Estrus Prediction Models for Dairy Gyr Heifers

Abstract

Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (p < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers.

Optimization of resistance to gastrointestinal nematodes in Santa Inês sheep: a genomic selection, machine learning and image analysis approach

“…These models are generally modifications or extensions of linear models, which allow for nonlinear relationships between variables and handling of collinearity and high-dimensional data (GOLDSTEIN et al, 2017;. ML algorithms have been utilized in different aspects of the livestock industry, such as detecting estrus in dairy cows (ANDRADE et al, 2021), classifying pathologies in animal necropsy reports (BOLLIG et al, 2020), and categorizing dairy cattle breeds (MOAWED et al, 2017). The best method for a particular task depends on the problem being studied.…”