Abstract
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.
Highlights
Soil particle-size distribution (PSD) is of prime importance for plant growth and soil management [1]
The present paper presented IR-Stochastic Gradient Boosting (SGB) results across several methodologies and features that are rarely addressed altogether in the literature
Particle-size distribution predicted by infrared spectroscopy the same direction, Viscarra Rossel et al (2006) [9] concluded that MIR was more suitable than NIR for texture and carbon determination, due to higher incidence of spectral bands combined with higher intensity and specificity of the signal compared to NIR
Summary
Soil particle-size distribution (PSD) is of prime importance for plant growth and soil management [1]. Mimicking particle sedimentation in natural water bodies, PSD has been traditionally quantified using the sieve-pipette method that determines particle mass, and the sievehydrometer or sieve-plummet balance method that measures changes in suspension density [2]. Sedimentation techniques are thought to overestimate the concentration of plate-like clay particles that do not fit into Stokes’ law [3]. Inc., Groupe Gosselin FG, Prochamps Inc., and Ferme Daniel Bolduc Inc. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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