Linear description (LD) of conformation traits was introduced in horse breeding to minimise subjectivity in scoring. However, recent studies have shown that LD traits show essentially the same problems as traditionally scored traits, such as data converging around the mean value with very small standard deviations. To improve the assessment of conformation traits of horses, we investigated the application of the recently described horse shape space model based upon 403 digitised photographs of 243 Franches-Montagnes (FM) stallions and extracted joint angles based on specific landmark triplets. Repeatability, reproducibility and consistency of the resulting shape data and joint angles were assessed with Procrustes ANOVA (Rep) and intra-class correlation coefficients (ICC). Furthermore, we developed a subjective score to classify the posture of the horses on each photograph. We derived relative warp scores (PCs) based upon the digitised photos conducting a principal component analysis (PCA). The PCs of the shapes and joint angles were compared to the posture scores and to the linear description data using linear mixed effect models including significant posture scores as random factors. The digitisation process was highly repeatable and reproducible for the shape (Rep = 0.72–0.99, ICC = 0.99). The consistency of the shape was limited by the age and posture (p < 0.05). The angle measurements were highly repeatable within one digitiser. Between digitisers, we found a higher variability of ICC values (ICC = 0.054–0.92), indicating digitising error in specific landmarks (e.g. shoulder point). The posture scores were highly repeatable (Fleiss’ kappa = 0.713–0.857). We identified significant associations (p(X2) < 0.05) with traits describing the withers height, shoulder length and incline, overall leg conformation, walk and trot step length. The horse shape data and angles provide additional information to explore the morphology of horses and therefore can be applied to improve the knowledge of the genetic architecture of LD traits.

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.

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The evaluation of conformation traits is an important part of selection for breeding stallions and mares. Some of these judged conformation traits involve joint angles that are associated with performance, health, and longevity. To improve our understanding of the genetic background of joint angles in horses, we have objectively measured the angles of the poll, elbow, carpal, fetlock (front and hind), hip, stifle, and hock joints based on one photograph of each of the 300 Franches-Montagnes (FM) and 224 Lipizzan (LIP) horses. After quality control, genome-wide association studies (GWASs) for these traits were performed on 495 horses, using 374,070 genome-wide single nucleotide polymorphisms (SNPs) in a mixed-effect model. We identified two significant quantitative trait loci (QTL) for the poll angle on ECA28 (p = 1.36 × 10−7), 50 kb downstream of the ALX1 gene, involved in cranial morphology, and for the elbow joint on ECA29 (p = 1.69 × 10−7), 49 kb downstream of the RSU1 gene, and 75 kb upstream of the PTER gene. Both genes are associated with bone mineral density in humans. Furthermore, we identified other suggestive QTL associated with the stifle joint on ECA8 (p = 3.10 × 10−7); the poll on ECA1 (p = 6.83 × 10−7); the fetlock joint of the hind limb on ECA27 (p = 5.42 × 10−7); and the carpal joint angle on ECA3 (p = 6.24 × 10−7), ECA4 (p = 6.07 × 10−7), and ECA7 (p = 8.83 × 10−7). The application of angular measurements in genetic studies may increase our understanding of the underlying genetic effects of important traits in equine breeding.

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

Conformation traits such as joint angles are important selection criteria in equine breeding, but mainly consist of subjective evaluation scores given by breeding judges, showing limited variation. The horse shape space model extracts shape data from 246 landmarks (LM) and objective joint angle measurements from triplets of LM on standardized horse photographs. The heritability was estimated for 10 joint angles (seven were measured twice with different LM placements), and relative warp components of the whole shape, in 608 Franches-Montagnes (FM) horses (480 stallions, 68 mares and 60 geldings born 1940–2018, 3–25 years old). The pedigree data comprised 6986 horses. Genetic variances and covariances were estimated by restricted maximum likelihood model (REML), including the fixed effects birth year, age (linear and quadratic), height at withers (linear and quadratic), as well as postural effects (head, neck, limb position and body alignment), together with a random additive genetic animal component and the residual effect. Estimated heritability varied from 0.08 (stifle joint) to 0.37 (poll). For the shape, the type was most heritable (0.36 to 0.37) and evolved from heavy to light over time. Image-based phenotyping can improve the selection of horses for conformation traits with moderate heritability (e.g., poll, shoulder and fetlock).

In order to improve the housing conditions of stallions in individual boxes, we tested a so-called “social box” allowing increased physical contact between neighbouring horses. This study investigated whether housing stallions in social boxes changes the number of social interactions during carriage driving. We hypothesised that the stay in social boxes would decrease the number of unwanted social interactions between stallions when driven in pairs. Eight Franches-Montagnes breeding stallions were observed when driven in pairs with a “neutral” stallion housed in a so-called “conventional box”, strongly limiting physical contact. They were driven on a standardised route over the course of four days before, during, and after being housed in social boxes. The type and frequency of behaviours of the pairs and the interventions of the groom and the driver during the test drives were assessed live and using video recordings. Results from linear mixed-effect models show that unwanted social interactions decreased during and after the stallions were housed in the social box (p < 0.001). Stallions’ interactions also decreased over the four days (p < 0.01), suggesting a habituation to the test conditions by learning not to interact, or by subtly settling dominance. The social box tended to decrease unwanted social behaviours of stallions driven in pairs and could therefore be used as an environmental enrichment for horses.

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