RESUMEN
Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (-/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.
RESUMEN
Climate change (CC) is expected to increase temperatures and the frequency of extreme weather events, which renewed interest in heat stress (HS) effects on dairy cattle farms. Resilience is a key concept that should be considered to better understand the dairy farms exposure to HS and to combat CC-related risks. Thus, this study aimed to investigate the aspects of HS vulnerability for Tunisian dairy cattle farming systems. Historical milk test-day records from official milk recording were merged with temperature and humidity data provided by public weather stations. Firstly, different models relying in two heat load indices were applied for HS exposure assessment. Secondly, broken line models were used to estimate HS thresholds, milk losses, and rates of decline of milk production associated with temperature-humidity index (THI) across parities. Thirdly, individual cow responses to HS estimated using random regression model were considered as key measures of dairy farming system sensitivity assessment to HS. Dairy farms are annually exposed for 5 months to high THI values above 72 in Tunisia. The tipping points, at which milk yield started to decline over parities with 3-day average THI, ranged between 65 and 67. The largest milk decline per unit of THI above threshold values was 0.135 ± 0.01 kg for multiparous cows. The milk losses estimated due to HS in the Holstein breed during the summer period (June to August) ranged between 110 and 142 kg/cow in north and south, respectively. A high HS sensitivity was proved especially in dairy farms characterized by large herd size and high milk production level. Hence, providing knowledge of dairy farms vulnerability to HS may provide the basis for developing strategies to reduce HS effects and plan for CC adaptation.
