Abstract
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.
References
Baxter, S. J., & Oliver, M. A. (2005). The spatial prediction of soil mineral N and potentially available N using elevation. Geoderma, 128, 325–339.
Bishop, T. F. A., & Lark, R. M. (2006). The geostatistical analysis of experiments at the landscape-scale. Geoderma, 133, 87–106.
Bregt, A. K., Gesing, H. J., & Alkasuma, M. (1992). Mapping the conditional probability of soil variables. Geoderma, 53, 15–29.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Brevik, E. C., Calzolari, C., Miller, B. A., Pereira, P., Kabala, C., Baumgarten, A., et al. (2016). Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma, 264, 256–274.
Brevik, E. C., Fenton, T. E., & Jaynes, D. B. (2012). Use of electrical conductivity to investigate soil homogeneity in Story County, Iowa, USA. Soil Survey Horizon, 53(5), 50–54.
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.
Chilés, J. P., & Delfiner, P. (1999). Geostatistics. Modeling spatial uncertainty. New York: Wiley.
Dobson, A. J., & Barnett, A. G. (2008). An introduction to generalized linear models. London: Chapman and Hall.
Doolittle, J. A., & Brevik, E. C. (2014). The use of electromagnetic induction techniques in soils studies. Geoderma, 223–225, 33–45.
Everingham, Y., Sexton, J., Skocaj, D., & Inman-Bamber, G. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36, 27.
Farahani, H. J., Buchleiter, G. W., & Brodahl, M. K. (2005). Characterization of apparent soil electrical conductivity variability in irrigated sandy and non-saline fields in Colorado. Transactions of the ASAE, 48, 155–168.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Applied Geostatistics Series: Oxford University Press.
Goovaerts, P. (1999). Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89, 1–45.
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113–129.
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.
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.
Gray, L. C., & Morant, P. (2003). Reconciling indigenous knowledge with scientific assessment of soil fertility changes in southwestern Burkina Faso. Geoderma, 111, 425–437.
Guo, P. T., Li, M. F., Luo, W., Tang, Q. F., Liu, Z. W., & Lin, Z. M. (2015). Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma, 237, 49–59.
Hoffmann, U., Hoffmann, T., Jurasinski, G., Glatzel, S., & Kuhn, N. J. (2014). Assessing the spatial variability of soil organic carbon stocks in an alpine setting (Grindelwald, Swiss Alps). Geoderma, 232–234, 270–283.
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.
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.
Karnieli, A. (1990). Application of kriging technique to areal precipitation mapping in Arizona. GeoJournal, 22, 391–398.
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.
Kitchen, N. R., Drummond, S. T., Lund, E. D., Sudduth, K. A., & Buchleiter, G. W. (2003). Soil electrical conductivity and topography related to yield for three contrasting soil–crop systems. Agronomy Journal, 95, 483–495.
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.
Ladoni, M., Bahrami, H. A., Alavipanah, S. K., & Norouzi, A. A. (2010). Estimating soil organic carbon from soil reflectance: A review. Precision Agriculture, 11, 82–99.
Lal, R. (2007). Farming carbon. Soil and Tillage Research, 96, 1–5.
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.
Liaw, A., Wiener, M, Breiman, L., & Cutler, A. (2016). Package ‘random forest’. Retrieved May 18, 2016 from https://www.stat.berkeley.edu/~breiman/RandomForests/.
Lozano-García, B., Parras-Alcántara, L., & Brevik, E. C. (2016). Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Science of the Total Environment, 544, 963–970.
Lozano-García, B., Parras-Alcántara, L., & Del Toro, M. (2011). The effects of agricultural management with oil mill by-products on surface soil properties, runoff and soil losses in southern Spain. Catena, 85, 187–193.
Mabit, L., & Bernard, C. (2010). Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools. Earth Surface Processes and Landforms, 35, 278–283.
Mallarino, A. P., & Wittry, D. J. (2004). Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precision Agriculture, 5, 131–144.
Marchetti, A., Piccini, C., Francaviglia, R., & Mabit, L. (2012). Spatial distribution of soil organic matter using geostatistics: A key indicator to assess soil degradation status in central Italy. Pedosphere, 22(2), 230–242.
Martínez, G., Vanderlinden, K., Ordóñez, R., & Muriel, J. L. (2009). Can apparent electrical conductivity improve the spatial characterization of soil organic carbon? Vadose Zone Journal, 8, 586–593.
McBratney, A. B., & Webster, R. (1983). Optimal interpolation and isarithmic mapping of soil properties. V. Co-regionalization and multiple sampling strategy. Journal of Soil Science, 34, 137–162.
Miller, B. A., Koszinski, S., Wehrhan, M., & Sommer, M. (2015). Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks. Soil, 1, 217–233. doi:10.5194/soil-1-217-2015.
Moral, F. J., Terrrón, J. M., & Marques da Silva, J. R. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research, 106, 335–343.
Nerini, D., Momnestiez, P., & Manté, C. (2010). Cokriging for spatial functional data. Journal of Multivariate Analysis, 101, 409–418.
Nussbaum, M., Papritz, A., Baltensweiler, A., & Walthert, L. (2014). Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging. Geosciences Model Development, 7, 1197–1210.
Pachepsky, Y. A., Timlin, D. J., & Rawls, W. J. (2001). Soil water retention as related to topographic variables. Soil Science Society of America Journal, 65, 1787–1795.
Paz-Gonzalez, A., Vieira, S. R., & Taboada Castro, M. T. (2000). The effect of cultivation on the spatial variability of selected properties on an umbric horizon. Geoderma, 97, 273–292.
Pebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691.
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.
Piccini, C., Marchetti, A., & Francaviglia, R. (2014). Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment. Ecological Indicators, 36, 301–314.
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/.
Roberts, D. F., Adamchuck, V. I., Shanahan, J. F., Ferguson, R. B., & Schepers, J. S. (2011). Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery. Precision Agriculture, 12, 82–102.
Robinson, T. P., & Metternicht, G. (2006). Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculure, 50, 97–108.
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.
Roy, S. K., Shibusawa, S., & Okayama, T. (2006). Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor. Precision Agriculture, 7, 419–436.
Rudiyanto, Minasny, B., Setiawan, B. I., Arif, C., Saptomo, S. K., & Chadirin, Y. (2016). Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands. Geoderma, 272, 20–31.
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.
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.
Stadler, A., Rudolph, S., Kupischa, M., Langensiepen, M., van der Kruk, J., & Ewert, F. (2015). Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements. European Journal of Agronomy, 64, 8–20.
Sudduth, K. A., Kitchen, N. R., Wiebold, W. J., Batchelor, W. D., Bollero, G. A., Bullock, D. G., et al. (2005). Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture, 46, 263–283.
Sun, Y., Cheng, Q., Lin, J., Schellberg, J., & Lammers, P. S. (2013). Investigating soil physical properties and yield response in a grassland field using a dual-sensor penetrometer and EM38. Journal of Plant Nutrition and Soil Science, 176, 209–216.
Tarr, A., Moore, K. J., Burras, C. L., Bullock, D. G., & Dixon, P. M. (2005). Improving map accuracy of soil variables using soil electrical conductivity as a covariate. Precision Agriculture, 6, 255–270.
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.
Viscarra Rossel, R. A., & Chen, C. (2011). Digitally mapping the information content of visible near infrared spectra of superficial Australian soils. Remote Sensing of Environment, 15, 1443–1455.
Vitharana, U. W. A., Van Meirvenne, M., Cockx, L., & Bourgeois, J. (2006). Identifying potential management zones in a layered soil using several sources of ancillary information. Soil Use and Management, 22, 405–413.
Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29–38.
Wang, K., Zhang, C. R., Li, W. D., Lin, J., & Zhang, D. X. (2014). Mapping soil organic matter with limited sample data using geographically weighted regression. Journal of Spatial Science, 59, 91–106.
Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists (p. 149)., Statistics in practice series Chichester: Wiley.
Wu, C., Wu, J., Luo, Y., Zhang, L., & DeGloria, S. D. (2009). Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal, 73(4), 1202–1208.
Zhang, S. W., Huang, Y. F., Shen, C. Y., Ye, H. C., & Du, Y. C. (2012). Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma, 171, 35–43.
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
García-Tomillo, A., Mirás-Avalos, J.M., Dafonte-Dafonte, J. et al. Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region. Precision Agric 18, 882–897 (2017). https://doi.org/10.1007/s11119-016-9481-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-016-9481-6