Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-03T05:40:57.628Z Has data issue: false hasContentIssue false
  • English
  • Français

Simulation of sow herd dynamics with emphasis on performance and distribution of periodic task events

Published online by Cambridge University Press:  23 May 2008

G. MARTEL ,
B. DEDIEU  and
J.-Y. DOURMAD
G. MARTEL
Affiliation:
INRA, UMR1079, Systèmes d'Elevage Nutrition Animale et Humaine, F-35590 Saint Gilles, France INRA, UMR1273, Mutations des Activités, des Espaces et des Formes d'Organisation dans les Territoires Ruraux, F-63122 Saint-Genès-Champanelle, France
B. DEDIEU
Affiliation:
INRA, UMR1273, Mutations des Activités, des Espaces et des Formes d'Organisation dans les Territoires Ruraux, F-63122 Saint-Genès-Champanelle, France
J.-Y. DOURMAD*
Affiliation:
INRA, UMR1079, Systèmes d'Elevage Nutrition Animale et Humaine, F-35590 Saint Gilles, France
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Currently, the diversity of sow herd management strategies has been described but there are no tools that explore how it promotes sow herd performance nor how it or performance are linked to work organization problems. The goal of the current study was to build a herd dynamic, stochastic object-oriented model capable of representing the herd dynamics and performance, and to predict the number of events workers will have to deal with. Each sow is individually represented in the model and the model works as a discrete event simulator with a predefined time step of 1 h. At each time step of simulation, the model searches for an event to be processed. An event may imply change of sow physiological state (e.g. oestrus, farrowing and insemination) and/or request an action from a worker (e.g. oestrous detection and farrowing supervision). This action may result in the planning of a new event (e.g. farrowing after mating) and/or modification of sow state (e.g. from oestrus to pregnant). The occurrences of some technical activities such as weaning are defined in time and frequency according to the management strategy of the farmer. The model is stochastic as sow biology is represented by several normal univariate distributions according to parity or by a threshold (fertility, abortion and mortality rates). When sows return into oestrus after mating they can be moved to another batch or culled depending on batch management strategy and culling policy. Outputs of this model focus on productivity of sows and distribution of tasks over the week. Definitions of the duration of simulation and number of replications to obtain the steady state and the variability of results are presented. The model is able to simulate several batch farrowing systems (BFS) and results of 1-, 3- and 4-week BFS are presented. Several simulations with modified management (no oestrous detection during the weekend and change of the weaning day) or with modified sow biology (increased variability of the weaning-to-oestrus interval and lower fertility rate) are performed. Results indicate that these modifications have specific consequences on performance and task distribution according to the BFS. The model provides useful information concerning the effects of herd management strategies on productivity and distribution of events over time and their sensitivity to biological criteria.

Type
Modelling Animal Systems Paper
Information
The Journal of Agricultural Science , Volume 146 , Issue 4 , August 2008 , pp. 365 - 380
Copyright
Copyright © 2008 Cambridge University Press

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

REFERENCES

Allen, M. & Stewart, T. (1983). A simulation model for a swine breeding unit producing feeder pigs. Agricultural Systems 10, 193–211.CrossRefGoogle Scholar
Barthez, A. (1986). Du labeur paysan au métier d'agriculteur: l'élaboration statistique en agriculture. Cahiers d'Economie et Sociologie Rurales 3, 45–72.CrossRefGoogle Scholar
Boulot, S. (2004). L'hyperprolificité en 2002: quels résultats, quel impact sur la longévité des truies. Journées de la Recherche Porcine en France 36, 429–434.Google Scholar
Carr, J. (2006). The maintenance of health. In Whittemore's Science and Practice of Pig Production, 3rd edn (Eds Kyriazakis, I. & Whittemore, C. T.), pp. 263–316. Oxford: Blackwell Publishing.CrossRefGoogle Scholar
Cournut, S. & Dedieu, B. (2004). A discrete events simulation of flock dynamics: a management application to three lambings in two years. Animal Research 53, 383–403.CrossRefGoogle Scholar
Dedieu, B., Chabosseau, J.-M., Benoit, M. & Laignel, G. (1997). L'élevage ovin extensif du Montmorillonais entre recherche d'autonomie, exigences des filières et simplicité de conduite. INRA Productions Animales 10, 207–218.CrossRefGoogle Scholar
Durand, D. (2002). La systémique. Paris, France: PUF.Google Scholar
Guillaumin, A., Kling-Eveillard, F. & Marty, M. (2004). Attentes des éleveurs laitiers d'Aquitaine en matière de qualité de vie et de conditions de travail. Rencontres autour des Recherches sur les Ruminants 11, 208.Google Scholar
IFIP (2007). Gestion Technique des Troupeaux de Truies année 2006. Available online at http://www.ifip.asso.fr/actu/gttt.htm (verified 7 April 2008).Google Scholar
Ingrand, S., Cournut, S., Dedieu, B. & Antheaume, F. (2003). La conduite de la reproduction du troupeau de vaches allaitantes: modélisation des prises de décision. INRA Productions Animales 16, 263–270.CrossRefGoogle Scholar
Jalvingh, A., Dijkhuizen, A. & Arendonk, J. (1992). Dynamic probabilistic modelling of reproduction and replacement management in sow herds. General aspects and model description. Agricultural Systems 39, 133–152.CrossRefGoogle Scholar
Jean, N., Lacroix, A., Maamoun, M. & Mollard, A. (1988). Durée et intensité du travail des agriculteurs dans la crise économique. INRA Actes et Communications 3, 45–82.Google Scholar
Jørgensen, E. & Kristensen, A. R. (1995). An object oriented simulation model of a pig herd with emphasis on information flow. In Farm Animal Computer Technologies Conference, pp. 206–215. Orlando, FL: FACT.Google Scholar
Madelrieux, S., Dedieu, B. & Dobremez, L. (2006). ATELAGE: un modèle pour qualifier l'organisation du travail dans les exploitations d'élevage. INRA Productions Animales 19, 47–58.CrossRefGoogle Scholar
Martel, G., Dedieu, B. & Dourmad, J. Y. (2006). Les représentations biotechniques du fonctionnement des troupeaux de truies: analyse comparative et perspectives. Journées de la Recherche Porcine en France 38, 255–262.Google Scholar
Pettigrew, J., Cornelius, S., Eidman, V. & Moser, R. (1986). Integration of factors affecting sow efficiency: a modelling approach. Journal of Animal Science 63, 1314–1321.CrossRefGoogle Scholar
Plà, L., Pomar, C. & Pomar, J. (2003). A Markov decision sow model representing the productive lifespan of herd sows. Agricultural Systems 76, 253–272.CrossRefGoogle Scholar
Plà, L. M. (2007). Review of mathematical models for sow herd management. Livestock Science 106, 107–119.CrossRefGoogle Scholar
Pomar, C., Harris, D., 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
Pomar, J. & Pomar, C. (2005). A knowledge-based decision support system to improve sow farm productivity. Expert Systems with Applications 29, 33–40.CrossRefGoogle Scholar
Python Software Foundation (2004). Python Programming Language 2.4. Hampton, NH: Python Software Foundation. Available online at: http://www.python.org (verified 4 February 2008).Google Scholar
Romera, A. J., Morris, S. T., Hodgson, J., Stirling, W. D. & Woodward, S. J. R. (2004). A model for simulating rule-based management of cow-calf systems. Computers and Electronics in Agriculture 42, 67–86.CrossRefGoogle Scholar
Rozeboom, D. W., Pettigrew, J. E., Moser, R. L., Cornelius, S. G. & El Kandelgy, S. M. (1996). Influence of gilt age and body composition at first breeding on sow reproductive performance and longevity. Journal of Animal Science 74, 138–150.CrossRefGoogle ScholarPubMed
Sauvant, D. (2005). La modélisation de la réponse des porcs aux pratiques alimentaires et d'élevage. Journées de la Recherche Porcine en France 37, 283–290.Google Scholar
Schwartz, D. (1993). Méthodes statistiques à l'usage des médecins et des biologistes. Paris, France: Flammarion.Google Scholar
Singh, D. (1986). Simulation of swine herd population dynamics. Agricultural Systems 22, 157–183.CrossRefGoogle Scholar
Steverink, D. W. B. (1999). Optimising insemination strategies in pigs. Ph.D. thesis, Wageningen University, Wageningen, The Netherlands.Google Scholar
Teffène, O., Salaun, Y. & Querné, M. (1986). La gestion prévisionnelle à court-terme en élevage porcin, un outil: PORGEP. Journées de la Recherche Porcine en France 18, 189–202.Google Scholar
Tess, M. W., Bennet, G. L. & Dickerson, G. E. (1983). Simulation of genetic changes in life cycle efficiency of pork production. I. A bioeconomic model. Journal of Animal Science 56, 336–353.CrossRefGoogle Scholar
White, K. R., Anderson, D. M. & Bate, L. A. (1996). Increasing piglet survival through an improved farrowing management protocol. Canadian Journal of Animal Science 76, 491–495.CrossRefGoogle Scholar
Willenburg, K. L., Miller, G. M., Rodriguez-Zas, S. L. & Knox, R. V. (2003). Effect of boar exposure at time of insemination on factors influencing fertility in gilts. Journal of Animal Science 81, 9–15.CrossRefGoogle ScholarPubMed
Young, L. G., King, G. J., Walton, J. S., Mcmillan, I. & Klevorick, M. (1990). Age, weight, backfat and time of mating effects on performance of gilts. Canadian Journal of Animal Science 70, 469–481.CrossRefGoogle Scholar