Diagnosis and simulation: a suitable combination to support farming systems design
Guillaume Martin A B C , Jean-Pierre Theau A , Olivier Therond A , Roger Martin-Clouaire B and Michel Duru A
+ Author Affiliations
- Author Affiliations
A INRA, UMR 1248 AGIR, F-31326 Castanet Tolosan, France.
B INRA, UR 875 UBIA, F-31326 Castanet Tolosan, France.
C Corresponding author. Email: guillaume.martin@toulouse.inra.fr
Crop and Pasture Science 62(4) 328-336 https://doi.org/10.1071/CP10361
Submitted: 12 November 2010 Accepted: 21 March 2011 Published: 19 April 2011
Abstract
Designing or improving farming systems requires understanding their dynamics so as to predict their behaviour in response to management. Simulation tools can potentially support the process by which farmers and scientists might obtain such an encompassing understanding. The usability of these tools is, however, partially inhibited by the inherent complexity of the interactions at work in farm-scale models. Whereas such models are generally used in isolation, here we present an approach in which a field-scale diagnosis method complements a farm-scale simulation model. This diagnosis method lends itself easily to an intelligible presentation of field-specific knowledge that can be fed to the simulation tool for more encompassing considerations. Our approach is used to support the design of novel management strategies in grassland-based beef systems and proved to be effective when applied to two farms in the French Pyrenees. Thanks to the integrative representation of the various processes, including the management ones, simulation contributed to deeper learning of both scientists and farmers about room for manoeuvre for increasing self-sufficiency for forage. The diagnosis phase enhanced the learning process by providing a simpler framework in which elementary problems at field scale could be considered separately before being examined concurrently at farm scale in the simulation phase.
Additional keywords: design, farm management, farming system, grassland, learning, modelling.
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