Stability of biochar in five soils: Effects from soil property

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Biochar-compost addition benefits Phragmites australis growth and soil property in coastal wetlands

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.145166 ◽

2021 ◽

Vol 769 ◽

pp. 145166

Author(s):

Jin-Feng Liang ◽

Qian-Wei Li ◽

Jun-Qin Gao ◽

Jiu-Ge Feng ◽

Xiao-Ya Zhang ◽

...

Keyword(s):

Phragmites Australis ◽

Coastal Wetlands ◽

Soil Property

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Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4041350 ◽

2019 ◽

Vol 14 (2) ◽

Cited By ~ 4

Author(s):

K. K. Choi ◽

Paramsothy Jayakumar ◽

Matthew Funk ◽

Nicholas Gaul ◽

Tamer M. Wasfy

Keyword(s):

Normal Distribution ◽

Soil Property ◽

Region Of Interest ◽

Mobility Model ◽

Maximum Speed ◽

Next Generation ◽

Input Parameters ◽

Terrain Elevation ◽

Dynamic Kriging ◽

Geotechnical Databases

A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the next generation NATO reference mobility model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) and reliability assessment for Speed Made Good and GO/NOGO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction, and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of input parameters, a dynamic kriging (DKG) surrogate model is obtained for the maximum speed of the nevada automotive test center (NATC) wheeled vehicle platform complex terramechanics model. Finally, inverse reliability analysis using Monte Carlo simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and GO/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

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Research trends of microplastics in the soil environment: Comprehensive screening of effects

Soil Ecology Letters ◽

10.1007/s42832-021-0077-3 ◽

2021 ◽

Author(s):

Shin Woong Kim ◽

Matthias C. Rillig

Keyword(s):

Soil Properties ◽

Additional Data ◽

Soil Property ◽

Dry Weight ◽

Current Data ◽

Soil Biota ◽

Soil Parameters ◽

Soil Environment ◽

Soil System ◽

Sensitivity Distribution

AbstractWe collated and synthesized previous studies that reported the impacts of microplastics on soil parameters. The data were classified and integrated to screen for the proportion of significant effects, then we suggest several directions to alleviate the current data limitation in future experiments. We compiled 106 datasets capturing significant effects, which were analyzed in detail. We found that polyethylene and pellets (or powders) were the most frequently used microplastic composition and shape for soil experiments. The significant effects mainly occurred in broad size ranges (0.1–1 mm) at test concentrations of 0.1%–10% based on soil dry weight. Polyvinyl chloride and film induced significant effects at lower concentrations compared to other compositions and shapes, respectively. We adopted a species sensitivity distribution (SSD) and soil property effect distribution (SPED) method using available data from soil biota, and for soil properties and enzymes deemed relevant for microplastic management. The predicted-no-effect-concentration (PNEC)-like values needed to protect 95% of soil biota and soil properties was estimated to be between 520 and 655 mg kg−1. This study was the first to screen microplastic levels with a view toward protecting the soil system. Our results should be regularly updated (e.g., quarterly) with additional data as they become available.

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A Comparative Study of Various Methods of Handling Missing Data in UNSODA

Agriculture ◽

10.3390/agriculture11080727 ◽

2021 ◽

Vol 11 (8) ◽

pp. 727

Author(s):

Yingpeng Fu ◽

Hongjian Liao ◽

Longlong Lv

Keyword(s):

Missing Data ◽

Missing Values ◽

Soil Property ◽

Particle Density ◽

Organic Matter Content ◽

Nonparametric Tests ◽

Matter Content ◽

Support Vector ◽

Soil Database ◽

Property Data

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

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A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Land ◽

10.3390/land10050544 ◽

2021 ◽

Vol 10 (5) ◽

pp. 544

Author(s):

Jetse J. Stoorvogel ◽

Vera L. Mulder

Keyword(s):

Comparative Analysis ◽

Soil Properties ◽

Soil Property ◽

Evaluation Criteria ◽

Clay Content ◽

Soil Database ◽

Global Environmental ◽

Global Soil ◽

Mean Square Difference ◽

Methodological Evaluation

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

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Soil property distribution following oil well access road removal in North Dakota, USA

Canadian Journal of Soil Science ◽

10.1139/cjss-2017-0141 ◽

2018 ◽

Vol 98 (2) ◽

pp. 369-380 ◽

Cited By ~ 2

Author(s):

Heather L. Matthees ◽

David G. Hopkins ◽

Francis X.M. Casey

Keyword(s):

North Dakota ◽

Soil Property ◽

Oil Well ◽

Property Distribution ◽

Access Road

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Combining two soil property rasters using an adaptive gating approach

Soil Research ◽

10.1071/sr14275 ◽

2015 ◽

Vol 53 (8) ◽

pp. 907 ◽

Cited By ~ 5

Author(s):

David Clifford ◽

Yi Guo

Keyword(s):

Confidence Intervals ◽

Soil Property ◽

Mean Squared Error ◽

Geographic Space ◽

Covariate Information ◽

Squared Error ◽

Inverse Variance ◽

Machine Learning Approach ◽

Further Development ◽

Better Than

Given the wide variety of ways one can measure and record soil properties, it is not uncommon to have multiple overlapping predictive maps for a particular soil property. One is then faced with the challenge of choosing the best prediction at a particular point, either by selecting one of the maps, or by combining them together in some optimal manner. This question was recently examined in detail when Malone et al. (2014) compared four different methods for combining a digital soil mapping product with a disaggregation product based on legacy data. These authors also examined the issue of how to compute confidence intervals for the resulting map based on confidence intervals associated with the original input products. In this paper, we propose a new method to combine models called adaptive gating, which is inspired by the use of gating functions in mixture of experts, a machine learning approach to forming hierarchical classifiers. We compare it here with two standard approaches – inverse-variance weights and a regression based approach. One of the benefits of the adaptive gating approach is that it allows weights to vary based on covariate information or across geographic space. As such, this presents a method that explicitly takes full advantage of the spatial nature of the maps we are trying to blend. We also suggest a conservative method for combining confidence intervals. We show that the root mean-squared error of predictions from the adaptive gating approach is similar to that of other standard approaches under cross-validation. However under independent validation the adaptive gating approach works better than the alternatives and as such it warrants further study in other areas of application and further development to reduce its computational complexity.

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