Evaluation of ruminal motility in cattle by a bolus-type wireless sensor

Monitoring ventral tail base surface temperature for fever detection in calves

In this study, we investigated whether monitoring the ventral tail base surface temperature (ST) using a wearable wireless sensor could be effective for fever detection in calves with experimentally induced pneumonia after inoculation with Histophilus somni strain 2336. We found a significant difference in the changes in ST values between the control and H. somni-inoculated groups after 24 h of inoculation and detected fever; however, the rectal temperature showed a significant difference between the groups after 12 h of inoculation. When a significant difference in the ST between the two groups was observed, serum haptoglobin concentration and exacerbation of clinical score increased in the H. somni-inoculated group compared with those in the control group. Pneumonia was observed in the H. somni-inoculated group at necropsy, indicating that the changes in ST may reflect fever with inflammation caused by H. somni infection. Our results demonstrated that monitoring ST using a sensor attached to the ventral tail base can detect fever in calves and may be a useful and labor-saving tool for the health management of calves.

Evaluation of an indwelling bolus equipped with a triaxial accelerometer for the characterisation of the diurnal pattern of bovine reticuloruminal contractions

This observational study aimed to describe the diurnal pattern of reticuloruminal contraction rate (RRCR) and the proportion of time spent ruminating by cattle, using two commercial devices equipped with triaxial accelerometers: an indwelling bolus (placed in the reticulum) and a neck collar. The three objectives of this study were firstly to determine whether the indwelling bolus provided observations consistent with RRCR as determined by clinical examination using auscultation and ultrasound, secondly to compare estimates of time spent ruminating using the indwelling bolus and a collar-based accelerometer, and finally to describe the diurnal pattern of RRCR using the indwelling bolus data. Six rumen-fistulated, non-lactating Jersey cows were fitted with an indwelling bolus (SmaXtec Animal Care GmbH, Graz, Austria) and a neck collar (Silent Herdsman, Afimilk Ltd. Kibbutz Afikim, Israel), and data were collected over two weeks. Cattle were housed together in a single straw-bedded pen and fed ad libitum hay. To assess the agreement between the indwelling bolus and traditional methods of assessing reticuloruminal contractility in the first week, the RRCR was determined over 10 min, twice a day, by ultrasound and auscultation. Mean inter-contraction intervals (ICI) derived from bolus and ultrasound, and from auscultation were 40.4 ± 4.7, 40.1 ± 4.0 and 38.4 ± 3.3 s. Bland-Altmann plots showed similar performance of the methods with small biases. The Pearson correlation coefficient for the time spent ruminating derived from neck collars and indwelling boluses was 0.72 (highly significant, P < 0.001). The indwelling boluses generated a consistent diurnal pattern for all the cows. In conclusion, a robust relationship was observed between clinical observation and the indwelling boluses for estimation of ICI and, similarly, between the indwelling bolus and neck collar for estimating rumination time. The indwelling boluses showed a clear diurnal pattern for RRCR and time spent ruminating, indicating that they should be useful for assessing reticuloruminal motility.

Evaluation of stirring time through a rumen simulation technique: Influences on rumen fermentation and bacterial community

Introduction

Rumen motility is a key element that influences ruminant nutrition, whereas little is known about the effects of rumen contraction duration on rumen fermentation and ruminal microbiome. We previously reported that proper rotation speed of a rumen simulation technique (RUSITEC) system enhanced rumen fermentation and microbial protein (MCP) production. In the present study, different contraction durations and intervals were simulated by setting different stirring times and intervals of the stirrers in a RUSITEC system. The objective of this trial was to evaluate the influences of stirring time on rumen fermentation characteristics, nutrient degradation, and ruminal bacterial microbiota in vitro.

Methods

This experiment was performed in a 3 × 3 Latin square design, with each experimental period comprising 4 d for adjustment and 3 d for sample collection. Three stirring time treatments were set: the constant stir (CS), the intermittent stir 1 (each stir for 5 min with an interval of 2 min, IS1), and the intermittent stir 2 (each stir for 4 min with an interval of 3 min, IS2).

Results

The total volatile fatty acid (TVFA) concentration, valerate molar proportion, ammonia nitrogen level, MCP density, protozoa count, disappearance rates of dry matter, organic matter, crude protein, neutral detergent fiber, and acid detergent fiber, emissions of total gas and methane, and the richness index Chao 1 for the bacterial community were higher (p < 0.05) in the IS1 when compared to those in the CS. The greatest TVFA, MCP, protozoa count, nutrient disappearance rates, gas productions, and bacterial richness indices of Ace and Chao 1 amongst all treatments were observed in the IS2. The relative abundance of the genus Treponema was enriched (p < 0.05) in CS, while the enrichment (p < 0.05) of Agathobacter ruminis and another two less known bacterial genera were identified in IS2.

Discussion

It could be concluded that the proper reduction in the stirring time might help to enhance the feed fermentation, MCP synthesis, gas production, and the relative abundances of specific bacterial taxa.

Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods

It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels.

A Non-Invasive Sound Technology to Monitor Rumen Contractions

This technical report used a wireless device (CURO MkII) that recorded high-quality rumen sound waves from cows of different production statuses (dry cow vs. lactating cow) and physiological stages (pregnant vs. non-pregnant). Recordings from a dry Jersey heifer fed a diet based on haylage and straw showed a few high-amplitude spikes (3 at 6 dB) but mostly infrequent signals (9 at 12 dB and 22 at 18 dB), with pauses of approx. 2 min with no rumen sounds in between. Analysis of a few individual spikes in the 12 dB range showed that wave frequencies ranged from 230 to 250 Hz and lasted 4 s. Recordings of the high-yielding Red Danish cow fed a total mixed ration (TMR) showed an almost constant frequency of the rumen sounds with considerable amplitude of the waves. Rumen sounds from the Red Danish dry and pregnant cow fed on TMR were less frequent, with a lower amplitude than those from the high-yielding cow. These preliminary results demonstrate that wireless sound recording units are capable of measuring rumen sounds in a production setting and can discern between animals of different production and physiological stages, but more studies are needed to confirm our findings.

Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.

Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal's Left Flank

Simple Summary

This study analyzed the possibility of automatically detecting dairy cow behavior by combining the use of a single triaxial accelerometer applied to the animal’s left flank with a machine learning technique. This combination enabled the detection of posture and the main types of behavior that are extremely useful in evaluating the animal’s welfare and health such as resting, feeding, and rumination with a high degree of accuracy. The novelty of the study was the success in reaching a high accuracy in detecting five different behaviors and the animal posture by using a single sensor and allowing farmers to save money. To the best of our knowledge, this is the first study that has successfully explored the feasibility of locating a sensor on the animal’s left flank, showing the opportunity of automatically measuring some physiological parameters, such as those ones related to respiration and rumen health, in a non-invasive way.

Abstract

The aim of the present study was to develop a model to identify posture and behavior from data collected by a triaxial accelerometer located on the left flank of dairy cows and evaluate its accuracy and precision. Twelve Italian Red-and-White lactating cows were equipped with an accelerometer and observed on average for 136 ± 29 min per cow by two trained operators as a reference. The acceleration data were grouped in time windows of 8 s overlapping by 33.0%, for a total of 35,133 rows. For each row, 32 different features were extracted and used by machine learning algorithms for the classification of posture and behavior. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75.0 and 25.0% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB), and Support Vector Machine. The XGB model showed the best accuracy (0.99) and Cohen’s kappa (0.99) in predicting posture, whereas the Random Forest model had the highest overall accuracy in predicting behaviors (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. Overall, very accurate detection of the posture and resting behavior were achieved.

Pressure measurement in the reticulum to detect different behaviors of healthy cows

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and ‘other’, as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows.

Long-Term Recording of Reticulo-Rumen Myoelectrical Activity in Sheep by a Telemetry Method

The reticulum and rumen are considered a single functional unit (the reticulo-rumen) with regards to myoelectrical and contractile activities. The specialized contractions of the reticulo-rumen provide constant mixing of partially digested material (cycle A), its flow into the omasum during eructation (cycle B), and regurgitation-rumination (cycle C). This study aimed to investigate the feasibility of electromyography (EMG) registered by a long-term telemetry method for assessment of the basic reticulo-rumen myoelectrical activity in sheep, to develop the effective recognition of the reticulo-rumen cycles at rest with no food stimulation, and to investigate the relationship between cycles A, B, and C in such basic conditions. The experiment was carried out on nine ewes. Myoelectric activity of the rumen, reticulum, and abomasum was recorded by the combination of three silver bipolar electrodes and a 3-channel transmitter implant. The myoelectrical activity registered successfully in the reticulum and rumen was determined as three characteristic patterns of cycles A, B, and C. The percentage of each type of cycle changed at different intervals from equally cycles A (43-50%) and B (50-56%), occurring when cycle C was not observed to the domination of cycle C (57-73%) with a decrease of cycles A (6-14%) and B (20-28%). The long-term EMG telemetry registration is feasible in the assessment of the reticulo-rumen myoelectrical activity in sheep.

Evaluation of ruminal motility using an indwelling 3-axis accelerometer in the reticulum in cattle

Attempts to increase production and improve farm environments have been made for several years. Rumen motility (RM) is one of the biological parameters that provides essential information of individuals in ruminants, and it is usually evaluated by auscultation. The study was aimed to examine RM using the 3-axis accelerometer (3XA) in cattle. The manufactured 3XA were placed in the reticulum (3XA-R) and implanted in the subcutaneous layer of the brisket (3XA-SC), respectively, and the accelerations were compared following intramuscular injection of xylazine (0.05 mg/kg) or saline in experiment 1 and of xylazine (0.05 mg/kg) or atropine (0.04 mg/kg) in experiment 2. In experiment 3, the dose-dependent decrease of RM was evaluated following xylazine administration (0, 0.05, 0.1 mg/kg) in the 3XA-R equipped cows via a 3 × 3 Latin square method. In experiment 1, saline-treated animals showed a continuous fluctuation while the frequency and amplitude of 3XA-R in xylazine-injected cows were reduced after administration. The acceleration of 3XA-SC was changed after administration, but not abruptly. Among the motion parameters, V2 was calculated only using X- and Z-axis acceleration in consideration of the cylindrical shape, and it showed the apparent difference between pre- and post-xylazine administration. In experiment 2, the V2 of 3XA-R was decreased after atropine administration while that of 3XA-SC was maintained. In experiment 3, a dose-dependent V2 decrement of 3XA-R after xylazine administration was observed and lasted for 40 and 80 min in doses of 0.05 mg/kg and 0.1 mg/kg, respectively. In conclusion, The 3XA detected the decrease in RM efficiently and processed the data wirelessly without interference from body movement. This technology will help detect problems early and prevent a decline in cattle productivity.

Transforming the Adaptation Physiology of Farm Animals through Sensors

Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.