the 7th Framework Programme funded by the European Union. Model and method validation Bellocchi's work in validation has had implications for model and analytical method assessment. Genuine insights in model results, as well as results from an analytical method, imply concomitant understandings of multiple aspects of quality assessment to be taken into account and formalized. The fuzzy set theory formalized by Professor Lofti Zadeh at the University of California in 1965 was pointed out as having a direct use to assess numerical outcomes for its ability to aggregate multiple, possibly contradictory, evaluation measures. Many of the more basic principles of this theory are now generally accepted in many areas. Its application in a context of validation opened up to a new way to investigate results from a modelling process or an analytical method. In 2001, Bellocchi and co-workers firstly introduced the possibility to use fuzzy logic to evaluate model estimates at the Second International Symposium on Modelling Cropping Systems (Florence, Italy), and in 2002 the same approach was internationally acknowledged. Further extensions and applications followed (as reported in Authorship). Fuzzy logic Multiple-metric aggregation The procedure based on the multi-valued fuzzy set introduced by Professor Lofti Zadeh, follows the Sugeno method of fuzzy inference. Three membership classes are basically defined for all metrics used in the validation work, according to an expert judgment, i.e. Favorable (F), Unfavorable (U), and partial (or fuzzy) membership, using S-shaped curves as transition possibilities in the range F to U: where: x = the value of the basic input; a = the lower bound of the transition interval [min(F, U)]; b = the upper bound of the transition interval [max(F, U)]; c = (a + b) /2. According to the equation, if a = F, then x ≤ a means x = F, and S(x;a;b) gives the degree of membership of the index value x to the set U. Its complement, 1 - S(x;a;b), gives the degree of membership of the index value x to the set F. A two-stage design of a fuzzy-based rules' inferring system is applied where firstly inputs with similar characteristics are aggregated into modules and then, using the same procedure, the modules can be aggregated into a second level integrated index called indicator. Both modules and indicator range from 0 to 1. The control rules for estimating module values are based on logic relationships between inputs and outputs, expressed in linguistic