A modified model for estimating the full description of soil particle size distribution

<p>The PARIO system is a novel technique for the measurement of soil particle-size distribution. It is a computerized sedimentation-based system that will yield a quasi-continuous particle-size distribution curve. Given that it is semi-automated, continuous and sedimentation-based, this system promises to become a good and compatible alternative to the traditional pipette or hydrometer techniques. Through hundreds of measurements we have acquired practical operational knowledge that this poster will share with potential future users. We will also present quantitative information on the technique&#8217;s sensitivity to e.g. temperature shift or intermittent vibration during measurement. We also used a set of 45 soil samples of various texture from Norway to compare particle-size distribution measured by the PARIO system, the traditional pipette technique and laser diffractometry. We discuss measurement results as well as related sample-preparation aspects.</p>

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Natural succession of grassland on the Loess Plateau of China affects multifractal characteristics of soil particle-size distribution and soil nutrients

Ecological Research ◽

10.1007/s11284-016-1399-y ◽

2016 ◽

Vol 31 (6) ◽

pp. 891-902 ◽

Cited By ~ 11

Author(s):

Caili Sun ◽

Guobin Liu ◽

Sha Xue

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Soil Nutrients ◽

Loess Plateau ◽

Soil Particle ◽

The Loess Plateau ◽

Natural Succession ◽

Soil Particle Size ◽

Soil Particle Size Distribution

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Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling

PLoS ONE ◽

10.1371/journal.pone.0233242 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0233242

Author(s):

Elizabeth Jeanne Parent ◽

Serge-Étienne Parent ◽

Léon Etienne Parent

Keyword(s):

Machine Learning ◽

Particle Size ◽

Particle Size Distribution ◽

Soil Carbon ◽

Size Distribution ◽

Soil Particle ◽

Blue Color ◽

Soil Particle Size ◽

Soil Particle Size Distribution ◽

Method R

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.

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