The sensitivity analysis, using the global variance-based Sobolˇı and E-FAST methods, provided information on the 13 factors and indicated those with the strongest influence on variations in the model results. From these results it can be seen that the most important factors are: physiography (0.36), crop types (0.20), vegetation (0.16) and demand (0.09), which together accounted for 81% of variance. The conclusion that can be drawn from this, and which should be taken into account when solving similar studies, is that these four factors, being those with the greatest influence on the results, must be based on reliable basic information. In the same way, the slope, biomass and transport cost factors also have a very significant influence, although they are somewhat overshadowed by the fact that the environmental factors had been assigned high weights at level 1. This indicates that the weights of these three factors should be given higher values to show the true reflection of their worth in the results. With regard to weight variations, we should also highlight transport, vegetation and physiography, which were highly influential in the variance of results, which indicates that the three factors are extremely sensitive and thus have a strong influence on the model.