Models for prediction of soil precompression stress from readily available soil properties

Models for prediction of soil precompression stress from readily available soil properties Compaction of the subsoil is an almost irreversible damage to the soil resource. Modern machinery exerts high mechanical stresses to the subsoil, and a range of studies report significant effects on soil functions. There is an urgent need for quantitative knowledge of soil strength in order to evaluate sustainability of current field traffic. The aim of this study was to identify the most important drivers of soil precompression stress, σpc, and to develop pedotransfer functions for prediction of σpc. We revisited previously published data on σpc for a silty clay loam soil at a range of soil matric potentials. σpc was estimated from the original stress-strain curves by a novel, numerical method for estimating the stress at maximum curvature, assumingly partitioning the curve into elastic and plastic sections. Multiple regression was used to identify the drivers best describing the variation in σpc data. For the plough layer, σpc increased with bulk density (BD), which explained 77% of the variation. For the subsoil layer just beneath the ploughing depth, the model best describing σpc data included the drivers BD and pF, with pF defined as the log to the negative matric potential. The model was strongly significant with R2 = 0.90. The same trend was found for three subsoil layers from 0.35–0.95 m depth, but the model accounted for only 16% of the variation in σpc. A model involving samples from all soil layers and including BD, pF and soil clay content accounted for 38% of the variation. This model predicted σpc to be constant at pF ~2 across soil clay contents for a given soil BD. For pF < 2, σpc was predicted to be higher for sandy soils than for soils rich in clay. In contrast, σpc increased with clay content for dryer conditions (pF > 2). Model predictions correlated well with measured data in two independent data sets from the literature. However, the predictions were approximately double those of one of the data sets. This may relate to the longer stress application used in laboratory compression tests for these data compared to the other calibration data set and to the procedure used in this study. We encourage further studies of the effect of stress application procedures in compression tests. The prediction equations established in this investigation have to be verified based on measurements of σpc for a range of soil types, soil horizons and soil moisture conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoderma Elsevier

Models for prediction of soil precompression stress from readily available soil properties

Loading next page...
 
/lp/elsevier/models-for-prediction-of-soil-precompression-stress-from-readily-kUkH1Cx0AT
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0016-7061
eISSN
1872-6259
D.O.I.
10.1016/j.geoderma.2018.01.028
Publisher site
See Article on Publisher Site

Abstract

Compaction of the subsoil is an almost irreversible damage to the soil resource. Modern machinery exerts high mechanical stresses to the subsoil, and a range of studies report significant effects on soil functions. There is an urgent need for quantitative knowledge of soil strength in order to evaluate sustainability of current field traffic. The aim of this study was to identify the most important drivers of soil precompression stress, σpc, and to develop pedotransfer functions for prediction of σpc. We revisited previously published data on σpc for a silty clay loam soil at a range of soil matric potentials. σpc was estimated from the original stress-strain curves by a novel, numerical method for estimating the stress at maximum curvature, assumingly partitioning the curve into elastic and plastic sections. Multiple regression was used to identify the drivers best describing the variation in σpc data. For the plough layer, σpc increased with bulk density (BD), which explained 77% of the variation. For the subsoil layer just beneath the ploughing depth, the model best describing σpc data included the drivers BD and pF, with pF defined as the log to the negative matric potential. The model was strongly significant with R2 = 0.90. The same trend was found for three subsoil layers from 0.35–0.95 m depth, but the model accounted for only 16% of the variation in σpc. A model involving samples from all soil layers and including BD, pF and soil clay content accounted for 38% of the variation. This model predicted σpc to be constant at pF ~2 across soil clay contents for a given soil BD. For pF < 2, σpc was predicted to be higher for sandy soils than for soils rich in clay. In contrast, σpc increased with clay content for dryer conditions (pF > 2). Model predictions correlated well with measured data in two independent data sets from the literature. However, the predictions were approximately double those of one of the data sets. This may relate to the longer stress application used in laboratory compression tests for these data compared to the other calibration data set and to the procedure used in this study. We encourage further studies of the effect of stress application procedures in compression tests. The prediction equations established in this investigation have to be verified based on measurements of σpc for a range of soil types, soil horizons and soil moisture conditions.

Journal

GeodermaElsevier

Published: Jun 15, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off