e-infrastructure Roadmap for Open Science in Agriculture

The attempt to use simulation models as management-support tools puts human decision and action to the fore. Although it is well-known that there is a strong intricacy between decision and action, action representation is here the focus. A formalization to implement in simulation models this concept is proposed and discussed in the light of the 'situated action' paradigm (Suchman 1987), Allen's theory of action and time (Allen 1984), and BRAHMS, a model to simulate people's actual practice (Sierhuis 2001). This tentative theory originates in modelling and simulation experiences in the field of agricultural production systems. These systems are dealt with at various scales of observation: from livestock enterprises or crop plots to whole-farm systems or groups of farms (Guerrin and Paillat 2003). Within such systems, material (and information) fluxes are issued from processes operated by human agents or natural causes. Two types of fluxes are distinguished: those mainly driven by human agents (workable fluxes) and those mainly driven by natural causes (biophysical fluxes). These fluxes interact through human action that aims at orienting biophysical fluxes by acting on workable fluxes. The emphasis put on action simulation is justified by how is conceived the use of models in decision-support for managing such systems. Putting aside the prescriptive approach (the model provides the user with the decision) a simulation model is thought of as a reflexive tool aimed at fostering experimentation and apprenticeship by the user on its own practice. What-If? simulation mirroring the interplay of intended actions within the system is deemed useful to support stakeholders' decision-making (Mc Cown 2002). Hence, the model needs not represent the decision cognitive process, but rather, 'what' is being done in fact. The main concern is thus to simulate the actions and their consequences resulting from scenarios described in terms of situations, plans, management rules, constraints, to help the user compare policy trade-offs. The modelling ontology of action proposed here generalizes and builds upon the features developed within two dynamic simulation models applied to livestock waste management: MAGMA (Guerrin 2001), which simulates the application on crops of manure from various livestock in a one-to-many or many-to-one fashions; APPROZUT (Guerrin 2004; Guerrin and Medoc 2005), which simulates the deliveries of slurry from multiple pig farms to a unique treatment plant in a many-to-one fashion. An action is represented as a dynamic process by a binary function of time. Action may be singular (occurring once) or cyclic (repeating occurrences over time). The state of an action (0 or 1 values holding on time intervals) is distinguished from the temporal events bounding its occurrences. These are quasi-instantaneous state transitions: 0 1 determining the start dates of actions' occurrences; 1 0 their end dates. They are generated by changes in other processes playing the role of triggering or interrupting conditions. As long as these changes are not detected, action is maintained in its current state. These processes, continuous or discrete, may be a combination of predefined schedules or clocks, external processes accounting for the environment, or other actions. In turn, an action exerts an immediate or delayed effect on target processes (e. g. fluxes controlling stocks) and system performance indicators. This binary formalization of action gives rise to the use of propositional or predicate calculus to reason upon action in a dynamical system framework. The management of actions involves mainly action coordination. It can be achieved by several means namely planning, action composition, and allocation over time of continuous or discrete resources shared by concurrent actions according to their demands and priorities. An advantage of this dynamical system approach is to ease the connection of action models with classical dynamic models accounting for the biophysical processes at work in production systems. The mathematical functions used to represent these concepts are given and their use is illustrated, for the sake of clarity, on simple toy-example simulations. However, references are made to real issues from livestock effluent management experimented with the MAGMA and APPROZUT models. This modelling ontology of action (still under work) has been implemented in the Vensim simulation software based on systems dynamics.