• A bio-economic model for cost analysis of alternative management strategies in beef finishing systems

      Kamilaris, C.; Dewhurst, R.J.; Vosough Ahmadi, B.; Crosson, Paul; Alexander, P.; SRUC PhD studentship; Teagasc Walsh Fellowship Programme; Scottish Government (Elsevier BV, 2019-10-26)
      Global population growth together with rising incomes is increasing the demand for meat-based products. This increases the need to optimize livestock production structures, whilst ensuring viable returns for the farmers. On a global scale, beef producers need tools to assist them to produce more high-quality products whilst maintaining economic efficiency. The Grange Scottish Beef Model (GSBM) was customized to simulate beef finishing enterprises using data from Scottish beef finishing studies, as well as agricultural input and output price datasets. Here we describe the model and its use to determine the cost-effectiveness of alternative current management practices (e.g. forage- and cereal-based finishing) and slaughter ages (i.e. short, medium or long finishing duration). To better understand drivers of profitability in beef finishing systems, several scenarios comparing finishing duration, gender, genetic selection of stock for growth rate or feed efficiency, as well as financial support were tested. There are opportunities for profitable and sustainable beef production in Scotland, for both cereal and forage based systems, particularly when aiming for a younger age profile at slaughtering. By careful choice of finishing systems matched to animal potential, as well as future selection of high performing and feed efficient cattle, beef finishers will be able to enhance performance and increase financial returns.
    • Social network properties predict chronic aggression in commercial pig systems

      Foister, Simone; Doeschl-Wilson, Andrea; Roehe, Rainer; Arnott, Gareth; Boyle, Laura; Turner, Simon; Teagasc Walsh Fellowship Programme; Scottish Government; 2015004 (Public Library of Science (PLoS), 2018-10-04)
      Post-mixing aggression in pigs is a harmful and costly behaviour which negatively impacts both animal welfare and farm efficiency. There is vast unexplained variation in the amount of acute and chronic aggression that dyadic behaviours do not fully explain. This study hypothesised that certain pen-level network properties may improve prediction of lesion outcomes due to the incorporation of indirect social interactions that are not captured by dyadic traits. Utilising current SNA theory, we investigate whether pen-level network properties affect the number of aggression-related injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM). Furthermore we compare the predictive value of network properties to conventional dyadic traits. A total of 78 pens were video recorded for 24hr post-mixing. Each aggressive interaction that occurred during this time period was used to construct the pen-level networks. The relationships between network properties at 24hr and the pen level injuries at 24hr-PM and 3wk-PM were analysed using mixed models and verified using permutation tests. The results revealed that network properties at 24hr could predict long term aggression (3wk-PM) better than dyadic traits. Specifically, large clique formation in the first 24hr-PM predicted fewer injuries at 3wk-PM and high betweenness centralisation at 24hr-PM predicted increased rates of injury at 3wk-PM. This study demonstrates that network properties present during the first 24hr-PM have predictive value for chronic aggression, and have potential to allow identification and intervention for at risk groups.