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Spatial Variability of Apparent Electrical Conductivity and Cone Index as Measured with Sensing Technologies: Assessment and Comparison

Jabro, Jay D., Kim, Y., Evans, Robert G., Stevens, Bart W., Iversen, Williams
Paper 2005 no.PNW05-1017
sandy loam soils, electrical conductivity, soil strength, soil analysis, sensors, geostatistics, spatial variation, soil heterogeneity
Assessment and interpretation of spatial variability of soil chemical and physical properties are very important for precision farming. The spatial variability of apparent electrical conductivity (ECa) and penetration resistance expressed as cone index (CI) for soil compaction was investigated with Veris 3100 and Veris 3000 sensing technologies. The study was conducted at the research farm located near Williston, ND on a sandy loam soil (Sandy, mixed, frigid Entic Haplustoll). Measurements of soil ECa were taken using Veris 3100 guided by a parallel swathing light bar monitored with the Trimble Ag132 DGPS unit providing spatial coordinates for each measurement at shallow (0-30 cm) and deep (0-90 cm) depths. A Veris 3000 equipped with the GPS unit was also used to collect measurements of ECa and CI that were recorded in 2 cm intervals to a depth of 90 cm on a grid sampling system. The experimental plot area mapped with this technology was approximately 1.4 ha. The ECa data from both Veris 3100 and Veris 3000 exhibited similar spatial trends across the field that may characterize the variability of soil for a variety of important physical and chemical properties. The coefficient of variations of ECa from Veris 3100 and Veris 3000 were 19.2 and 11.3%, respectively. However, the averages of ECa measurements for Veris 3100 and Veris 3000 were 4.92 and 3.31 mS/m, respectively. The ECa mean difference, Md between these two devices was also significantly different from zero (Md= 1.71 mS/m; t=34.23, n=138; pr<0.01). Geostatistical tools were used to evaluate spatial dependency and assess the overall soil variability. It was found that soil ECa and CI parameters were spatially distributed and presented weak to medium spatial dependency within the mapped field area. Further, ECa measurements from both sensors exhibited approximately log normal distribution and the CI values were normally distributed using probability distribution functions. The spatial data produced from this new direct sensing technology can be used as baseline for precision farming and making future management decisions.