Symposium review: Linking activity-sensor data and physiology to improve dairy cow fertility

Smart sensors in Thai dairy reproduction: A case study

Background and Aim

Movement activity sensors are known for their potential to boost the reproductive performance of dairy cows. This study evaluated the effectiveness of these sensors on three Thai dairy farms (MK, NF, and CC), each using different sensor brands. We focused on reproductive performance at these farms and expanded our evaluation to include farmer satisfaction with sensor technology on five farms (MK, NF, CC, AP, and IP), allowing for a thorough analysis of both operational outcomes and user feedback.

Materials and Methods

A total of 298 lactation records and interviewing five experienced farm owners with over a year of sensor usage were our research methods. To measure the effect on the first service timing and post-parturition pregnancy rates, Cox regression models were utilized for sensor usage.

Results

Biosensors' implementation enhanced data precision while quickening the first service within 100 days and pregnancy within 200 days. The MK and NF farms showed significant progress. Within 100 and 200 days post-implementation, the overall improvement was 30%-34% in the first service rate and 39%-67% in the conception rate across all assessed farms. Farmers acknowledged improved reproductive performance from the sensors, overcoming language barriers.

Conclusion

The study highlighted the advantages of using movement activity sensors in enhancing both cattle reproductive success and farmers' satisfaction on Thai dairy farms. These sensors led to more accurate management decisions, increasing overall farm productivity.

Performance and Cost-Efficiency of Single Hormonal Treatment Protocols in Tropical Anestrous Dairy Cows

This retrospective study aimed to evaluate the performance of hormone treatment protocols, determine the factors associated with pregnancy success after hormone treatment, and compare the cost-efficiencies of two types of hormone treatment among cyclic and noncyclic anestrous dairy cows. The clinical records of 279 anestrous cows that received hormone treatment for artificial insemination (AI) from 64 herds in the western region of Thailand were obtained from Kasetsart University Veterinary Teaching Hospital from January to August 2017. The performance of the hormone treatment protocols, fixed-time AI (TAI) and estrus detection before AI (EAI), showed that the pregnancy risk for the TAI protocol was higher than that for the EAI protocol, but pregnancy per AI did not differ significantly between the two protocols in cyclic and noncyclic cows. Multivariate logistic regression analysis showed that cows receiving the TAI protocol were more likely to be pregnant compared to those treated with the EAI protocol. Cows with a 3.00 body condition score (BCS) < 3.75 after treatment and loose-housed cows were more likely to become pregnant. Treatment during winter showed higher pregnancy success than that in the summer and rainy seasons. The cost-efficiency analysis showed that the TAI protocol was the most cost-efficient option for noncyclic cows, whereas the EAI protocol was the most cost-efficient option for cyclic cows.

Validation of a leg-mounted pedometer for the measurement of steps in lactating Holstein cows

The aim of this study was to validate the pedometer AfiAct II (AfiMilk) for the measurement of steps in lactating Holstein cows housed in a freestall design by assessing its agreement with visual observation of step counts. A total of 41 primiparous (n = 12) and multiparous (n = 29) cows were enrolled in the study between August and September 2018. Steps were monitored continuously by the pedometer and visually assessed for a 24-h period using video recordings. Visually observed steps were categorized as walking and stationary steps. The total number of steps taken per cow was calculated using the sum of walking and stationary steps. Unprocessed step count data from the study day were retrieved from the AfiMilk system in time-blocks of approximately 15 min. Repeated measures correlation was used to quantify the association between the pedometer measurements and visual observation of step counts. Nonindependence among observations were accounted adjusting for interindividual (cow) variability with an analysis of covariance. Pearson correlation coefficients (r) were categorized from negligible (0.00-0.30) to very high (0.90-1.00). Bland-Altman plots were created to evaluate the bias between the pedometer and visual observations. A total of 2,261 time-blocks were used in this study with an average (mean ± standard deviation) of 55.14 ± 8.1 time-blocks per cow. A high correlation was found for the evaluation between the pedometer and observed walking steps (r = 0.74; 95% confidence interval [CI] = 0.73-0.76), stationary steps (r = 0.71; 95% CI = 0.69-0.63), and total steps (r = 0.88; 95% CI = 0.87-0.89). The results of the Bland-Altman plot suggested limited bias between the pedometer step counts and visual observation of steps, independent of the type of steps. Numerical differences and several time-block differences outside of the 95% interval of agreement suggested an overestimation of step counts by the pedometer, which increased as the number of steps increased. The pedometer measured, on average, 97.6 ± 118.5 (28%), 249.2 ± 126.2 (125%), and 297.2 ± 205.4 (196%) steps/day more than the visual observed total steps, stationary steps, and walking steps, respectively. Our findings indicate that the pedometer counts all movement in which the pedometer leg is lifted off the floor without distinguishing if there was body movement of the animal.

Precision Livestock Farming: What Does It Contain and What Are the Perspectives?

Precision Livestock Farming (PLF) describes the combined use of sensor technology, the related algorithms, interfaces, and applications in animal husbandry. PLF technology is used in all animal production systems and most extensively described in dairy farming. PLF is developing rapidly and is moving beyond health alarms towards an integrated decision-making system. It includes animal sensor and production data but also external data. Various applications have been proposed or are available commercially, only a part of which has been evaluated scientifically; the actual impact on animal health, production and welfare therefore remains largely unknown. Although some technology has been widely implemented (e.g., estrus detection and calving detection), other systems are adopted more slowly. PLF offers opportunities for the dairy sector through early disease detection, capturing animal-related information more objectively and consistently, predicting risks for animal health and welfare, increasing the efficiency of animal production and objectively determining animal affective states. Risks of increasing PLF usage include the dependency on the technology, changes in the human-animal relationship and changes in the public perception of dairy farming. Veterinarians will be highly affected by PLF in their professional life; they nevertheless must adapt to this and play an active role in further development of technology.

Continuous real-time cow identification by reading ear tags from live-stream video

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.

«The role of reproductive biology in SDGs» Global warming and cattle reproduction: Will increase in cattle numbers progress to global warming?

The livestock industry produces a large amount of greenhouse gases (GHG) that cause global warming. A high percentage of GHG emissions are derived from cattle and has been suggested to be a factor in global warming. With the global increase in the consumption of livestock products, the number of farm animals has increased. In addition, the reduction in productivity and reproductive capacity of cattle has resulted in accelerated GHG emissions. In a high-temperature environment, the pregnancy rate decreases, leading to an increase in animals that do not contribute to production. Consequently, GHG emission per unit product increases, thereby accelerating global warming. To reduce this environmental impact, it is important to improve the breeding efficiency of cattle by the use of reproductive technology and, thus, reduce the number of non-productive animals. Thus, reproductive biology plays a major role in mitigating global warming related to the livestock industry.

Administration of GnRH at Onset of Estrus, Determined by Automatic Activity Monitoring, to Improve Dairy Cow Fertility during the Summer and Autumn

We examined gonadotropin-releasing hormone (GnRH) administration at onset of estrus (OE), determined by automatic activity monitoring (AAM), to improve fertility of dairy cows during the summer and autumn. The study was performed on two dairy farms in Israel. The OE was determined by AAM recorded every 2 h, and a single im dose of GnRH analogue was administered shortly after OE. Pregnancy was determined by transrectal palpation, 40 to 45 d after artificial insemination (AI). Conception risk was analyzed by the GLIMMIX procedure of SAS. Brief visual observation of behavioral estrus indicated that about three-quarters of the events (n = 40) of visually detected OE occurred within 6 h of AAM-detected OE. Accordingly, the GnRH analogue was administered within 5 h of AAM-detected OE, to overlap with the expected endogenous preovulatory LH surge. Overall, pregnancy per AI (P/AI) was monitored over the entire experimental period (summer and autumn) in 233 first, second or third AI (116 and 117 AI for treated and control groups, respectively). Least square means of P/AI for treated (45.8%) and control (39.4%) groups did not differ, but group-by-season interaction tended to differ (p = 0.07), indicating no effect of treatment in the summer and a marked effect of GnRH treatment (n = 58 AI) compared to controls (n = 59 AI) on P/AI in the autumn (56.6% vs. 28.5%, p < 0.03). During the autumn, GnRH-treated mature cows (second or more lactations), and postpartum cows exhibiting metabolic and uterine diseases, tended to have much larger P/AI than their control counterparts (p = 0.07-0.08). No effect of treatment was recorded in the autumn in first parity cows or in uninfected, healthy cows. In conclusion, administration of GnRH within 5 h of AAM-determined OE improved conception risk in cows during the autumn, particularly in those exhibiting uterine or metabolic diseases postpartum and in mature cows. Incorporation of the proposed GnRH treatment shortly after AAM-detected OE into a synchronization program is suggested, to improve fertility of positively responding subpopulations of cows.

Daily activity measures and milk yield immediately before and after a fertile estrus and during the period of expected return to estrus after insemination in dairy cows

The objective of this study was to characterize changes in milk yield and other physical measures during a 7-d periestrual period encompassing estrus (d 0) and during a 16-d period of expected return to estrus beginning at d 17 after artificial insemination (AI) until pregnancy status was determined on d 32. Lactating dairy cows milked thrice daily were fitted with CowManager SensOor ear tags (Agis) capable of assessing real-time eating, rumination, resting, high activity (estrus), ear-surface temperature, and heat alerts. Data were uploaded to the cloud, downloaded daily into Excel (Microsoft Corp.) spreadsheets, averaged to produce daily means for each activity, and analyzed as repeated measures relative to estrus or to d 17 after AI. Daily milk was unchanged during the periestrual period but was greater in nonpregnant cows that failed to return to estrus (NP-NR) during d 21 through 26 compared with NP cows that returned to estrus (NP-R) and pregnant (PREG) cows during that same period. Daily ear-surface temperature was greater during d 1 to 3 compared with d 0 and averaged 0.6 to 1.7°C greater from d 17 through 32 in NP-NR cows compared with NP-R and PREG cows. Daily rumination and resting times reached nadirs on d 0, with decreases occurring 48 h before estrus. Both rumination and resting times increased by 25 or 81% on the day after estrus, respectively. Rumination and resting times were less in NP-R cows during d 22 through 26 compared with NP-NR and PREG cows. In contrast, daily eating time was greatest on the day of estrus compared with 3 d before and after estrus. The NP-R cows spent more time eating during d 17 through 32 compared with NP-NR and PREG cows. High activity increased by 97% during 48 h before estrus, peaked at estrus, and decreased to a constant level during d 1 through 3. The NP-R cows had greater high activity on d 22 through 26 compared with NP-NR and PREG cows. We conclude that resting and rumination activity decreased to daily nadirs, whereas eating and high activity peaked on the day of estrus. Fertile estrus was associated with 12% greater high activity, 11% less resting time, and 6% less rumination time. In addition, cows that returned to estrus after AI had greater daily eating and high activity times and less rumination and resting time during the period of expected return to estrus after AI compared with pregnant cows and cows failing to return to estrus.

Estrus Detection Using Background Image Subtraction Technique in Tie-Stalled Cows

Simple Summary

With increasing herd sizes and labor costs in recent decades, visual estrus detection by farmers has become more difficult because of the reduced manpower input per cow. To address this problem, various wearable devices have been developed for automatic estrus detection in cows, such as neck- or leg-mounted activity meters for monitoring estrus-associated increments in the amount of activity. However, these animal-contact devices have several limitations; namely, it can be dangerous to attach or remove the device and it can cause discomfort. Recently, a background image subtraction technique has been proposed as a non-contact method for monitoring activity changes in livestock animals. In this study, a new method was developed by combining the background subtraction technique and the thresholding method to detect estrus-associated activity increases in tie-stalled cows. Using this method, a substantial increase in activity in estrus was detectable, and the estrus detection sensitivity reached as high as 90% with a precision of 50%, where the sensitivity and precision were calculated as: (true-positive/[true-positive + false-negative]) × 100% and (true-positive/[true-positive + false-positive]) × 100%, respectively. This result may indicate that activity monitoring using the background subtraction technique has the potential to be a non-contact estrus detection method in tie-stalled cows.

Abstract

In this study, we determined the applicability of the background image subtraction technique to detect estrus in tie-stalled cows. To investigate the impact of the camera shooting direction, webcams were set up to capture the front, top, and rear views of a cow simultaneously. Video recording was performed for a total of ten estrous cycles in six cows. Standing estrus was confirmed by testing at 6 h intervals. From the end of estrus, transrectal ultrasonography was performed every 2 h to confirm ovulation time. Foreground objects (moving objects) were extracted in the videos using the background subtraction technique, and the pixels were counted at each frame of five frames-per-second sequences. After calculating the hourly averaged pixel counts, the change in values was expressed as the pixel ratio (total value during the last 24 h/total value during the last 24 to 48 h). The mean pixel ratio gradually increased at approximately 48 h before ovulation, and the highest value was observed at estrus, regardless of the camera shooting direction. When using front-view videos with an appropriate threshold, estrus was detected with 90% sensitivity and 50% precision. The present method in particular has the potential to be a non-contact estrus detection method for tie-stalled cows.