Cattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to clas-sify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feed-ing, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and com-pared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R2 was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R2 = 0.99, P < 0.001). Performance was lower in the drinking area (R2 = 0.90, P < 0.01) and the concentrate feeder (R2 = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R2 of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerom-eter alone, such as eating concentrates and drinking (R2 = 0.85 and 0.90, respectively). This study demon-strates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Electronic boluses with biotelemetry capabilities enable wireless monitoring of animals’ physiological data (e.g., temperature, pH). The aim of this study was to design and experimentally validate a novel multiband (434, 868, and 1400)-MHz conformal patch antenna for in-body biotelemetry applications for cows. The optimal frequency band was studied prior to the design of the antenna, based on the dielectric measurements of the antenna environment (i.e., rumen). The antenna was integrated in a 13.5- $\times {\varnothing }3$ -cm bolus and simulated in a ${\varnothing }300$ -mm spherical phantom with electromagnetic properties of cows’ rumen fluid. The proposed antenna presented a high performance with a realized gain of (−38.5, −41.2, and −45.7) dBi and a radiation efficiency of (0.012%, 0.0045%, and 0.001%) at 434, 868, and 1400 MHz, respectively. Following the numerical analysis and optimization, a prototype was manufactured to experimentally evaluate the antenna performance. Good agreement was obtained between the measurements and simulations both for reflection coefficients and radiation performance. The measured gain was −36.3, −40.4, and −43.6 dBi, at 434, 868, and 1400 MHz, respectively. The proposed multiband antenna will enable the development of a new generation of boluses for animal biotelemetry applications to enhance the performance of the animal monitoring systems.

Automated systems based on wearable sensors for livestock monitoring are becoming increasingly popular. Specifically, wireless in-body sensors could yield relevant data, such as ruminal temperature. The collection of such data requires an accurate characterization of the in-to-out body wireless channel between the in-body sensor and the gateway. The aim of this study is to experimentally characterize the in-to-out-body propagation loss for cows and horses at 433, 868, and 1400 MHz. Measurements were conducted in vivo on five different fistulated cows and five horse cadavers using specialized robust in-body capsule antennas inside the animals' abdomen. Next, the in-body antenna gain was deembedded from the wireless channel, and the in-to-out body propagation loss was obtained as the difference between measured unobstructed line-of-sight path losses and in-to-out-body path losses. The measurements showed a body propagation loss of (mean ± standard deviation) 30.8 ± 4.1 dB, 44.5 ± 4.8 dB, and 54.2 ± 4.7 dB for cows at 433, 868, and 1400 MHz, respectively. For horses, the body propagation losses were 23.2 ± 3.8 dB, 31.0 ± 4.7 dB, and 44.4 ± 3.2 dB at 433, 868, and 1400 MHz, respectively. These results are important to determine the wireless range of WBANs to optimize the network topology and estimate the associated network cost for large-scale monitoring systems.

The objective is to investigate whether NH3 and odour emissions can be lowered by implementing three different ventilation control settings (VCS) as low-cost mitigation techniques. In treatment 1 (T1) the set-point temperature (Tset) was set +2 degrees C higher than the reference strategy (CON). In treatment 2 (T2), the minimum (V-min) and maximum (V-max) ventilation settings were set to 75% and 90% of the CON, respectively. For treatment 3 (T3), the Tset was +1 degrees C higher than the CON, while the V-min and V-max settings were initially set at 25% and 80% of the CON but gradually increased during the fattening period. The results implied that T1 was the best VCS, as it significantly lowered odour emissions compared to the CON by 34%. Despite the significant decrease in ventilation rate, the overall hourly average NH3 emissions did not differ between treatments T1, T2, T3 and CON. However, based on the VERA protocol (https://www.vera-verification.eu/test-protocols/), annual NH3 emission factors were calculated and demonstrated the potential of T1 to decrease NH3 emissions by 11% compared to the CON. The lack of significant differences in the overall hourly average NH3 emissions between T1, T2, T3 and the CON was in part due to the seasonal variations in pig housing airflow patterns affecting the air exchange rate in the slurry pit. This indicates that, despite the importance of the ventilation rate on emissions, the effect of the indoor airflow pattern on the gaseous release from the slurry pit into the building is crucial. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

In this paper, for the first time, the in-to-out-body path loss between a capsule antenna placed inside the cows’ rumen and a distant gateway was characterized at 868 MHz. Measurements were conducted on five different fistulated cows in a dairy barn. The in-body antenna gain was then de-embedded from the wireless channel. The difference between free space measurements and in-to-out-body path loss assessment was used to quantity the path loss increase due to the cows’ body. Results have shown an increase of the path loss on average (all cows) by 50.6 dB, with a variation between 43.7 and 55.3 dB. The obtained results were used to calculate the range of a LoRa (Long range) based network accounting for the antenna channel. With an input transmit power of 14 dBm, ranges up to 175 m in indoor and 364 m in outdoor were obtained depending on the used bit rate.

Automated tracking of indoor housed farm animals is gaining an increasing interest in farming practice and research for monitoring animal behaviour and health. Several positioning solutions have become commercially available for different animal species, but the insight on how these systems perform in pig facilities is still limited. In this study the feasibility of tracking sows in a barn using an ultra-wideband (UWB) based indoor positioning system was investigated. The system consisted of 7 base stations and multiple tracker tags, and the determination of tags' positions was based on time of arrival (ToA). For locating stationary tags the system achieved 0.37 m accuracy at 1 m height and 0.50 m accuracy at 0.3 m height. While tracking moving tags at 1 m height 0.38 m accuracy was achieved. Three filters were tested to further improve the positioning performance. The median filter had the highest improvement on locating stationary and moving tags. The bilateral filter had a balanced capability of uncovering complex moving trajectories. The double exponential moving average filter was more suited when real-time updates were required. The overall results showed that tracking group housed sows inside a pig barn was feasible using the UWB positioning system. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

Ammonia emissions are an important issue in livestock production. Many mitigation measures have been proposed in order to reduce the environmental impact of livestock farms, and reliable field measurements are required to evaluate the amount of released or reduced ammonia while applying these measures. Following the guideline of the Verification of Environmental Technologies for Agricultural Production test protocol, five commercially available gas analysers, i.e., INNOVA 1314, Picarro G2103, Rosemount CT5100, Gasmet CX4000, and Axetris LGD F200-A, were validated as alternative methods to the wet-chemistry method (reference method) for measuring ammonia in livestock houses. High correlations (r>0.99) were found between the analysers and the reference method. The measurement errors of the tested analysers were below 2 ppm(v)or 10%. Equivalence to the wet-chemistry method was demonstrated for the INNOVA and Rosemount analysers without a recalibration and for the Picarro and Axetris analysers with a recalibration. The Gasmet analyser was seemingly subjected to an interference from carbon-dioxide and, after compensating for the cross-sensitivity, the equivalence to the wet-chemistry method could also be demonstrated. Calibration curves that were based on a certified gas cylinder were inconsistent with that based on wet-chemistry measurements, which suggested that field calibration might be necessary for optimal measurement accuracy.

Ammonia (NH3) emission is one of the major environmental issues in livestock farming. Gas measurements are required to study the emission process, to establish emission factors, and to assess the efficiency of emission reduction techniques. However, the current methods for acquiring reference measurements of NH3 are either high in cost or labor intensive. In this study, a cost-effective ammonia monitoring system (AMS) was constructed from a commercially-available gas analyzing module based on tunable diode laser absorption (TDLA) spectroscopy. To cope with the negative measurement biases caused by differing inlet pressures, a set of correction equations was formulated. Field validation of the AMS on NH3 measurement was conducted in a fattening pig barn, where the system was compared to a Fourier-transform infrared (FTIR) spectroscopy analyzer. Under two test conditions in a fattening pig barn, the absolute error of the AMS measurements with respect to the average obtained values between the AMS and the FTIR was respectively 0.66 and 0.08 ppm(v), corresponding to 5.9% and 0.5% relative error. Potential sources of the measurement uncertainties in both the AMS and FTIR were discussed. The test results demonstrated that the AMS was capable of performing high-quality measurement with sub-ppm accuracy, making it a promising cost-effective tool for establishing NH3 emission factors and studying NH3 emission processes in pig houses.

Accelerometers (neck- and leg-mounted) and ultra-wide band (UWB) indoor localization sensors were combined for the detection of calving and estrus in dairy cattle. In total, 13 pregnant cows and 12 cows with successful insemination were used in this study. Data were collected two weeks before and two weeks after delivery for calving. Similarly, data were collected two weeks before and two weeks after artificial insemination (AI) for estrus. Different cow variables were extracted from the raw data (e.g., lying time, number of steps, ruminating time, travelled distance) and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three) for both calving and estrus. Moreover, the detection performance within different time intervals (24 h, 12 h, 8 h, 4 h, and 2 h) before calving and AI was investigated. In general, for both calving and estrus, the performance of the detection within 2-4 h was lower than for 8 h24 h. However, the use of a combination of sensors increased the performance for all investigated detection time intervals. For calving, similar results were obtained for the detection within 24 h, 12 h, and 8 h. When one sensor was used for calving detection within 24-8 h, the localization sensor performed best (Precision (Pr) 73-77%, Sensitivity (Se) 57-58%, Area under curve (AUC) 90-91%), followed by the leg-mounted accelerometer (Pr 67-77%, Se 54-55%, AUC = 88-90%) and the neck-mounted accelerometer (Pr 50-53%, Se 47-48%, AUC = 86-88%). As for calving, the results of estrus were similar for the time intervals 24 h-8 h. In this case, similar results were obtained when using any of the three sensors separately as when combining a neck- and a leg-mounted accelerometers (Pr 86-89%, Se 73-77%). For both calving and estrus, the performance improved when localization was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (73-91%). Finally, for the detection with one sensor within a time interval of 4 h or 2 h, the Pr and Se decreased to 55-65% and 42-62% for estrus and to 40-63% and 33-40% for calving. However, the combination of localization with either leg or neck-mounted accelerometer as well as the combination of the three sensors improved the Pr and Se compared to one sensor (Pr 72-87%, Se 63-85%). This study demonstrates the potential of combining different sensors in order to develop a multi-functional monitoring system for dairy cattle.

The study aims to develop a test platform (TP) compartment that could mimic diurnal variations in indoor climate and NH 3 emission in a real pig compartment and to compare diurnal indoor climate and NH 3 production between two TP compartments and a real compartment. The objectives were achieved by using a real and TP compartment followed by an ad hoc test that used two TP compartments and another real compartment. The TP had two compartments equipped with mock-up pigs as heat source and automatic urea solution spraying installation to mimic pig urination at the pen floor. The study evaluated indoor climate and NH 3 production in a 4-day comparative test between a TP and a real compartment followed by a 3-day comparative test between two TP compartments and a real compartment where exhaust and slurry pit NH 3 concentrations, ventilation rate, indoor temperature and relative humidity were simultaneously measured. The TP reproduced comparable diurnal measured parameters in the real compartment. The TP compartment overestimated NH 3 emissions in the real compartment by 23% (R 2 = 0.27) in the first experiment. In the second experiment, the two TP compartments overestimated NH 3 emissions in the real compartment by 38% (R 2 = 0.36) and 44% (R 2 = 0.37). The overestimated NH 3 emission in the TP was probably due to differences in urea solution vs. pig urine chemistry and floor fouling characteristics. The two TP compartments when compared showed similar diurnal trends in NH 3 concentration and emission rate with hourly averages of 11.5 ± 4.1 vs. 11.6 ± 2.8 ppm and 13.2 ± 3.0 vs. 12.1 ± 2.9 g/h, respectively. The study shows the TP could simulate indoor climate dynamics and NH 3 emissions trends in a real compartment and therefore could be used to study NH 3 volatilization processes and emission reduction techniques studies on relative emission basis.

In this Letter, for the first time, the in-to-out-body path loss between an antenna placed inside the cows' rumen and a distant gateway was characterised at 433 MHz. Measurements were conducted on seven different fistulated cows using a signal generator and a spectrum analyser. Subsequent measurement of the antenna in free space was used to quantify the path loss increase due to the cow body. Results have shown an increase of the path loss by 45.5 dB on average (all cows), with a variation between 39.7 and 51.1 dB. Also, the measured path loss values as a function of the transmitter-receiver distance in a dairy barn were well fitted by a log-normal path loss model. The obtained models were used to calculate the range of a LoRa (Long range) based network. Ranges up to 100 m were obtained depending on the used transmit power and bit rate.

In this letter, for the first time, the in-to-out-body path loss between an antenna placed inside the cows’ rumen and a distant gateway was characterized at 433 MHz. Measurements were conducted on seven different fistulated cows using a signal generator and a spectrum analyser. A subsequent measurement of the antenna in free space was used to quantify the path loss increase due to the cow body. Results have shown an increase of the path loss by 45.5 dB on average (all cows), with a variation between 39.7 dB and 51.1 dB. In addition, the measured path loss values as a function of the transmitter-receiver distance in a dairy barn were well fitted by a log-normal path loss model. The obtained models were used to calculate the range of a LoRa (Long range) based network. Ranges up to 100 meters were obtained depending on the used transmit power and bit rate.

A new simple decision-tree (DT) algorithm was developed using the data from a neck-mounted accelerometer for real-time classification of feeding and ruminating behaviours of dairy cows. The performance of the DT was compared to that of a support vector machine (SVM) algorithm and a RumiWatch noseband sensor and the effect of decreasing the sampling rate of the accelerometer on the classification accuracy of the developed algorithms was investigated. Ten multiparous dairy cows were used in this study. Each cow was fitted with a RumiWatch halter and an accelerometer attached to the cow's collar with both sensors programmed to log data at 10 Hz. Direct observations of the cows' behaviours were used as reference (baseline data). Results indicate that the two sensors have similar classification performances for the considered behavioural categories (i.e., feeding, ruminating, other activity), with an overall accuracy of 93% for the accelerometer with SVM, 90% for the accelerometer with DT, and 91% for the Rumiwatch sensor. The difference between the predicted and the observed ruminating time (in min/h) was less than 1 min. h (1.5% of the observed time) for the SVM and less than 2 min. h (2.8%) for both DT and the RumiWatch. Similarly, the difference in feeding time was 1.3 min. h (2.1%) for the SVM compared to 2.5 min. h (4.3%) and 2.4 min. h (4.1%) for both RumiWatch and DT, respectively. These preliminary findings illustrate the potential of the collar-mounted accelerometer to classify feeding and ruminating behaviours with accuracy measures comparable to the Rumiwatch noseband sensor.

Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6 h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1 Hz to 0.05 Hz.

In this paper, we assessed the exposure of a cow to the electromagnetic fields (EMFs) induced by a wireless power transfer (WPT) system working at 92 kHz in a dairy barn. Cow exposure to the radiated EMFs was evaluated and compared to safety guidelines. We modeled a realistic WPT system for dairy cows in Sim4Life, a 3D electromagnetic simulation tool. We validated the model with electric field measurements; simulated fields deviated on average 6% from measured fields. We used the proposed WPT model to evaluate the stimulation and thermal effects based on the internal electric field and the specific absorption rate (SAR), respectively. Results showed that the exposure mainly varied with the distance of the transmitter to the body: variation of 5 dB of the induced electric field when the transmitter was set at 20 cm and 10 cm from the body. The distance of the receiver to the body influenced the exposure less (10%). We also compared the exposure with the limits provided by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). The internal electric fields were more conservative than SAR, which showed values far below exposure limits.

The aim of this study was to compare the impact of 3 ventilation set-point temperatures (T-set = 21, 23 & 25 degrees C) on indoor climate and NH3 emission, using 2 pig compartments with underfloor air distribution (UFAD) and equipped with artificial pigs. The artificial pigs consisted of mock-up pigs to simulate heat production and a spraying installation to mimic pig urination by applying urea solution onto the fully slatted pen floors in the test compartments. The study identified ground channel temperature (T-GC) as a key factor affecting NH3 emission (P < 0.001). T-GC also interacted with Tset (P < 0.001) in effects on NH3 emission. When the reference Tset increased from 23 degrees C to 25 degrees C, NH3 emission decreased by 43% at T-GC 15 degrees C and 29% at T-GC 18 degrees C, due to the relative reduction in ventilation rate (VR). At T-GC of 22 degrees C, NH3 emission did not differ between T-set (23 degrees C) and T-set (25 degrees C). However, when the reference T-set decreased from 23 degrees C to 21 degrees C, NH3 emission did not differ between the 2 set-point temperatures at T-GC of 15, 18 and 22 degrees C. Further tests under practical conditions are needed to confirm these findings and to check for the impact of T-set on pig performance and carcass quality. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.