Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data

Abstract

Soil textural components are the most common inputs in pedotransfer functions (PTFs), although other soil properties, such as bulk density, organic matter content and structural indexes, are also used. The possibility of using the geometric mean d g and the geometric standard deviation r g of soil particle diameters instead of soil particle size distribution to derive PTFs was investigated. Soil samples (234) having different particle size distributions were collected randomly. The predictor variables were separated into two groups: (i) particle-size distribution and bulk density, (ii) bulk density, geometric mean and geometric standard deviation of particle diameters. Two point PTF types and two parametric PTF forms were developed to predict six points on the retention curve and the parameters of the van Genuchten model, using the stepwise regression method. The derived PTFs were compared with the Rosetta package. The results indicated that the second set of variables accounted for 65 and 90% of the variation in a and h s parameters of the van Genuchten, model respectively. The results also indicated that these variables better explained the variation of the n parameter (the shape parameter of the van Genuchten model) and the water contents at )30, )100, )300, )500 and )1500 kPa matric potentials. It was concluded that, at the drier end of the retention curve, d g and r g predict the water contents better than the soil bulk density. Although the coefficients of determination of the derived PTFs with the second set of variables were higher than that of the first group, the first set of variables gave more reliable estimates. Results showed that the descriptors of the particle size distribution (d g and r g ) did not improve the quality of soil water retention prediction. The reliability test indicated that the derived PTFs predict soil hydraulic properties better than the Rosetta package.

Role of soil carbon in the landscape functioning of the Alto São Bartolomeu watershed in the Cerrado region, Brazil

“…Even though the matrices were more similar compared with pH, Ca 2+ , Mg 2+ , they presented a more dispersed ordering, probably because of the specific management characteristics of every sampling unit. The similarity of the soils in cerrado stricto sensu patches can be explained by the similarity in the chosen elements of the landscape (Martins et al, 2004a), conceived during sampling planning and reflecting in the good performances of the PTFs, as indicated by Dashtaki et al (2010), keeping in mind the relatively high regression coefficients.…”

Estimation of Evapotranspiration Based on Surface Energy Balance Algorithm for Land (Sebal) Using Landsat 8 and Modis Images

“…Root Means Square Error and Mean Bias Error were used for statistical assessment of the models accuracy. The value of RMSE presents the amount of model error (Dashtaki et al, 2010). MBE index shows trend to overestimation or underestimation.…”

Pedotransfer functions for water retention in different soil classes from the center-southern Rio Grande do Sul State

“…It is noted, though, that with the intention of reducing the prediction errors of the estimated attribute, a specific PTF should not be extrapolated beyond the region and soil class for which it was developed (DASHTAKI et al, 2010;NEMES et al, 2009). This is because the more homogeneous the soils that compose the PTF database, the better their performance will be (DASHTAKI et al, 2010). It is important to mention that the effect of soil mineralogy can sometimes overlap the effect of the soil class in this context.…”

“…In other words, PTFs, or simply pedofunctions, are mathematical models that allow transforming basic soil information, available for instance in soil survey reports, into other information more difficult to obtain and of higher cost (MICHELON et al, 2010). It is noted, though, that with the intention of reducing the prediction errors of the estimated attribute, a specific PTF should not be extrapolated beyond the region and soil class for which it was developed (DASHTAKI et al, 2010;NEMES et al, 2009). This is because the more homogeneous the soils that compose the PTF database, the better their performance will be (DASHTAKI et al, 2010).…”

“…This aspect and the fact that the entries of data of generated PTFs are different from the ones generated here do not allow comparison of models. Also there is need to develop PTFs using simpler and cheaper data entry (DASHTAKI et al, 2010) as performed in this work.…”