Social network analysis to predict social behavior in dairy cattle
Dairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social inte...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | JDS Communications |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266691022400067X |
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| author | H. Marina W.F. Fikse L. Rönnegård |
| author_facet | H. Marina W.F. Fikse L. Rönnegård |
| author_sort | H. Marina |
| collection | DOAJ |
| description | Dairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows' expected social behavior. |
| format | Article |
| id | doaj-art-4fbb02a38797473b9227077b1cf9ad76 |
| institution | Kabale University |
| issn | 2666-9102 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JDS Communications |
| spelling | doaj-art-4fbb02a38797473b9227077b1cf9ad762024-11-22T07:39:16ZengElsevierJDS Communications2666-91022024-11-0156608612Social network analysis to predict social behavior in dairy cattleH. Marina0W.F. Fikse1L. Rönnegård2Department of Animal Biosciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden; Corresponding authorVäxa, Swedish University of Agricultural Sciences, SE-756 51 Uppsala, SwedenDepartment of Animal Biosciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden; School of Information and Engineering, Dalarna University, SE-791 88 Falun, Sweden; The Beijer Laboratory for Animal Science, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, SwedenDairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows' expected social behavior.http://www.sciencedirect.com/science/article/pii/S266691022400067X |
| spellingShingle | H. Marina W.F. Fikse L. Rönnegård Social network analysis to predict social behavior in dairy cattle JDS Communications |
| title | Social network analysis to predict social behavior in dairy cattle |
| title_full | Social network analysis to predict social behavior in dairy cattle |
| title_fullStr | Social network analysis to predict social behavior in dairy cattle |
| title_full_unstemmed | Social network analysis to predict social behavior in dairy cattle |
| title_short | Social network analysis to predict social behavior in dairy cattle |
| title_sort | social network analysis to predict social behavior in dairy cattle |
| url | http://www.sciencedirect.com/science/article/pii/S266691022400067X |
| work_keys_str_mv | AT hmarina socialnetworkanalysistopredictsocialbehaviorindairycattle AT wffikse socialnetworkanalysistopredictsocialbehaviorindairycattle AT lronnegard socialnetworkanalysistopredictsocialbehaviorindairycattle |