Abstract

In the context of climate change, the frequency and severity of extreme weather events are expected to increase in temperate regions, and potentially have a severe impact on farmed cattle through production losses or deaths. In this study, we used distributed lag non-linear models to describe and quantify the relationship between a temperature–humidity index (THI) and cattle mortality in 12 areas in France. THI incorporates the effects of both temperature and relative humidity and was already used to quantify the degree of heat stress on dairy cattle because it does reflect physical stress deriving from extreme conditions better than air temperature alone. Relationships between daily THI and mortality were modeled separately for dairy and beef cattle during the 2003–2006 period. Our general approach was to first determine the shape of the THI–mortality relationship in each area by modeling THI with natural cubic splines. We then modeled each relationship assuming a three-piecewise linear function, to estimate the critical cold and heat THI thresholds, for each area, delimiting the thermoneutral zone (i.e. where the risk of death is at its minimum), and the cold and heat effects below and above these thresholds, respectively. Area-specific estimates of the cold or heat effects were then combined in a hierarchical Bayesian model to compute the pooled effects of THI increase or decrease on dairy and beef cattle mortality. A U-shaped relationship, indicating a mortality increase below the cold threshold and above the heat threshold was found in most of the study areas for dairy and beef cattle. The pooled estimate of the mortality risk associated with a 1°C decrease in THI below the cold threshold was 5.0% for dairy cattle [95% posterior interval: 4.4, 5.5] and 4.4% for beef cattle [2.0, 6.5]. The pooled mortality risk associated with a 1°C increase above the hot threshold was estimated to be 5.6% [5.0, 6.2] for dairy and 4.6% [0.9, 8.7] for beef cattle. Knowing the thermoneutral zone and temperature effects outside this zone is of primary interest for farmers because it can help determine when to implement appropriate preventive and mitigation measures.

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