Modelling in environmental sciences is becoming increasingly complex because ever-increasing numbers of processes are combined, thus making model-based decision aids both more relevant but more difficult to develop. Our approach focused on water quality and aimed to identify spatial tree patterns that represent surface flow and pollutant pathways from plot to plot involved in water pollution by herbicides. First, a simulation model predicted herbicide transfer rate, the proportion of applied herbicide that reaches water courses, based on the spatial and temporal distribution of weed-control activities. These predictions were used as a set of learning examples for symbolic learning techniques to induce rules based on qualitative and quantitative attributes and explain two classes of transfer rate. In this study we compared two automatic symbolic learning techniques applied to a set of simulations: (1) a relational- inductive method using the inductive logic programming (ILP) approach to induce spatial tree patterns; and (2) an attribute-value method to induce aggregated attributes of the trees. Twenty-eight and thirty-three rules were learnt by the ILP and attribute-value approaches which explained 81% and 88% of the examples, respectively. The ILP approach provided relevant indicators of plot-to-plot connectivity. The integrated attribute-value approach is simpler and quicker, but the ILP patterns are more useful for stakeholders.