TY - JOUR
T1 - Models for prediction of soil precompression stress from readily available soil properties
AU - Schjønning, Per
AU - Lamandé, Mathieu
PY - 2018
Y1 - 2018
N2 - 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, σ
pc, and to develop pedotransfer functions for prediction of σ
pc. We revisited previously published data on σ
pc for a silty clay loam soil at a range of soil matric potentials. σ
pc 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 σ
pc data. For the plough layer, σ
pc increased with bulk density (BD), which explained 77% of the variation. For the subsoil layer just beneath the ploughing depth, the model best describing σ
pc data included the drivers BD and pF, with pF defined as the log to the negative matric potential. The model was strongly significant with R
2 = 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 σ
pc. 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 σ
pc to be constant at pF ~2 across soil clay contents for a given soil BD. For pF < 2, σ
pc was predicted to be higher for sandy soils than for soils rich in clay. In contrast, σ
pc 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 σ
pc for a range of soil types, soil horizons and soil moisture conditions.
AB - 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, σ
pc, and to develop pedotransfer functions for prediction of σ
pc. We revisited previously published data on σ
pc for a silty clay loam soil at a range of soil matric potentials. σ
pc 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 σ
pc data. For the plough layer, σ
pc increased with bulk density (BD), which explained 77% of the variation. For the subsoil layer just beneath the ploughing depth, the model best describing σ
pc data included the drivers BD and pF, with pF defined as the log to the negative matric potential. The model was strongly significant with R
2 = 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 σ
pc. 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 σ
pc to be constant at pF ~2 across soil clay contents for a given soil BD. For pF < 2, σ
pc was predicted to be higher for sandy soils than for soils rich in clay. In contrast, σ
pc 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 σ
pc for a range of soil types, soil horizons and soil moisture conditions.
KW - Bulk density
KW - Matric potential
KW - Pedotransfer function
KW - Soil texture
UR - http://www.scopus.com/inward/record.url?scp=85042912862&partnerID=8YFLogxK
U2 - 10.1016/j.geoderma.2018.01.028
DO - 10.1016/j.geoderma.2018.01.028
M3 - Journal article
SN - 0016-7061
VL - 320
SP - 115
EP - 125
JO - Geoderma
JF - Geoderma
IS - June
ER -