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Heat stress represents a key factor that negatively affects the productive and reproductive performance of farm animals. In the present work, a new measure of tolerance to heat stress for dairy cattle was developed using principal component analysis. Data were from 590,174 test-day records for milk yield, fat and protein percentages, and somatic cell score of 39,261 Italian Holstein cows. Test-day records adjusted for main systematic factors were grouped into 11 temperature-humidity index (THI) classes. Daughter trait deviations (DTD) were calculated for 1,540 bulls as means of the adjusted test-day records for each THI class. Principal component analysis was performed on the DTD for each bull. The first 2 principal components (PC) explained 42 to 51% of the total variance of the system across the 4 traits. The first PC, a measure of the level at which the curve is located, was interpreted as a measure of the level at which the DTD curve was located. The second PC, which shows the slope of increasing or decreases DTD curves, synthesized the behavior of the DTD pattern. Heritability of the 2 component scores was moderate to high for level across all traits (range = 0.23–0.82) and low to moderate for slope (range = 0.16–0.28). For each trait, phenotypic and genetic correlations between level and slope were equal to zero. A genome-wide association analysis was carried out on a subsample of 423 bulls genotyped with the Illumina 50K bovine bead chip (Illumina, San Diego, CA). Two single nucleotide polymorphisms were significantly associated with slope for milk yield, 4 with level for fat percentage, and 2 with level and slope of protein percentage, respectively. The gene discovery was carried out considering windows of 0.5 Mb surrounding the significant markers and highlighted some interesting candidate genes. Some of them have been already associated with the mechanism of heat tolerance as the heat shock transcription factor (HSF1) and the malonyl-CoA-acyl carrier

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help

The transition period (3 weeks before and after calving) of high-yielding dairy cows is commonly characterized by the occurrence of high disease rates, and represents the most critical phase in the production cycle. Around calving, cows usually develop a systemic inflammatory response with no signs of infections, and show lower dry matter intake and energy efficiency, as well as distinct changes of carbohydrate and fat metabolism, impaired liver capacity to metabolize the fatty acids and increased risk of liver lipidosis. The acute phase response is evident in terms of both positive (APP+: i.e. haptoglobin, ceruloplasmin, Serum Amyloid A) and negative (APP-: i.e. albumin, paraoxonase, “carriers” of vitamins and hormones, lipoproteins) acute phase proteins. The raise of APP+ synthesis in the liver partly competes with the usual synthesis of liver proteins (APP- response), which contributes – along with early metabolic and tissue distribution losses– to lower levels of such proteins, and therefore to reduced liver functions and ensuing systemic repercussions. Interestingly, cows with a severe acute phase reaction after calving period show reduced feed intake even before calving. Cows classified according to APP- levels after calving show a clear relationship with performance (i.e. cows with lower levels and/or slower recovery of APP- produce less milk and are less fertile). The above changes take place in the framework of a clear inflammatory cytokine response, distinct signs of immunosuppression and a generalized rise of intra- and extra-cellular Heat Shock Protein (HSP) 72kDa. An important role of cytokines is also demonstrated by the clear worsening of inflammatory and metabolic responses following administration of interferon alpha. The above changes start in the late pregnancy period in terms of a clear interleukin-6 (IL-6) and ceruloplasmin (APP+) response. Moreover, cows that suffer from high inflammatory conditions at the beginning of lactation show a lower en