Correlation Of Soil Properties Research Articles

Soil greenhouse gas emissions from drained and rewetted agricultural bare peat mesocosms are linked to geochemistry

In view of climate considerations regarding the management of peatlands, there is a need to assess whether rewetting can mitigate greenhouse gas (GHG) emissions, and notably how site-specific soil-geochemistry will influence differences in emission magnitudes. However, there are inconsistent results regarding the correlation of soil properties with heterotrophic respiration (Rh) of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from bare peat. In this study, we determined 1) soil-, and site-specific geochemical components as drivers for emissions from Rh on five Danish fens and bogs, and 2) emission magnitudes under drained and rewetted conditions. For this, a mesocosm experiment was performed under equal exposure to climatic conditions and water table depths controlled to either -40 cm, or -5 cm. For the drained soils, we found that annual cumulative emissions, accounting for all three gases, were dominated by CO2, contributing with, on average, 99 % to a varying global warming potential (GWP) of 12.2–16.9 t CO2eq ha−1 yr−1. Rewetting lowered annual cumulative emissions from Rh by 3.2–5.1 t CO2eq ha−1 yr−1 for fens and bogs, respectively, despite a high variability of site-specific CH4 emissions, contributing with 0.3–3.4 t CO2 ha−1 yr−1 to the GWP. Overall, analyses using generalized additive models (GAM) showed that emission magnitudes were well explained by geochemical variables. Under drained conditions, significant soil-specific predictor variables for CO2 flux magnitudes were pH, phosphorus (P), and the soil substrate's relative water holding capacity (WHC). When rewetted, CO2 and CH4 emissions from Rh were affected by pH, WHC, as well as contents of P, total carbon and nitrogen. In conclusion, our results found the highest GHG reduction on fen peatlands, further highlighting that peat nutrient status and acidity, and the potential availability of alternative electron acceptors, might be used as proxies for prioritising peatland areas for GHG mitigation efforts by rewetting.

Correlation of Soil Properties with Costus cupreifolius Maas Admixture during Stabilization

The study investigated the changes in soil properties as a function bagasse ash proportion during soil stabilization. Addition of bagasse ash in soil during stabilization is carried out to improve the properties of soil susceptible to deformation under load effect. The bagasse ash was obtained from Costus cupreifolius mass. The experimental values obtained from maximum dry density (MDD), optimum moisture content (OMC), consistency limits, California bearing ratio (CBR) and unconfined compressive strength (UCS) were fitted to a linear model to ascertain the degree of correlation between the properties and the percentage of bagasse ash. Experimental results showed that bagasse ash improved the properties of the soil positively. Thus, the maximum dry density, optimum moisture content, and the consistency limits of the stabilized soil reduced with addition of bagasse ash, while the California bearing ratio and unconfined compressive strength of the soil were increased. The model also interpreted the fitted experimental observations with correlation coefficients ranging from 0.7501 to 0.9792. Therefore, using a mathematical model will be useful to predict the properties of soil for a given mix design without direct measurement, especially in the case where the instrument is prone to error. Application of model would also be useful for design and analysis.

Quantifying the relationships of soil properties and crop growth with yield in a NPK fertilizer application maize field

Spatial variability of soil properties, crop performance, fertilization management, and tillage methods play an important role in yield prediction and improvement. However, the farm nutrient management in most regions of the world is still based on conventional and uniform management strategies, without take into consideration the spatial variability of the above factors. Therefore, the objectives of this study were to delineate potential management zones (MZs), with the considering of spatial variability of soil properties, crop performance, fertilization management, tillage methods, and investigate their relationships with yield, and their contributions to yield in each management zone. A 1.60 ha maize field with fertilizer and tillage management, in Lishu County, Jilin Province, China, was selected as the study area and soil samples with soil organic matter, total nitrogen and soil pH measured values, crop samples with crop height, leaf nitrogen content, above ground biomass at V6, silking, and maturity growth stages, and yield measured values were collected in 2019 and 2020. Firstly, the rectangular MZs were defined to divide the study area into relatively homogeneous areas based on the spatial variability of soil properties, crop performance, and yield of 2019 and 2020. Secondly, the correlations of soil properties and crop performance with yield were obtained across MZs and growth stages in 2019 and 2020. Lastly, the yield prediction models were established based on the above four types of factors by using random forest (RF) model and the contributions of main factors were obtained quantitatively across different MZs and years. The results showed that (1) the small maize field was divided into three rectangular MZs and most of the soil and crop attributes presented homogeneous characteristics in each zone; (2) correlations of soil properties and crop performance with yield varied across different MZs and growth stages, and even different years; (3) RF model obtained good performance across MZs and years, with R2 = 0.86 and RMSE = 0.77 kg/ha in 2019 and R2 = 0.89 and RMSE = 1.04 kg/ha in 2020 both for the whole field. The contribution of main factors for yield varied across both MZs and years, while some characteristics of the main factors’ contributions remain unchanged. This study would open the opportunity to farmers and agricultural experts for site-specific nutrient management in the maize field.

Slope reliability evaluation based on multi-objective grey wolf optimization-multi-kernel-based extreme learning machine agent model

Slope reliability assessment is an efficient methodology for landslide risk mitigation. However, it is also a challenging task owing to various uncertainties and randomness of soil properties in geotechnical engineering. As the slope is a complicated system with high nonlinearity, the stability analysis with high precision and reliability fails to be obtained from the traditional response surface method. To counteract this limitation, this paper proposes a technique based on machine learning for slope reliability evaluation, namely a multi-objective grey wolf optimization-multi-kernel-based extreme learning machine model based on strength reduction method. The probability of slope failure is estimated by using the developed model when connected with Monte Carlo Simulation. Model application to probabilistic evaluation of slope was conducted by two typical case studies. The results show that as compared with Monte Carlo Simulation and other traditional techniques, the developed machine learning method, which combines the advantages of strength reduction method, the multi-objection grey wolf optimization and multi-kernel-based extreme learning machine, demonstrates better computational performance and applicability, and achieves more accurate and reliable failure probability. Further, the proposed approach can also be applied to predict probability of slope failure with consideration of different coefficient of variation and correlation of soil properties. Hence, improved failure probability can be obtained from the developed method, which could offer crucial information for decisions with regard to early landslide warning.

Spatial variability and correlation of soil properties in the Annaba plain (North-East Algeria)

Spatial variability and correlation of soil properties in the Annaba plain (North-East Algeria)

Assessment of important soil properties related to Chinese Soil Taxonomy based on vis–NIR reflectance spectroscopy

As a rapid, inexpensive and accurate analysis technique, vis–NIR spectra has shown great advantages for determining a wide variety of soil properties, such as soil organic matter content, mineral composition, water content, particle size and color. Thus, this technique is becoming increasingly popular in soil science. We aim to assess the applicability of using vis–NIR spectra to estimate eighteen different soil properties that are important for Chinese Soil Taxonomy (CST). In this study, vis–NIR reflectance spectra were measured under laboratory conditions. First, partial least-squares regression (PLSR) was used to predict the following soil properties related to soil classification: coarse crumb, sand, silt, and clay contents, bulk density (BD), pH (H2O), pH (KCl), soil organic matter (SOM), total nitrogen (TN), total potassium (TK), and total phosphorus (TP) contents, cation exchange capacity (CEC), free iron (Fe2O3), soluble salts (salt), available phosphorus (AP), exchangeable aluminum (ExAl), aluminum saturation (AS) and base saturation (BS). Then, the important bands for modeling these soil properties were selected based on the selectivity ratio (SR). Finally, the spectral chromophores of the soil and the correlations of soil properties were analyzed. The results showed that (1) the prediction accuracy based on the PLSR algorithm was good for pH, SOM, TN, Fe2O3, salt, AS and BS (RPD > 2.0, R2 between 0.70 and 0.90). For sand, silt, clay, BD, TP, TK, CEC, AP and ExAl, the PLSR model could achieve acceptable estimates (1.4 < RPD < 2.0, R2 between 0.56 and 0.72), while for coarse crumb, the PLSR model was unable to make reliable predictions (RPD < 1.4, R2 below 0.50). (2) As chromophore properties, SOM, TN, Fe2O3, clay and salt are and can be predicted by spectroscopy. Besides, BD, pH, TK, TP, CEC, AP, ExAl, AS and BS have significant correlations with chromophore properties and can also be predicted by vis–NIR spectroscopy. Therefore, except for coarse crumb, the soil properties important to CST can be quantitatively predicted by PLSR based on vis–NIR reflectance spectroscopy. This study is significant to CST, and it provides a fast and efficient method for soil classification.

Manganese status in upland and lowland rubber-growing soils in Songkhla province, southern Thailand

Rubber trees, the most economic crop in southern Thailand, are generally cultivated in acid upland soils and currently have been extended into lowland areas which induce higher solubility of manganese (Mn) which may result in Mn toxicity to rubber. This study investigated soil Mn and leaf Mn levels in rubber trees grown in upland and lowland soils. Ninety soil samples from upland and lowland rubber plantations in Songkhla province were collected and analyzed for water soluble Mn, NH4OAc Mn, diethylenetriaminepentaacetic acid test (DTPA) Mn, reducible Mn and total Mn forms. Leaf Mn analysis was also undertaken on samples of each plantation. The results revealed that the Mn concentrations of all soil Mn forms and leaf Mn in the lowland soils were higher than those of the upland samples. Comparing the optimum level reported by the Rubber Research Institute, both DTPA Mn and leaf Mn in the upland and lowland samples were high. Correlation analysis showed high and significant positive correlations of DTPA Mn and water Mn (r = 0.715), NH4OAc Mn (r = 0.975), reducible Mn (r = 0.953) and total Mn (r = 0.809) in the lowland samples. Medium to high positive correlations among Mn forms were also found in the upland samples. The correlation of soil properties (pH, electrical conductivity, organic matter, available K, exchangeable Ca, Mg and Na and cation exchange capacity) and soil Mn were clearly defined. These results indicated that soil properties affect the release of Mn. However, a correlation between soil Mn and leaf Mn was not observed.

Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines

In this work, a novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures has been developed, and a reliability-based temporal and spatial maintenance strategy is presented. The spatial correlation of soil properties is modelled via hidden Markov random field. The temporal correlation of the corrosion rate is characterised by the geometric Brownian bridge process. A Bayesian inferential framework is employed to estimate the model parameters of the corrosion growth model using in-line inspection data. The proposed corrosion growth model was validated with actual inspection data. In the reliability analysis, the impact of device detectability is considered and hence the estimated failure probability is more realistic. The proposed maintenance strategy is directly based on the time-specific and location-specific failure probability. The application of the proposed model and maintenance strategy is illustrated through a real-life pipeline system. The results indicate that the proposed maintenance strategy is an adaptive and dynamic scheme that is able to improve the efficiency of inspections.

Correlation of soil properties with weed ocurrence in sugarcane areas

Soil properties can influence weed community composition and weed density agricultural area. Knowing this relationship would allow to choose the best strategy for the control of such plants. This study aimed to investigate the correlation between weed density and chemical and physical attributes of soil in three areas (UCO, USC, and UPA) for commercial sugarcane cultivation in Campos dos Goytacazes, RJ. Grids of 40 m x 40 m were established in the areas, and soil samples were collected at the intersection points for physical and chemical analysis and evaluation of the soil seed bank (SSB), followed by a phyto-sociological survey of the weeds present. Samples were collected during two periods: February/March and June/July, 2010. SSB presented the greatest number of species per vegetation evaluated in the two sampling periods. Clay content had a positive effect leading to greater weed density in all areas (UCO, USC and UPA) in at least one of the densities (0-10 and 10-20 cm). On the other hand, sand content, when significant, presented a negative correlation with plant density in all the SSB areas analyzed. The pH negatively influenced the density of the species found through the phyto-sociological survey at USC and UPA. Cyperus rotundus, dominant in all areas, correlated positively with phosphorus, potassium, and clay content and negatively with pH and high sand content.

Performance-Based Reliability Design for Deep Foundations Using Monte Carlo Statistical Methods (DFI Student Paper Competition 2012)

Deep foundation designs for service limit state are still deterministic in the current AASHTO LRFD Specifications. To address this deficiency, a performance-based reliability design methodology is developed using the Monte Carlo statistical techniques. In the proposed methodology, the design criteria are defined in terms of the allowable displacement. The spatial variability of soil parameters is considered in the proposed methodology by modeling soil parameters as random fields. Failure is defined as the event that the induced displacement exceeds the limiting displacement. The probability of failure by Monte Carlo approach is the ratio of the number of unsatisfactory performance events to the sample size. Three numerical examples are given to illustrate the application of the proposed methodology for laterally loaded and axially loaded drilled shafts, respectively. The spatial variability and correlation of soil properties were shown to exert significant influences on the foundation design.

Correlation of soil properties and fruit size of &lt;I&gt;Callgonum mongolicum&lt;/I&gt; and related species

Correlations of soil properties and fruit size of Calligonum mongolicum Turcz. and related species were analyzed. The results showed that the particle size characteristics of soils for growing Calligonum mongolicum and related species were similar, and the soils belonged to sandyones. The organic matter contents of soils were low (averaging 1.772 to 3.054 g/kg), and the total salt content of soils was low too (averaging 0.471 g/kg). The pH val- ues indicated that the soils were alkaline, varying from pH 7.65 to 10.25. The results of Principal Component Analysis (PCA) and hierarchical cluster showed that 9 habitats could be divided into 6 types according to salt con- tents and pH values. Fruit sizes of different populations and the soil properties had significant linear correlations (P<0.000). It was concluded that the differences in fruit sizes among the populations of Calligonum mongolicum and related species were related to soil variability, especially the nutrient contents of soils, and fruit size can not be considered as a taxonomic index of Calligonum mongolicum and related species.

Delineating site‐specific irrigation management zones

ABSTRACTSince spatial heterogeneity of soil physical properties has a significant influence on site‐specific irrigation management, field moisture capacity, saturated moisture content, wilting point and soil dry bulk density were used as the data source to derive irrigation management zones. The spatial heterogeneity and correlations of soil properties were firstly characterized using geostatistics and principal component analysis. Then, irrigation management zones were delineated by running management zone analysis software based on the fuzziness performance index and normalized classification entropy, which were used to determine the least number of subzones. Finally, the zoning of irrigation management was verified by performing a statistical analysis as a statistical control of the above classifications. The study site was divided into two subzones having distinct differences between them, indicating that the delineation of irrigation management zones was reasonable. In additional, a stratified sampling method was also adopted to calculate the optimal sampling size of the soil properties in each subzone. Saturated moisture content has the optimal sampling size of 12 and soil water capacity and wilting point have the least, with only 3. The delineation of irrigation management zones and optimal sampling size could provide data support and science‐based information for site‐specific irrigation management. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Effect of spatial correlation structure and transformation model on the design of shallow strip footing

Directional variability of spatial correlation is observed in natural soils due to their depositional characteristics and it influences the response of structures founded on these deposits. Nonetheless, the results presented in most of the available literature are based on the assumption of either isotropic spatial correlation or perfect spatial correlation of soil properties in horizontal and vertical directions. It is also observed from past studies that the effect of transformation model on the total uncertainty is quite significant. Hence, an effort has been made in this paper to study the effect of anisotropy of autocorrelation characteristics of cone tip resistance (qc) and the transformation model on the bearing capacity of a shallow strip footing, founding on the surface of a spatially varying soil mass. The statistics in the vertical direction of the soil mass are taken from 8 Cone Penetration Test (CPT) records and statistics in the horizontal direction are assumed. For the case considered, it is observed that the transformation model significantly influences the degree of variability of design parameter. The results also show that isotropic correlation structure based on the vertical autocorrelation distance underestimates the variability of design parameter. On the other hand, perfect correlation in horizontal or vertical, or both directions, overestimates the variability of design parameters, and produces conservative estimates of allowable bearing capacity.

Seismic reliability analysis of earth slopes under short term stability conditions

Different models were developed for evaluating the probabilistic three-dimensional (3-D) stability analysis of earth slopes and embankments under earthquake loading. The 3-D slope stability model assumed is that of a simple cylindrical failure surface. The probabilistic models evaluate the probability of failure under seismic loading considering the randomness of earthquake occurrence, and earthquake induced acceleration and uncertainties stemming from the discrepancies between laboratory-measured and in-situ values of shear strength parameters. The models also takes into consideration the spatial variabilities and correlations of soil properties. The probabilistic analysis and design approach is capable of obtaining the 2-D and 3-D static and dynamic safety factors, the probability of slope failure, the earthquake induced acceleration coefficient, the yield acceleration coefficient, the earthquake induced displacement, and the probability of allowable displacement exceedance taking into account the local site effect. The approach is applied to a well known landslide case: Congress Street Landslide in Chicago. A sensitivity analysis was conducted on the different parameters involved in the models by applying those models to the Congress Street landslide considering different levels of seismic hazard. Also, a sensitivity analysis was carried out to study the sensitivity of computed results to input parameters of undrained shear strength, and corrective factors. A comparison was made between the different models of failure. The parametric study revealed that the hypocentral distance and earthquake magnitude have major influence on the earthquake induced displacement, probability of failure and dynamic 2-D and 3-D safety factors.

SARETL: an expert system for probabilistic displacement-based dynamic 3-D slope stability analysis and remediation of earthquake triggered landslides

Different models were developed for evaluating the probabilistic three-dimensional (3-D) stability analysis of earth slopes and embankments under earthquake loading using both the safety factor and the displacement criteria of slope failure. In the 3-D analysis, the critical and total slope widths become two new and important parameters. The probabilistic models evaluate the probability of failure under seismic loading and consider the different sources of uncertainties involved in the problem, i.e. uncertainties stemming from the discrepancies between laboratory-measured and in situ values of shear strength parameters, randomness of earthquake occurrence, and earthquake-induced acceleration. The models also take into consideration the spatial variabilities and correlations of soil properties. The developed models are incorporated in a computer program, PTDDSSA (probabilistic three-dimensional dynamic slope stability analysis). These developed analysis/design procedures are incorporated within a code named SARETL (stability analysis and remediation of earthquake-triggered landslides) that was developed in this study for stability analysis and remediation of earthquake-triggered landslides. In addition to the dynamic inertia forces; the developed system takes into consideration the local site effects. The code is capable of: 1. Prediction of permanent deformations that result from landslides under seismic loading using both probabilistic and deterministic approaches. 2. The assessment of landslide hazard that affects major transportation routes in the event of earthquakes, and the preparation of earthquake-induced landslide hazard maps (i.e. maps that show expected displacements and probability of slope/embankments failure) for different earthquakes magnitudes and environmental conditions. 3. Proposing a mitigation strategy against landslides and suggesting guidelines for remedial measures. The developed expert system is applied to a major highway case study. Design maps are developed for the highway under seismic loading.

Reliability analysis of three-dimensional dynamic slope stability and earthquake-induced permanent displacement

Different models were developed for evaluating the probabilistic three-dimensional (3D) stability analysis of earth slopes and embankments under earthquake loading using both the safety factor and the displacement criteria of slope failure. In the 3D analysis, the critical and total slope widths become two new and important parameters. The probabilistic models evaluate the probability of failure under seismic loading considering the different sources of uncertainties involved in the problem, i.e. uncertainties stemming from the discrepancies between laboratory-measured and in-situ values of shear strength parameters, randomness of earthquake occurrence, and earthquake-induced acceleration. The models also takes into consideration the spatial variabilities and correlations of soil properties. Five probabilistic models of earthquake-induced displacement were developed based on the non-exceedance of a limited value criterion. Moreover, a probabilistic model for dynamic slope stability analysis was developed based on 3D dynamic safety factor. These models are formulated and incorporated within a computer program (PTDDSSA). A sensitivity analysis was conducted on the different parameters involved in the developed models by applying those models to a well-known landslides (Selset landslide) under different levels of seismic hazard. The parametric study was conducted to evaluate the effect of different input parameters on the resulting critical failure width, 3D dynamic safety factor, earthquake-induced displacement and the probability of failure. Input parameters include: average values and coefficients of variations of water table, cohesion and angle of friction for effective stress analysis, scales of fluctuations in both distance and time, hypocentral distance, earthquake magnitude, earthquake strong shaking period, etc. The hypocentral distance and earthquake magnitude were found to have major influence on the earthquake-induced displacement, probability of failure (i.e. probability of allowable displacement exceedance), and dynamic 2D and 3D safety factors.

Probabilistic evaluation of lateral extent of soil liquefaction under earthquake loading

Abstract A Monte Carlo simulation method for calculating the probability of soil liquefaction of a finite area in which the ground motion is propagated upward from the bedrock through the soil layers is presented. The vertical propagation of the ground motion through the soil layers is analyzed using random vibration analysis. To account for the variability of soil properties with depth, the soil deposit is divided into layers. Within each layer the horizontal statistical correlation of the undrained cyclic shearing strength against liquefaction is taken into consideration by describing it as a two-dimensional homogeneous random field. The method computes the probability of liquefaction spreading over a specified area under a given earthquake loading considering the effects of spatial correlation of soil properties.

Correlation of Soil Properties with phosphate fixation in some alkaline-calcareous soils of Northwest India

Effects of soil properties on the rate of phosphate fixation and transformation of added phosphate into different inorganic P fractions were examined on 28 alkaline and calcareous soils of different agroclimatic zones of Haryana and Uttar Pradesh, India. Surface samples were treated with 200 μg P g‐1 soil and incubated at 25 ± 2°C and at 60% water holding capacity. Available P was determined at 7, 14, 28, and 56 days of incubation. The rate of decrease of available P was satisfactorily described by a second‐order kinetic rate equation. Clay, organic carbon, Al, Fe, and cation exchange capacity (CEC) was positively and pH was negatively related with rate constant. An 81.7% variation in rate constant was due to soil properties, while clay content alone contributed a 78% variation. CaCO3 affected the rate constant only in soils not varying in other properties. The variation due to soil properties in the transformation of added phosphate into different inorganic P fractions was: saloid‐P 77.7%, Al‐P 43.9%, Fe...

Comparison of mehlich‐1 and mehlich‐3 extractable soil boron with hot‐water extractable boron

Abstract The standard hot water extractant for soil boron (B) was compared with two extractants currendy in use by routine soil testing laboratories, Mehlich‐1 (M‐1) and Mehlich‐3 (M‐3). If routine extractants could be used for B, it would save the time and expense required for a separate extraction. Six states (Delaware, Maryland, Virginia, North Carolina, South Carolina, and Georgia) each selected 100 soil samples sent by growers to their State laboratories which were analyzed for routine properties (pH, P, K, Ca, Mg, and organic matter) and B by M‐1 or M‐3. The samples were then analyzed for hot‐water B (HWB) using a plastic pouch procedure and inductively‐coupled plasma emission spectrometry (ICP). M‐3 extracted more B than M‐1, and the correlation coefficients for M‐3 B with HWB were higher (r = 0. 82∗∗) than were M‐l with HWB (r = 0. 74∗∗). Correlations of soil properties with M‐l and M‐3 B were higher than with HWB, but none were notable with two exceptions. Extractable Ca was positively correlated...

Probabilistic three-dimensional stability analysis of slopes

A probabilistic model is described to evaluate the three-dimensional (3-D) stability of earth slopes under long-term conditions. The model takes into consideration the spatial variabilities and correlations of soil properties, as well as the uncertainties stemming from the discrepancies between laboratory-measured and in-situ values of shear strength parameters. It also accounts for the effects of modeling errors and progressive failure. In the 3-D analysis, the critical and total slope widths become two new and important parameters. An actual failure case is analysed in detail taking into consideration all sources of uncertainties. Based on the best estimates of the different soil parameters, the failure width is predicted to be 63 m versus 55 m of observed failure width and the probability of slope failure is computed to be 0.11. The results agree well with those actually observed supporting the predictive ability of the PTDSSA (Probabilistic Three-Dimensional Slope Stability Analysis) model. The numerical evaluation of the model is carried out by the PTDSSA computer program prepared for this purpose.