Error in models and their inputs can be propagated to outputs. This is impo
rtant for modelling soil processes because soil properties used as paramete
rs commonly contain error in the statistical sense, that is, variation. Mod
el error can be assessed by validation procedures, but tests are needed for
the propagation of (statistical) error from input to output. Input error i
nteracts with non-linearity in the model such that it contributes to the me
an of the output as well as its error. This can lead to seriously incorrect
results if input error is ignored when a non-linear model is used, as is d
emonstrated for the Arrhenius equation. Tests for non-linearity and error p
ropagation are suggested. The simplest test for non-linearity is a graph of
the output against the input. This can be supplemented if necessary by tes
ting whether the mean of the output changes as the standard deviation of th
e input increases. The tests for error propagation examine whether error is
suppressed or exaggerated as it is propagated through the model and whethe
r changes in the error in one input influence the propagation of another. A
pplying these tests to a leaching model with rate and capacity parameters s
howed differences between the parameters, which emphasized that statements
about non-linearity must be for specific inputs and outputs. In particular,
simulations of mean annual concentrations of solute in drainage and concen
trations on individual days differed greatly in the amount of non-linearity
revealed and in the way error was propagated. This result is interpreted i
n terms of decoherence.