Publikationsdatenbank

Sensor-based analysis of behaviour time budgets in young warmblood stallions entering initial preparation for licensing (2026)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Eichler, Fabienne

Klitzing, Lara (WE 11)

Strutzke, Saskia

Pilger, Franziska

Kroschel, Laura

Thöne-Reineke, Christa (WE 11)

Hoffmann, Gundula

Kirsch, Katharina (WE 11)

Quelle

Applied animal behaviour science

Bandzählung: 294

Seiten: 106873

ISSN: 0168-1591

Sprache

Englisch

Verweise

URL (Volltext): https://linkinghub.elsevier.com/retrieve/pii/S0168159125003727

DOI: 10.1016/j.applanim.2025.106873

Kontakt

Institut für Tierschutz, Tierverhalten und Versuchstierkunde

Königsweg 67
14163 Berlin
+49 30 838 61146
tierschutz@vetmed.fu-berlin.de

Abstract / Zusammenfassung

Activity sensors provide an objective means to track movement and allow for automatic recognition of behavioural patterns. This study validates the use of sensor-based analysis for assessing daily patterns and behavioural differences related to housing system, age, and season at the onset of preparation for licensing of young warmblood stallions. Warmblood stallions (n = 29), aged 2 years (born between March and June), were categorised based on their housing system: indoor group housing (T1-group: n = 9) or individual stables (n = 20). The individually stabled horses were further divided by month at the onset of preparation for licensing (T1-Ind: June (n = 10) vs. T2-Ind: October (n = 10)), resulting in three cohorts of comparable size. Each stallion wore an activity sensor on a collar for 24 coherent hours per week during the first three months of the preparation period (T1: June-August; T2: October-December). Behavioural analysis was conducted using a deep neural network trained to classify distinct individual activities, including eating, resting and active behaviours. The time budgets of these activities per horse were compared between the cohorts. All the horses in the study displayed appropriate time budgets for each behavioural category, spending approximately 50 % of the day eating, followed by 30–38 % resting and 14–18 % being active, which is consistent with typical species patterns. Group-housed stallions presented more active behaviour within the indoor housing system (χ² (4) = 17.1, P < 0.01), likely due to greater space and social interactions. Stallions in group housing also displayed stronger correlations with synchronous eating (χ² (2) = 77.5, P < 0.001), resting (χ² (2) = 68.8, P < 0.001), and active (χ² (2) = 63.8, P < 0.001) behaviours, reflecting improved social cohesion. In contrast, individually stabled horses were more active in the paddock (χ² (4) = 44.6, P < 0.001), and covered greater distances, possibly compensating for reduced movement opportunities and limited social contact in the stable. Stallions trained in summer (T1) expressed more eating (χ² (4) = 25.6, P < 0.001) and less resting (χ² (4) = 28.7, P < 0.001) behaviours compared to winter (T2), likely due to seasonal factors such as daylight duration. In conclusion, housing systems had subtle effects on the expression of activity time budgets. Group housing was associated with greater behavioural synchrony and increased voluntary movement. A shorter daylight duration led to reduced eating and increased resting behaviour. Limitations of this study include the absence of recorded social behaviours, and a fourth cohort of group-housed stallions entering the preparation period in October. Sensor-based activity monitoring has proven to be an effective method for objectively quantifying behavioural time budgets, providing a valuable basis for future studies linking behaviour and welfare indicators.