Paysages virtuels et analyse de scénarios pour évaluer les impacts ...

Discussion
Comparing observations and simulations
The preliminary analysis of data collected on the Naizin catchment during 1993-2002 allowed us to quantify
some features of the spatial allocation of management practices in farming landscapes. The dairy farm-type
exhibited the tightest field pattern (AWD = 636 m), which results from accumulated constraints related to the
field distance-to-farmstead: daily trips of dairy cows between the pastures and the milking room, water and
food supplies to grazing heifers. In Naizin, pigs are bred in buildings associated with the farmstead, thus this
farm-type require farmstead-field trips only for crop management and explains why the pig farm-type had a
wider AWD (798 m). Although it has already been observed that (i) farm-types have specific crop objectives
and (ii) crop management influences the crop pattern around the farmstead (Thenail and Baudry, 2004), the
challenge of the decision trees construction was to expect that these driving factors would remain noticeable
when mining the dataset at the field scale (the decision tree growth phase). Although Fig. III.23.a showed
that all the related field attributes (soil waterlogging, field area, and field distance-to-farmstead) were
actually incorporated in the decision trees, the different agronomic constraints were not fulfilled with the
same level of satisfaction. Each driving factor is discussed hereafter.
Crop production objectives
Actually, the root node of all the decision trees tested the “current crop” attribute, which made them very
similar to Markovian transition matrices, whose mathematical properties (no negative term in the matrix,
ergodicity) are known to induce or maintain stationarity of crop proportions when used to simulate crop
transitions (Usher, 1992; Logofet and Lesnaya, 2000; Castellazzi et al., 2007b). The trained transition
matrices had such properties and the observed stability of crop proportions during the 1993-2002 period
(Fig. III.20) suggests that the system already was at a stationary state. The structural similarity of the
stochastic decision trees, and the fact that more than half of decision leaves only involved test on the current
crop, explain why Stochastree and Rotomatrix showed similar performances in terms of crop production
objectives. Stratifying the learning dataset by farm-type allowed to build specific models, which simulated
crop proportions closer to the observed ones. However the stationarity of the observed crop areas induced a
high sensitivity in the Wilcoxon tests when comparing the observed and the simulated crop proportions: a
difference of 3% (in absolute value) of the farm-type total area revealed to be statistically significant but may
be acceptable from an agronomic perspective (Table III.3). However, the common structure of SDT and
matrices made Stochastree simulations also sensitive to “absorbing” crops (Usher, 1979): the area dedicated
to permanent pastures kept on increasing but at a very slow speed (around 0.5 ha.year -1 ) since the only
transition leading to permanent pastures was from the vegetable crop class, which was very marginally
represented in Naizin (Fig. III.20) and with low probabilities of apparition (Fig. III.22).
III. Stochastree, un modèle de successions de cultures basé sur des arbres de décision stochastique – p. 93