Vaginal temperature modeling in Holstein cows and cluster analysis by the cophenetic correlation coefficient.

The objective was to establish a model for the prediction and characterization of vaginal temperature in Holstein cows, based on environmental predictors and thermal comfort indices, through cluster analysis, validation by the cophenetic correlation coefficient, and multiple regression analysis. The micrometeorological characterization of the site was carried out by recording the air temperature (Tair), the relative humidity (RH), the black globe temperature (BGT), the black globe temperature and humidity (BGHI), and dew point temperature (TDP). The recording of vaginal temperature (Tv) was performed in eight dairy cows using temperature sensors, equipped with data loggers, coupled with intravaginal devices. The data were analyzed using descriptive statistics and cluster analysis (CA) by using the hierarchical agglomerative method based on the value of the cophenetic correlation coefficient (CCC >0.70), in which representative physiological models were established, characterizing the Tv through multiple regression. In the afternoon the coefficient of variation (CV) was low for all variables, indicating homogeneity of the meteorological variables and efficiency of the ventilation system. The temperature and humidity index (THI) was mild only on the morning. There was a variation of 0.28 °C of Tv between shifts, sufficient to characterize the condition of comfort and stress of the animal, with values above 39 °C indicating animal stress. Tv showed strong correlation with BGT, Tair, TDP and RH, assuming that physiological variables, such as Tv, tend to have greater relationship with abiotic variables. Empirical models were established for estimating Tv based on the analyses performed in this study. Model 1 is recommended for TDP ranges of 14.00-21.00 °C and RH of 30-100%, while model 2 can be used for Tair situations up to 35 °C. The regression models for estimating Tv are promising for characterizing the thermal comfort of dairy cows housed in compost barn systems.

Spatial variability and exploratory inference of abiotic factors in barn compost confinement for cattle in the semiarid.

The assessment of environmental conditions in the creation of dairy cattle in feedlots must be carried out to identify possible causes of stress and assist producers in the decision-making process. The objective was to characterize the abiotic factors and bed temperature, in the comfort conditions of dairy cows, through geostatistics and exploratory data inference, in a Compost Barn System in the Brazilian semiarid region. The data were obtained in a milk production unit, located in the municipality of Lajedo, Pernambuco, Brazil. The variables air temperature (Tair), relative humidity (RH), wind speed (WS), illuminance (Lux), skin temperature (Ts), bed temperature (Tb) were recorded and the temperature index was determined and humidity (THI). Data were recorded at 9:00 a.m., 12:00 p.m., and 03:00 p.m., over 5 days in the summer season. For geostatistical analysis, the classic semivariances were determined. The principal component analysis was performed to establish an index that characterized the condition of animal comfort. The variables Tair, RH, Tb, and THI showed a low coefficient of variation for all times. The best fit to the models of the semivariograms was the Gaussian at 9:00 a.m. and 03:00 p.m., and the spherical at 12:00 p.m. The Tb spatial variability was low for all studied hours. Tair showed a strong correlation with Tb, due to the process of heat transfer by convection from the floor to the environment. Geostatistics and exploratory data analysis allowed the establishment of a comfort index for Compost Barn production systems in the Brazilian semiarid region (R2 = 0.996; p < 0.0001).