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.

Multilevel sensor for monitoring external and internal environment of eggs.

Although it is well known that incubation environment has a great influence on embryogenesis and post-hatching performance of birds, not much is known about how external thermal, sound and light stimuli are isolated by eggshells and perceived by embryos. In this context, this study aimed to develop, calibrate and evaluate a multilevel sensor for integrated monitoring of the external (incubator) and internal environment of eggs. The variables of interest for the external environment were air temperature and relative humidity. For the internal environment, shell temperature, internal temperature, luminosity and sound pressure level were considered. The sensor was developed with an ATmega328 microcontroller, in open-source prototyping, using electronic components which are compatible with the egg's physical structure. Calibrations were carried out in a controlled environment, comparing the multilevel sensor with commercial equipment, obtaining coefficients of determination of R 2 > 0.90 for all variables studied. The multilevel sensor was also validated, simulating a commercial incubation situation and comparing eggs with 2 shell colors (white and brown) and internal volume (intact and empty). Validation results showed that white-shelled eggs insulate less external light (P < 0.001) and full eggs presented higher internal temperatures, greater light and lower sound pressure levels compared to empty eggs (P < 0.001). The multilevel sensor developed here is an innovative proposal for monitoring, simultaneously and in real time, different variables of interest in the commercial incubation environment.