Erosion Tolerance for Cropland: Application of the Soil Survey Data Base

2015 ◽  
pp. 113-130 ◽  
Author(s):  
R. B. Grossman ◽  
C. R. Berdanier
Keyword(s):  
Data Base ◽  
Survey Data ◽  
Soil Survey

Use of Soil Survey Data for Regional Soil Water Simulation Models

1985 ◽  
Vol 49 (5) ◽  
pp. 1238-1244 ◽  
Author(s):  
J. H. M. Wösten ◽  
J. Bouma ◽  
G. H. Stoffelsen
Keyword(s):  
Soil Water ◽  
Survey Data ◽  
Simulation Models ◽  
Soil Survey ◽  
Water Simulation

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

scholarly journals Processing of conventional soil survey data using geostatistical methods

2011 ◽  
Vol 50 (No. 8) ◽  
pp. 352-357 ◽  
Author(s):  
V. Penížek ◽  
L. Borůvka
Keyword(s):  
Survey Data ◽  
Extreme Values ◽  
Clay Content ◽  
Original Data ◽  
Soil Survey ◽  
Geostatistical Methods ◽  
Suitable Treatment ◽  
Simple Exclusion ◽  
Soil Units ◽  
Zonal Soils

The aim of this study is to find a suitable treatment of conventional soil survey data for geostatistical exploitation. Different aims and methods of a conventional soil survey and the geostatistics can cause some problems. The spatial variability of clay content and pH for an area of 543 km<sup>2</sup> was described by variograms. First the original untreated data were used. Then the original data were treated to overcome the problems that arise from different aims of conventional soil survey and geostatistical approaches. Variograms calculated from the original data, both for clay content and pH, showed a big portion of nugget variability caused by a few extreme values. Simple exclusion of data representing some specific soil units (local extremes, non-zonal soils) did not bring almost any improvement. Exclusion of outlying values from the first three lag classes that were the most influenced due to a relatively big portion of these extreme values provided much better results. The nugget decreased from pure nugget to 50% of the sill variability for clay content and from 81 to 23% for pH.

scholarly journals Iras Minor Planet Survey

1994 ◽  
Vol 160 ◽  
pp. 463-466 ◽  
Author(s):  
Edward F. Tedesco
Keyword(s):  
Data Base ◽  
Survey Data ◽  
Minor Planet ◽  
Hard Copy ◽  
Machine Readable

This chapter describes the contents and organization of the IRAS Minor Planet Survey Data Base and tells how to obtain hard-copy and machine-readable versions of the data.

GROUPING OF LOWER FRASER VALLEY SOILS OF BRITISH COLUMBIA BY NUMERICAL METHODS

1973 ◽  
Vol 53 (4) ◽  
pp. 435-443
Author(s):  
B. KLOOSTERMAN ◽  
L. M. LAVKULICH
Keyword(s):  
British Columbia ◽  
Cluster Analysis ◽  
Survey Data ◽  
Soil Classification ◽  
Data File ◽  
Soil Survey ◽  
Data Sets ◽  
Lower Fraser Valley ◽  
Average Profile ◽  
Fraser Valley

The British Columbia Soil Survey Data File was used to numerically classify soils of the Lower Fraser Valley of British Columbia. The data employed in the numerical-classification procedure were routine soil survey data and this classification was compared with the Canadian Soil Classification System. Three types of soil-profile data sets were used: average surface slice, selected average profile, and average profile. Methods of statistical analysis were cluster analysis and hierarchial grouping analysis. No marked differences in grouping resulted by the two methods of analyses. The average profile method seemed to give better correspondence with the Canadian System of Soil Classification. Consideration of surface layers alone did not correspond with the Canadian Soil Classification. The hierarchical grouping scheme resulted in better defined groups than the cluster analysis approach.

A method of storage and rapid retrieval of soil survey data

1978 ◽  
Vol 14 (1) ◽  
pp. 41-43
Author(s):  
P. J. Cole ◽  
K. A. Watson
Keyword(s):  
Survey Data ◽  
Soil Survey

The application of soil survey data in opencast coal sites in Scotland

1983 ◽  
Vol 3 (3) ◽  
pp. 225-238 ◽  
Author(s):  
J.H. Gauld ◽  
K.W.M. Brown
Keyword(s):  
Survey Data ◽  
Soil Survey
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