Early pregnancy diagnoses based on physiological indexes of dairy cattle: a review.

With the development of standardization and scaling in the dairy farming industry, timely and accurate pregnancy diagnosis is required to improve the benefits of breeding by shortening the calving interval. However, the current pregnancy diagnostic methods cannot meet the requirements of the industry. Here, we review changes in the physiological indexes and in the function and morphological status of the reproductive organs of dairy cows at the early stages of pregnancy. In addition, the corresponding pregnancy diagnostic methods based on certain indexes are well development, and the pregnancy diagnostic approaches based on remote sensing and automation technology have become inevitable trends in the industry. These applications will reveal physiological regularities in pregnancy and benefit the detailed management of dairy cows during early pregnancy.

Automated measurement of cattle surface temperature and its correlation with rectal temperature.

The body temperature of cattle varies regularly with both the reproductive cycle and disease status. Establishing an automatic method for monitoring body temperature may facilitate better management of reproduction and disease control in cattle. Here, we developed an Automatic Measurement System for Cattle's Surface Temperature (AMSCST) to measure the temperature of metatarsus by attaching a special shell designed to fit the anatomy of cattle's hind leg. Using AMSCST, the surface temperature (ST) on the metatarsus of the hind leg was successively measured during 24 hours a day with an interval of one hour in three tested seasons. Based on ST and rectal temperature (RT) detected by AMSCST and mercury thermometer, respectively, a linear mixed model was established, regarding both the time point and seasonal factors as the fixed effects. Unary linear correlation and Bland-Altman analysis results indicated that the temperatures measured by AMSCST were closely correlated to those measured by mercury thermometer (R2 = 0.998), suggesting that the AMSCST is an accurate and reliable way to detect cattle's body temperature. Statistical analysis showed that the differences of STs among the three seasons, or among the different time points were significant (P<0.05), and the differences of RTs among the different time points were similarly significant (P<0.05). The prediction accuracy of the mixed model was verified by 10-fold cross validation. The average difference between measured RT and predicted RT was about 0.10 ± 0.10°C with the association coefficient of 0.644, indicating the feasibility of this model in measuring cattle body temperature. Therefore, an automated technology for accurately measuring cattle body temperature was accomplished by inventing an optimal device and establishing the AMSCST system.