Heat stress and cold stress have a negative influence on cattle welfare and productivity. There have been some studies investigating the influence of cold stress on cattle, however the emphasis within this review is the influence of heat stress on cattle. The impact of hot weather on cattle is of increasing importance due to the changing global environment. Heat stress is a worldwide phenomenon that is associated with reduced animal productivity and welfare, particularly during the summer months. Animal responses to their thermal environment are extremely varied, however, it is clear that the thermal environment influences the health, productivity, and welfare of cattle. Whilst knowledge continues to be developed, managing livestock to reduce the negative impact of hot climatic conditions remains somewhat challenging. This review provides an overview of the impact of heat stress on production and reproduction in bovines.

The study was aimed at assessing heat load-related risk of clinical mastitis (CM) in dairy cows. Records of CM for the years 2014 and 2015 were obtained from a large conventional dairy farm milking about 1,200 Holstein cows in central Italy. A case of CM was defined by the presence of clinical signs and veterinary confirmation. Quarter milk samples were collected and bacteriological investigated for each CM. Etiological agents were identified and classified as environmental or contagious pathogens. Hourly weather data from the nearest weather station were used to calculate heat load index (HLI). Upper and lower thresholds of HLI, at which the animal accumulates or dissipates heat, were settled and used to measure heat load balance through the accumulated heat load (AHL) model. Zero and positive values of AHL indicate periods of thermo-neutral and heat accumulation, respectively. Each case of CM was associated with HLI-AHL values recorded 5 d before the event. The risk of CM was evaluated using a case-crossover design. A conditional logistic regression model was used to calculate the odds ratio and 95% confidence intervals of CM recorded in thermo-neutral (AHL = 0) or heat load (AHL > 0) days, pooled or stratified for pathogen type (environmental or contagious). Classes of AHL as low (35) were included in the model. Other variables included in the model were milk yield as liters (30), days in milk (150), and parity (1, 2-3, and >3). A total of 1,086 CM cases were identified from 677 cows. Escherichia coli, Streptococcus spp., and Streptococcus uberis were the environmental pathogens isolated with the highest frequency; Staphylococcus aureus prevailed within contagious species. The analysis of pooled data indicated a significant effect of heat load on the occurrence of CM in the contagious pathogen stratum. Higher milk yield, middle and late stage of lactation, and older parity increased the risk of CM under heat load conditions. However, the association between pathogen type and these factors was not clear because the model provided significant odds ratios within all pathogen categories. The present study provided the first evidence of an association between HLI and CM in dairy cattle and suggested the ability of the AHL model to assess the risk of mastitis associated with heat load.

Previous studies have indicated that cattle with more excitable temperaments exhibit an increased stress response. The objective of this study was to investigate the relationship between temperament traits, handling, and stress-induced hyperthermia (SIH) in beef cattle. Rectal temperatures (T_REC, °C) of 60 purebred Angus cattle (30 heifers, 30 steers; 235.2 ± 5.11 kg) were recorded at 20 s intervals from 30 min prior to handling until two hours post handling. All cattle were exposed to a standardized handling procedure consisting of (i) being restrained in a weighing box for 30 s; (ii) being held within the crush for 30 s; and then (iii) being restrained in a head bail for 60 s. Cattle temperaments were evaluated via three traits: (1) agitometer score (AG); (2) crush score (CS); and (3) flight speed (FS) during the handling procedure. Agitometer scores and FS measures were used to describe an AG category (AG_CAT) and an FS category (FS_CAT) that were used to classify animals into three temperament categories: 1, calm; 2, intermediate; and 3, temperamental. Pearson’s correlation coefficients were used to evaluate the associations between (i) AG, CS, FS, and T_REC 30 min prior to entry into the weighing box (T-30) and then at 1 min intervals between time of entry into the weighing box (T0) until 10 min post-weighing (T10); and (ii) the relationship between AG, CS, and FS. The relationship between T_REC and temperament traits over the 2.5 h were modeled by using a first-order autoregressive repeated measures model. Flight speed had strong to moderate associations with T_REC at T-30 (r ≥ 0.37; p ≤ 0.006) and between T0 and T10 (r ≥ 0.36; p ≤ 0.01). There were moderate associations amongst T_REC between T0 and T10 and CS (r ≥ 0.31; p ≤ 0.01). A weak relationship existed with CS (r = 0.16; p = 0.16). There were no associations between AG and T_REC at T-30 (r ≥ −0.15; p = 0.84) or between T0 and T10 (r ≤ 0.04; p ≥ 0.4). Rectal temperature, irrespective of sex and temperament traits, was influenced by time (p < 0.0001), and maximum T_REC (39.3 ± 0.04 °C) occurred between 4 and 5.7 min after entry into the weighing box. In addition, CS (p = 0.007) influenced T_REC in these cattle. There were also time × temperament trait × sex interactions with the CS (p = 0.0003) and FS_CAT (p = 0.043) categories; however, time × temperament trait interactions were not statistically significant. Results from this study suggest that cattle with excitable temperaments, as evaluated by FS and CS, have a greater increase in T_REC. In addition, these results suggest that a relationship exists between basal T_REC and FS and CS. Together, these results highlight that temperament, as assessed by FS and CS, influences both basal T_REC and the peak temperature recorded following handling but does not influence the magnitude of change in T_REC post handling.