Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

Šinkovičová, Miroslava; Igaz, Dušan; Kondrlová, Elena; Jarošová, Miriam

doi:10.1088/1757-899x/245/7/072025

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…The experimental area was used for cultivating potatoes in the season before our experiment and the conventional procedure of deep ploughing was then performed [27,28]. The pH, carbonates content [29], grain-size distribution [30,31], concentrations of micronutrients [32], and Zn availability in soil were measured using DTPA extraction [33] before tillage (Table 1) and fertilizers were then applied manually.…”

Section: Methodsmentioning

confidence: 99%

Effect of Foliar Spray Application of Zinc Oxide Nanoparticles on Quantitative, Nutritional, and Physiological Parameters of Foxtail Millet (Setaria italica L.) under Field Conditions

et al. 2019

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It has been shown that the foliar application of inorganic nano-materials on cereal plants during their growth cycle enhances the rate of plant productivity by providing a micro-nutrient source. We therefore studied the effects of foliarly applied ZnO nanoparticles (ZnO NPs) on Setaria italica L. foxtail millet’s quantitative, nutritional, and physiological parameters. Scanning electron microscopy showed that the ZnO NPs have an average particle size under 20 nm and dominant spherically shaped morphology. Energy dispersive X-ray spectrometry then confirmed ZnO NP homogeneity, and X-ray diffraction verified their high crystalline and wurtzite-structure symmetry. Although plant height, thousand grain weight, and grain yield quantitative parameters did not differ statistically between ZnO NP-treated and untreated plants, the ZnO NP-treated plant grains had significantly higher oil and total nitrogen contents and significantly lower crop water stress index (CWSI). This highlights that the slow-releasing nano-fertilizer improves plant physiological properties and various grain nutritional parameters, and its application is therefore especially beneficial for progressive nanomaterial-based industries.

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Section: Methodsmentioning

confidence: 99%

Effect of Foliar Spray Application of Zinc Oxide Nanoparticles on Quantitative, Nutritional, and Physiological Parameters of Foxtail Millet (Setaria italica L.) under Field Conditions

et al. 2019

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“…min [57] and 30 kHz for 30 min [2]. Although fragile quartz grains may be broken using ultrasonication [58], the [silt | sand] balance for the sedimentation method showed results close to the 2-min sonicated laser method.…”

Section: Plos Onementioning

confidence: 83%

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

Parent

2021

PLoS ONE

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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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“…The 40 kHz sonication for a duration of 2 min appeared to be suitable for sandy soils [54]. Variants include 36 kHz for 3 min [55] and 30 kHz for 30 min [2]. Although fragile quartz grains may be broken using ultrasonication [56], the [silt | sand] balance for the Bouyoucos method showed results close to the 2-min sonicated laser method.…”

Section: Laser Methodsmentioning

confidence: 94%

Machine learning prediction of particle-size distribution from infrared spectra, methodologies and soil features

Parent

2020

Preprint

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13Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends 14 on soil preparation, methodology (sedimentation, laser), settling times and relevant soil 15 features. Compositional soil data may require log ratio (ilr) transformation to avoid 16 Infrared spectroscopy (IRS) more accurately predicts clay than sand and silt contents [10] 59 because the IR spectrum is sensitive to clay mineralogy [10][11][12] and total reflectance 60 decreases as grain size increases [13,14]. Light absorption is also influenced by soil 61 features such as particle roundness [15][16][17][18], soil pH, and the Mehlich-3 soil test for Ca, 62 Mg and Mn [19]. The IRS detects Al-OH (2200 nm) and Fe-OH (2290 nm) [20,21] that 63 in turn have an impact on soil structure [22] and IR reflectance [23]. The VIS-NIR 64 spectra are sensitive to soil moisture and C content [24]. Organic matter, multi-nutrient 65 extraction and reconstituted bulk density from scooped soil samples are also common 66 features quantified in routine laboratories.67 The sedimentation methods provide percentages of sand, silt, and clay from the log-log 68 relationship between settling time and suspension density. The slope and intercept return 69 proportions of sand, silt and clay at pre-selected settling times that may vary between 70 laboratories [1], thus affecting the accuracy of IRS models. Providing more flexibility 71 using the slope and intercept of the log-log relationship and reducing the arbitrariness of 72 the settling time selection may allow increased reliability of IRS models calibrated 73 against Bouyoucos methods. 74 There are unattended sources of error in IRS calibration. There is systematic negative 75 covariance between sand, silt and clay fractions due to resonance within the ternary 76 diagram [25]. Indeed, there are D-1 degrees of freedom in a D-part composition [26]. Not 77 considering the problem of closure to 100% in statistical analysis, confidence intervals 78 about means of proportions may take values outside of the compositional space, i.e. < 0 79 or > 100% [27], and the measures of distance and dissimilarity are non-Euclidian [28]. To 80 return unbiased statistical results, orthonormal balances among subsets of components 81 can be computed as D-1 isometric log ratios (ilr) [29]. The back-transformed ilr values 82 allow recovering proportions of sand, silt, and clay totalling exactly 100% within the 83 limits of the ternary diagram. 84 For both the sedimentation and laser techniques there are several soil pre-treatments 85 (peroxide, sodium hypochlorite, sodium hexametaphosphate, sonication intensity), soil 86 features, calibration techniques, options (NIR 2X, NIR 4X, settling times or suspension 87 density function for sedimentation; pump and stirrer spin, refractive index of the medium, 88 real or imaginary refractive index, density for laser) and expressions (percentages, ratios) 89 that influence results of particle-size distribution. Machine learning (ML) is an emerging 90 data mining technique of ...

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Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

Cited by 17 publications

References 7 publications

Effect of Foliar Spray Application of Zinc Oxide Nanoparticles on Quantitative, Nutritional, and Physiological Parameters of Foxtail Millet (Setaria italica L.) under Field Conditions

Effect of Foliar Spray Application of Zinc Oxide Nanoparticles on Quantitative, Nutritional, and Physiological Parameters of Foxtail Millet (Setaria italica L.) under Field Conditions

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

Machine learning prediction of particle-size distribution from infrared spectra, methodologies and soil features

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