Sampling To Monitor Soil In England And Wales View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1999

AUTHORS

M. Scholz , M. A. Oliver , R. Webster , P. J. Loveland , S. P. McGrath

ABSTRACT

The National Soil Inventory of England and Wales contains records of the soil, as it was in the early 1980s, at 5671 positions on an orthogonal 5-km grid. It constitutes a baseline against which to assess change in more than 100 soil properties, both over the country as a whole and regionally. The data have been analysed to determine their spatial structure: many properties appear to behave similarly. With the aid of a principal component analysis we identified the concentration of zinc (Zn) as a typical representative, and we use it in this paper to illustrate the more general situation. The experimental variogram was computed from the common logarithm of Zn using data from all of the sites. The double (nested) spherical model provided the best tit. Its correlation ranges were 26 km and 85 km. The original grid was then sub-sampled at grid spacings of 10 km x 10 km, 15 km x 15 km and 20 km x 20 km to assess whether less intense sampling could be used for future monitoring. The experimental variograms became increasingly erratic as the grid interval increased. Those from the 10-km and 15-km grids still showed spatial structure and could be modelled reasonably, but at 20 km, the variogram became too erratic to model confidently. The log Zn was then kriged at 2.5-km intervals from both the original data on the 5-km grid and from the sub-samples, in each case using the variogram model computed from it. The results show that it is possible to reveal the regional pattern, but not more, by sampling on 10-km or 15-km grids. More... »

PAGES

465-476

Book

TITLE

geoENV II — Geostatistics for Environmental Applications

ISBN

978-90-481-5249-0
978-94-015-9297-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-015-9297-0_39

DOI

http://dx.doi.org/10.1007/978-94-015-9297-0_39

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049066076


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