Heat stress related dairy cow mortality during heat waves and control periods in rural Southern Ontario from 2010-2012.

Katherine E Bishop-Williams, Olaf Berke, David L Pearl, Karen Hand, David F Kelton
Author Information
  1. Katherine E Bishop-Williams: Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd East, Guelph, ON, N1G 2 W1, Canada. kbishop@uoguelph.ca.
  2. Olaf Berke: Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd East, Guelph, ON, N1G 2 W1, Canada. oberke@uoguelph.ca.
  3. David L Pearl: Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd East, Guelph, ON, N1G 2 W1, Canada. dpearl@uoguelph.ca.
  4. Karen Hand: Strategic Solutions Group, 142 Hume Rd, Puslinch, ON, Canada. karen.hand@strategicsolns.ca.
  5. David F Kelton: Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd East, Guelph, ON, N1G 2 W1, Canada. dkelton@uoguelph.ca.

Abstract

BACKGROUND: Heat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in Dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 °C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm Dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm Dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010-2012 were associated with increased on-farm Dairy cow mortality in Southern Ontario. Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI.
RESULTS: For every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = ≤ 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times.
CONCLUSIONS: Southern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.

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MeSH Term

Animals
Cattle
Cattle Diseases
Heat Stress Disorders
Ontario
Retrospective Studies

Word Cloud

Created with Highcharts 10.0.0heatmortalitySouthernOntariostresswaveson-farmHeatdairycowHSI1extremeincreasedcontrolincreasecanresult3weatherriskdescribedrelatedincreases2010-20126periodsdataacrosslevelsunittimes=risksBACKGROUND:physiologicalresponseenvironmentalcowsrapidlychanginglongdurationclimatechangedefineddaystemperatures32°CincreasinglyfrequentphenomenonincreasingindicesHSIsgenerallybasedtemperaturehumidityproviderelativemeasurediscomfortusedpredictfollowsdistributionspacepresentedmapsSimilarlymappedgoalstudydemonstrateassociatedMortalityrecordsfarmlocationsfarmsregisteredCanWestDairyHerdImprovementProgramretrievedthree-day20102012randomsamplecontrols2:1takensetcreaterisk-basedhybriddesignOn-farmestimatedusing37stationssubsequentlyinterpolatedgeostatisticalkrigingPoissonregressionmodelappliedassessrelationvaryingRESULTS:everyonerate03CI95%IRR025035p0001typical8periodwaveratespredicted27CONCLUSIONS:affecteddemonstratedhighFarmersawareinformedappropriatemethodsmitigaterural

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