The prediction of cation exchange capacity from
readily available soil properties remains a challenge. In this study,
firstly, we extended the entire particle size distribution curve from
limited soil texture data and, at the second step, calculated the
fractal parameters from the particle size distribution curve. Three
pedotransfer functions were developed based on soil properties,
parameters of particle size distribution curve model and fractal
parameters of particle size distribution curve fractal model using
the artificial neural networks technique. 1 662 soil samples were
collected and separated into eight groups. Particle size distribution
curve model parameters were estimated from limited soil texture
data by the Skaggs method and fractal parameters were calculated
by Bird model. Using particle size distribution curve model parameters
and fractal parameters in the pedotransfer functions
resulted in improvements of cation exchange capacity predictions.
The pedotransfer functions that used fractal parameters as predictors
performed better than the those which used particle size
distribution curve model parameters. This can be related to the
non-linear relationship between cation exchange capacity and
fractal parameters. Partitioning the soil samples significantly increased
the accuracy and reliability of the pedotransfer functions.
Substantial improvement was achieved by utilising fractal parameters
in the clusters