A. Nemes

Software data news: Software to estimate -33 and -1500kPa soil water retention using the non-parametric k-Nearest Neighbor technique

A computer tool has been developed that uses a k-Nearest Neighbor (k-NN) lazy learning algorithm to estimate soil water retention at -33 and -1500kPa matric potentials and its uncertainty. The user can customize the provided source data collection to accommodate specific local needs. Ad hoc calculations make this technique a competitive alternative to publish pedotransfer equations, as re-development of such equations is not needed when new data become available.

USING THE NRCS NATIONAL SOILS INFORMATION SYSTEM (NASIS) TO PROVIDE SOIL HYDRAULIC PROPERTIES FOR ENGINEERING APPLICATIONS

Modern agricultural, biological, and environmental engineers have a multitude of uses for soil hydraulic parameters that quantify the ability of soils and sediments to retain and transmit water. These parameters are difficult and costly to obtain, especially if large areas of land need to be characterized. An active search for the relationships of soil hydraulic parameters with readily available soil properties began in the 1970s based on compilations of data from various sources. Although substantial progress was made, further developments were hampered by the inhomogeneity of the data compendiums in terms of soil variables included, methods of their measurements, ranges of parameters, regional representation, and uncertain data quality. New opportunities to supply soil hydraulic parameters to the end users have been created by the public domain availability of soils information provided in the USDA-NRCS National Soils Information System (NASIS). These data coupled with analytical advances have enhanced the development of new relationships describing soil hydraulic properties. The database currently contains analytical data for more than 50,000 pedons describing U.S. soils. The data set has provided the opportunity to study the effects of qualitative information such as soil structure and topography properties, which improves our ability to estimate hydraulic soil properties. The size of the database also allowed experimentation with new data analysis methods that were not previously usable. A summary of methods that have used the NASIS dataset to predict the soil hydraulic properties for a range of scales is presented along with examples of engineering applications that use such estimates. Opportunities for future research based on the NASIS dataset are given.