Articles | Volume 3, issue 4
https://doi.org/10.5194/soil-3-235-2017
https://doi.org/10.5194/soil-3-235-2017
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: 2,335 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,351 800 184 2,335 154 125 135
  • HTML: 1,351
  • PDF: 800
  • XML: 184
  • Total: 2,335
  • Supplement: 154
  • BibTeX: 125
  • EndNote: 135
Views and downloads (calculated since 21 Aug 2017)
Cumulative views and downloads (calculated since 21 Aug 2017)

Viewed (geographical distribution)

Total article views: 2,335 (including HTML, PDF, and XML) Thereof 2,156 with geography defined and 179 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 01 Nov 2024
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.