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

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. • Wearable sensors and AI effectively tracked behaviour for analysis of daily patterns • Less resting and more active behaviour was expressed in group than in individual housing • Eating behaviour was expressed more during summer than winter • Compared with individual stables, group housing led to stronger activity synchronisation • Individually stabled horses were more active in the daily offered sand paddock

Authors

Institutions

Publication Details

Journal
Applied Animal Behaviour Science
Published
2025-11-27
DOI
https://doi.org/10.1016/j.applanim.2025.106873
Primary Topic
Veterinary Equine Medical Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Katharina Kirsch, Saskia Strutzke, Lara Klitzing, Gundula Hoffmann et al.
Applied Animal Behaviour Science
Veterinary Equine Medical Research
article

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

Katharina Kirsch, Saskia Strutzke, Lara Klitzing, Gundula Hoffmann, F. Eichler, F. Pilger, Christa Thöne‐Reineke, L. Kroschel
article en

Abstract

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. • Wearable sensors and AI effectively tracked behaviour for analysis of daily patterns • Less resting and more active behaviour was expressed in group than in individual housing • Eating behaviour was expressed more during summer than winter • Compared with individual stables, group housing led to stronger activity synchronisation • Individually stabled horses were more active in the daily offered sand paddock

Applied Animal Behaviour ScienceVol. 294
Animal Welfare Institute (US), Leibniz Institute for Agricultural Engineering and Bioeconomy (DE)
Openalex Percentile: Top 51%
Veterinary Equine Medical Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.