PhD position « Optimising the pig fattening unit with an individual-based model »

PigOptim : Economic and environmental optimization of pig fattening units facing uncertainties of market prices and feed nutritive value

PhD supervisors : Jean-Yves DOURMAD (IR INRA), UMR PEGASE (Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage), Domaine de la Prise 35590 Saint-Gilles, Eric DARRIGRAND (MC Université Rennes 1) Equipe d’Analyse Numérique, UMR IRMAR (Institut de Recherche Mathématique de Rennes) Campus de Beaulieu, 35042, Rennes. Alexandre GOHIN (DR INRA), UMR SMART-LERECO (Structures et Marchés Agricoles, ressources et territoires, Laboratoire d’études et de recherche en Economie), 4 allée Adolphe Bobierre - CS 61103 - 35011 Rennes.

Co-supervisors : Florence GARCIA-LAUNAY (IR INRA), UMR PEGASE, Fabrice MAHE (MC Université Rennes 1), UMR IRMAR.

Contact for information and to apply (before end of September 2017): florence.garcia-launay@inra.fr (02.23.48.50.87), fabrice.mahe@univ-rennes1.fr (02.23.23.60.48).

Team and location: The PhD will be conducted at the SysPorc group "The pig into the livestock systems”  ofUMR 1348 PEGASE (https://www.rennes.inra.fr/pegase).

Summary: Feeding strategies in pig fattening units are key factors of the economic result and environmental impacts of pig production. The choice of the feeding strategy is made under uncertainty of the economic context and additionally under uncertainty of feed nutritive value, associated to a growing use of coproducts. The phD will answer to the following issue: How optimising the feeding strategy of a batch of fattening pigs on economic and environmental criteria under uncertainty of the context? The project will aim at i) producing a model able to optimise the feeding strategy for a batch of fattening pigs under uncertainty of the context, ii) using the produced model to establish the effect of uncertainty of market prices and of feed nutritive value on optimal feeding strategies. For this purpose; the model will associate a stochastic growth model of pigs (issued from InraPorc®), an evaluation of environmental impacts of pig production by Life Cycle Assessment, a feed formulator and a procedure of optimisation of the feeding strategy.

Keywords : Multiobjective optimization, individual-base model, Life Cycle Assessment, Python programming language, Numeric resolution, uncertainty of market prices, feed formulation.

Skills of the applicant and funding : The candidate will need a master (or equivalent) in Applied Mathematics and/or Engineering sciences, with a strong interest for the application in biology and agronomy OR a master in agronomy or animal sciences with a strong interest for the utilisation of applied mathematics and computing. Skills and experience in optimisation and operational research, as well as in simulation of system dynamics would be also appreciated. The project is funded by the GloFoods metaprogram INRA-CIRAD (http://www.glofoods.inra.fr/) and Brittany (Western France).

Context and research question: Economic results of pig farming systems are very variable and depend on the prices of feed ingredients used to formulate feeds as well as on the pork price. Pig farming systems also contribute to some environmental impacts, such as climate change or eutrophication. The environmental impacts usually associated to pig farming systems (eutrophication, acidification climate change,…) are also largely influenced by the feeding strategy of the farmer. The pig fattening unit also contributes to the major part of the environmental impacts of pig production.

A recent project involving the PEGASE research unit as well as IFIP the pig institute has produced a model of the pig fattening unit which accounts for the various farming practices (e.g. feeding practices) on the growth performance of the animals, and calculates the economic output and the environmental impacts (with Life Cycle Assessment) which result from the animal performances.

The research question is: How optimising the feeding strategy for a batch of fattening pigs on economic and environmental criteria?

Hypothesis 1: The optimization of the feeding strategy allows to improve efficiently the economic result and the environmental impacts of pig production

Hypothesis 2: The market situation (prices of feed ingredients and of pork) has an effect on the optimal feeding strategies

Hypothesis 3: Accounting for uncertainties of market situation and of feed nutritional value allows finding optimal feeding strategies that are more robust to the market situation than feeding strategies optimised without accounting for uncertainties.

Research program: The program will be organized with the following steps:

Task 1: Selection of a series of optimization procedures to i) optimize the feeding strategy according to market situation with high speed and ability to find the global optimum, and ii) to optimize the feeding strategy in uncertain market situation

Task 2: Programming of the various candidate procedures for optimization

Task 3: choice of the economic scenarios to test the procedures  / test and choice of the best procedures

Task 4: Identification of optimal feeding strategies for various criteria (economic and/or environmental) and for various economic contexts

Task 5: comparison of economic and environmental outputs of feeding strategies optimised with and without consideration of the market situation and feed value uncertainties.

Expected results: This multidisciplinary work (applied mathematics, animal sciences for livestock systems, environment, economy, computing) will provide a model able to optimise the feeding strategies of the pig fattening unit. It will also provide knowledge and a generic approach to conduct the optimisation of the livestock systems management. This work will also give insights for the integration of results of optimal strategies from the farm to the territory and national scales/

References :

Cadero, A., Aubry, A., Brossard, L., Dourmad, J.-Y., Salaün, Y., Garcia-Launay, F. (2016). Modelling fattening pig production systems: use of a dynamic, stochastic, mechanistic model. In: Book of Abstract of the 67th Annual Meeting of the European Federation of Animal (p. 441). Wageningen, NLD : Wageningen Academic

InraPorc® 2006. Un outil pour évaluer des stratégies alimentaires chez le porc. Version 1.0.4.0. INRA-UMR SENAH, www.rennes.inra.fr/inraporc

Vautier, B., Quiniou, N., van Milgen, J., Brossard, L., 2013. Accounting for variability among individual pigs in deterministic growth models. Animal 7, 1265-1273.