects has been developed to analyse the relationship between milk production, animal behaviour and environmental parameters based on data surveyed in 2016 in an Italian dairy farm. Each cow has been characterised in terms of her response to heat conditions, and the results led to define three classes of susceptibility to heat stress within the herd. These attributes have then been related to the various phenotypic parameters collected by the precision livestock farming devices used in the farm. The study provides a model to understand the effects of heat stress conditions on individual animals in relation to the main parameters describing their rearing conditions; moreover, the results contribute to improve the herd management by lending indications to define targeted treatments according to the cow’s characteristics.

Keywords

precision livestock farmingtemperature-humidity indexsusceptibilitydata analysismilk yield
Type
Research Article
Information
animal , Volume 14 , Issue 2 , February 2020 , pp. 418 - 424
Copyright
© The Animal Consortium 2019 

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Footnotes

*

This article has been amended since original publication. Throughout the article ‘addictive’ has been changed to ‘additive’.

References

Armstrong, DV 1994. Heat stress interaction with shade and cooling. Journal of Dairy Science 77, 2044–2050.CrossRefGoogle ScholarPubMed
Asabe 1986. Design of ventilation systems for poultry and livestock shelters. ASAE 270.5. ASABE American Society of Agricultural and Biological Engineers, St. Joseph, MI (USA), 1–33.Google Scholar
Barkema, HW, von Keyserlingk, MAG, Kastelic, JP, Lam, TJGM, Luby, C, Roy, J-P, LeBlanc, SJ, Keefe, GP and Kelton, DF 2015. Invited review: changes in the dairy industry affecting dairy cattle health and welfare. Journal of Dairy Science 98, 7426–7445.CrossRefGoogle ScholarPubMed
Berckmans, D 2017. General introduction to precision livestock farming. Animal Frontiers 7, 6.CrossRefGoogle Scholar
Bernabucci, U, Biffani, S, Buggiotti, L, Vitali, A, Lacetera, N and Nardone, A 2014. The effects of heat stress in Italian Holstein dairy cattle. Journal of Dairy Science 97, 471–486.CrossRefGoogle ScholarPubMed
Bonora, F, Pastell, M, Benni, S, Tassinari, P and Torreggiani, D 2018. ICT monitoring and mathematical modelling of dairy cows performances in hot climate conditions: a study case in Po valley (Italy). Agricultural Engineering International: CIGR Journal 20, 1–12.Google Scholar
Bouraoui, R, Lahmar, M, Majdoub, A, Djemali, M and Belyea, R 2002. The relationship of temperature-humidity index with milk production of dairy cows in a Mediterranean climate. Animal Research 51, 479–491.CrossRefGoogle Scholar
CIGR (Commission Internationale du Génie Rural) International Commission of Agricultural and Biosystems Engineering 1984. Report of working group on climatization of animal houses. Scottish Farm Buildings Investigation Unit, Aberdeen, Scotland.Google Scholar
CIGR Section II Working Group N 14 – Cattle Housing 2014. Recommendations of dairy cow and replacement Heifer housing. CIGR, Gainesville, FL, USA.Google Scholar
Dragovich, D 1979. Effect of high temperature-humidity conditions on milk production of dairy herds grazed on farms in a pasture-based feed system. International Journal of Biometeorology 23, 15–20.CrossRefGoogle Scholar
Gasqui, P and Trommenschlager, JM 2017. A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level. Scientific Reports 7, 1–11.CrossRefGoogle ScholarPubMed
Hagiya, K, Hayasaka, K, Yamazaki, T, Shirai, T, Osawa, T, Terawaki, Y, Nagamine, Y, Masuda, Y and Suzuki, M 2017. Effects of heat stress on production, somatic cell score and conception rate in Holsteins. Animal Science Journal 88, 3–10.CrossRefGoogle ScholarPubMed
Hastie, T, Tibshirani, R and Friedman, J 2009. The elements of statistical learning. Bayesian Forecasting and Dynamic Models 1, 1–694.Google Scholar
Herbut, P and Angrecka, S 2018. Relationship between THI level and dairy cows’ behaviour during summer period. Italian Journal of Animal Science 17, 226–233.CrossRefGoogle Scholar
Hillman, PE, Lee, CN and Willard, ST 2005. Thermoregulatory responses associated with lying and standing in heat-stressed dairy cows. Transactions of the ASABE 48, 795–801.CrossRefGoogle Scholar
James, G, Witten, D, Hastie, T and Tibshirani, R 2013. An introduction to statistical learning with applications in R. Current medicinal chemistry 7, 995–1039.Google Scholar
Kadzere, CT, Murphy, MR, Silanikove, N and Maltz, E 2002. Heat stress in lactating dairy cows: a review. Livestock Production Science 77, 59–91.CrossRefGoogle Scholar
Lessire, F, Hornick, JL, Minet, J and Dufrasne, I 2015. Rumination time, milk yield, milking frequency of grazing dairy cows milked by a mobile automatic system during mild heat stress. Advances in Animal Biosciences 6, 12–14.CrossRefGoogle Scholar
Lin, X and Zhang, D 1999. Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society. Series B: Statistical Methodology 61, 381–400.CrossRefGoogle Scholar
Macciotta, NPP, Vicario, D and Cappio-Borlino, A 2005. Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. Journal of Dairy Science 88, 1178–1191.CrossRefGoogle ScholarPubMed
R Core Team 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing 1, 1–2630.Google Scholar
Samal, L 2013. Heat stress in dairy cows - reproductive problems and control measures. International Journal of Livestock Research 3, 14–22.Google Scholar
Wood, SN 2004. Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99, 673–686.CrossRefGoogle Scholar
Wood, SN 2017. Generalized additive models: an introduction with R, 2nd edition, Chapman and Hall/CRC Press Taylor & Francis Group, Boca Raton, London, New York, 1–476.CrossRefGoogle Scholar
Yano, M, Shimadzu, H and Endo, T 2014. Modelling temperature effects on milk production: a study on Holstein cows at a Japanese farm. SpringerPlus 3, 129.CrossRefGoogle Scholar
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