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a USDA-ARS Hydrology and Remote Sensing Lab., Bldg. 007, Rm. 104, BARC-WEST, Beltsville, MD 20705
b USDA-ARS Alternate Crops and Systems Lab., Bldg. 007, Rm. 116, BARC-WEST, Beltsville, MD 20705
* Corresponding author (ypachep{at}hydrolab.arsusda.gov)
Digital elevation models were proposed and used as a data source to estimate soil properties. This study evaluated variability of texture and water retention of soils for a gently sloping 3.7-ha field located in the long-term precision farming research site at the Beltsville Agricultural Research Center, MD. The specific objectives of this research were (i) to characterize variability of water retention across the hillslope, and (ii) determine and describe any correlations of soil water retention with soil texture and surface topography. Soil was sampled along four 30-m transects and in 39 points within the study area. Textural fraction contents, bulk density, and water retention at 0, 2.5, 5.0, 10, 33, 100, 500, and 1500 kPa were measured in samples taken from 4- to 10-cm depth. A 30-m digital elevation model (DEM) was constructed from aerial photography data. Slopes, profile curvatures, and tangential curvatures were computed in grid nodes and interpolated to the sampling locations. Regressions with spatially correlated errors were used to relate water retention and texture to computed topographic variables. Sand, silt, and clay contents depended on slope and curvatures. Soil water retention at 10 and 33 kPa correlated with sand and silt contents. The regression model relating water retention to the topographic variables explained more than 60% of variation in soil water content at 10 and 33 kPa, and only 20% of variation at 100 kPa. Increases in slope values and decreases in tangential curvature values, i.e., less concavity or more convexity across the slope, led to the decreases in water retention at 10 and 33 kPa. Results of this work show a potential for topographic variables to be used in interpretation of field-scale variability of soil properties and, possibly, yield maps in precision agriculture.
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