Precision Agriculture

, Volume 18, Issue 5, pp 882–897 | Cite as

Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region

  • Aitor García-Tomillo
  • José Manuel Mirás-AvalosEmail author
  • Jorge Dafonte-Dafonte
  • Antonio Paz-González
Brief Communication


Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (ECa), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The ECa measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg−1, whereas ECa varied from 6.7 to 14.7 mS m−1. These two variables were significantly correlated (r = −0.6, p < 0.05); hence, ECa was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and ECa. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.


Cokriging Geostatistics Ordinary kriging Random forest Regression kriging Soil quality 



This work was supported by Spanish Ministry of Economy and Competitiveness (Project CGL2013-47814-C2). The helpful comments from two anonymous reviewers are deeply acknowledged. The authors thank two anonymous reviewers for their helpful insights on previous versions of this manuscript.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aitor García-Tomillo
    • 1
  • José Manuel Mirás-Avalos
    • 1
    • 2
    Email author
  • Jorge Dafonte-Dafonte
    • 3
  • Antonio Paz-González
    • 1
  1. 1.Área de Edafología y Química Agrícola, Facultad de CienciasUniversidade da CoruñaA CoruñaSpain
  2. 2.Departamento de Riego, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC)Campus Universitario de EspinardoMurciaSpain
  3. 3.Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de LugoUniversidade de Santiago de CompostelaLugoSpain

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