Although heat detection makes a relevant contribution to good reproduction performance of dairy cattle, available studies on the economic evaluations of automatic heat detection systems are limited. Therefore, the objective of this article is to provide an economic evaluation of using automatic heat detection. The effect of different heat detection rates on gross margin is modelled with SimHerd (SimHerd A/S, Denmark). The analysis considers all additional investment costs in automatic heat detection. The economic evaluation is carried out on the assumption of two different herds of Simmental cattle with milk production levels of 7000 and 9000 kg and herd sizes of 70 and 210 cows, respectively. Furthermore, we distinguish between two investment scenarios: In scenario 1, only cows are equipped with automatic heat detection, while scenario 2 assumes that cows and heifers are equipped with automatic heat detection. Because some variables are relatively uncertain (heat detection rates; time for heat control), they are modelled with triangle distributions using the Monte Carlo method in @RISK (Palisade Corporation software, Ithaca NY USA). This makes it possible to model a probability distribution for the net returns of investment in automatic heat detection.  

The simulation results show that the net return of investing in an automatic heat detection system ranges in all scenarios from -33 to +111 € per cow and year, with mean values of +6 to +35 € per cow and year. In general, the net return is independent of the milk production level assumed. A comparison amongst all scenarios shows higher net returns for bigger herd sizes, due to fixed cost degression effects. Considering all scenarios, the probability of a positive net return of using an automatic heat detection system is 82 %. The economic advantage or disadvantage depends strongly on the current fertility management of a dairy farm without automatic heat detection. Additionally equipping heifers with the system has a strong positive effect on the economy of automatic heat detection systems, due to the resulting reduction in the age at first calving. 

Livestock farming technologies have a tremendous potential to improve and support farmers in herd management decisions, in particular in reproductive management. Nowadays, estrus detection in cows is challenging and many detection tools are available. The company Smartbow (Weibern, Austria) developed a novel ear-tag sensor, which consists of a 3D-accelerometer that records head and ear movements of cows as basis for algorithm development and further analyses. Estrus detection by the SMARTBOW system is primarily based on an increased activity combined with behavioral changes. In this study, the system was installed on a commercial dairy farm in Slovakia and Holstein-Friesian cows were equipped with SMARTBOW Eartag sensors. Exceeding cow specific thresholds for activity and behavioral changes, an estrus alert was generated. Retrospectively, reproductive performance data were used to evaluate the accuracy of estrus alerts generated by the SMARTBOW system. Sensitivity, specificity, positive and negative predictive value, accuracy, and error rate for detecting estruses were 97%, 96%, 98%, 94%, 96%, and 2%, respectively. In summary, the SMARTBOW system is suitable for an automatic estrus detection of estrus events in indoor housed dairy cows.

The objective of this study was to evaluate the ear tag based accelerometer SMARTBOW (Smartbow, Weibern, Austria) for detecting rumination time, chewing cycles and rumination bouts in dairy cows. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. Additionally, the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings was tested. Ten Simmental cows were equipped with 10 Hz accelerometer ear tags and kept in a pen separated from the other herd mates. During the study, cows’ rumination and other activities were directly observed for 20 h by 2 trained observers. Additionally, cows were video recorded, 24 h a day. After exclusion of unsuitable videos, 2,490 h of cow individual 1-hour video sequences were eligible for further analyses. Out of this, 100 video sequences were randomly selected and analyzed by a trained observer using professional video analyses software. Based on these analyses, half of the data were used for development and testing of the SMARTBOW algorithm, respectively. Inter-and intra-observer reliability as well as the comparison of direct against video observations revealed in high agreements for rumination time and chewing cycles with Pearson correlation coefficients of r > 0.99. The rumination time, chewing cycles as well as rumination bouts detected by SMARTBOW were highly associated (r > 0.99) with the analyses of video recordings. Testing the algorithm revealed in an underestimation of the average ± standard deviation (SD) rumination time per 1-hour period by SMARTBOW of 17.0 ± 35.3 s (i.e. ‑1.2%), compared with visual observations. The average number ± SD of chewing cycles and rumination bouts was overestimated by the SMARTBOW system by 59.8 ± 79.6 (i.e. 3.7%) and by 0.5 ± 0.9 (i.e. 1.8%), respectively compared with the video analyses. From a practical and clinical point of view, the detected differences were negligible.

The US is the largest producer of beef in the world. Last year alone, it produces nearly 19% of the world’s beef.  This translate to about almost $90 billion in economic impact in the country. Aside from being a producer, the US also consumed more than 26 billion pounds of beef which have a retail value of the entire beef industry to more than $74B. For this level of production and consumption, each rancher in the US must produce a herd size of at least 100 or more to sustain the current demand. Ranchers often employ different techniques to minimized cattle losses by regularly monitoring their cattle’ health and well-being. With the recent advances in Global Positioning System (GPS) and the miniaturization of GPS module and internet of things (IoT) provide the medium of developing small tracking device for cattle.

This paper describes the development of cattle tracking device using GPS and IoT technology. The wireless tracking module may provide a framework that may be used for a large scale tracking system to help in monitoring cattle location and health. A prototype system for cattle tracking was developed. The system consists of a GPS Receiver module, Wifi, and sensors such as temperature and accelerometer+magnetometer. The GPS receiver module was tested to determine the minimum time to get a fixed signal. The shortest warm-up/cold start was determined to be around 32-35 sec. which was consistent with the datasheet of the receiver module. The tracking device was tested in a 3 sec. lag time and used ubidots cloud (cloud-based iot platform) for real-time data tracking. Three ubidots widgets were used, GPS data which was overlayed with a local Google map, data points, and graph style display for temperature data. The test under the latest prototype (version 6) produced real-time tracking.

The impact of using a mathematical model estimating real-time daily lysine requirements in a sustainable precision feeding program for growing pigs was investigated in two performance trials. Three treatments were tested in the first trial (60 pigs of 41.2±0.5 kg): a three-phase feeding program (3P) obtained by blending fixed proportions of feeds A (high nutrient concentration) and B (low nutrient concentration); and two daily-phase feeding programs in which the blended proportions of feeds A and B were adjusted daily to meet the estimated nutritional requirements of the group (MPG) or of each pig individually (MPI). The five treatments tested during second trial (70 pigs of 30.4±2.2) were a three-phase group-feeding program; against four individual daily-phase feeding programs in which the blending proportions of feeds A and B were updated daily to meet 110%, 100%, 90%, or 80% of the lysine requirements estimated using a mathematical model. Feeders identified each pig and delivered, in response to each animal request, a blend of feeds A and B containing the estimated concentration of lysine required by this pig this day according to the assigned trialal treatment. Feed intake was recorded automatically by the feeders, and the pigs were weighed weekly. These individual data was used to estimate the required concentration of lysine for each pig. Nitrogen excretion was calculated by the difference between intake and retention. In the first project, the MPG and MPI programs showed similar performance results than 3P feeding program. However, compared with the 3P, the MPI feeding program reduced (P<0.05) the standardized ileal digestible lysine intake by 27% and the estimated nitrogen excretion by 22%. In the second project, feeding pigs in a daily-basis program providing 110%, 100%, or 90% of the estimated individual lysine requirements also did not influence performance results in comparison with the 3P. However, feeding pigs individually with diets tailored to match 100% of nutrient requirements made it possible to reduce (P<0.05) digestible lysine intake by 26% and estimated nitrogen excretion by 30% relative to 3P. Precision feeding is an effective approach to make pig production more sustainable without compromising growth performance.