TECHNICAL ARTICLEMapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction MeasurementsGarcía-Tomillo, Aitor1; Mirás-Avalos, José Manuel2; Dafonte-Dafonte, Jorge3; Paz-González, Antonio1Author Information 1Centro de Investigaciones Científicas Avanzadas (CICA), Facultad de Ciencias, Grupo AQUASOL, Universidade da Coruña, A Coruña, Spain. 2Departamento de Riego, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), Campus Universitario de Espinardo, Murcia, Spain. 3Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de Ingeniería, Universidade de Santiago de Compostela, Lugo, Spain. Guest Editor: Dr. José Manuel Monteiro Gonçalves. Address for correspondence: Dr. Aitor García-Tomillo. Centro de Investigaciones Científicas Avanzadas (CICA), Grupo AQUASOL, Facultad de Ciencias, Universidad de La Coruña, As Carballeiras s/n, Campus de Elviña 15071, A Coruña, Spain. E-mail: [email protected] Financial Disclosures/Conflicts of Interest: This work was supported by Spanish Ministry of Economy and Competitiveness (project CGL2013-47814-C2) and cofunded by FEDER (European Fund for Regional Development). Received February 14, 2017. Accepted for publication September 9, 2017. Soil Science: August 2018 - Volume 182 - Issue 8 - p 278-284 doi: 10.1097/SS.0000000000000213 Buy Metrics Abstract 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. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.