Community structure of domesticated pigs in livestock facilities

Pinpointing pigs: performance and challenges of an ultra-wideband real-time location system for tracking growing-finishing pigs under practical conditions

Real-Time Location Systems (RTLSs) are promising precision livestock farming tools and have been employed in behavioural studies across various farm animal species. However, their application in research with fattening pigs is so far unexplored. The implementation of these systems has great potential to gain insight into pigs' spatial behaviour such as the use of functional areas and pigs' proximity to each other as indicators for social relationships. The aim of this study was therefore to validate the accuracy, precision, and data quality of the commercial Noldus Information Technology BV TrackLab system. We conducted different measurement sets: first, we performed static measurements in 12 pens at four different locations in each pen at three heights each using a single ultra-wideband tag (UWB). We recorded unfiltered x- and y-coordinates at 1 Hz. We repeated these measurements with six tags aligned in a 2 × 3 grid with varied spacing to test interference between the tags. We also tested dynamic performance by moving the tags along the centre line of the pens. Finally, we measured the data quality with 55 growing pigs in six pens, including the identification of location 'jumps' from the inside to the outside of the pen. Each pen housed ten animals fitted with a UWB tag attached to their farm ear tag. We collected data for 10 days and analysed seven 24-h periods of raw and filtered data. The mean accuracy of the RTLS measurements was 0.53 m (precision: 0.14 m) for single and 0.46 m (precision: 0.07 m) for grouped tags. Accuracy improved with increasing measurement height for single tags but less clearly for grouped tags (P [height single] = 0.01; P [height grouped] = 0.22). When tags were fitted to animals, 63.3% of the filtered data was lost and 21.8% of the filtered location estimates were outside the pens. Altogether, the TrackLab system was capable of fairly accurate and precise assignment of the functional areas where individual animals were located, but was insufficient for the analysis of social relationships. Furthermore, the frequent occurrence of gaps in signal transmission and the overall high data loss rates presented significant limitations. Additionally, the challenging hardware requirements for attaching sensors to the animals underline the need for further technological advances in RTLS for the application with growing-finishing pigs.

Preferential associations in an unstable social network: applying social network analysis to a dynamic sow herd

Preferential associations are fitness-enhancing ties between individuals, documented in a range of taxa. Despite this, research into preferential associations remains underrepresented in commercial species, particularly pigs. This study investigates the development of preferential associations in a dynamic sow herd. Preferential associations were defined as approaching a resting sow and then sitting or lying with physical contact with the selected sow, separated by < 1 m from the head or directly next to her, with interaction tolerated for > 60 s. For individual identification, each sow was marked with colored dots, stripes, or both, corresponding to their ear-tag number. Preferential associations were measured over one production cycle of 21 days. Behavioral observations took place on 7 days of the study, with 3 h of behavior per day recorded during peak activity times (08:00-09:00, 15:00-16:00, 20:00-21:00 h). Behaviors were recorded using five cameras, each positioned within the barn to provide coverage of the functional areas. The network metrics applied included in-degree centrality (received ties), out-degree centrality (initiated ties), centralization (the extent to which an individual is central within the network), clustering coefficient (a measure of tie strength), and the E-I Index (a measure of assortment by trait: parity, familiarity, and sociality). Individuals were added and removed during the study, so the centrality metrics of missing sows were weighted. To describe the structure of the network, brokerage typologies were applied. Brokerage typologies include five positions, including coordinators, gatekeepers, representatives, consultants, and liaisons. The results revealed social discrimination in assortment by connectedness even when ties were not reciprocal, and the most connected sows were significantly more likely to be approached than less connected individuals. The most connected sows had significantly higher in-degree and out-degree centrality. With the application of brokerage typologies, the results showed a relationship between connectedness and brokering type, with the most connected sows predominantly engaging in coordinating behavior. The results suggest that the motivation for discrimination in the unstable preferential association network was not founded upon bidirectional interactions. These findings highlight the complexities involved when forming social preferences and present a platform for further exploring the motivations for preferential associations among intensively farmed pigs.