Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape

For quantitative water management, fine analysis of spatial and temporal interactions between cropping systems and water resources helps identify time and site-specific withdrawal situations. However, it is a methodological challenge to provide fine-resolution analyses at the scale of large watersheds used for crises management. In this study, we present a methodology based on multiple methods and mixed sources of information to finely model an agricultural landscape (AL) that represents the spatial distribution of cropping systems. Our approach is based on progressively hybridizing databases and local actors' and experts' knowledge to produce a spatially explicit and dynamic model. The Land Parcel Identification System database, which resulted from the European Common Agricultural Policy, is crucial for our method since it provides the spatial and temporal basis of our model (i.e., geographic delineation of islets and information about crop sequences). Local knowledge is used to identify factors determining spatial distribution of cropping systems and to build a generic model that simulates farmers' crop-management strategies. The model was qualitatively and quantitatively evaluated using a multi-agent simulation platform (MAELIA). We asked local experts on quantitative water management to evaluate the ability of the platform to reproduce intra- and inter-annual dynamics at different levels when using our model of the AL as input. The experts were satisfied with the results; they validated the predicted dynamics of the variables, and some discussed the objectivity of the values. We discuss the method's contribution to current challenges in modeling large agricultural areas and the associated tradeoffs. The approach is promising for policy makers who wish to develop integrated, locally adapted land-management strategies.