Using classification trees to detect induced sow lameness with a transient model

A workflow for automatic, high precision livestock diagnostic screening of locomotor kinematics

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g., body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (Sus domesticus) were filmed from lateral perspective during their first 10 h of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy was validated for strides from piglets of normal birth weight (i.e., the category it was trained on), but the models infer the body mass and size of low birth weight (LBW) piglets (which were left out of training, out-of-sample inference) to be "normal." The age of some (but not all) low birth weight individuals was underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time.

Measure and characterization of lameness in gestating sows using force plate, kinematic, and accelerometer methods

The objective was to assess sows' lameness by measuring weight distribution on limbs using a force plate made up of 4 individual platforms each resting on 4 single-ended beam load cells. The weight was recorded at an average rate of 14 readings per s over a 15 min period. Ten sows (5 lame sows and 5 sound sows) were weighed twice on 2 different days to assess the repeatability of the measure. Sixty-one sows were then selected in 2 different sites and visually scored for lameness, using a 3-point scoring system (0=normal gait; 1=abnormal gait, and/or stiffness; and 2=shortened stride, and/or the sow puts less weight or avoids putting weight on 1 leg). Various measures were recorded from each sow using the force plate (percentage of weight, the ratio between the weights applied by contralateral legs, weight shifting, and amplitude of weight bearing and weight removing), kinematics (speed, stride length, swing time, stance time, foot height, and carpal and tarsal joints angle average and amplitude), and accelerometers (time spent standing during 24 h, frequency of stepping behavior during feeding, and latency to lie down after feed delivery). The within-sow CV for each measure taken from the force plate were lower than 15%, which reflects a good repeatability. Among force plate measures, only the weight shifting frequency and the ratio between the weights applied by contralateral legs differed between lameness scores. Sows that scored 2 had a higher frequency of weight shifting for fore legs (P=0.0003) and hind legs (P=0.0007) than sows scored 0 and 1. The ratio between the weights applied by contralateral legs decreased with the increase of lameness score for the hind limbs (P=0.014). However, these measures also differed between sites (P<0.01). These differences may be due to various reasons, including but not limited to genetics and housing systems. Nevertheless, the results suggest that force plate measures such as the asymmetry in the weight applied between a pair of legs and weight shifting are good indicators of lameness. Multivariate analysis on fore and hind legs showed independency between variables related to animals in movement (measures from kinematics) and variables related to static animals (measures from the force plate and accelerometers). Therefore, both static and dynamic methods need to be used to detect various lame sows.