Automatic detection of lameness in gestating group-housed sows using positioning and acceleration measurements

Monitoring of Non-Lame Horses and Horses with Unilateral Hindlimb Lameness at Rest with the Aid of Accelerometers

The aim of this study was to determine whether horses exhibiting unilateral hindlimb lameness unload (rest) the lame limb more than the contralateral limb. The resting/unloading of the hindlimbs and the time spent lying down were measured using accelerometers. Ten non-lame horses and 20 lame horses were recruited for participation and monitored for 11 h overnight with accelerometers (MSR145, sampling rate: 1 Hz, and measuring range: ±15 g) attached to the lateral metatarsal and metacarpal regions of each limb. Metatarsal and metacarpal orientation were used to determine whether the limb was unloaded (rested) or loaded, respectively, or whether the horses were lying down. The relation of resting time between non-lame and lame limbs (non-lame/lame: 0.85 ± 1.2) of the lame horses differed significantly (p = 0.035) from that of the non-lame horses (right/left: 1.08 ± 0.47). Non-lame horses rested their hindlimbs evenly (left: 15 ± 10%; right: 17 ± 16%). Horses with unilateral hindlimb lameness unloaded the lame limb longer (lame limb: 61.8 ± 25.3%, non-lame limb: 38.2 ± 25.3%) than their contralateral limb. The lame horses (13 ± 11%) lay down longer (p = 0.012) than the non-lame horses (3 ± 6%). The degree of lameness determined by the participating veterinarians (Vet Score) (r = -0.691, p < 0.01) and the asymmetry evaluated by the lameness locator (ALL) (r = -0.426, p = 0.019) correlated with the resting ratio (rest time ratio). Both factors were also correlated with the time spent lying down (Vet Score (r = 0.364, p = 0.048) and the ALL (r = 0.398, p = 0.03)). The ALL and VET Score were significantly correlated (r = 0.557, p = 0.01). The results of this study provide a good baseline for future research into how individual resting patterns may help to detect pain.

Spatial distribution, movement, body damage, and feather condition of laying hens in a multi-tier system

The welfare and health of laying hens in the multitier system raise concern in public. The flock distributions during feeding time at 51 and 89 wk were studied in a multitier system. Furthermore, the ultra-high frequency radio frequency identification (UHF RFID) equipment was used to identify the transition between tiers and time spent in each tier of 48 focal hens (12 hens from each tier-group of the multitier system) at 92 wk of age. The body weight, tibia size (length and width), body damage (comb and rear part), and feather condition (neck, breast, back, tail, cloaca, and wings) of focal hens from different tier-groups were further compared. The results showed that the spatial distribution in flocks changed from top to bottom with increasing age. The hens at 51 wk of age were mainly distributed in the 4th tier (19.6 ± 5.0% in 1st tier, 9.6 ± 1.1% in 2nd tier, 23.6 ± 2.9% in 3rd tier and 47.3 ± 2.6% in 4th tier), and hens at 89 wk of age were mainly distributed in the lower tiers (33.5 ± 1.5% in 1st tier, 31.9 ± 5.1% in 2nd tier, 15.7 ± 3.4% in 3rd tier and 16.6 ± 3.1% in 4th tier). The spatial distribution of hens at 89 wk of age was more even than that at 51 wk of age. At 92 wk of age, the proportion of time spent in original tier of 4 tier-groups was 91.0 ± 5.7%, 51.9 ± 5.7%, 59.0 ± 7.0% and 63.0 ± 6.7%, respectively. Focal hens preferred to stay in the original tier and spent significantly less time in other tiers (P < 0.05). There was no significant difference in body weight, body damage score, tibia width and partial feather scores (neck, breast, tail, and cloaca) of focal hens among 4 tier-groups (P > 0.05). However, focal hens from 1st tier had worse feather scores on wings and back, and shorter tibia length compared to other tiers suggesting that there were more lower ranking birds that located in lower tier to avoid competition, but had equal access to resource, which is good for their welfare and health. In summary, the overcrowding situation was improved near the end of the laying cycle in the multitier system, thereby mitigating the potential negative effects to the lower ranking hens and maintain a satisfactory level of welfare and health for laying hens near the end of the laying cycle.

Evaluating the Activity of Pigs with Radio-Frequency Identification and Virtual Walking Distances

Monitoring the activity of animals can help with assessing their health status. We monitored the walking activity of fattening pigs using a UHF-RFID system. Four hundred fattening pigs with UHF-RFID ear tags were recorded by RFID antennas at the troughs, playing devices and drinkers during the fattening period. A minimum walking distance, or virtual walking distance, was determined for each pig per day by calculating the distances between two consecutive reading areas. This automatically calculated value was used as an activity measure and not only showed differences between the pigs but also between different fattening stages. The longer the fattening periods lasted, the less walking activity was detected. The virtual walking distance ranged between 281 m on average in the first fattening stage and about 141 m in the last fattening stage in a restricted environment. The findings are similar to other studies considering walking distances of fattening pigs, but are far less labor-intensive and time-consuming than direct observations.

The Use of Animal's Body, Scrotal Temperature and Motion Monitoring in Evaluating Boar Semen Production Capacity

Biomedical measurements by specialized technological equipment have been used in farm animals to collect information about nutrition, behavior and welfare. This study investigates the relation of semen quality (CASA analysis, viability, morphology, membrane biochemical activity and DNA fragmentation) with boar behavior during ejaculation. Sensors were placed on the boar’s body. Movement features were collected using an inertial measurement unit (IMU), comprising an accelerometer, a gyroscope and a magnetometer. Boar, scrotal and dummy temperatures were measured by an infrared (IR) camera and an IR thermometer, while the face salivation of the boar was recorded by a moisture meter (also based on IR technology). All signals and images were logged on a mobile device (smartphone or tablet) using a Bluetooth connection and then transferred wirelessly to the cloud. The data files were then processed using scripts in MATLAB 2021a (MathWorks, Natick, Massachusetts) to derive the necessary indices. Ninety-four ejaculates from five boars were analyzed in this study. The statistical analysis was performed in the Statistics and Machine Learning Toolbox of MATLAB 2021a using a linear mixed effects model. Significant and strong negative correlations (R2 > 0.5, p ≤ 0.05) were observed between boar, dummy and scrotal temperature with the progressive, rapid and slow movement of spermatozoa, VCL (curvilinear velocity), VSL (straight line velocity) and ALH (amplitude of lateral head displacement) kinematics. The volume of the ejaculate was correlated with the scrotal and dummy temperature. Dummy’s temperature was negatively correlated with BCF (beat/cross-frequency), viability and total time of ejaculation, while it was positively correlated with abnormal morphology. Body temperature was negatively correlated with BCF. Positive correlations were noticed between VAP (average path velocity) and total time of ejaculation with body acceleration features, as well as between the overall dynamic body acceleration (ODBA) and total time of ejaculation. In conclusion, the use of biomedical sensors can support the evaluation of boar sperm production capacity, providing valuable information about semen quality.

Comparative effects of nonsteroidal anti-inflammatory drugs at castration and tail-docking in neonatal piglets

This study assessed the efficacy of meloxicam, flunixin, and ketoprofen in piglets undergoing routine castration and tail-docking. Six-day-old male piglets (8/group) received one of five randomized treatments: intramuscular saline (SAL PROC), meloxicam (MEL; 0.4 mg/kg), flunixin (FLU; 2.2 mg/kg), ketoprofen (KETO; 3.0 mg/kg) or sham (SAL SHAM; saline injection, no processing). Two hours post-dose, piglets were castrated and tail-docked. Plasma cortisol, interstitial fluid (ISF) prostaglandin E2 (PGE2) and activity levels via Actical® monitoring were used to estimate pain. SAL SHAM and FLU exhibited lower cortisol concentrations than SAL PROC at the time of processing (p = 0.003 and p = 0.049, respectively), and all NSAIDs exhibited lower PGE2 than SAL PROC at 3.69 hours (MEL p = 0.050; FLU p = 0.043 and KETO p = 0.031). While not statistically significant, PGE2 was higher in SAL PROC piglets vs. other treatment groups at most time points. There was also a high degree of variability between piglets, especially for SAL PROC. Activity levels were significantly decreased at multiple time points in SAL PROC and MEL piglets following processing. However, FLU and KETO piglets had increased activity levels closer to that of the SAL SHAM group, suggesting that these NSAIDs are more effective than MEL in providing analgesia. These results demonstrate that management strategies including administration of intramuscular flunixin or ketoprofen to reduce pain associated with processing will likely improve piglet health and welfare in the United States.

Accelerometer systems as tools for health and welfare assessment in cattle and pigs - A review

Welfare assessment has traditionally been performed by direct observation by humans, providing information at only selected points in time. Recently, this assessment method has been questioned, as 'Precision Livestock Farming' technologies may be able to deliver more valid, reliable and feasible real-time data at the individual level and serve as early monitoring systems for animal welfare. The aim of this paper is to describe how accelerometers can be used for welfare assessment based on the principles of the Welfare Quality assessment protocol. Algorithm development is based mainly on the detection of behavioural traits. So far, high accuracies have been found for movement and resting behaviours in cows and pigs, while algorithm development for feeding and drinking behaviours in pigs lag behind progress in cows where valid algorithms are already available. Welfare studies have used accelerometer technology to address the effects on behaviour of diet, daily cycle, enrichment, housing, social mixing, oestrus, lameness and disease. Additional aspects to consider before a decision is made upon its use in research and in practical applications include battery life and sensor location. While accelerometer systems for cows are already being used by farmers, application in pigs has mainly remained at the research level.

Symposium review: Precision technologies for dairy calves and management applications

There is an increasing interest in using precision dairy technologies (PDT) to monitor real-time animal behavior and physiology in livestock systems around the world. Although PDT in adult cattle is extensively reviewed, PDT use for the management of preweaned dairy calves has not been reviewed. We systematically reviewed research on the use and application of precision technologies in calves. Accelerometers have the potential to be used to monitor lying behavior, step activity, and rumination, which are useful to detect changes in behavior that may be indicative of disease, responses to painful procedures, or positive welfare behaviors such as play. Automated calf feeding systems can control delivery of nutritional plans to individualize feeding and weaning of calves; changes in feeding behaviors (such as milk intake, drinking speed, and unrewarded visits) may also be used to identify early onset of disease. The PDT devices also measure physiological and physical attributes in dairy calves. For instance, temperature monitoring devices such as infrared thermography, ruminal boluses, and implanted microchips have been assessed in calves, but no herd management-based commercial system is available. Many other PDT are in development with potential to be used in dairy calf management, such as image and acoustic-based monitoring, real-time location, and use of enrichment items for monitoring positive emotional states. We conclude that PDT have great potential for application in dairy calf management, enabling precise behavioral and physiological monitoring, targeted feeding programs, and identification of calves with poor health or behavioral impairments. We strongly encourage further development and validation of commercially available technologies for on-farm application of the monitoring of dairy calf welfare, performance, and health.

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

Simple Summary

The increasing implementation of technological advances originally developed for video gaming (PlayStation, Xbox) is helping to progress livestock production so that it is both more efficient and more focused on the welfare of the animals. Such advances are necessary to ensure that innovations can emerge from applications using cameras, microphones and sensors to enhance the farmers’ eyes, ears and nose in everyday farming. This technology for remote monitoring of livestock, termed precision livestock farming, is the ability to automatically track individual livestock in real time. The goal of this review is to apprise swine veterinarians and their clientele on precision livestock farming with a general introduction to the technology available, a review of research and commercially available technology and the implications and opportunities for swine practitioners and farmers. Drawing from pig welfare criteria in the Common Swine Industry Audit, this review explains how these applications can be used to improve swine welfare within current pork production stakeholder expectations. Swine veterinarians and specialists, by virtue of their animal advocacy role, interpretation of benchmarking data, and stewardship in regulatory and commodity programs, can play a broader role in facilitating the transfer of precision livestock farming and technology to their clients.

Abstract

The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients.

Detecting lameness in sows from ear tag-sampled acceleration data using wavelets

The objective of this study was to develop an automated monitoring system to detect lameness in group-housed sows early and reliably on the basis of acceleration data sampled from ear tags. To this end, acceleration data from ear tags were acquired from an experimental system deployed at the Futterkamp Agriculture Research Farm from May 2012 until November 2013. The developed method performs a wavelet transform for each individual sow's time series of total acceleration. Feature series are then computed by locally estimating the energy, variation and variance in a small moving window. These feature series are then further decomposed into uniform level sets. From these series of level sets, the highest and lowest levels are monitored for lameness detection. To that end, they are split into a past record to serve as reference data representing a sow's expected behaviour. The deviations between the reference and the remaining detection part of current data, termed feature activated, were then utilised to possibly indicate a lameness condition. The method was applied to a sample of 14 sows, seven of which were diagnosed as lame by a veterinarian on the last day of the sampling period of 14 days each. A prediction part of 3 days was set. Feature activated were clearly separable for the lame and healthy group with means of 8.8 and 0.8, respectively. The day-wise means were 1.93, 9.47 and 15.16 for the lame group and 0.02, 1.13 and 1.44 for the healthy group. A threshold could be set to completely avoid false positives while successfully classifying six lame sows on at least one of the 2 last days. The accuracy values for this threshold were 0.57, 0.71 and 0.78 when restricting to data from the particular day. A threshold that maximised the accuracy achieved values of 0.57, 0.79 and 0.93. Thus, the method presented seems powerful enough to suggest that an individual classification from ear tag-sampled acceleration data into lame and healthy is feasible without previous knowledge of the health status, but has to be validated by using a larger data set.