Advertisement

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

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.

Keywords

Cokriging Geostatistics Ordinary kriging Random forest Regression kriging Soil quality 

Notes

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.

References

  1. Baxter, S. J., & Oliver, M. A. (2005). The spatial prediction of soil mineral N and potentially available N using elevation. Geoderma, 128, 325–339.CrossRefGoogle Scholar
  2. Bishop, T. F. A., & Lark, R. M. (2006). The geostatistical analysis of experiments at the landscape-scale. Geoderma, 133, 87–106.CrossRefGoogle Scholar
  3. Bregt, A. K., Gesing, H. J., & Alkasuma, M. (1992). Mapping the conditional probability of soil variables. Geoderma, 53, 15–29.CrossRefGoogle Scholar
  4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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
  8. Chilés, J. P., & Delfiner, P. (1999). Geostatistics. Modeling spatial uncertainty. New York: Wiley.Google Scholar
  9. Dobson, A. J., & Barnett, A. G. (2008). An introduction to generalized linear models. London: Chapman and Hall.Google Scholar
  10. Doolittle, J. A., & Brevik, E. C. (2014). The use of electromagnetic induction techniques in soils studies. Geoderma, 223–225, 33–45.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Applied Geostatistics Series: Oxford University Press.Google Scholar
  14. Goovaerts, P. (1999). Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89, 1–45.CrossRefGoogle Scholar
  15. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113–129.CrossRefGoogle Scholar
  16. 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
  17. 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.
  18. Gray, L. C., & Morant, P. (2003). Reconciling indigenous knowledge with scientific assessment of soil fertility changes in southwestern Burkina Faso. Geoderma, 111, 425–437.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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
  22. 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
  23. Karnieli, A. (1990). Application of kriging technique to areal precipitation mapping in Arizona. GeoJournal, 22, 391–398.CrossRefGoogle Scholar
  24. 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
  25. 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.CrossRefGoogle Scholar
  26. 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
  27. 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.CrossRefGoogle Scholar
  28. Lal, R. (2007). Farming carbon. Soil and Tillage Research, 96, 1–5.CrossRefGoogle Scholar
  29. 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
  30. Liaw, A., Wiener, M, Breiman, L., & Cutler, A. (2016). Package ‘random forest’. Retrieved May 18, 2016 from https://www.stat.berkeley.edu/~breiman/RandomForests/.
  31. 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.CrossRefPubMedGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. Nerini, D., Momnestiez, P., & Manté, C. (2010). Cokriging for spatial functional data. Journal of Multivariate Analysis, 101, 409–418.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. Pebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691.CrossRefGoogle Scholar
  45. Pebesma, E., & Graeler, B. (2016). Package ‘GSIF’gstat’. Retrieved May 18, 2016 from https://cran.r-project.org/web/packages/gstat/gstat.pdf.
  46. 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
  47. 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.CrossRefGoogle Scholar
  48. 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/.
  49. 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/.
  50. 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.CrossRefGoogle Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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.
  53. 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.CrossRefGoogle Scholar
  54. 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.CrossRefGoogle Scholar
  55. 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
  56. 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
  57. 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.CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. 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.CrossRefGoogle Scholar
  61. 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
  62. 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.CrossRefGoogle Scholar
  63. 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.CrossRefGoogle Scholar
  64. 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.CrossRefGoogle Scholar
  65. 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.CrossRefGoogle Scholar
  66. Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists (p. 149)., Statistics in practice series Chichester: Wiley.Google Scholar
  67. 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.CrossRefGoogle Scholar
  68. 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.CrossRefGoogle Scholar

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

Personalised recommendations