Efficient detection of estrus is a permanent challenge for successful reproductive performance in dairy cattle. In this context, comprehensive knowledge of estrus-related behaviors is fundamental to achieve optimal estrus detection rates. This review was designed to identify the characteristics of behavioral estrus as a necessary basis for developing strategies and technologies to improve the reproductive management on dairy farms. The focus is on secondary symptoms of estrus (mounting, activity, aggressive and agonistic behaviors) which seem more indicative than standing behavior. The consequences of management, housing conditions and cow- and environmental-related factors impacting expression and detection of estrus as well as their relative importance are described in order to increase efficiency and accuracy of estrus detection. As traditional estrus detection via visual observation is time-consuming and ineffective, there has been a considerable advancement of detection aids during the last 10 years. By now, a number of fully automated technologies including pressure sensing systems, activity meters, video cameras, recordings of vocalization as well as measurements of body temperature and milk progesterone concentration are available. These systems differ in many aspects regarding sustainability and efficiency as keys to their adoption for farm use. As being most practical for estrus detection a high priority – according to the current research – is given to the detection based on sensor-supported activity monitoring, especially accelerometer systems. Due to differences in individual intensity and duration of estrus multivariate analysis can support herd managers in determining the onset of estrus. Actually, there is increasing interest in investigating the potential of combining data of activity monitoring and information of several other methods, which may lead to the best results concerning sensitivity and specificity of detection. Future improvements will likely require more multivariate detection by data and systems already existing on farms.

Despite the widespread adoption of hormonal synchronization protocols that allow for timed artificial insemination (AI), detection of estrus plays an important role in the reproductive management program on most dairies in the United States. Increased physical activity is a secondary sign of estrus in dairy cattle, and a new generation of electronic systems that continuously monitor physical activity to predict timing of AI have been developed and marketed to the dairy industry. A variety of management and physiologic challenges inhibit detection of behavioral estrus on farms, but the prevalence of anouvular cows near the end of the voluntary waiting period is particularly problematic. Only 70% of lactating Holstein cows were detected in estrus when using an activity monitoring system, with the remaining 20% of cows classified as anovular and 10% ovulating without showing signs of activity. Mean time of AI in relation to ovulation based on the activity monitoring system was acceptable for most of the cows with increased activity, however, variability in the duration of estrus and timing of AI in relation to ovulation could result in poor pregnancy outcomes in some cows. Use of a Presynch–Ovsynch protocol for submission of cows for first AI has been widely adopted by dairies in the United States, and a combined approach in which AI based on activity is followed by submission of cows not detected with activity to timed AI after synchronization of ovulation may be an effective strategy for submission of cows to first AI. Based on a field trial on a large commercial dairy in the United States, the activity monitoring system detected 70% of cows with increased activity after the second PGF2α injection of a Presynch–Ovsynch protocol, however, cows inseminated to increased activity had fewer pregnancies per AI (P/AI) compared with cows with increased activity after the second PGF2α injection that received timed AI after completing the Presynch–Ovsynch protocol. Based on an economic model comparing reproductive management programs with varying levels of AI to estrus v. timed AI, the rate of estrus detection and the P/AI to inseminations based on AI to detected estrus v. timed AI affected the decision to inseminate based on activity v. timed AI. In conclusion, an activity monitoring system detected increased activity in about 70% of lactating Holstein cows on a large commercial dairy in the United States, however, synchronization of ovulation and timed AI was beneficial to inseminate cows not detected with increased activity by the activity monitoring system.