Motivations and attitudes of Brazilian dairy farmers regarding the use of automated behaviour recording and analysis systems.

In this Research Communication we investigate the motivations of Brazilian dairy farmers to adopt automated behaviour recording and analysis systems (ABRS) and their attitudes towards the alerts that are issued. Thirty-eight farmers participated in the study distributed into two groups, ABRS users (USERS, n = 16) and non-users (NON-USERS, n = 22). In the USERS group 16 farmers accepted being interviewed, answering a semi-structured interview conducted by telephone, and the answers were transcribed and codified. In the NON-USERS group, 22 farmers answered an online questionnaire. Descriptive analysis was applied to coded answers. Most farmers were young individuals under 40 years of age, with undergraduate or graduate degrees and having recently started their productive activities, after a family succession process. Herd size varied with an overall average of approximately 100 cows. Oestrus detection and cow's health monitoring were the main reasons given to invest in this technology, and cost was the most important factor that prevented farmers from purchasing ABRS. All farmers in USERS affirmed that they observed the target cows after receiving a health or an oestrus alert. Farmers believed that they were able to intervene in the evolution of the animals' health status, as the alerts gave a window of three to four days before the onset of clinical signs of diseases, anticipating the start of the treatment.The alerts issued by the monitoring systems helped farmers to reduce the number of cows to be observed and to identify pre-clinically sick and oestrous animals more easily. Difficulties in illness detection and lack of definite protocols impaired the decision making process and early treatment, albeit farmers believed ABRS improved the farm's routine and reproductive rates.

An attempt at estrus detection in cattle by continuous measurements of ventral tail base surface temperature with supervised machine learning.

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.

The Internet of Things enhancing animal welfare and farm operational efficiency.

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows.

Estrus in dairy cattle varies in duration and intensity, highlighting the need for accurate and continuous monitoring to determine optimal breeding time. The objective of this study was to evaluate precision dairy monitoring technologies (PDMT) for detecting estrus. Estrus was synchronized in lactating Holstein cows (n = 109) using a modified G7G-Ovsynch protocol (last GnRH injection withheld to permit expression of estrus) beginning at 45 to 85 d in milk. Resumption of ovarian cyclicity at enrollment was verified by transrectal ultrasonography for presence of a corpus luteum. Cows were observed visually during 30 min (4 times per day) for behavioral estrus on d -1 to 2 (d 0 = day of estrus). Periods peri-estrus were defined by the temporal blood plasma progesterone patterns on d -5, -4, -3, -2, -1, 0, 2, 4, 6, and 8. Estrous detection by PDMT, an estrous behavior scoring system, and by visual observation of standing estrus were compared with the reference (gold) standard. Only 56% of cows that ovulated were observed standing by visual observation. Sensitivity and specificity for estrous detection were not different among all PDMT. Devices in this study measuring activity in steps, neck movement, high activity of head movement, or a proprietary motion index increased on the day of estrus 69 to 170% from the baseline before estrus. The change in rumination time on the day of estrus decreased for both neck and ear-based technologies (-2 to -16%). Temperature of the reticulorumen, vagina, and ear skin were not different on the day of estrus than day peri-estrus. Daily lying times decreased on average to 24.6% on the day of estrus for IceQube (IceRobotics Ltd., Edinburgh, Scotland). In contrast, lying time increased 15.5 and 33.1% for AfiAct Pedometer Plus (Afimilk, Kibbutz Afikim, Israel) and Track a Cow (ENGS Systems Innovative Dairy Solutions, Rosh Pina, Israel), respectively. All PDMT tested were capable of detecting estrus at least as effectively as visual observation. Four of the 6 PDMT that reported estrous alerts correctly detected 15 to 35% more cows than visual observation 4 times per day. Use of temporal progesterone patterns correctly identified more cows than visual observation alone. Dairy producers considering PDMT should focus on (1) the reference (gold) standard used to test efficacy of a device's alerts and (2) the device that will have the fewest false readings in their operations.

Automated detection of estrous behavior in tie-stall housing using a barometer and accelerometer.

Tie-stall housing inhibits movement in cows, thereby restricting the behavioral indicators used by farmers for detecting estrous. In this study, we investigated the changes in patterns of lying and standing times at estrous, and evaluated the potential for automated detection of estrous within tie-stalls using a barometer and accelerometer. On estrous days, total daily standing time was significantly longer than that during non-estrous days (P < 0.05). A practical method was developed for detecting slight altitude changes using a novel device, which consisted of a barometer and accelerometer, and was attached to the neckband. Total daily standing time predicted using this new device was found to be highly correlative with the observed measured data (r = 0.95, P < 0.01), indicating the accuracy of the device in measuring daily standing time in tie-stall housed cows. In addition, the device detected an overall increase in total daily standing time during estrous days.

Delaying investments in sensor technology: The rationality of dairy farmers' investment decisions illustrated within the framework of real options theory.

The adoption rate of sensors on dairy farms varies widely. Whereas some sensors are hardly adopted, others are adopted by many farmers. A potential rational explanation for the difference in adoption may be the expected future technological progress in the sensor technology and expected future improved decision support possibilities. For some sensors not much progress can be expected because the technology has already made enormous progress in recent years, whereas for sensors that have only recently been introduced on the market, much progress can be expected. The adoption of sensors may thus be partly explained by uncertainty about the investment decision, in which uncertainty lays in the future performance of the sensors and uncertainty about whether improved informed decision support will become available. The overall aim was to offer a plausible example of why a sensor may not be adopted now. To explain this, the role of uncertainty about technological progress in the investment decision was illustrated for highly adopted sensors (automated estrus detection) and hardly adopted sensors (automated body condition score). This theoretical illustration uses the real options theory, which accounts for the role of uncertainty in the timing of investment decisions. A discrete event model, simulating a farm of 100 dairy cows, was developed to estimate the net present value (NPV) of investing now and investing in 5 yr in both sensor systems. The results show that investing now in automated estrus detection resulted in a higher NPV than investing 5 yr from now, whereas for the automated body condition score postponing the investment resulted in a higher NPV compared with investing now. These results are in line with the observation that farmers postpone investments in sensors. Also, the current high adoption of automated estrus detection sensors can be explained because the NPV of investing now is higher than the NPV of investing in 5 yr. The results confirm that uncertainty about future sensor performance and uncertainty about whether improved decision support will become available play a role in investment decisions.

Efficiency of a pedometer device for detecting estrus in standing heat and silent heat in Japanese Black cattle.

The usefulness of a radiotelemetric pedometer for estrus detection in standing (ST) heat, or in silent heat without ST events, but in which ovulation is observed, in Japanese Black cattle was investigated. The duration of an increase in steps in ST heat was 11.8 ± 1.3 hr, and it was similar to that of ST events (duration: 10.1 ± 0.8 hr). Even in silent heat, the change pattern and the duration (11.6 ± 0.2 hr) of the period with an increase in steps during estrus were not different compared with ST heat. When artificial insemination (AI) was performed at 15.5 ± 0.6 hr from the onset of estrus detected by the pedometer in ST heat cases, the conception rate was 57.1% (8/14). Furthermore, fertility in cattle that underwent silent heat was evaluated. When AI was performed at 14.4 ± 2.0 hr from the onset of estrus detected by the pedometer, the conception rate was 60% (3/5) in silent heat cases. The overall results suggest that the radiotelemetric pedometer is a valid device for detecting estrus and it can even detect silent heat in Japanese Black cattle. Moreover, even silent heat cattle are fertile when AI is performed at the appropriate time.

Potential of connected devices to optimize cattle reproduction.

Estrus and calving are two major events of reproduction that benefit from connected devices because of their crucial importance in herd economics and the amount of time required for their detection. The objectives of this review are to: 1) provide an update on performances reached by sensor systems to detect estrus and calving time; 2) discuss current economic issues related to connected devices for the management of cattle reproduction; 3) propose perspectives for these devices. The main physiological parameters monitored separately or in combination by connected devices are the cow activity, body temperature and rumination or eating behavior. The combination of several indicators in one sensor may maximize the performances of estrus and calving detection. An effort remains to be made for the prediction of calvings that will require human assistance (dystocia). The main reasons to invest in connected devices are to optimize herd reproductive performances and reduce labor on farm. The economic benefit was evaluated for estrus detection and depends on the initial herd performances, herd size, labor cost and price of the equipment. Major issues associated with the use of automated sensor systems are the weight of financial investment, the lack of economic analysis and limited skills of the users to manage associated technologies. In the near future, connected devices may allow a precise phenotyping of reproductive and health traits on animals and could help to improve animal welfare and public perception of animal production.

Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle.

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.

The effect of housing condition on the performance of two types of activity meters to detect estrus in dairy cows.

When the daily routine of a cow is disturbed, it may have a detrimental effect on the performance of activity meters to detect estrus. It is possible that during the pasture period, the daily routine of cows is disturbed, adversely affecting the performance of activity meters to detect estrus which does not happen when the cows are housed indoors. The objective of this study was to investigate whether housing conditions (pasture or indoor) affected the performance of activity meters to detect estrus in dairy cows. In this research, two types of activity meters were used, an activity meter attached to the leg and one mounted on the neck. Cows of two different herds were equipped with the Smarttag Leg and the Smarttag Neck (Nedap livestock management, Groenlo, the Netherlands). The study began during the pasture period (September) and ended during the indoor period (January). The pasture period ended at the beginning of November. So, about two months of pasture period and two months of indoor period were studied. Milk samples were collected twice a week during the morning milking and true estrus was determined by milk progesterone concentrations. In total, the dataset consisted of 95 true estrous periods and 1992 true non-estrous days of 56 cows for the pasture period and 138 true estrous periods and 3164 true non-estrous days of 65 cows for the indoor period. Overall, no differences in sensitivity, positive predictive value (PPV) and specificity were found between the pasture and indoor period for both types of sensors. There was also no difference in the performance between leg and neck activity meters. Sensitivity was between 76 and 82%, PPV was between 87 and 92% and specificity was between 99 and 100%. In conclusion, the sensitivity, PPV and specificity did not differ between the pasture and indoor period. This means that, in our study, the performance of both types of activity meters to detect estrus is not affected by housing conditions.

Estrous detection by monitoring ventral tail base surface temperature using a wearable wireless sensor in cattle.

In the present study, the ventral tail base surface temperature (ST) was monitored using a wearable wireless sensor for estrus detection in cattle. Relationships among ST, behavioral estrus expression, ovulation, and changes in hormone profiles during the estrous cycle were examined. Holstein Friesian or Japanese Black female cattle were used in summer (August-September), autumn (October-November) and winter (January-February; three animals per season). On Day 11 of the estrous cycle (Day 0=the day of ovulation), the sensor was attached to the surface of the ventral tail base and ST was measured every 2min until Day 11 of the next estrous cycle. Hourly maximum ST values were used for analysis. To exclude circadian rhythm and seasonal effects, ST changes were expressed as residual temperatures (RT=actual ST - mean ST for the same hour on the previous 3days). Obvious circadian rhythms of the ST were observed and daily changes in the ST significantly differed among seasons. There was no significant seasonal difference, however, in the RT. The mean RT increased significantly ∼24 compared with ∼48h before ovulation. The mean maximum RT was 1.27±0.30°C, which was observed 5.6±2.4h after the onset of estrus, 2.4±1.3h before LH peak, and 26.9±1.2h before ovulation. The ST of the ventral tail base could be monitored throughout the estrous cycle and could detect a substantial change around the time of expression of behavioral estrus. Calculation and analysis of the RT could be useful for automatic estrous detection.

Site of PGF2α injection does not alter effectiveness of the Select Synch + controlled internal drug release and timed artificial insemination protocol.

Beef Quality Assurance programs have contributed to significant improvements in the wholesomeness of beef available for consumption. Injection site blemishes in the round have declined since the promotion of administering intramuscular injections in the neck. Unfortunately, many producers continue to administer estrus synchronization (ES) drugs in the rump. The objective of this study was to compare the effectiveness of injection site of PGF2α, in ES protocols, on steroid hormone concentrations and pregnancy rates. A Select Synch + 7-day controlled internal drug release ES protocol was conducted with the site of PGF2α injection alternated between neck and rump in beef cattle (n = 312) at the Ohio State University Agricultural Technical Institute and North Carolina State University. Blood samples (n = 75) were collected at controlled internal drug release insertion and at the time of artificial insemination (AI) to determine if progesterone (P4) and estrogen (E2) concentrations varied due to PGF2α injection site. All cattle were confirmed pregnant by ultrasonography at approximately 30 and 90 days after insemination in North Carolina and approximately 70 days after insemination in Ohio. Data were analyzed as randomized complete block designs in PROC GLIMMIX with animal as the experimental unit. Differences were declared significant at P < 0.05. Site of PGF2α injection, in either the neck or rump, did not affect (P > 0.05) overall conception rates in response to AI (58.4% and 55.6%, respectively). Altering PGF2α injection site did not impact P4, E2 concentrations, or the P4:E2 ratio at AI (P > 0.05). However, cattle inseminated after displaying estrus had greater (P < 0.05) pregnancy rates than timed AI (67.8 vs. 47.5%, respectively). First service conception rates and pregnancy rates were consistent with previous reports. Overall, altering the location of the PGF2α injection during ES did not change circulating hormone concentrations at AI or pregnancy rates; therefore, cattle producers should follow Beef Quality Assurance guidelines when administering ES protocols.

Using estrus detection patches to optimally time insemination improved pregnancy risk in suckled beef cows enrolled in a fixed-time artificial insemination program.

A multilocation study examined pregnancy risk (PR) after delaying AI in suckled beef cows from 60 to 75 h when estrus had not been detected by 60 h in response to a 7-d CO-Synch + progesterone insert (CIDR) timed AI (TAI) program (d -7: CIDR insert concurrent with an injection of GnRH; d 0: PGF injection and removal of CIDR insert; and GnRH injection at TAI [60 or 75 h after CIDR removal]). A total of 1,611 suckled beef cows at 15 locations in 9 states (CO, IL, KS, MN, MS, MT, ND, SD, and VA) were enrolled. Before applying the fixed-time AI program, BCS was assessed, and blood samples were collected. Estrus was defined to have occurred when an estrus detection patch was >50% colored (activated). Pregnancy was determined 35 d after AI via transrectal ultrasound. Cows ( = 746) detected in estrus by 60 h (46.3%) after CIDR removal were inseminated and treated with GnRH at AI (Control). Remaining nonestrous cows were allocated within location to 3 treatments on the basis of parity and days postpartum: 1) GnRH injection and AI at 60 h (early-early = EE; = 292), 2) GnRH injection at 60 h and AI at 75 h (early-delayed = ED; = 282), or 3) GnRH injection and AI at 75 h (delayed-delayed = DD; = 291). Control cows had a greater ( < 0.01) PR (64.2%) than other treatments (EE = 41.7%, ED = 52.8%, DD = 50.0%). Use of estrus detection patches to delay AI in cows not in estrus by 60 h after CIDR insert removal (ED and DD treatments) increased ( < 0.05) PR to TAI when compared with cows in the EE treatment. More ( < 0.001) cows that showed estrus by 60 h conceived to AI at 60 h than those not showing estrus (64.2% vs. 48.1%). Approximately half (49.2%) of the cows not in estrus by 60 h had activated patches by 75 h, resulting in a greater ( < 0.05) PR than their nonestrous herd mates in the EE (46.1% vs. 34.5%), ED (64.2% vs. 39.2%), and DD (64.8% vs. 31.5%) treatments, respectively. Overall, cows showing estrus by 75 h (72.7%) had greater ( < 0.001) PR to AI (61.3% vs. 37.9%) than cows not showing estrus. Use of estrus detection patches to allow for a delayed AI in cows not in estrus by 60 h after removal of the CIDR insert improved PR to TAI by optimizing the timing of the AI in those cows.

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection.

Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.

Effect of sensor systems for cow management on milk production, somatic cell count, and reproduction.

To improve management on dairy herds, sensor systems have been developed that can measure physiological, behavioral, and production indicators on individual cows. It is not known whether using sensor systems also improves measures of health and production in dairy herds. The objective of this study was to investigate the effect of using sensor systems on measures of health and production in dairy herds. Data of 414 Dutch dairy farms with (n=152) and without (n=262) sensor systems were available. For these herds, information on milk production per cow, days to first service, first calving age, and somatic cell count (SCC) was provided for the years 2003 to 2013. Moreover, year of investment in sensor systems was available. For every farm year, we determined whether that year was before or after the year of investment in sensor systems on farms with an automatic milking system (AMS) or a conventional milking system (CMS), or whether it was a year on a farm that never invested in sensor systems. Separate statistical analyses were performed to determine the effect of sensor systems for mastitis detection (color, SCC, electrical conductivity, and lactate dehydrogenase sensors), estrus detection for dairy cows, estrus detection for young stock, and other sensor systems (weighing platform, rumination time sensor, fat and protein sensor, temperature sensor, milk temperature sensor, urea sensor, ß-hydroxybutyrate sensor, and other sensor systems). The AMS farms had a higher average SCC (by 12,000 cells/mL) after sensor investment, and CMS farms with a mastitis detection system had a lower average SCC (by 10,000 cells/mL) in the years after sensor investment. Having sensor systems was associated with a higher average production per cow on AMS farms, and with a lower average production per cow on CMS farms in the years after investment. The most likely reason for this lower milk production after investment was that on 96% of CMS farms, the sensor system investment occurred together with another major change at the farm, such as a new barn or a new milking system. Most likely, these other changes had led to a decrease in milk production that could not be compensated for by the use of sensor systems. Having estrus detection sensor systems did not improve reproduction performance. Labor reduction was an important reason for investing in sensor systems. Therefore, economic benefits from investments in sensor systems can be expected more from the reduction in labor costs than from improvements in measures of health and production in dairy herds.

Characterization of Dutch dairy farms using sensor systems for cow management.

To improve cow management in large dairy herds, sensors have been developed that can measure physiological, behavioral, and production indicators on individual cows. Recently, the number of dairy farms using sensor systems has increased. It is not known, however, to what extent sensor systems are used on dairy farms, and the reasons why farmers invest or not in sensor systems are unclear. The first objective of this study was to give an overview of the sensor systems currently used in the Netherlands. The second objective was to investigate the reasons for investing or not investing in sensor systems. The third objective was to characterize farms with and without sensor systems. A survey was developed to investigate first, the reasons for investing or not in sensor systems and, then, how the sensor systems are used in daily cow management. The survey was sent to 1,672 Dutch dairy farmers. The final data set consisted of 512 dairy farms (response rate of 30.6%); 202 farms indicated that they had sensor systems and 310 farms indicated that they did not have sensor systems. A wide variety of sensor systems was used on Dutch dairy farms; those for mastitis detection and estrus detection were the most-used sensor systems. The use of sensor systems was different for farms using an automatic milking system (AMS) and a conventional milking system (CMS). Reasons for investing were different for different sensor systems. For sensor systems attached to the AMS, the farmers made no conscious decision to invest: they answered that the sensors were standard in the AMS or were bought for reduced cost with the AMS. The main reasons for investing in estrus detection sensor systems were improving detection rates, gaining insights into the fertility level of the herd, improving profitability of the farm, and reducing labor. Main reasons for not investing in sensor systems were economically related. It was very difficult to characterize farms with and without sensor systems. Farms with CMS and sensor systems had more cows than CMS farms without sensor systems. Furthermore, farms with sensor systems had fewer labor hours per cow compared with farms without sensor systems. Other farm characteristics (age of the farmer, availability of a successor, growth in herd size, milk production per cow, number of cows per hectare, and milk production per hectare) did not differ for farms with and without sensor systems.

Using UHF proximity loggers to quantify male-female interactions: a scoping study of estrous activity in cattle.

Reproductive efficiency is an important determinant of profitable cattle breeding systems and the success of assisted reproductive techniques (ART) in wildlife conservation programs. Methods of estrous detection used in intensive beef and dairy cattle systems lack accuracy and remain the single biggest issue for improvement of reproductive rates and such methods are not practical for either large-scale extensive beef cattle enterprises or free-living mammalian species. Recent developments in UHF (ultra high frequency) proximity logger telemetry devices have been used to provide a continuous pair-wise measure of associations between individual animals for both livestock and wildlife. The objective of this study was to explore the potential of using UHF telemetry to identify the reproductive cycle phenotype in terms of intensity and duration of estrus. The study was conducted using Belmont Red (interbred Africander Brahman Hereford-Shorthorn) cattle grazing irrigated pasture on Belmont Research Station, northeastern Australia. The cow-bull associations from three groups of cows each with one bull were recorded over a 7-week breeding season and the stage of estrus was identified using ultrasonography. Telemetry data from bull and cows, collected over 4 8-day logger deployments, were log transformed and analyzed by ANOVA. Both the number and duration of bull-cow affiliations were significantly (P<0.001) greater in estrous cows compared to anestrus cows. These results support the development of the UHF technology as a hands-off and noninvasive means of gathering socio-sexual information on both wildlife and livestock for reproductive management.

An ex ante analysis on the use of activity meters for automated estrus detection: to invest or not to invest?

The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a sensor system for detection of estrus and appraises the feasibility of an investment in such a system. A stochastic dynamic simulation model was used to simulate reproductive performance of a dairy herd. The number of cow places in this herd was fixed at 130. The model started with 130 randomly drawn cows (in a Monte Carlo process) and simulated calvings and replacement of these cows in subsequent years. Default herd characteristics were a conception rate of 50%, an 8-wk dry-off period, and an average milk production level of 8,310 kg per cow per 305 d. Model inputs were derived from real farm data and expertise. For the analysis, visual detection by the farmer ("without" situation) was compared with automated detection with activity meters ("with" situation). For visual estrus detection, an estrus detection rate of 50% and a specificity of 100% were assumed. For automated estrus detection, an estrus detection rate of 80% and a specificity of 95% were assumed. The results of the cow simulation model were used to estimate the difference between the annual net cash flows in the "with" and "without" situations (marginal financial effect) and the internal rate of return (IRR) as profitability indicators. The use of activity meters led to improved estrus detection and, therefore, to a decrease in the average calving interval and subsequent increase in annual milk production. For visual estrus detection, the average calving interval was 419 d and average annual milk production was 1,032,278 kg. For activity meters, the average calving interval was 403 d and the average annual milk production was 1,043,398 kg. It was estimated that the initial investment in activity meters would cost €17,728 for a herd of 130 cows, with an additional cost of €90 per year for the replacement of malfunctioning activity meters. Changes in annual net cash flows arising from using an activity meter included extra revenues from increased milk production and number of calves sold, increased costs from more inseminations, calvings, and feed consumption, and reduced costs from fewer culled cows and less labor for estrus detection. These changes in cash flows were caused mainly by changes in the technical results of the simulated dairy herds, which arose from differences in the estrus detection rate and specificity between the "with" and "without" situations. The average marginal financial effect in the "with" and "without" situations was €2,827 for the baseline scenario, with an average IRR of 11%. The IRR is a measure of the return on invested capital. Investment in activity meters was generally profitable. The most influential assumptions on the profitability of this investment were the assumed culling rules and the increase in sensitivity of estrus detection between the "without" and the "with" situation.

Comparison of three devices for the automated detection of estrus in dairy cows.

Considerable technological advances have been made in the automated detection of estrus in dairy cattle, but few studies have evaluated their relative performance on the same animals or assessed cow-related factors that affect their performance. Our objective was to assess the performance and reliability of three devices commercially available in France for cow estrus detection. The devices were a pedometer (PM; Afitag) and two activity meters (AM1; Heatime-RuminAct, and AM2; HeatPhone). Two algorithms were tested for AM2. We fitted 63 lactating Holstein cows with the three detectors from calving to 90 days after calving. The onset and pattern of cyclicity were monitored from 7 to 90 days postpartum measuring progesterone concentration in milk twice weekly. A total of 211 ovulations were identified. Cyclicity was classified as normal in 60% of cows (38/63). Calculated over the operating period of all the devices (179 periods of estrus), the sensitivities and positive predictive values were, respectively, 71% and 71% for PM, 62% and 84% for AM1, 61% and 67% for the first algorithm of AM2, and 62% and 87% for the second algorithm of AM2. Both activity meters had a lower sensitivity but a higher positive predictive value than the PM (P < 0.05). For all devices, the performance in estrus detection was much poorer at the first postpartum ovulation than at subsequent ovulations (P < 0.05). Lactation rank and milk production affected some devices (P < 0.05). These devices could be used to reinforce visual observations, especially after 50 days postpartum, the minimum recommended delay to insemination. However, their full benefit remains to be verified in different farming systems and taking into account the specific objectives of the dairy farmer.

Invited review: sensors to support health management on dairy farms.

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.