Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-23T13:04:56.142Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling batch farrowing management within a farrow-to-finish pig herd: influence of management on contact structure and pig delivery to the slaughterhouse

Published online by Cambridge University Press:  01 January 2008

A. Lurette ,
C. Belloc ,
S. Touzeau ,
T. Hoch ,
H. Seegers  and
C. Fourichon
A. Lurette*
Affiliation:
UMR708 Unit of Animal Health Management, Veterinary School, ENVN, INRA, 44000 Nantes, France
C. Belloc
Affiliation:
UMR708 Unit of Animal Health Management, Veterinary School, ENVN, INRA, 44000 Nantes, France
S. Touzeau
Affiliation:
UR341 Unit of Applied Mathematics and Computer Science, INRA, 78350 Jouy-en-Josas, France
T. Hoch
Affiliation:
UMR708 Unit of Animal Health Management, Veterinary School, ENVN, INRA, 44000 Nantes, France
H. Seegers
Affiliation:
UMR708 Unit of Animal Health Management, Veterinary School, ENVN, INRA, 44000 Nantes, France
C. Fourichon
Affiliation:
UMR708 Unit of Animal Health Management, Veterinary School, ENVN, INRA, 44000 Nantes, France
*
E-mail: lurette@vet-nantes.fr
Get access

Abstract

Pathogen spread within pig host populations can vary depending on within-herd interactions among pigs also called the contact structure. The recommended batch farrowing management, allowing for a fixed-interval mating for groups of sows of equal size, called batches, leads to an all-in/all-out management of pigs in which animals in different batches have no contact. To maintain a profitable pig delivery, producers have to deliver groups of pigs at a given weight, what needs sometimes herd management adaptations. However, producers’ adaptations that avoid delivering pigs below slaughtering weight (out-of-range pigs), result in increasing the contact between animals from different batches. To study the influence of herd management on contact structure and on pig delivery, a stochastic mathematical model representing population dynamics within a farrow-to-finish herd was elaborated. Sixteen management systems were represented combining or not the all-in/all-out management system with producers’ decisions: batch mixing, use of an extra room, suppression of the drying period and sale of post-weaning batches. Two types of contact were considered: via the animals themselves, when batch mixing occurred; and via the room, when decontamination was not complete. The impact of producers’ decisions on contact structure and on pig delivery, differed radically when pig growth was normal and when it was slow (i.e. mean age at slaughtering weight increased by 20%). When pig growth was normal, the all-in/all-out management prevented both contact via the animals and via the room but resulted in 9% of pigs delivered out of range. The use of an extra room or batch mixing decreased this percentage, the latter resulting in very frequent contact between batches via the animals. When pig growth was slow, the all-in/all-out management led to a very high percentage of pigs delivered out of range (almost 80%). The suppression of the drying period at the end of the finishing period and the sale of post-weaning batches induced a significant decrease in this percentage (down to 2% to 20%), the latter allowing to reduce the percentage of batches that made contact via the room (40% instead of 80%). This pig herd model helped to understand the compromise for producers between implementing internal biosecurity or maintaining a profitable pig delivery. Our results show that there was no unique optimal system and that efficient producers’ decisions (for biosecurity and delivery) may differ, depending on pig growth.

Keywords

batch systemscontact structuremodelspigs
Type
Full Paper
Information
animal , Volume 2 , Issue 1 , January 2008 , pp. 105 - 116
Copyright
Copyright © The Animal Consortium 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, MA, Stewart, TS 1983. A simulation model for a swine breeding unit producing feeder pigs. Agricultural Systems 10, 193–211.Google Scholar
Berends, BR, Urlings, HAP, Snijders, JMA, VanKnapen, F 1996. Identification and quantification of risk factors in animal management and transport regarding Salmonella spp. in pigs. International Journal of Food Microbiology 30, 37–53.Google Scholar
Brown, P 2006. Advantages and disadvantages of batch farrowing. Farm Animal Practice 28, 94–96.Google Scholar
Dahl, J, Wingstrand, A, Nielsen, B, Baggesen, DL 1997. Elimination of Salmonella typhimurium infection by the strategic movement of pigs. The Veterinary Record 26, 679–681.Google Scholar
Eblé, P, De Koeijer, A, Bouma, A, Stegeman, A, Dekker, A 2006. Quantification of within- and between-pen transmission of Foot-and-Mouth disease virus in pigs. Veterinary Research 37, 647–654.CrossRefGoogle ScholarPubMed
Hébert, H, Lurette, A, Fourichon, C, Seegers, H, Belloc, C 2007. Batch farrowing implementation in pig herds and influence on contact among animals. Comptes Rendus des Journées de la Recherche Porcine 39, 345–350.Google Scholar
Institut Technique du Porc 2000. Mémento de l’éleveur de porc, sixième editionInstitut Technique du Porc, Paris, France.Google Scholar
Institut Technique du Porc 2006. Le porc par les chiffres 2005. Institut Technique du Porc, Paris, France.Google Scholar
Ivanek, R, Snary, EL, Cook, AJC, Gröhn, YT 2004. A mathematical model for the transmission of Salmonella typhimurium within a grower-finisher pig herd in Great Britain. Journal of Food Protection 67, 2403–2409.Google Scholar
Jalvingh, AW, Dijkhuizen, AA, Van Arendonk, JAM, Brascamp, EW 1992. An economic comparison of management strategies on reproduction and replacement in sow herds using a dynamic probabilistic model. Livestock Production Science 32, 331–350.CrossRefGoogle Scholar
Jorgensen E and Kristensen AR 1995. An object oriented simulation model of a pig herd with emphasis on information flow. Proceedings of the Farm Animal Computer Technologies Conference (FACTs 95), 7–9 March 1995, Orlando, Florida, pp. 206–215.Google Scholar
Klinkenberg, D, De Bree, J, Laevens, H, De Jong, MCM 2002. Within- and between-pen transmission of classical swine fever virus: a new method to estimate the reproduction ratio from transmission experiments. Epidemiology and Infection 128, 293–299.CrossRefGoogle ScholarPubMed
Kristensen, AR, Sollested, TA 2004. A sow replacement model using Bayesian updating in a three-level hierarchic Markov process. I. Biological model. Livestock Production Science 87, 13–24.CrossRefGoogle Scholar
MacKenzie, K, Bishop, SC 2001. Developing stochastic epidemiological models to quantify the dynamics of infectious diseases in domestic livestock. Journal of Animal Science 79, 2047–2056.Google Scholar
Madec, F, Bridoux, N, Bounaix, S, Jestin, A 1998. Measurement of digestive disorders in the piglet at weaning and related risk factors. Preventive Veterinary Medicine 35, 53–72.Google Scholar
Plà, LM, Pomar, C, Pomar, J 2003. A Markov decision sow model representing the productive lifespan of herd sows. Agricultural Systems 76, 253–272.Google Scholar
Pomar, C, Harris, DL, Savoie, P, Minvielle, F 1991. Computer simulation model of swine production systems: III. A dynamic herd simulation model including reproduction. Journal of Animal Science 69, 2822–2836.CrossRefGoogle Scholar
Rose, N, Larour, G, Le Diguerher, G, Eveno, E, Jolly, JP, Blanchard, P, Oger, A, Le Dimna, M, Jestin, A, Madec, F 2003. Risk factors for porcine post-weaning multisystemic wasting syndrome (PMWS) in 149 French farrow-to-finish herds. Preventive Veterinary Medicine 61, 209–225.Google Scholar
Schinckel, A, Smith, JW, Tokach, MD, Dritz, SS, Einstein, M, Nelssen, JL, Goodband, RD 2002. Two on-farm data collection methods to determine dynamics of swine compositional growth and estimates of dietary lysine requirements. Journal of Animal Science 80, 1419–1432.Google Scholar
Schinckel A, Richert B and Foster K 2005. Management of pigs in the pork production chain. Proceedings of the Fifth London Swine Conference, 6–7 April 2005, London, Ontario, pp. 115–131.Google Scholar
Singh, D 1986. Simulation of swine herd population dynamics. Agricultural Systems 22, 157–183.Google Scholar
Stalder, KJ, Knauer, M, Baas, TJ, Rotschild, MF, Mabry, JW 2004. Sow longevity. Pig News and Information 25, 53N–74N.Google Scholar
Toft, N, Kristensen, AR, Jorgensen, E 2005. A framework for decision support related to infectious diseases in slaughter pig fattening units. Agricultural Systems 85, 120–137.Google Scholar
Van der Gaag, MA, Vos, F, Saatkamp, HW, Van Boven, M, Van Beek, P, Huirne, RBM 2004. A state-transition simulation model for the spread of Salmonella in the pork supply chain. European Journal of Operational Research 156, 782–798.Google Scholar