Hierarchical cluster analysis: A statistical tool to study sheep behavior under heat stress
1 Faculty of Agriculture, University of Ciego de Ávila, Ciego de Ávila, 69450, Cuba.
2 Instituto Superior Politécnico de Huíla (ISPH), Universidad de Mandume Ya Ndemufayo, Angola.
3 Plant Improvement; Agricultural Research Council-Tropical and Subtropical Crops; Private Bag X11208, Nelspruit, 1200, South Africa.
4 Centro de Estudio de Producción Animal (CEPA), Universidad de Granma (UDG), carretera de Manzanillo km 17 ½ CP: 85100, Granma, Cuba.
5 Laboratory for Plant Breeding and Conservation of Genetic Resources, Bioplant Center, University of Ciego de Avila, Ciego de Ávila, 69450, Cuba.
Research Article
World Journal of Advanced Pharmaceutical and Life Sciences, 2021, 01(01), 001–007.
Article DOI: 10.53346/wjapls.2021.1.1.0013
Publication history:
Received on 13 February 2021; revised on 18 March 2021; accepted on 21 March 2021
Abstract:
Sheep farming, and the income generated from this endeavor, contributes significantly to the global economy and rural livelihoods. Therefore, it is vital to maintain the productivity of this industry in the face of changing climate patterns. In the context of sheep farming, animals are exposed to a higher heat load as global temperatures are increasing, leading to heat stress. This heat stress conditions can adversely affect animal productivity and welfare resulting in reduced feed intake, physiological (panting, higher respiration, higher heart rate, etc.) and behavioral changes (lying down, standing, seeking shade, etc.) to compensate for increased heat load ultimately leading to lowered productivity. Considering this, it is important to monitor sheep behavior in order to implement improved management practices to compensate for changes in climate. The current short study investigated sheep behavior at different times throughout the day. The data generated was analyzed using the hierarchical cluster analysis method in order to integrate all variables into a single representative dendrogram that could summarize sheep behavior. While hierarchical cluster analysis has been applied in diverse scientific fields, as far as we know, the statistical application reported here is novel in the context of sheep behavior in response to climate change. We studied sheep behavior throughout the day (9:00 – 9:50; 10:00 – 10:50; 11:00 – 11:50; 14:00 – 14:50; 15:00 – 15:50; 16:00 – 16:50). Shade was provided in the form of trees. The following indicators were recorded over a period of twelve months at two week intervals: number of animals eating grass, ruminating and resting. The statistical evaluations undertaken resulted in the generation of a dendrogram which integrated all evaluated variables to categorize the behaviors undertaken at different times during the day. The dendrogram indicated three groupings of sheep behavior that were distinctly different from each other. The analysis shown here indicates that the use of hierarchical cluster analysis culminating in the construction of a dendrogram can effectively synthesize large datasets to outline similar relationships (in this study, this was in the context of observed behaviors). This statistical method applied to sheep physiological studies may help interpret experimental data in the context of climatic change.
Keywords:
Animal physiological stress; Biostatistics; Climate change; Heat stress; Ovis aries
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