SOIL
SOIL
SOIL
Articles | Volume 3, issue 4
Articles | Volume 3, issue 4
https://doi.org/10.5194/soil-3-235-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-3-235-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 3, issue 4
Original research article
 | 
13 Dec 2017
Original research article |  | 13 Dec 2017

Planning spatial sampling of the soil from an uncertain reconnaissance variogram

R. Murray Lark, Elliott M. Hamilton, Belinda Kaninga, Kakoma K. Maseka, Moola Mutondo, Godfrey M. Sakala, and Michael J. Watts

Viewed

Total article views: 2,493 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,444 852 197 2,493 156 144 152
  • HTML: 1,444
  • PDF: 852
  • XML: 197
  • Total: 2,493
  • Supplement: 156
  • BibTeX: 144
  • EndNote: 152
Views and downloads (calculated since 21 Aug 2017)
Cumulative views and downloads (calculated since 21 Aug 2017)

Viewed (geographical distribution)

Total article views: 2,493 (including HTML, PDF, and XML) Thereof 2,304 with geography defined and 189 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 09 May 2025
Download
Short summary
An advantage of geostatistics for mapping soil properties is that, given a statistical model of the variable of interest, we can make a rational decision about how densely to sample so that the map is sufficiently precise. However, uncertainty about the statistical model affects this process. In this paper we show how Bayesian methods can be used to support decision making on sampling with an uncertain model, ensuring that the probability of meeting certain levels of precision is high enough.
Read more
Share