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Effects of Climatic Conditions on the Lying Behavior of a Group of Primiparous Dairy Cows
SIMPLE SUMMARY: Dairy cow welfare has become a significant topic in recent years. Lying (down) behavior is considered a useful indicator for dairy cow health, welfare, reproductive and productive status. The study evaluated the interaction of climatic conditions on the lying behavior of a group of d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912646/ https://www.ncbi.nlm.nih.gov/pubmed/31717823 http://dx.doi.org/10.3390/ani9110869 |
Sumario: | SIMPLE SUMMARY: Dairy cow welfare has become a significant topic in recent years. Lying (down) behavior is considered a useful indicator for dairy cow health, welfare, reproductive and productive status. The study evaluated the interaction of climatic conditions on the lying behavior of a group of dairy cows. The developed model seems helpful to identify and predict this important indicator of the welfare of the herd. The prediction model developed, with automatic monitoring of cow behavior, could be a valid early warning system to identify any deviation from the expected behavior, and could be also used to evaluate the goodness of management and to evaluate the heat stress mitigation strategy. ABSTRACT: Currently, lying behavior can be assessed using continuous observations from sensors (e.g., accelerometers). The analysis of digital data deriving from accelerometers is an effective tool for studying livestock behaviors. Despite the large interest in the lying behavior of dairy cows, no reference was found in literature regarding the prediction of lying behavior as a function of the interaction of environmental parameters. The present study aimed to evaluate the influence of climatic conditions (temperature-humidity index, solar radiation, air velocity and rainfalls) on the lying behavior of a group of primiparous dairy cows, using data from accelerometers, and develop a prediction model to identify and predict the lying behavior of dairy cows as a function of the effects of environmental conditions. Results from the. GLM Procedure (SAS) showed that the model was highly significant (p < 0.001) and the r(2) was 0.84. All of the effects in the model resulted in being highly significant (p < 0.001). This model, if validated properly, could be a valid early warning system to identify any deviation from the expected behavior, and to assess the effectiveness of thermal stress mitigation strategies. |
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