INCORPORATING SPATIAL TRENDS AND ANISOTROPY IN GEOSTATISTICAL MAPPINGOF SOIL PROPERTIES

Citation
Cag. Crawford et Gw. Hergert, INCORPORATING SPATIAL TRENDS AND ANISOTROPY IN GEOSTATISTICAL MAPPINGOF SOIL PROPERTIES, Soil Science Society of America journal, 61(1), 1997, pp. 298-309
Citations number
42
Categorie Soggetti
Agriculture Soil Science
ISSN journal
03615995
Volume
61
Issue
1
Year of publication
1997
Pages
298 - 309
Database
ISI
SICI code
0361-5995(1997)61:1<298:ISTAAI>2.0.ZU;2-L
Abstract
The spatial variation in soil parameters often differs with direction. These differences may occur naturally or may be due to management pra ctices. Regardless of their origin, they present a challenge in geosta tistical mapping of soil parameters. Recommendations pertaining to the selection of an appropriate geostatistical method based on the curren t literature are often incomplete or contradictory. The purpose of thi s investigation was to provide a unified description, comparison, and discussion of different geostatistical methods for handling trend and anisotropy that may be present in measured soil properties. Soil organ ic matter content of the 0- to 20-cm depth from a field in continuous ridge-tilled corn (Zea mays L.) was used to compare five geostatistica l methods: ordinary kriging with an isotropic semivariogram (OKA); ord inary kriging with an anisotropic semivariogram (OKA); ordinary krigin g within local neighborhoods (OKN); universal kriging (UK); and median polish kriging (MPK). Organic matter maps produced from the five meth ods showed similar large-scale features but marked differences in the finer features. A comparison of percentage of total area in each organ ic matter range among mapping methods also showed strong similarities; however, the proportion of the field assigned to each range differed by as much as 7%. Larger differences would be expected at large sample spacing. Although the five methods produced similar maps, selection o f the ''best'' technique should be based on selection of an associated model that best accounts for and describes the nature of the cause of the variation.