2013
DOI: 10.3168/jds.2012-5947
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Evaluation of heat stress effects on production traits and somatic cell score of Holsteins in a temperate environment

Abstract: This study was aimed to evaluate the degree of thermal stress exhibited by Holsteins under a continental temperate climate. Milk, fat, protein, and somatic cell count test-day records collected between 2000 and 2011 from 23,963 cows in 604 herds were combined with meteorological data from 14 public weather stations in Luxembourg. Daily values of 6 different thermal indices (TI) weighted in term of temperature, relative humidity, solar radiation, and wind speed were calculated by averaging hourly TI over 24h. H… Show more

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Cited by 191 publications
(187 citation statements)
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“…The importance of direct temperature metrics in explaining productivity is consistent with a wealth of studies on the impact of heat stress in dairy cattle (Renaudeau et al, 2012). Relatively few studies have assessed the impact of other weather variables on milk traits, but thermal indices that account for WS and solar radiation perform better than those that do not (Hammami et al, 2013). In our study, weather metrics summarised across a week's timescale from the TD usually explained milk traits (particularly yield and fat content) better than shorter-scale summaries.…”
Section: Discussionsupporting
confidence: 55%
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“…The importance of direct temperature metrics in explaining productivity is consistent with a wealth of studies on the impact of heat stress in dairy cattle (Renaudeau et al, 2012). Relatively few studies have assessed the impact of other weather variables on milk traits, but thermal indices that account for WS and solar radiation perform better than those that do not (Hammami et al, 2013). In our study, weather metrics summarised across a week's timescale from the TD usually explained milk traits (particularly yield and fat content) better than shorter-scale summaries.…”
Section: Discussionsupporting
confidence: 55%
“…AIC has been used previously to compare temperature indices in explaining milk traits (Bruegemann et al, 2012;Hammami et al, 2013). As the metrics for summarising a given element were closely correlated, and high proportions of shared variance can lead to unreliable estimates, we fitted each metric in a separate linear mixed effects model (LMM) (equation (3)) using maximum likelihood to produce a series of non-nested models.…”
Section: Methodsmentioning
confidence: 99%
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