Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region
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.
KeywordsCokriging 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.
- Chen, C., Hu, K., Li, H., Yun, A., & Li, B. (2015). Three-dimensional mapping of soil organic carbon by combining kriging method with profile depth function. PLoS ONE, 10, e012903.Google Scholar
- Chilés, J. P., & Delfiner, P. (1999). Geostatistics. Modeling spatial uncertainty. New York: Wiley.Google Scholar
- Dobson, A. J., & Barnett, A. G. (2008). An introduction to generalized linear models. London: Chapman and Hall.Google Scholar
- Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Applied Geostatistics Series: Oxford University Press.Google Scholar
- Gozdowski, D., Stępień, M., Samborski, S., Dobers, E. S., Szatyłowicz, J., & Chormański, J. (2015). Prediction accuracy of selected spatial interpolation methods for soil texture at farm field scale. Journal of Soil Science and Plant Nutrition, 15, 639–650.Google Scholar
- GRASS Development Team. (2015). Geographic Resources Analysis Support System (GRASS) Software, Version 7.0.3 Open Source Geospatial Foundation. Retrieved June 9, 2016 from http://grass.osgeo.org.
- IUSS Working Group WRB. (2014). World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. Rome: FAO.Google Scholar
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Number 103 in Springer texts in statistics. New York: Springer.Google Scholar
- King, J. A., Dampney, P. M. R., Lark, R. M., Wheeler, H. C., Bradley, R. I., & Mayr, T. R. (2005). Mapping potential crop management zones within fields: Use of yield-map series and patterns of soil physical properties identified by electromagnetic induction sensing. Precision Agriculture, 6, 167–181.CrossRefGoogle Scholar
- Köppen, W. (1936) Das geograsphica system der Klimate [On a geographic system of climate]. In W. Köppen & G. Geiger (Eds.), Handbuch der Klimatologie [Handbook of Climatology], 1.C. (pp. 1–44). Gebr, Bontraerger.Google Scholar
- Lesch, S. M., Rhoades, J. D., & Corwin, D. L. (2000). ESAP-95 version 2.01R. User manual and tutorial guide. Research Report Nº 146, June 2000. USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, CA.Google Scholar
- Liaw, A., Wiener, M, Breiman, L., & Cutler, A. (2016). Package ‘random forest’. Retrieved May 18, 2016 from https://www.stat.berkeley.edu/~breiman/RandomForests/.
- Pebesma, E., & Graeler, B. (2016). Package ‘GSIF’gstat’. Retrieved May 18, 2016 from https://cran.r-project.org/web/packages/gstat/gstat.pdf.
- Peralta, N. R., Cicore, P. L., Marino, M. A., Marques da Silva, J. R., & Costa, J. L. (2015). Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production. Spanish Journal of Agricultural Research, 13(4), e1103. doi: 10.5424/sjar/2015134-8032.CrossRefGoogle Scholar
- QGIS Development Team. (2016). QGIS geographic information system 2.14.3. Open Source Geospatial Foundation Project. Retrieved May 18, 2016 from http://www.qgis.org/.
- R Core Team. (2016). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved February 22, 2016 from http://www.R-project.org/.
- Rossiter, D. G. (2016). An introduction to geostatistics with R/gstat. Cornell University. Retrieved May 18, 2016 from http://www.css.cornell.edu/faculty/dgr2/teach/R/gs_short_ex.pdf.
- Siqueira, G. M. (2009). Medida de la conductividad eléctrica aparente del suelo por inducción electromagnética y variabilidad espacial de propiedades físicas y químicas del suelo [Measurement of soil apparent electrical conductivity by electromagnetic induction and spatial variability of physical and chemical soil properties]. Ph.D. Thesis. Universidade de Santiago de Compostela, Spain.Google Scholar
- Siqueira, G. M., Dafonte Dafonte, J., Valcárcel Armesto, M., & França e Silva, E. F. (2014). Using multivariate geostatistics to assess patterns of spatial dependence of apparent soil electrical conductivity and selected soil properties. The Scientific World Journal,. doi: 10.1155/2014/712403.Google Scholar
- Vieira, S. R. (2000). Uso de geoestatística em estudos de variabilidade espacial de propriedades do solo [Use of geostatistics in studies of spatial variability of soil properties]. In R. F. Novais (Ed.), Tópicos em Ciência do Solo [Topics on soil science] (pp. 3–87). Viçosa: Sociedade Brasileira de Ciência do Solo.Google Scholar
- Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists (p. 149)., Statistics in practice series Chichester: Wiley.Google Scholar