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DIVISION S-1—SOIL PHYSICS

Soil Consistence and Structure as Predictors of Water Retention

USDA-ARS Animal Waste Pathogen Lab., Bldg. 173, Rm. 203, BARC-EAST, Beltsville, MD 20705

* Corresponding author (ypachepsky{at}anri.barc.usda.gov)

It is impractical to measure water retention for large-scale hydrologic, agronomic, and ecological applications or at the design stages of many projects; therefore, water retention estimates are often used. Field soil descriptions routinely include structure and consistence characterization. The objective of this work was to use the National Resource Conservation Service (NRCS) database to evaluate the potential for structural and consistence properties to serve as predictors of soil hydraulics properties. Total of 2140 samples were found that had (i) values of water contents at -33 kPa and -1500 kPa, (ii) structure characterized with grade, size, and shape, (iii) consistence characterized with dry and moist consistency, stickiness, and plasticity, and (iv) textural class determined in the field and from lab textural analysis. Because structural and consistence parameters were represented by categories rather than numbers, regression trees were used for recursive partitioning of the data sets into groups to decrease overall variability measured as the sum of squared errors within groups. Plasticity class, grade class, and dry consistency class were leading predictors of water retention at both -33 kPa and -1500 kPa matric potentials. The accuracy of estimates from structural and consistence parameters was lower than from textural classes. Using soil structural and consistence parameters along with textural classes provided a small, although significant improvement in accuracy of water retention estimates as compared with estimation from texture alone. Soil structural and consistence parameters can serve as predictors of soil water retention because those parameters reflect soil basic properties that affect soil hydraulic properties.

Abbreviations: , water content at the matric potential of interest • M_group, minimum number of samples in a group after partitioning • M_split, minimum number of samples before a partitioning • NRCS, National Resource Conservation Service • RMSE, root mean squared error

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