According to Gibon et al. (Reference Gibon, Sibbald, Flamant, Lhoste, Revilla, Rubino and Sørensen1999), the overall objectives of Livestock Farming Systems (LFS) research are as follows:
1. To increase knowledge about livestock farming systems and their diversity.
2. To build management aids for livestock farmers and tools for advisors.
3. To build tools to aid negotiation between actors within a given framework at local, regional or national scales for questions dealing with rural development and animal industries.
Annual meetings of the European Association for Animal Production (EAAP) are good opportunities to assess the very latest developments in modelling in the field of animal sciences (not only from European teams but also from others). Concerning the specific area of LFSFootnote 1 (see communications made within the sessions managed or co-managed by members of the LFS commission), there is a specific challenge in modelling, as on one hand, several research areas are involved (firstly animal sciences, but with strong links to social sciences), and on the other hand, several components, including decisional and biological ones, animal, herbage and crops, must be included in the model, as they are parts of the system to be modelled (through both space and time).
The following three papers were presented in the 58th and 59th annual meetings of EAAP in 2007 and 2008, respectively (Dublin and Vilnius).
Birds are involved in the first paper (Tichit et al., Reference Tichit, Barbottin and Makowski2010) that deals with the necessity for LFS systems to cope with environmental issues, that is, the multifunctional aspect of farming. Other modelling papers dealing with the environment at system level are, for example, Vayssières et al. (Reference Vayssières, Lecomte, Guerrin and Nidumolu2007), biomass flow operations); Dourmad et al. (Reference Dourmad, Hassouna, Robin, Guingand, Meunier-Salaün and Lebret2009), manure composition); Rigolot et al. (Reference Rigolot, Espagnol, Robin, Hassouna, Béline, Paillat and Dourmad2010), manure management); Wall et al. (Reference Wall, Simm and Moran2010), greenhouse gas emissions), To be multifunctional means to manage trade-offs, to define some indicators and to be able to assess the impact of the ‘whole’ system (what is a whole system?) using multicriteria assessment methods. The question then is what indicators to choose (again a multicriteria issue!), especially between their accuracy and their cost. The authors present some methodological results applied to the assessment of the impact of LFS on the environment, that is, for predicting the presence of two bird species in a French marsh area covered by grassland.
Cows are the main target in the second paper (Gouttenoire et al., Reference Gouttenoire, Fiorelli, Trommenschlager, Coquil and Cournut2010) that deals with a non-systemic issue (reproductive performances of dairy cows), but with the necessity to choose a systemic approach to asses and solve it. More precisely, the authors propose to combine both analytical and systemic approaches arguing with the complementarity of physiological knowledge on animals and management strategies of the decision-maker (the stockbreeder). Thus far, the objective is not to propose a model for decision-making about a biological function (see Gardner et al., Reference Gardner, Lea and Sinclair2008; Benoit et al., Reference Benoit, Tournadre, Dulphy, Laignel, Prache and Cabaret2009; Lesnoff and Lancelot, Reference Lesnoff and Lancelot2009; Friggens et al., Reference Friggens, Disenhaus and Petit2010), but to show the added value of mixing different points of view to solve a biotechnical problem within the herd.
The third paper is centred around the farmers (Magne et al., Reference Magne, Cerf and Ingrand2010), as it deals with modelling their informational activity. The aim here is to be able to propose some ideas to renew advising methods with the hypothesis that we first have to understand which information is relevant for farmers (i.e. stockbreeders) to deal with the situations they encounter and to adapt their farming practices. Such approaches at the farm scale, making the link between performances and farmer goals, are also present in recent studies, such as in Fiorelli et al., Reference Fiorelli, Dedieu and Pailleux2007; Madelrieux and Dedieu, Reference Madelrieux and Dedieu2008; García-Martínez et al., Reference García-Martínez, Olaizola and Bernués2009), but no formalisation of the farmer’s system of information is proposed.
In conclusion, we assume that these three papers show how modelling within the farming systems can contribute to solve the challenge of putting back agriculture in concordance with the demands and expectations of society, including (we assume that is the case…) cows, birds and farmers together in the countryside.
