Holstein heifers in desert climate: effect of coat color on physiological variables and prediction of rectal temperature

Two hundred Holstein heifers were divided by hair coat color in black (n₁ = 60), white (n₂ = 62), and mixed (n₃ = 78) to accomplish two objectives: (1) to compare physiological variables using an analysis of variance, and (2) to construct regression equations to predict rectal temperature. In each heifer, rectal temperature (RT), respiration frequency (RF), and body surface temperatures (obtained with infrared thermography in eye, nose, forehead, head, neck, ear, shoulder, flank, belly, leg, loin, rump, and vulva) were measured. Black heifers had more RF and RT (P < 0.01) than mixed and white coat heifers; white heifers had similar RT than mixed color heifers, but they exhibited less RF (P < 0.05). In general, black and mixed coat color heifers had higher BST (P < 0.01) than white heifers in the majority of the anatomical regions measured. For black coat heifers, the best regression model to predict RT included three predictor variables: [RT = 35.59 - 0.013 (RH) + 0.045 (RF) + 0.019 (T_Ear); R² = 71%]. For white coat heifers, the best model included two predictor variables: [RT = 35.29 + 0.035 (RF) + 0.033 (T_Forehead); R² = 71%]; and for mixed coat color heifers, the best model included two predictor variables: [RT = 35.07 + 0.022 (RF) + 0.038 (T_Head); R² = 44%]. Heifers with dark hair coat color showed higher physiological constants than white heifers; the prediction of rectal temperature was more precise in heifers with well-defined hair coat color. Physiological and climatic variables, along with infrared thermography, represent an appropriate combination to predict rectal temperature in Holstein heifers with predominant white or black hair coat color.

Climate change vulnerability of confined livestock systems predicted using bioclimatic indexes in an arid region of México

Climate change is a major world-wide challenge to livestock production because food security is likely to be compromised by increased heat stress of the animals. The objective of this study was to characterize, using bioclimatic indexes, two livestock regions located in an arid zone of México, and to use this information to predict the impact of global warming on animal production systems of these regions located in the state of Baja California (México). A 5-year database (i.e., 2011 to 2015) consisting of about one million data points from two zones (i.e., coast, valley) from four meteorological stations in the north of Baja California were used. Bioclimatic indexes were constructed for the four types of livestock production systems most common in this region, being: dairy cattle, beef cattle, sheep, pigs. The temperature-humidity index (THI) thresholds used to classify heat stress were determined and scaled for each livestock species as: THI_beef and THI_pig 74 units; THI_milk 72 units; and THI_sheep 23 units. Statistical differences between indices were detected (P < 0.01) during summer for the valley and coast zones as (THI_beef = 72.9 and 51.8; THI_milk = 80.6 and 67.4; THI_pigs = 83.9 and 65.2; THI_sheep = 29.5 and 20.1 units). Coast zone weather did not suggest vulnerability of livestock production systems to heat stress at any time of the year, but heat stress risk during summer for valley zone dairy cattle, sheep and pigs was classified as severe, but lower for feedlot cattle. Prediction models showed significant adjustment just in the coastal zone for THImilk, THIsheep, and THIsheep, suggesting more impact of global warming during summer in the coastal zone. Use of management strategies to reduce heat load of domestic animals during summer in northern Baja California is essential to maintain their productivity, with more emphasis in the valley zone.