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

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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.
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