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Assessment and management of spatial variability of soil chemical and physical properties (e.g., soil texture, organic matter, salinity, compaction, and nutrient content) are very important for precision farming. With current advances in sensing technology, soil electrical conductivity (EC) mapping is considered the most efficient and inexpensive method that can provide useful information about soil variability within agricultural fields. The objectives of this research study were to determine if Coulter and penetrometer-type EC sensors produce similar descriptions of soil variability, and if EC and cone index (CI) measured using a penetrometer-type sensor are correlated. The spatial variability of apparent EC (ECa) and penetration resistance expressed as CI for soil compaction were investigated with Coulter and penetrometer sensing technologies. The study was conducted in April 2005 at the research farm located near Williston, North Dakota, on a Lihen sandy loam (sandy, mixed, frigid Entic Haplustoll). The ECa and CI values generated by the penetrometer sensor were averaged over a 0- to 30-cm depth for comparison with values measured using the Coulter sensor over the same 0- to 30-cm depth. Classical and spatial statistics were used to evaluate spatial dependency and assess the overall soil variability within the experimental site. The statistical results indicated that the ECa data from both Coulter and penetrometer sensors exhibited similar spatial trends across the field that may be used to characterize the variability of soil for a variety of important physical and chemical properties. The coefficients of variation (CVs) of log-transformed ECa data from Coulter and penetrometer sensors were 11.3% and 18.9%, respectively. The mean difference, Md, of log-transformed ECa measurements between these two devices was also significantly different from zero (Md = 0.44 mS/m; t = 31.5, n = 134; P < 0.01). Soil ECa and CI parameters were spatially distributed and presented strong to medium spatial dependency within the mapped field area. Results from this study indicate the effectiveness of the ECa and CI sensors for identifying spatial variability of soil properties, and thus, the sensors may be useful tools for managing spatial variability in agricultural fields.

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