Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T08:27:05.063Z Has data issue: false hasContentIssue false
  • English
  • Français

Local adaptation of a national digital soil map for use in precision agriculture

Published online by Cambridge University Press:  01 June 2017

K. Piikki ,
M. Söderström  and
H. Stadig
K. Piikki*
Affiliation:
Swedish University of Agricultural Sciences Department of Soil and Environment, Division of Precision Agriculture, P.O. Box 34, SE-532 23 Skara, Sweden
M. Söderström
Affiliation:
Swedish University of Agricultural Sciences Department of Soil and Environment, Division of Precision Agriculture, P.O. Box 34, SE-532 23 Skara, Sweden
H. Stadig
Affiliation:
Hushållningssällskapet Skaraborg, Järnvägsgatan. 18, SE-532 30, SkaraSweden
*
†E-mail: kristin.piikki@slu.se
Get access

Abstract

The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50 m×50 m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.

Keywords

digital soil mappingdownscalinglocal adaptationsoilvariable-rate seedingliming
Type
Soil Sensing and Variability
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 430 - 432
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hengl, T, de Jesus, JM, Heuvelink, GBM, Gonzalez, MR, Kilibarda, M et al 2016. SoilGrids250m: Global Gridded Soil Information Based on Machine Learning. Preprint submitted to PLOS One on August 1st 2016.Google Scholar
McBratney, AB, Santos, MM and Minasny, B 2003. On digital soil mapping. Geoderma 117, 3–52.CrossRefGoogle Scholar
R Core, Team 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.Google Scholar
Söderström, M, Sohlenius, G, Rodhe, L and Piikki, K 2016a. Adaptation of regional digital soil mapping for precision agriculture. Precision Agriculture published online, 1–20.Google Scholar
Söderström, M and Piikki, K 2016. Digitala åkermarkskartan. (The Digital Soil Map of Sweden). Precision Agriculture Sweden, POS teknisk rapport 37, 22 pages. (in Swedish language).Google Scholar
Söderström, M, Piikki, K and Cordingley, J 2016b. Improved usefulness of continental soil databases for agricultural management through local adaptation. South African Journal of Plant and Soil, 1–11. http://dx.doi.org/10.1080/02571862.2016.2016.1166400.CrossRefGoogle Scholar
Wetterlind, J and Stenberg, B 2010. Near‐infrared spectroscopy for within‐field soil characterization: small local calibrations compared with national libraries spiked with local samples. European Journal of Soil Science 61, 823–843.Google Scholar