Pedotransfer functions related to spatial variability of water retention attributes for lowland soils

• Main Author: Nebel,Álvaro Luiz Carvalho
• Publication Date: 2010
• Other Authors: Timm,Luís Carlos, Cornelis,Wim, Gabriels,Donald, Reichardt,Klaus, Aquino,Leandro Sanzi, Pauletto,Eloy Antonio, Reinert,Dalvan José
• Format: Article
• Language: eng
• Source: Revista Brasileira de Ciência do Solo (Online)
• Download full: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832010000300008
• topic: soil water content; Planosol; Gleisol; hydraulic properties; geostatistics; regression models

The estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field measured data, using statistical and geostatistical tools, like mean error, root mean square error, semivariogram, cross-validation, and regression coefficient. The eight tested PTFs to evaluate gravimetric soil water contents (Ug) at the tensions of 33 kPa and 1,500 kPa presented a tendency to overestimate Ug 33 kPa and underestimate Ug1,500 kPa. The PTFs were ranked according to their performance and also with respect to their potential in describing the structure of the spatial variability of the set of measured values. Although none of the PTFs have changed the distribution pattern of the data, all resulted in mean and variance statistically different from those observed for all measured values. The PTFs that presented the best predictive values of Ug33 kPa and Ug1,500 kPa were not the same that had the best performance to reproduce the structure of spatial variability of these variables.