Estimation of direct and social effects of feeding duration in growing pigs using records from automatic feeding stations
Automatic feeding systems in pig production allow for the recording of individual feeding behavior traits, which might be influenced by the social interactions among individuals. This study fitted mixed models to estimate the direct and social effects on visit duration at the feeder of group-housed...
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2021angarita.pdf LINK AL EDITOR |
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| 022 | |a 0021-8812 | ||
| 024 | |a 10.1093/jas/skab042 | ||
| 040 | |a AR-BaUFA |c AR-BaUFA | ||
| 245 | 1 | 0 | |a Estimation of direct and social effects of feeding duration in growing pigs using records from automatic feeding stations |
| 520 | |a Automatic feeding systems in pig production allow for the recording of individual feeding behavior traits, which might be influenced by the social interactions among individuals. This study fitted mixed models to estimate the direct and social effects on visit duration at the feeder of group-housed pigs. The dataset included 74,413 records of each visit duration time (min) event at the automatic feeder from 135 pigs housed in 14 pens. The sequence of visits at the feeder was employed as a proxy for the social interaction between individuals. To estimate animal effects, the direct effect was apportioned to the animal feeding (feeding pig), and the social effect was apportioned to the animal that entered the feeder immediately after the feeding pig left the feeding station (follower). The data were divided into two subsets: “non-immediate replacement” time (NIRT, N = 6,256), where the follower pig occupied the feeder at least 600 s after the feeding pig left the feeder, and “immediate replacement” time (IRT, N= 58,255), where the elapsed time between replacements was less than or equal to 60 s. The marginal posterior distribution of the parameters was obtained by Bayesian method. Using the IRT subset, the posterior mean of the proportion of variance explained by the direct effect (Prp 2 d) was 18% for all models. The proportion of variance explained by the follower social effect (Prp 2 f ) was 2%, and the residual variance (2 e) decreased, suggesting an improved model fit by including the follower effect. Fitting the models with the NIRT subset, the estimate of Prp 2 d was 20% but the Prp 2 f was almost zero and 2 e was identical for all models. For the IRT subset, the predicted best linear unbiased predictor (BLUP) of direct (Direct BLUP) and social (Follower BLUP) random effects on visit duration at the feeder of an animal was calculated. Feeder visit duration time was not correlated with traits, such as weight gain or average feed intake (P major 0.05), whereas for the daily feeder occupation time, the estimated correlation was positive with the Direct BLUP (r= 0.51, P minor to 0.05) and negative with the Follower BLUP (r= −0.26, P minor to 0.05). The results suggest that the visit duration of an animal at the single-space feeder was influenced by both direct and social effects when the replacement time between visits was less than 1 min. Finally, animals that spent a longer time per day at the feeder seemed to do so by shortening the meal length of the preceding individual at the feeder. | ||
| 650 | |2 Agrovoc |9 26 | ||
| 653 | |a FEEDING BEHAVIOR | ||
| 653 | |a PIGS | ||
| 653 | |a SOCIAL EFFECTS | ||
| 700 | 1 | |a Angarita Barajas, Belcy Karine |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 72825 | |
| 700 | 1 | |a Han, Junjie |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 73937 | |
| 700 | 1 | |a Cantet, Rodolfo Juan Carlos |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 12817 | |
| 700 | 1 | |a Chewning, Sarah K. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 73938 | |
| 700 | 1 | |a Wurtz, Kaitlin E. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 73543 | |
| 700 | 1 | |a Siegford, Janice M. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 73545 | |
| 700 | 1 | |a Ernst, Catherine W. |u Michigan State University. Department of Animal Science. East Lansing, MI. |9 73546 | |
| 700 | 1 | |a Steibel, Juan Pedro |u Michigan State University. Department of Animal Science. East Lansing, MI. |u Michigan State University. Department of Fisheries and Wildlife. East Lansing, MI. |9 13048 | |
| 773 | 0 | |t Journal of animal science |g Vol.99, no.5 (2021), p.1–8, tbls., grafs., fot. |w (AR-BaUFA)SECS000110 | |
| 856 | |f 2021angarita |i En reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2021angarita.pdf |x ARTI202206 | ||
| 856 | |u https://www.asas.org |z LINK AL EDITOR | ||
| 942 | |c ARTICULO | ||
| 942 | |c ENLINEA | ||
| 976 | |a AAG | ||