Predicting soil depth in a humid tropical watershed: A comparative analysis of best-fit regression and geospatial models

Caires, Sunshine A. De; Martin, Chaney C. G. St.; Wuddivira, Mark N.; Farrick, Kegan K.; Zebarth, Bernie J.

doi:10.1016/j.catena.2022.106843

Cited by 6 publications

(5 citation statements)

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“…Soil texture is one of, if not the most important, soil parameters used by the World Reference Base as the international standard for soil classification. Soil texture determines the water‐holding capacity, porosity, and water flow dynamics of soils and is intrinsically linked to the distribution of chemical soil properties such as ECa (De Caires et al., 2023), pH (R. Francis et al., 2021), ECe (De Caires et al., 2015), macro, and micronutrients (Atwell et al., 2023), as well as aggregate stability (J. R. Francis et al., 2023; Wuddivira et al., 2013), slope stability, and suction (Roopnarine et al., 2014). Clustering analysis on bibliometric data revealed that although there has been a significant advancement in PSD measurements with remote and proximal sensing techniques being employed, sedimentation methods using hydrometer and pipette techniques are still overwhelmingly the methods of choice (Figure 2).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, regression models have been developed using hygroscopic water content at varying humidities (Arthur et al, 2015;Chen et al, 2014Chen et al, , 2015Wuddivira et al, 2012) and permanent wilting point measured with a chilledmirror dewpoint meter (Farrick et al, 2019) to estimate PSD. Despite these advances, PSD determination from sedimentation procedures (Day, 1965) has remained indispensable as it is often used on samples collected for ground-truthing, which is required for the calibration of proximal sensing (De Caires et al, 2023) and laboratory point meters to develop predictive regression models (Farrick et al, 2019). Therefore, its accuracy remains an integral part of the advances in soil texture determination.…”

Section: Introductionmentioning

confidence: 99%

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Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method

Wuddivira,

Bramble,

Ramlochan

et al. 2023

J. Plant Nutr. Soil Sci.

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BackgroundParticle size distribution (PSD) is a fundamental soil measurement that is crucial for agriculture, engineering, hydrological, and environmental applications as well as in basic research such as in developing water release curves in flow and transport modeling. The accurate determination of PSD, particularly clay content, is especially critical in ascertaining the magnitude and rates of many physical, chemical, and hydrological processes in soils. The accuracy of the hydrometer method for PSD, which relies on the settling velocity of soil particles in a soil suspension, could be affected by the density of the suspension, which in turn, depends on the initial sample mass, texture, and mineralogy of the soil.AimsWe hypothesized that (1) initial mass determines the concentration and the physical interaction of particles in a soil suspension and affects particle settlement, recovery, and the accuracy of the hydrometer method, and (2) the widely used initial air‐dry mass of 50 g underestimates the clay percentage of medium to heavy textured tropical soils. The objective of this study was to investigate the effect of mass and KCl on clay recovery in tropical soils with varying texture and mineralogy using the hydrometer method.MethodsThe experiment comprised seven soils, four soil masses (10, 20, 30, 50 g), and two KCl treatments in a 7 × 4 × 2 factorial design replicated three times.ResultsResults showed a general trend of increasing estimates of clay % as the soil mass decreased from 50 to 10 g and the KCl concentration increased from 0 to 0.0025 M. This implies that using an initial sample mass of 50 g in sedimentation methods, underestimates, and 10 g overestimates the clay % in predominantly medium to heavy textured tropical soils. Interestingly, there was a drastic change in texture for most of the soils as the sample mass decreased to 10 g.ConclusionsWe conclude that the initial air‐dry mass affects the clay recovery of medium to heavy textured humid tropical soils. Results suggest that the ideal initial mass for these soils lies between 20 and 30 g. Furthermore, the mechanistic pathway that drives the observed decrease in clay recovery as soil sample mass increase could be due to the flocculating tendency of KCl.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method

Wuddivira,

Bramble,

Ramlochan

et al. 2023

J. Plant Nutr. Soil Sci.

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“…The goal was to identify robust spatial relationships between the dependent and independent variables. For further analysis, we utilized OLS to explore suitable variables with spatial autocorrelation, as evaluated through global Moran’s I ( 47 ).…”

Section: Methodsmentioning

confidence: 99%

Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

Almasi,

Bakhshi Lomer,

Khaksar

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road crashes are a major cause of fatalities worldwide, posing significant challenges for road-safety experts in selecting appropriate crash-frequency estimation models. This study introduces localized safety performance functions (C-SPFs), which explore the spatial variation of crash frequency and the spatial correlation between dependent variables. The exploratory spatial regression method is employed to identify optimal spatial associations. The study further predicts crashes using geographically weighted Poisson regression (GWPR) and generalized Poisson regression. Results indicate that C-SPFs offer greater accuracy than do models calibrated solely on annual average daily traffic. Moreover, the proposed model is especially relevant for jurisdictions facing higher heavy-vehicle traffic and frequent crashes. The development of C-SPFs and the use of GWPR provide valuable tools for policymakers and road-safety experts in enhancing crash-frequency estimation accuracy. Implementing these techniques can aid in prioritizing safety measures and countermeasures, especially in regions with significant heavy-vehicle traffic and crash occurrences. Additionally, the integration of spatial-analysis techniques and localized models can lead to more effective transportation planning and targeted road-safety interventions, ultimately contributing to reducing the burden of road crashes on a global scale.

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“…This error is related to the variations between ECa and estimated ECe values. Despite their benefits, as regards their predictive efficacy, they cannot reach the same degree of accuracy as the geostatistical models [7,93]. Nonetheless, the regression modeling approach yields viable results in most cases of regional scales [94,95]; hence, it remains one of the most appealing and preferable calibration techniques.…”

Section: Deterministic and Stochastic Conversion Of Ecamentioning

confidence: 99%

“…Additionally, since most of these calibration models rely on linear equations, they use a best fit line through the data, usually an ordinary least-squares (OLS) regression, a technique that is accompanied by certain attributes. This prediction method is associated with key assumptions such as the normality and independence of errors and homoscedasticity for obtaining valid coefficients [93,114]. Therefore, deviations from meeting these criteria can compromise the reliability of the coefficients and result in inaccurate predictions and potentially biased conclusions for the salt-affected area.…”

Section: Regression-based Models (Linear Mlr Simple Depth-weighted Co...mentioning

confidence: 99%

Assessment and Mapping of Soil Salinity Using the EM38 and EM38MK2 Sensors: A Focus on the Modeling Approaches

Petsetidi,

Kargas

2023

Land

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Soil salinization and its detrimental agricultural, environmental, and socioeconomic impact over extended regions represent a major global concern that needs to be addressed. The sustainability of agricultural lands and the development of proper mitigation strategies require effective monitoring and mapping of the saline areas of the world. Therefore, robust modeling techniques and efficient sensors that assess and monitor the spatial and temporal variations in soil salinity within an area, promptly and accurately, are essential. The aim of this paper is to provide a comprehensive and up-to-date review of the modeling approaches for the assessment and mapping of saline soils using data collected by the EM38 and EM38MK2 (MK2) sensors at different scales. By examining the current and latest approaches and highlighting the most noteworthy considerations related to their accuracy and reliability, the intention of this review is to elucidate and underline the role of the EM38 and the MK2 type in the recent needs of detecting and interpreting soil salinity. Another aim is to assist researchers and users in selecting the optimal approach for future surveys and making well-informed decisions for the implementation of precise management practices. The study’s findings revealed that the integration of the EM38 and MK2 sensors with remote sensing data and advanced methods like machine learning and inversion is a promising approach to the accurate prediction and mapping of the spatiotemporal variations in soil salinity. Therefore, future research focused on validating and expanding such sophisticated modeling applications to regional and global scales should be increased.

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Predicting soil depth in a humid tropical watershed: A comparative analysis of best-fit regression and geospatial models

Cited by 6 publications

References 60 publications

Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method

Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method

Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

Assessment and Mapping of Soil Salinity Using the EM38 and EM38MK2 Sensors: A Focus on the Modeling Approaches

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