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Impact of Cold Stress on Physiological, Endocrinological, Immunological, Metabolic, and Behavioral Changes of Beef Cattle at Different Stages of Growth.

Won-Seob KimJalil Ghassemi NejadHong Gu Lee
Published in: Animals : an open access journal from MDPI (2023)
The purpose of this study was to investigate the effect of cold stress (CS) on the physiological, blood, and behavioral parameters of beef cattle according to their growth stage. Twelve calves in the growing stages (220.4 ± 12.33 kg, male and non-castrated) and twelve steers in the early fattening stages (314.2 ± 18.44 kg) were used in this experiment. The animals were randomly distributed into three homogenized groups (four animals each) for 14 days, namely threshold, mild-moderate cold stress (MCS), and extreme cold stress (ECS), according to the outside ambient temperature. The feed and water intakes were recorded daily. The physiological parameters, blood parameters, and behavioral patterns were measured weekly. All data were analyzed using repeated-measures analysis. The calves exposed to the ECS decreased ( p < 0.064, tendency) their dry matter intake compared to the threshold and MCS groups. The HR and RT increased ( p < 0.001) in the ECS compared to the threshold in calves and steers. Moreover, increased ( p < 0.05) blood cortisol, non-esterified fatty acids (NEFA), and time spent standing were observed after exposure to ECS in calves and steers. However, the calves exposed to the ECS had decreased ( p = 0.018) blood glucose levels compared to the threshold. In conclusion, ECS affects the dry matter intake, HR, RT, blood cortisol, NEFA, and behavioral patterns in beef calves and steers. This phenomenon indicated that beef cattle exposed to CS modulated their behavior and blood parameters as well as their physiological response to maintain homeostasis regardless of the growth stage.
Keyphrases
  • blood glucose
  • air pollution
  • fatty acid
  • electronic health record
  • heat stress
  • skeletal muscle
  • type diabetes
  • machine learning
  • climate change
  • particulate matter
  • weight gain
  • neural network
  • deep learning