Andrade Valesca Vilela, Bernardes Priscila Arrigucci, Vicentini Rogério Ribeiro, Oliveira André Penido, Veroneze Renata, Ujita Aska, Negrão João Alberto, El Faro Lenira
Beef Cattle Research Center, Institute of Animal Science (IZ), Rod Carlos Tonani, Km 92, Sertãozinho 14174-000, Brazil.
Agricultural Research Company of Minas Gerais (EPAMIG), Rua Afonso Rato 1301, Uberaba 38001-970, Brazil.
Animals (Basel). 2021 Oct 30;11(11):3103. doi: 10.3390/ani11113103.
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 ( < 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.
技术设备在畜牧活动中越来越常见，比如用于识别奶牛的繁殖状态。为此，预测模型必须准确且能在生产环境中使用。本研究的目的是评估安装有瘤网胃测压丸的吉尔奶牛小母牛发情相关的瘤网胃温度（RRT）和活动（ACT）变化，并测试不同模型预测发情的能力。使用瘤网胃测压丸记录了45头接受发情同步处理的小母牛的RRT和ACT。比较了发情表现前一天和发情当天不同时间间隔的RRT和ACT平均值。使用混合模型对RRT和ACT进行方差分析。第二种方法采用逻辑回归、随机森林和线性判别分析模型，将RRT、ACT、一天中的时间以及温湿度指数（THI）作为预测因子。与发情前一天和发情后一天的同一时期相比，发情时RRT和ACT有所增加（<0.05）。随机森林模型表现最佳，灵敏度为51.69%，特异性为93.1%。目前的结果表明，RRT和ACT有助于识别吉尔奶牛小母牛的发情情况。
