Hostname: page-component-7479d7b7d-m9pkr Total loading time: 0 Render date: 2024-07-08T11:52:43.439Z Has data issue: false hasContentIssue false
  • English
  • Français

A systematic literature mapping and meta-analysis of animal-based traits as indicators of production diseases in pigs

Published online by Cambridge University Press:  30 October 2018

S. Stavrakakis ,
F. Loisel ,
P. Sakkas ,
N. Le Floc’h ,
I. Kyriazakis ,
G. Stewart  and
L. Montagne
S. Stavrakakis
Affiliation:
School of Agriculture, Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
F. Loisel
Affiliation:
PEGASE, Agrocampus Ouest, INRA, 35590 Saint-Gilles, France
P. Sakkas
Affiliation:
School of Agriculture, Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
N. Le Floc’h
Affiliation:
PEGASE, Agrocampus Ouest, INRA, 35590 Saint-Gilles, France
I. Kyriazakis
Affiliation:
School of Agriculture, Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
G. Stewart
Affiliation:
School of Agriculture, Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
L. Montagne*
Affiliation:
PEGASE, Agrocampus Ouest, INRA, 35590 Saint-Gilles, France
*
†E-mail: lucile.montagne@agrocampus-ouest.fr
Get access

Abstract

The choice of animal-based traits to identify and deal with production diseases is often a challenge for pig farmers, researchers and other related professionals. This systematic review focused on production diseases, that is, the diseases that arise from management practices, affecting the digestive, locomotory and respiratory system of pigs. The aim was to classify all traits that have been measured and conduct a meta-analysis to quantify the impact of diseases on these traits so that these can be used as indicators for intervention. Data were extracted from 67 peer-reviewed publications selected from 2339 records. Traits were classified as productive (performance and carcass composition), behavioural, biochemical and molecular traits. A meta-analysis based on mixed models was performed on traits assessed more than five times across studies, using the package metafor of the R software. A total of 524 unique traits were recorded 1 to 31 times in a variety of sample material including blood, muscle, articular cartilage, bone or at the level of whole animal. No behavioural traits were recorded from the included experiments. Only 14 traits were measured on more than five occasions across studies. Traits within the biochemical, molecular and productive trait groups were reported most frequently in the published literature and were most affected by production diseases; among these were some cytokines (interleukin (IL) 1-β, IL6, IL8 and tumour necrosis factor-α), acute phase proteins (haptoglobin) and daily weight gain. Quantification of the influence of factors relating to animal characteristics or husbandry practices was not possible, due to the low frequency of reporting throughout the literature. To conclude, this study has permitted a holistic assessment of traits measured in the published literature to study production diseases occurring in various stages of the production cycle of pigs. It shows the lack of consensus and common measurements of traits to characterise production diseases within the scientific literature. Specific traits, most of them relating to performance characteristics or immunological response of pigs, are proposed for further study as potential tools for the prognosis and study of production diseases.

Keywords

healthgrowthperformancediagnosissystematic review
Type
Research Article
Information
animal , Volume 13 , Issue 7 , July 2019 , pp. 1508 - 1518
Copyright
© The Animal Consortium 2018 

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.)

Footnotes

a

These two authors contributed equally to this work.

References

Benhariz, M, Goulet, O, Salas, J, Colomb, V and Ricour, C 1997. Energy cost of fever in children on total parenteral nutrition. Clinical Nutrition 16, 251–255.Google Scholar
Brockmeier, SL, Halbur, PG and Thacker, EL 2002. Porcine respiratory disease complex. In Polymicrobial diseases (ed. KA Brogden and JM Guthmiller), pp. 231–258. ASM Press, Washington, DC, USA.Google Scholar
Clark, B, Stewart, GB, Panzone, LA, Kyriazakis, I and Frewer, LJ 2016. A systematic review of public attitudes, perceptions and behaviours towards production diseases associated with farm animal welfare. Journal of Agricultural and Environmental Ethics 29, 455–478.Google Scholar
Eckersall, PD, PK. Saini, PK and McComb, C 1996. The acute phase response of acid soluble glycoprotein, α1-acid glycoprotein, ceruloplasmin, haptoglobin and C-reactive protein, in the pig. Veterinary Immunology and Immunopathology 51, 377–385.Google Scholar
EMA Committee for Medicinal Products for Veterinary Use and EFSA Panel on Biological Hazards 2017. Joint Scientific Opinion on measures to reduce the need to use antimicrobial agents in animal husbandry in the European Union, and the resulting impacts on food safety (RONAFA). EFSA Journal 15, 4666.Google Scholar
Fraser, D, Duncan, IJH, Edwards, SA, Grandin, T, Gregory, NG, Guyonnet, V, Hemsworth, PH, Huertas, SM, Huzzey, JM, Mellor, DJ, Mench, JA, Spinka, M and Whay, HR 2013. General Principles for the welfare of animals in production systems: the underlying science and its application. British Veterinary Journal 198, 19–27.Google Scholar
Fraser, D, Weary, DM, Pajor, EA and Milligan, BN 1997. A scientific conception of animal welfare that reflects ethical concerns. Animal Welfare 6, 187–205.Google Scholar
Girardin, P, Bockstaller, C and van der Werf, H 1999. Indicators: tools to evaluate the environmental impacts of farming systems. Journal of Sustainable Agriculture 13, 5–21.Google Scholar
Gunnarsson, S 2006. The conceptualisation of health and disease in veterinary medicine. Acta Veterinaria Scandinavica 48, 20–26.Google Scholar
Higgins, JPT, Thompson, SG, Deeks, JJ and Altman, DG 2003. Measuring inconsistency in meta-analyses. British Medical Journal 327, 557–560.Google Scholar
Klasing, KC and Iseri, VJ 2013. Recent advances in understanding the interactions between nutrients and immunity in farm animals. In Energy and protein metabolism and nutrition in sustainable animal production (ed. JW Oltjen, E Kebreab and H Lapierre), pp. 353–359. Wageningen Academic Publishers, Wageningen, The Netherlands.10.3920/978-90-8686-781-3_124Google Scholar
Knap, PW 2005. Breeding robust pigs. Australian Journal of Experimental Agriculture 45, 763–773.Google Scholar
Konsman, JP and Dantzer, R 2001. How the immune and nervous systems interact during disease-associated anorexia. Nutrition 17, 664–668.Google Scholar
Kyriazakis, I 2014. Pathogen-induced anorexia: a herbivore strategy or an unavoidable consequence of infection? Animal Production Science 54, 1190–1197.Google Scholar
Kyriazakis, I and Tolkamp, BJ 2010. Disease. In The Encyclopedia of applied animal behaviour and welfare (ed. CAB International), pp. 176–177. Wallingford, UK.Google Scholar
Le Bail, PY, Bugeon, J, Dameron, O, Fatet, A, Hurtaud, C, Hue, I, Joret, L, Salaün, MC, Nédellec, C, Reichstadt, M and Vernet, J, Institut National de Recherche Agronomique (INRA) 2012. Animal trait ontology for livestock - ATOL. Retrieved on 19 March 2014 from http://www.atol-ontology.com/index.php/fr/.Google Scholar
Le Floc’h, N, Melchior, D and Obled, C 2004. Modifications of protein and amino acid metabolism during inflammation and immune system activation. Livestock Science 87, 37–45.Google Scholar
Martineau, GP and Morvan, H 2010. Maladies d’élevage des porcs, 2nd revised edition. La France Agricole, Paris, France.Google Scholar
Matthews, SG, Miller, AL, Clapp, J, Plötz, T and Kyriazakis, I 2016. Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal 217, 43–51.Google Scholar
National Research Council 2012. Nutrient requirements of swine, 11th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
Niemi, JK, Jones, P, Tranter, R and Heinola, K 2016. The economic significance of production diseases and their control in pig farms. Retrieved on July 2017 from www.fp7-prohealth.eu/documents/26/Jarkko_Niemi.pdf.Google Scholar
Pastorelli, H, van Milgen, J, Lovatto, P and Montagne, L 2012. Meta-analysis of feed intake and growth responses of growing pigs after a sanitary challenge. Animal 6, 952–961.Google Scholar
Rauw, WM, Kanis, E, Noordhuizen-Stassen, EN and Grommers, FJ 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 15–33.Google Scholar
R Core Team 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0. Retrieved June 2016 from http://www.R-project.org/.Google Scholar
Spurlock, ME 1997. Regulation of metabolism and growth during immune challenge: an overview of cytokine function. Journal of Animal Science 75, 1773–1783.Google Scholar
Viechtbauer, W 2010. Conducting meta-analysis in R with the metaphor package. Journal of Statistical Software 36, 1–48.Google Scholar
World Health Organization 2015. World Health Statistics, WHO Library Cataloguing-in-Publication Data, ISBN 978 92 4 069443 9 (PDF). Retrieved April 2015 from http://www.who.int/gho/publications/en/.Google Scholar
World Organisation for Animal Health 2017. Terrestrial animal health code, 27th revised edition. OIE, Paris, France.Google Scholar
Zimmerman, JJ, Karriker, LA, Ramirez, A, Schwartz, KJ and Stevenson, VW 2012. Diseases of Swine, 10th edition. Wiley-Blackwell, Oxford, UK.Google Scholar
Supplementary material: File

Stavrakakis et al. supplementary material

Stavrakakis et al. supplementary material

Download Stavrakakis et al. supplementary material(File)
File 966.7 KB