The domestication of the horse began about 5500 years ago in the Eurasian steppes. In the following millennia horses spread across the ancient world, and their role in transportation and warfare affected every ancient culture. Ownership of horses became an indicator of wealth and social status. The importance of horses led to a growing interest in their breeding and management. Many phenotypic traits, such as height, behavior, and speed potential, have been proven to be a subject of selection; however, the details of ancient breeding practices remain mostly unknown. From the fourth millennium BP, through the Iron Age, many literature sources thoroughly describe horse training systems, as well as various aspects of husbandry, many of which are still in use today. The striking resemblance of ancient and modern equine practices leaves us wondering how much was accomplished through four thousand years of horse breeding.

The Polish draft horse (PDH) breed is a result of crossing local mares with imported cold-blooded stallions, such as Belgians, Ardennes, Fjords, and others. A part of the broodmare stock investigated in this study was also imported from various countries, such as Denmark. In this study, we investigate the genetic composition of the PDH by analyzing the whole mitochondrial d-loop variability and comparing it to previously demonstrated whole d-loop sequences of other cold-blooded breeds: Ardennais, Belgian, Breton, Clydesdale, Noriker, Norwegian Fjord, Percheron, and Suffolk. Our results show high nucleotide diversity within the PDH population (π = 0.011), and the existence of two main haplogroups: one of relatively concise origin, with strong kinship to the Belgian breed, and the second showing close relation to the majority of other analyzed cold-blooded breeds. Some of the PDH maternal strains clustered separately, which can be a result of the influence of other unidentified breeds that served as a foundation stock for the present population. This present study explains the genetic relationship of the PDH to other cold-blooded breeds and indicates the high genetic diversity of the breed.

With a history tracing back to at least the 18th century and a substantial global influence on various breeds, Polish Arabian horse population is of paramount importance for both breeders and conservationists. However, its genetic makeup and the population dynamics are still not well understood. This study presents an analysis of the modern Polish Arabian horse population using pedigree data, focusing on the breed's genetic diversity and population structure. Our analysis encompassed 1 498 individuals defined as the reference population (RP) and their 11 065 ancestors, which resulted in the dataset of 12 254 individuals (total population). We traced their genealogy to assess inbreeding coefficients (F), founder effects, and genetic variability measures such as the effective number of founders (f_e), ancestors (f_a), or founder genome equivalents (f_ge). The results indicated a good pedigree quality with an average of 28.1 maximum traced generations, revealing high pedigree completeness for initial generations with a decline beyond the seventh generation. The genetic diversity parameters showed a considerable bottleneck effect, with an effective number of founders at 73, which reflects a substantial loss of genetic diversity over time. Despite the vast total number of founders (852), only a few have had a lasting impact on the current population, signaling the need for revised breeding strategies to maintain diversity. The study identified a slight but consistent rise in inbreeding over the last century, with a marginal recent decline, and a significant difference in the contribution of various founders. The average F was 5.8%, with 99.6% of the reference population being inbred. The analysis of effective population size (N_e) highlighted potential risks for genetic diversity, urging for revision of breeding goals to consider a wider array of founder lineages. The study indicated that stallions belonging to RP can be attributed to 15 distinct sirelines, whereas mares to 45 unique damlines, more than considered in the current breeding program (8 and 15, respectively). Conclusively, the study underlines the need for ongoing monitoring and strategic breeding to maintain and enhance the genetic diversity of Polish Arabians, considering the breed's historical significance and contemporary genetic challenges.

Horses have been used and bred for centuries for their movements. However, specific breeds are expected to have different movement capabilities. We have measured 425 horses from four different breeds at walk and trot on a straight line using an inertial measurement unit (IMU) system (EquiMoves®). This article describes how the data was collected, filtered and analysed to provide a useable dataset of 28 movement variables. It provides a full protocol for field measurements and requirements for adequate trials for analysis. Intra-class correlation coefficient estimates are also provided to assess repeatability of the measurements.

DeepLabCut has transformed behavioral and locomotor research by enabling markerless pose estimation through deep learning. Despite its broad adoption across species and behaviors, quantitative kinematic analyses remained limited by noisy outputs and the computational expertise required for refinement. To address this issue, we introduce refineDLC, a comprehensive post-processing pipeline that streamlines the conversion of noisy DeepLabCut outputs into robust, analytically reliable kinematic data. The pipeline incorporates essential cleaning steps, including inversion of the y-coordinates for intuitive spatial interpretation, removal of zero-value frames, and exclusion of irrelevant body part labels. It further applies dual-stage filtering based on likelihood scores and positional changes, enhancing data accuracy and consistency. Multiple interpolation strategies manage missing values while maintaining data continuity and integrity. We evaluated refineDLC using two datasets: controlled locomotion in cattle and field-recorded trotting horses. Across both contexts, the pipeline substantially improved data quality and interpretability, reducing variability, eliminating false-positive labeling errors, and transforming noisy trajectories into physiologically meaningful kinematic patterns. Outputs were reliable and analysis-ready regardless of recording conditions or species. By simplifying the transformation from raw DeepLabCut outputs to meaningful kinematic insights, refineDLC expands accessibility for researchers, particularly those with limited programming expertise, enabling precise quantitative analyses at scale. Future developments may incorporate adaptive filtering algorithms and real-time quality assessments, further optimizing performance and automation. These enhancements will extend the pipeline’s applicability to precision phenotyping, behavioral ecology, animal science, and conservation biology.