Continuous welfare assessment of dairy cows at individual level: A farmer-oriented tool based on normal daily ranges of sensor-recorded traits

Animal welfare on farms is currently assessed using human-evaluation protocols, which provide a single record of herd condition at a specific moment. Integrating sensor-based data and farm records, continuous information on each animal's welfare can be obtained. This study aims to create an algorithm to assess individual dairy cow welfare, contributing to the goal of building a platform to inform producers and consumers about dairy cattle welfare. It was built based on the Five Domains model of animal welfare. 221 cows from four commercial free-stall barn farms in Spain and Italy were fitted with accelerometry collars and rumen boluses and monitored for 92 days. Individual data were collected daily. Accelerometers recorded time spent ruminating, eating, lying, walking, and standing within a 24-h interval. Boluses recorded rumen pH and temperature every 10 min, averaged over 24 h. Farm records included parity, veterinary treatments, and milk conductivity. The model provides a daily global welfare index per cow, categorised into health, nutrition, and environment scores. Behaviour and mental state were not included due to a lack of relevant sensor data. Scores range from 0 to 10, indicating the likelihood of the cow experiencing welfare-compromising conditions. Normal thresholds, based on scientific literature, were set for each trait. The algorithm detected daily deviations in traits, assuming that cows with welfare issues deviate from normal behavioural and physiological patterns. When a cow's welfare index decreased, affected domains could be identified, enabling farmers to address potential welfare issues and implement corrective measures. • An algorithm was developed to automatically assess dairy cow welfare at the individual level. • The algorithm utilizes sensor data and farm records for welfare assessment. • Daily normal thresholds for sensor-recorded traits were established from literature. • Deviations from normal ranges assign daily scores across welfare domains. • This method complements human assessments, providing a multidimensional view of cow welfare.

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Publication Details

Journal
Biosystems Engineering
Published
2025-12-19
DOI
https://doi.org/10.1016/j.biosystemseng.2025.104365
Citations
1
Primary Topic
Animal Behavior and Welfare Studies
Type
article
Field-Weighted Citation Impact
2.35
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article

Continuous welfare assessment of dairy cows at individual level: A farmer-oriented tool based on normal daily ranges of sensor-recorded traits

Elisabetta Canali, Greta Veronica Berteselli, Kevin Chow, Joan Serra-Sagristà et al.
1 citations
Biosystems Engineering
Animal Behavior and Welfare Studies
2.35
article

Continuous welfare assessment of dairy cows at individual level: A farmer-oriented tool based on normal daily ranges of sensor-recorded traits

Elisabetta Canali, Greta Veronica Berteselli, Kevin Chow, Joan Serra-Sagristà, Y Antonio Gómez, Pol Llonch, Xavier Manteca, Artemis Llabrés, N. Blasco-Andreo
article en
1 citations

Abstract

Animal welfare on farms is currently assessed using human-evaluation protocols, which provide a single record of herd condition at a specific moment. Integrating sensor-based data and farm records, continuous information on each animal's welfare can be obtained. This study aims to create an algorithm to assess individual dairy cow welfare, contributing to the goal of building a platform to inform producers and consumers about dairy cattle welfare. It was built based on the Five Domains model of animal welfare. 221 cows from four commercial free-stall barn farms in Spain and Italy were fitted with accelerometry collars and rumen boluses and monitored for 92 days. Individual data were collected daily. Accelerometers recorded time spent ruminating, eating, lying, walking, and standing within a 24-h interval. Boluses recorded rumen pH and temperature every 10 min, averaged over 24 h. Farm records included parity, veterinary treatments, and milk conductivity. The model provides a daily global welfare index per cow, categorised into health, nutrition, and environment scores. Behaviour and mental state were not included due to a lack of relevant sensor data. Scores range from 0 to 10, indicating the likelihood of the cow experiencing welfare-compromising conditions. Normal thresholds, based on scientific literature, were set for each trait. The algorithm detected daily deviations in traits, assuming that cows with welfare issues deviate from normal behavioural and physiological patterns. When a cow's welfare index decreased, affected domains could be identified, enabling farmers to address potential welfare issues and implement corrective measures. • An algorithm was developed to automatically assess dairy cow welfare at the individual level. • The algorithm utilizes sensor data and farm records for welfare assessment. • Daily normal thresholds for sensor-recorded traits were established from literature. • Deviations from normal ranges assign daily scores across welfare domains. • This method complements human assessments, providing a multidimensional view of cow welfare.

Biosystems EngineeringVol. 262
Universitat Autònoma de Barcelona (ES), University of Milan (IT)
Openalex Percentile: Top 9%
Animal Behavior and Welfare Studies
2.35
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