EFFECT OF THE AUTOSORT SYSTEM ON SWINE WELL-BEING A. E. DeDecker*, J. M. Suchomel, and J.L. Salak-Johnson University of Illinois, Urbana, IL Introduction.

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EFFECT OF THE AUTOSORT SYSTEM ON SWINE WELL-BEING A. E. DeDecker*, J. M. Suchomel, and J.L. Salak-Johnson University of Illinois, Urbana, IL Introduction  The autosort system is a relatively new technology that producers have adopted without conclusive scientific data that indicate the effectiveness of the system. It is crucial for producers to understand the importance of properly implementing and managing the autosort system in order to be aware of the impact this system has on pig behavior and well-being. Objectives  Experiment 1. To evaluate the impact of autosort floor layouts on the behavior and physiology of wean-to-finish pigs.  Experiment 2. To determine the effect of food-court layouts on pig well-being. Materials and Methods  Approximately 2400 weanling pigs (~600 pigs/treatment) were randomly assigned to four treatments in exp.1 and an additional 2400 weanling pigs were used for exp. 2. Treatments were: (Exp. 1) (Exp. 2) 1. Food Court (FC) 1. Food Court 1 (FC 1) 2. Fast Lane (FL) 2. Food Court 2 (FC 2) 3. Water Court(WC) 3.*Lg. Pen Conventional (LCV) 4.*Lg. Pen Conventional (LCV) 4.*Small Pen Conventional (SCV) *Control  Behaviors were observed using continuous video recording and analyzed with Observer by Noldus. Scan sampling was used to determine the total number of animals performing a specific behavior at a specific time throughout wean-to-finish. Behaviors were observed during loading and training by a trained behaviorist.  Pre- and post- transportation b lood samples were collected via veni-puncture of jugular vein. Samples were used to measure cortisol, insulin-like growth factor 1 (IGF-1), total white blood cell count (WBC) and leukocyte cell types.  Statistical analysis of data were performed using SAS. Loading and training data were analyzed using Chi-Square analysis. All physiological data, continuous behaviors, leukocyte cell types and specific behaviors for loading and training were analyzed using PROC MIXED with repeated measures. Least-squares means, probability of differences, and standard errors were used to evaluate differences among treatments. Figure 2. Loading: Handling Ease, Exp. 1 Results  Exp. 1 results  Behavior – Within 1-wk all pigs adapted to the training process regardless of treatment (P<0.001). All autosort pigs loaded faster and required less prod use than LCV pigs (P<0.05). Continuous-behavior data indicated that autosort floor layouts affect maintenance behaviors, especially eating and drinking throughout wean-to-finish(P<0.01).  Physiology – Plasma IGF-1 concentration was greater (P<0.01) among those pigs in the FC compared to pigs in all other treatments. Total WBC counts and leukocyte cell types indicate that pigs in the FL were better able to cope with stress than pigs from other treatment groups (P<0.05).  Exp. 2 results  Behavior – Pigs in FC 1 and 2 habituated within 1-wk of training (P<0.05). During loading pigs from FC 1 and 2 were less difficult (P<0.001) and took less time to load than pigs from SCV (P<0.05). There were more lame pigs, more rears by pigs, and more prod use during loading among pigs from SCV compared to all other treatment groups (P<0.05).  Physiology – Pigs from FC 1 and SCV had greater cortisol concentration than pigs in other treatments (P<0.01). Pigs in FC 1 and 2 had a lower WBC count than conventional pigs (P<0.05). Conclusion  These data indicate that autosort floor layouts can effect various behaviors and physiological measures of pigs throughout the wean-to- finish phase, thus impacting well-being. Therefore, using various behavioral and physiological measures it is possible to optimize autosort layouts to improve pig well-being. Figure 3. Cortisol, Exp. 1 Figure 1. Schematics of treatments, Exp. 1 Food Court Fast Lane Water Court *Large Pen Conv. b c b,c a a b b a a,b b,c b a b a,b a,b a a, b, and c Means within a handling period with different letters differ P < 0.05 Score of 1-5, 5 is the most difficult. a, b, and c Means within transportation period with different letters differ P < 0.05 Abstract #28188