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Soil Electrical Conductivity Map Variability in Limestone Soils Overlain by Loess
Author(s) -
Mueller T. G.,
Hartsock N. J.,
Stombaugh T. S.,
Shearer S. A.,
Cornelius P. L.,
Barnhisel R. I.
Publication year - 2003
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2003.4960
Subject(s) - loess , soil water , soil science , bedrock , water content , saturation (graph theory) , soil horizon , environmental science , soil test , hydrology (agriculture) , spatial variability , geology , mineralogy , mathematics , geotechnical engineering , geomorphology , statistics , combinatorics
Sensors exist that allow rapid mapping of bulk soil electrical conductivity (EC); however, the utility of these sensors for Kentucky producers is unknown. The purpose of this study was to assess the nature and the causes of soil EC variability and to make a first assessment of its potential utility in Kentucky, particularly for fields containing soils derived from limestone residuum overlain by loess. Various geostatistical, correlation, and regression analyses were conducted at seven locations to examine EC map variability. Sensor drift and errors associated with changes in coulter depth were minimal. Bulk soil EC related fairly well with clay content across locations and sample dates ( r 2 = 0.40); however, many site‐ and time‐specific correlations were better. Clay (maximum r 2 = 0.75), moisture content (maximum r 2 = 0.76), Ca (maximum r 2 = 0.67), and Mg (maximum r 2 = 0.64) were positively correlated with EC, and depth to argillic or cambic horizon (maximum r 2 = 0.62), depth to fragipan (maximum r 2 = 0.81), and depth to bedrock (maximum r 2 = 0.32) were negatively correlated with EC. A multiple‐regression model ( R 2 = 0.70) was developed to predict EC that included nine factors: clay, sand, soil moisture, buffer pH, base saturation, Ca, soil temperature, depth to cambic and argillic horizon, and slope. Soil EC variability was spatially structured, and spatial patterns were stable over time; however, the degree to which these patterns could be observed depended on the mapping procedures used. Our research suggested that EC mapping may have utility for Kentucky farmers.