Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements

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Abstract

Soil texture influences many physical and chemical properties that affect fertility and productivity. Assessing the spatial distribution of soil texture is necessary to implement management practices that avoid soil degradation. The objective of this study was to evaluate the usefulness of soil's apparent electrical conductivity (ECa), as measured by electromagnetic induction, to improve the spatial estimation of soil texture. The study was carried out in a 10-ha prairie in NW Spain. The ECa measurements were used to design a sampling scheme of 80 locations, where soil samples were collected from 0- to 20-cm depth and from 20-cm depth to the boundary of the A horizon. Clay, silt, and sand contents were determined at both depths and then were weighted for the entire A horizon. Clay, silt, and sand contents were significantly correlated with ECa (r = 0.48, r = 0.24, r = −0.36, respectively; P < 0.05). Therefore, ECa was used as a secondary variable to interpolate texture maps through regression kriging. Soil texture and ECa showed a strong spatial dependence, and ECa and soil texture maps presented similar spatial distribution patterns. The ECa measurements were useful to design an appropriate sampling strategy, which captured the distribution of soil texture in the studied field. The information provided by the predictive maps is helpful in implementing sustainable soil management practices.

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