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Effects of interactions between feeding practices, animal health and farm infrastructure on technical, economic and environmental performances of a pig-fattening unit

Published online by Cambridge University Press:  03 March 2020

A. Cadéro ,
A. Aubry ,
J. Y. Dourmad ,
Y. Salaün  and
F. Garcia-Launay
A. Cadéro
Affiliation:
IFIP – Institut du porc, 35651Le Rheu, France INRAE Agrocampus Ouest, PEGASE, 35590Saint-Gilles, France
A. Aubry
Affiliation:
IFIP – Institut du porc, 35651Le Rheu, France
J. Y. Dourmad
Affiliation:
INRAE Agrocampus Ouest, PEGASE, 35590Saint-Gilles, France
Y. Salaün
Affiliation:
IFIP – Institut du porc, 35651Le Rheu, France
F. Garcia-Launay*
Affiliation:
INRAE Agrocampus Ouest, PEGASE, 35590Saint-Gilles, France
*
†E-mail: florence.garcia-launay@inra.fr
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Abstract

European pig production faces economic and environmental challenges. Modelling can help farmers simulate and understand how changes in their management practices affect the efficiency of their production system. We developed an individual-based model of a pig-fattening unit that considers individual variability in performance among pigs, farmers’ feeding practices and animal management and estimates environmental impacts (using life cycle assessment) and economic results of the unit. We previously demonstrated that this model provides reliable estimates of farm performance for different combinations of management practices, pig types and building characteristics. The objectives of this study were to quantify how interactions between feeding practices and animal management influence fattening unit results in healthy or impaired health conditions using the model. A virtual experiment was designed to evaluate effects of interactions between feeding practices, health status of the pig herd and infrastructure constraints on the technical performance, economic results and environmental impacts of the unit. The virtual experiment consisted of 96 scenarios, which combined chosen values of 6 input parameters of the model: batch interval (35 days and 7 days), use or non-use of a buffer room to manage the lightest pigs, feed rationing (ad libitum and restricted) and sequence plans (two-phase (2P), daily-phase (DP)), scale at which the feeding plan is applied (i.e. room, pen and individual) and health status of the pig herd (i.e. healthy v. impaired). Variance analysis was used to test effects of the factors in these 96 scenarios, and multivariate data analyses were used to classify the scenarios. Healthy populations obtained on average higher economic results (e.g. gross margin of 11.20 v. 1.50 €/pig) and lower environmental impacts (e.g. 2.24 v. 2.38 kg CO2-eq/kg pig live weight gain) than the population with impaired health. With 35 days batch interval and DP feeding, populations with impaired health reached gross margin similar to healthy populations with 2P ad libitum feeding and 7 days batch interval. Restricted, DP and individual feeding plans improved the economic and environmental performances of the unit for both health statuses. This study highlighted that health status of the pig herd is the main factor that affects technical, economic and environmental performances of a pig-fattening unit, and that adequate feeding strategies and animal management can compensate, to some extent, the effects of impaired health on environmental impacts but not on gross margin.

Keywords

virtual experimentgrowing pigindividual-based modelbatch managementhealth status
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s348 - s359
Copyright
© The Animal Consortium 2020

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References

AHDB 2016. 2015. Pig cost of production in selected countries. Agriculture and Horticulture Development Board Pork. Retrieved on 15 November 2019 from http://pork.ahdb.org.uk/media/274535/2016-pig-cost-of-production-in-selected-countries.pdfGoogle Scholar
Andretta, I, Pomar, C, Rivest, J, Pomar, J and Radunz, J 2016. Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs. Animal 10, 1137–1147.10.1017/S1751731115003067CrossRefGoogle ScholarPubMed
Brossard, L., Dourmad, J.Y., Rivest, J., van Milgen, J., 2009. Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy. Animal 3, 1114–1123.10.1017/S1751731109004546CrossRefGoogle ScholarPubMed
Brossard, L, Vautier, B, van Milgen, J, Salaün, Y and Quiniou, N 2014. Comparison of in vivo and in silico growth performance and variability in pigs when applying a feeding strategy designed by simulation to control the variability of slaughter weight. Animal Production Science 54, 1939–1945.10.1071/AN14521CrossRefGoogle Scholar
Cadero, A., 2017. Modélisation de l’atelier d’engraissement porcin pour prédire ses résultats économiques et ses impacts environnementaux. Doctoral thesis, Institut Supérieur des Sciences Agronomiques, Agroalimentaires, Horticoles et du Paysage, Rennes, France.Google Scholar
Cadero, A, Aubry, A, Brossard, B, Dourmad, JY, Salaün, Y and Garcia-Launay, F 2018a. Modelling interactions between farmer practices and fattening pig performances with an individual-based model, Animal 12, 1277–1286.10.1017/S1751731117002920CrossRefGoogle ScholarPubMed
Cadero, A, Aubry, A, Brun, F, Dourmad, JY, Salaün, Y and Garcia-Launay, F 2018b. Global sensitivity analysis of a pig fattening unit model simulating technico-economic performance and environmental impacts. Agricultural Systems 165, 221–229.10.1016/j.agsy.2018.06.016CrossRefGoogle Scholar
Cadero, A., Aubry, A., Dourmad, J.Y., Salaün, Y. and Garcia-Launay, F., 2018c. From an individual-based model of a pig fattening unit to a decision support tool. Computers and Electronics in Agriculture 147, 44–50.10.1016/j.compag.2018.02.012CrossRefGoogle Scholar
Doke, SK and Dhawale, SC 2015. Alternatives to animal testing: A review. Saudi Pharmaceutical Journal 23, 223–229.10.1016/j.jsps.2013.11.002CrossRefGoogle ScholarPubMed
Dourmad, J-Y and Jondreville, C 2007. Impact of nutrition on nitrogen, phosphorus, Cu and Zn in pig manure, and on emissions of ammonia and odours. Livestock Science 112, 192–198.10.1016/j.livsci.2007.09.002CrossRefGoogle Scholar
Dourmad, JY, Seve, B, Latimier, P, Boisen, S, Fernandez, J, van der Peet-Schwering, C and Jongbloed, AW 1999. Nitrogen consumption, utilisation and losses in pig production in France, The Netherlands and Denmark. Livestock Production Science 58, 261–264.10.1016/S0301-6226(99)00015-9CrossRefGoogle Scholar
Dubeau, F, Julien, PO and Pomar, C 2011. Formulating diets for growing pigs: economic and environmental considerations. Annals of Operations Research 190, 239–269.10.1007/s10479-009-0633-1CrossRefGoogle Scholar
Ferguson, NS 2015. Commercial Application of Integrated Models to Improve Performance and Profitability in Finishing Pigs. In Nutritional modelling for pigs and poultry (ed. Sakomura, NK, Gous, RM, Kyriazakis, I and Hauschild, L), pp. 141–156, CABI, Wallingford, UK.10.1079/9781780644110.0141CrossRefGoogle Scholar
Gouttenoire, L, Cournut, S and Ingrand, S 2011. Modelling as a tool to redesign livestock farming systems: a literature review. Animal 5, 1957–1971.10.1017/S175173111100111XCrossRefGoogle ScholarPubMed
Husson, F, Pagès, J and Lê, S, 2010. Exploratory multivariate analysis by example using R. Second Edition. Chapman and Hall/CRC, New York, NY, USA.10.1201/b10345CrossRefGoogle Scholar
IFIP 2016. Porc performances 2015. In IFIP, Le Rheu, France.Google Scholar
InraPorc® 2006. A model and decision support tool for the nutrition of growing pigs. INRA – UMR SENAH, Saint-Gilles, France. www.rennes.inra.fr/inraporc.Google Scholar
Lê, S, Josse, J and Husson, F 2008. FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software 25, 1–18.10.18637/jss.v025.i01CrossRefGoogle Scholar
Lurette, A., Belloc, C., Touzeau, S., Hoch, T., Ezanno, P., Seegers, H., Fourichon, C., 2008. Modelling Salmonella spread within a farrow-to-finish pig herd. Veterinary Research 39, 49.10.1051/vetres:2008026CrossRefGoogle ScholarPubMed
Lurette, A, Touzeau, S, Ezanno, P, Hoch, T, Seegers, H, Fourichon, C and Belloc, C 2011. Within-herd biosecurity and Salmonella seroprevalence in slaughter pigs: A simulation study. Journal of Animal Science 89, 2210–2219.10.2527/jas.2010-2916CrossRefGoogle ScholarPubMed
McAuliffe, GA, Chapman, DV and Sage, CL 2016. A thematic review of life cycle assessment (LCA) applied to pig production. Environmental Impact Assessment Review 56, 12–22.10.1016/j.eiar.2015.08.008CrossRefGoogle Scholar
Monteiro, A, Bertol, TM and Kessler, AD 2017. Applying precision feeding to improve the nitrogen efficiency of swine production: a review of possible practices and obstacles. Ciencia Rural 47, e20161029. doi: 10.1590/0103-8478cr20161029.CrossRefGoogle 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, 952–961.10.1017/S175173111100228XCrossRefGoogle ScholarPubMed
Pomar, C, Hauschild, L, Zhang, GH, Pomar, J and Lovatto, PA 2010. Precision feeding can significantly reduce feeding cost and nutrient excretion in growing animals. In Modelling nutrient digestion and utilisation in farm animals (ed Sauvant, D., van Milgen, J., Faverdin, P. and Friggens, N.). Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Pomar, C, Pomar, J, Dubeau, F, Joannopoulos, E and Dussault, JP 2014. The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing-finishing pigs. Animal 8, 704–713.10.1017/S1751731114000408CrossRefGoogle ScholarPubMed
R Development Core Team 2016. R: A language and environment for statistical computing. In R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rivest, J 2008. Etude des impacts zootechniques et économiques d’une augmentation du poids d’abattage chez le porc. Ph.D. dissertation, Laval University, Québec, Canada.Google Scholar
van der Meer, Y, Lammers, A, Jansman, AJM, Rijnen, M, Hendriks, WH and Gerrits, WJJ 2016. Performance of pigs kept under different sanitary conditions affected by protein intake and amino acid supplementation. Journal of Animal Science 94, 4704–4719.10.2527/jas.2016-0787CrossRefGoogle ScholarPubMed
van Milgen, J, Valancogne, A, Dubois, S, Dourmad, JY, Seve, B and Noblet, J 2008. InraPorc: A model and decision support tool for the nutrition of growing pigs. Animal Feed Science and Technology 143, 387–405.10.1016/j.anifeedsci.2007.05.020CrossRefGoogle Scholar
Vangroenweghe, F, Suls, L, Van Driessche, E, Maes, D and De Graef, E 2012. Health advantages of transition to batch management system in farrow-to-finish pig herds. Veterinarni Medicina 57, 83–91.10.17221/5254-VETMEDCrossRefGoogle Scholar
Vautier, B, Quiniou, N, van Milgen, J and Brossard, L 2013. Accounting for variability among individual pigs in deterministic growth models. Animal 7, 1265–1273.10.1017/S1751731113000554CrossRefGoogle ScholarPubMed
Wilfart, A, Espagnol, S, Dauguer, S, Tailleur, A, Gac, A and Garcia-Launay, F 2016. ECOALIM: A dataset of environmental impacts of feed ingredients used in French animal production. Plos One 11, e0167343. doi: 10.1371/journal.pone.0167343CrossRefGoogle ScholarPubMed
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