Ungaro F.; Calzolari C.; Busoni E., 2005, Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano-Veneta region of North Italy. Water retention properties., Geoderma (Amst.) 124 (2005): 293–317. doi_10.1016/j.geoderma.2004.05.007,
URL: http://www.cnr.it/prodotto/i/41491

Pedotransfer functions (PTFs) with quantified uncertainty for estimating water retention from soil structure and texture, organic carbon content, bulk density and derived variables are presented for the soils of the Pianura Padano-Veneta region of North Italy. A data set of 153 soil horizons from 59 soil profiles was used for calibration of parametric PTFs using a group method of data handling (GMDH); the proposed PTFs provide estimations for the Brooks and Corey water retention parameters and for water content at -5, -10, -33 and -1500 kPa matric potentials. Soil structure descriptors were retained as essential input variables to be included in the PTFs for the estimation of the Brooks and Corey water retention parameters. Calibrated PTFs were validated against two independent data sets_ a local one of 122 soil horizons and a data set of 78 soil horizons selected from the UNSODA data set. Point and parametric neural network PTFs were also trained and validated on the same data sets in order to evaluate which of the two methods is more accurate. In the case of point PTFs, GMDH PTFs performed better with the exception of water content at -1500 kPa; in the case of parametric PTFs, given an a posteriori correction of the parameters estimates outside calibration ranges, neural network provided slightly better validation results, but were less accurate in the case of the training data set. Results show that the proposed GMDH PTFs provide good results for the local validation data set and that for input data within the PTFs calibration range, the estimation holds the same degree of accuracy also for the UNSODA data set. Results suggest that whenever PTFs calibrated on a local data set are available, their use should be recommended, provided an a priori control of input variable ranges. Bootstrap results and Monte Carlo analysis highlighted the need for extending the calibration data set to include poorly represented coarse textured soils in order to reduce estimation variance and improve PTFs accuracy.

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