scholarly journals Evaluation of Phytoavailability of Heavy Metals to Chinese Cabbage (Brassica chinensisL.) in Rural Soils

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yao-Tsung Chang ◽  
Zeng-Yei Hseu ◽  
Franz Zehetner
Keyword(s):  
Soil Properties ◽  
Chinese Cabbage ◽  
Empirical Model ◽  
Correlation Coefficients ◽  
Clay Content ◽  
Extraction Methods ◽  
Crop Uptake ◽  
Chemical Agents ◽  
Extractable Metals ◽  
Metal Phytoavailability

This study compared the extractability of Cd, Cu, Ni, Pb, and Zn by 8 extraction protocols for 22 representative rural soils in Taiwan and correlated the extractable amounts of the metals with their uptake by Chinese cabbage for developing an empirical model to predict metal phytoavailability based on soil properties. Chemical agents in these protocols included dilute acids, neutral salts, and chelating agents, in addition to water and the Rhizon soil solution sampler. The highest concentrations of extractable metals were observed in the HCl extraction and the lowest in the Rhizon sampling method. The linear correlation coefficients between extractable metals in soil pools and metals in shoots were higher than those in roots. Correlations between extractable metal concentrations and soil properties were variable; soil pH, clay content, total metal content, and extractable metal concentration were considered together to simulate their combined effects on crop uptake by an empirical model. This combination improved the correlations to different extents for different extraction methods, particularly for Pb, for which the extractable amounts with any extraction protocol did not correlate with crop uptake by simple correlation analysis.

scholarly journals Correlation of physical and chemical attributes of soil with sugarcane yield

2012 ◽  
Vol 47 (4) ◽  
pp. 613-620 ◽  
Author(s):  
Domingos Guilherme Pellegrino Cerri ◽  
Paulo Sérgio Graziano Magalhães
Keyword(s):  
Soil Properties ◽  
Correlation Coefficients ◽  
Clay Content ◽  
Exchange Capacity ◽  
Test Area ◽  
Yield Variation ◽  
Sugarcane Yield ◽  
Physical Attributes ◽  
Chemical Attributes ◽  
Physical And Chemical

The objective of this work was to evaluate the correlation between sugarcane yield and some physical and chemical attributes of soil. For this, a 42‑ha test area in Araras, SP, Brazil, was used. Soil properties were determined from samples collected at the beginning of the 2003/2004 harvest season, using a regular 100x100 m grid. Yield assessment was done with a yield monitor (Simprocana). Correlation analyses were performed between sugarcane yield and the following soil properties: pH, pH CaCl2, N, C, cone index, clay content, soil organic matter, P, K, Ca, Mg, H+AL, cation exchange capacity, and base saturation. Correlation coefficients were respectively ‑0.05, ‑0.29, 0.33, 0.41, ‑0.27, 0.22, 0.44, ‑0.24, trace, ‑0.06, 0.01, 0.32, 0.14, and 0.04. Correlations of chemical and physical attributes of soil with sugarcane yield are weak, and, per se, they are not able to explain sugarcane yield variation, which suggests that other variables, besides soil attributes, should be analysed.

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

Land ◽  
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.

scholarly journals Using Multivariate Geostatistics to Assess Patterns of Spatial Dependence of Apparent Soil Electrical Conductivity and Selected Soil Properties

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Glécio Machado Siqueira ◽  
Jorge Dafonte Dafonte ◽  
Montserrat Valcárcel Armesto ◽  
Ênio Farias França e Silva
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Clay Content ◽  
Data Sets ◽  
Soil Electrical Conductivity ◽  
Multivariate Geostatistics ◽  
Northwestern Spain

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECadata sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECaand gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECaand clay content (ranging from 0.197 to 0.495, when different ECarecording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECadata sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECaas secondary variable with respect to the use of ordinary kriging.

scholarly journals Implications of carbon saturation model structure for simulated nitrogen mineralization dynamics

2014 ◽  
Vol 11 (6) ◽  
pp. 9667-9695 ◽  
Author(s):  
C. M. White ◽  
A. R. Kemanian ◽  
J. P. Kaye
Keyword(s):  
Soil Properties ◽  
N Mineralization ◽  
Storage Capacity ◽  
Clay Content ◽  
N Cycling ◽  
Transfer Efficiency ◽  
Net N Mineralization ◽  
Saturation Ratio ◽  
Carbon Saturation ◽  
C And N

Abstract. Carbon (C) saturation theory suggests that soils have a~limited capacity to stabilize organic C and that this capacity may be regulated by intrinsic soil properties such as clay content and mineralogy. While C saturation theory has advanced our ability to predict soil C stabilization, we only have a weak understanding of how C saturation affects N cycling. In biogeochemical models, C and N cycling are tightly coupled, with C decomposition and respiration driving N mineralization. Thus, changing model structures from non-saturation to C saturation dynamics can change simulated N dynamics. Carbon saturation models proposed in the literature calculate a theoretical maximum C storage capacity of saturating pools based on intrinsic soil properties, such as clay content. The extent to which current C stocks fill the storage capacity of the pool is termed the C saturation ratio, and this ratio is used to regulate either the efficiency or the rate of C transfer from donor to receiving pools. In this study, we evaluated how the method of implementing C saturation and the number of pools in a model affected net N mineralization from decomposing plant residues. In models that use the C saturation ratio to regulate transfer efficiency, C saturation affected N mineralization, while in those in which the C saturation ratio regulates transfer rates, N mineralization was independent of C saturation. When C saturation ratio regulates transfer efficiency, as the saturation ratio increases, the threshold C : N ratio at which positive net N mineralization occurs also increases because more of the C in the residue is respired. In a single-pool model where C saturation ratio regulated the transfer efficiency, predictions of N mineralization from residue inputs were unrealistically high, missing the cycle of N immobilization and mineralization typically seen after the addition of high C : N inputs to soils. A more realistic simulation of N mineralization was achieved simply by adding a second pool to the model to represent short-term storage and turnover of C and N in microbial biomass. These findings increase our understanding of how to couple C saturation and N mineralization models, while offering new hypotheses about the relationship between C saturation and N mineralization that can be tested empirically.

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

Relationships between extractable Al, selected soil properties, pH buffer capacity and lime requirement in some acidic Queensland soils

Soil Research ◽  
1992 ◽  
Vol 30 (2) ◽  
pp. 119 ◽  
Author(s):  
RL Aitken
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Properties ◽  
Buffer Capacity ◽  
Clay Content ◽  
Ph Buffering ◽  
Ph Buffer ◽  
Mineral Soils ◽  
Chloride Salts ◽  
Hydrolysis Of

The objectives of this study were to examine (1) interrelationships between various forms of extractable A1 and selected soil properties, (2) the contribution of extractable A1 to pH buffer capacity, and (3) investigate the use of extractable A1 to predict lime requirement. Aluminium was extracted from each of 60 Queensland soils with a range of chloride salts: 1 M KCl (AlK), 0.5 M CuCl2 (AlCu), 0.33 M LaCl3 (AlLa) and 0.01 M CaCl2 (AlCa). The amounts of A1 extracted were in the order AlCu > AlLa > Alk > AlCa. Little or no A1 was extracted by KC1 or Lac13 in soils with pHw values greater than 5.5 , whereas CuCl2 extracted some A1 irrespective of soil pH. The greater amounts of A1 extracted by CuCl2 were attributed mainly to A1 from organic matter, even though all of the soils were mineral soils (organic carbon 54.7%). Both AlCu and AlLa, were significantly (P < 0.001) correlated with organic carbon, whereas none of the extractable A1 measures was correlated with clay content. AlK and A~L, were poorly correlated to pH buffer capacity. The linear relationship between AlCu and pH buffer capacity (r2 = 0.49) obtained in this study supports the view of previous researchers that the hydrolysis of A1 adsorbed by organic matter is a source of pH buffering in soils. However, the change in CEC with pH accounted for 76% of the variation in pH buffer capacity, indicating that other mechanisms such as deprotonation of organic groups and variable charge minerals are also involved in pH buffering. The ability of CuCl2 and LaCl3extractable Al to estimate lime requirement depended on the target pH. The results suggest that lime requirements based on neutralization of AlLa would be sufficient to raise pHw to around 5.5, whereas requirements based on neutralization of AlCu substantially overestimated the actual lime requirement to pHw 5.5, but gave a reasonable estimation of the lime requirement to pHw 6 5.

scholarly journals Boron adsorption in soils from the State of São Paulo, Brazil

2000 ◽  
Vol 35 (2) ◽  
pp. 413-421 ◽  
Author(s):  
LUÍS REYNALDO FERRACCIÚ ALLEONI ◽  
OTÁVIO ANTONIO DE CAMARGO
Keyword(s):  
Aluminum Oxide ◽  
Correlation Coefficients ◽  
Clay Content ◽  
Sao Paulo ◽  
The State ◽  
São Paulo ◽  
Aluminum Oxides ◽  
Freundlich Isotherms ◽  
Physical Attributes ◽  
Boron Adsorption

Boron adsorption was studied in five representative soils (Rhodic Hapludox, Arenic Paleudalf and three Typic Hapludox) from the State of São Paulo, Brazil. Adsorption was higher in the clayey Oxisols, followed by the Alfisol and the coarser Oxisols. Calcium carbonate promoted an increase in the amount of adsorbed boron in all soils, with the most pronounced effect in the coarser-textured Oxisols. High correlation coefficients were found between adsorbed boron and clay and amorphous aluminum oxide contents and specific surface area (r = 0.79, 0.76 and 0.73, respectively, p < 0.01). Clay content, free aluminum oxide, and hot CaCl2 (0.01 mol L-1)-extracted boron explained 93% of the variation of adsorbed boron. Langmuir and Freundlich isotherms fitted well to the adsorbed data, and highest values for maximum boron adsorption were found in clayey soils, which were significantly correlated with contents of total, free and amorphous iron and aluminum oxides, as well with the physical attributes. Ninety four percent of the variation in the maximum adsorption could be related to the free iron content.

scholarly journals Relationship of soil properties to fractionation, bioavailability and mobility of lead and zinc in soil

2008 ◽  
Vol 53 (No. 5) ◽  
pp. 225-238 ◽  
Author(s):  
N. Finžgar ◽  
P. Tlustoš ◽  
D. Leštan
Keyword(s):  
Organic Matter ◽  
Regression Analysis ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Multiple Regression ◽  
Contaminated Soils ◽  
Organic Matter Content ◽  
Clay Content ◽  
Exchange Capacity ◽  
Matter Content

Sequential extractions, metal uptake by <i>Taraxacum officinale</i>, Ruby&rsquo;s physiologically based extraction test (PBET) and toxicity characteristic leaching procedure (TCLP), were used to assess the risk of Pb and Zn in contaminated soils, and to determine relationships among soil characteristics, heavy metals soil fractionation, bioavailability and leachability. Regression analysis using linear and 2nd order polynomial models indicated relationships between Pb and Zn contamination and soil properties, although of small significance (<i>P</i> < 0.05). Statistically highly significant correlations (<i>P</i> < 0.001) were obtained using multiple regression analysis. A correlation between soil cation exchange capacity (CEC) and soil organic matter and clay content was expected. The proportion of Pb in the PBET intestinal phase correlated with total soil Pb and Pb bound to soil oxides and the organic matter fraction. The leachable Pb, extracted with TCLP, correlated with the Pb bound to carbonates and soil organic matter content (<i>R</i><sup>2</sup> = 69%). No highly significant correlations (<i>P</i> < 0.001) for Zn with soil properties or Zn fractionation were obtained using multiple regression.

Sign in / Sign up

Export Citation Format

Share Document