Relationships of soil shrinkage parameters and indices with intrinsic soil properties and environmental variables in calcareous soils

Geoderma ◽  
2016 ◽  
Vol 277 ◽  
pp. 23-34 ◽  
Author(s):  
Z. Zolfaghari ◽  
M.R. Mosaddeghi ◽  
S. Ayoubi
Keyword(s):  
Soil Properties ◽  
Environmental Variables ◽  
Calcareous Soils ◽  
Soil Shrinkage

scholarly journals Phosphorus Supply of Typical Hungarian Soils

2006 ◽  
Vol 55 (1) ◽  
pp. 117-126 ◽  
Author(s):  
György Füleky
Keyword(s):  
Soil Properties ◽  
Inorganic Phosphate ◽  
Phosphorus Uptake ◽  
Calcareous Soils ◽  
Hot Water ◽  
Percolation Method ◽  
Water Percolation ◽  
Phosphorus Source ◽  
Fraction I ◽  
Test Plants

The new hot water percolation (HWP) method was introduced to determine the phosphorus supply of soils from the Soil Bank of 36 Hungarian soils. The present work aimed to explain the availability of phosphorus by determining the inorganic phosphate fractions and using ryegrass test plants. Four inorganic phosphate fractions were distinguished: Fraction I, the sorbed phosphates; Fraction II, the easily soluble Ca phosphates and the Al bound phosphates; Fraction III, the Fe phosphates; and Fraction IV, the hardly soluble Ca phosphates. Fraction II, in which the easily soluble Ca phosphates and Al phosphates accumulate, was the main phosphorus source for the test plants on both calcareous and non-calcareous soils. Fraction III (the iron phosphates) plays a greater role in non-calcareous soils, while Fraction IV (the hardly soluble Ca phosphates) in calcareous soils. Both fractions are closely connected with soil development, and with soil properties such as pH and CaCO 3 content. The hot water percolation method reflects the phosphorus supply of soil as well as that measured with ryegrass plants and with the AL method. This new HWP method is in good correlation with the main source of phosphate, with fraction II. For routine purposes the first collected HWP fraction can possibly be used to determine the phosphorus supply of soil correlating well with the phosphorus uptake of test plants.

Relationship between soil composition and retention capacity of terbumeton onto chalky soils

2009 ◽  
Vol 6 (3) ◽  
pp. 245 ◽  
Author(s):  
Achouak El Arfaoui ◽  
Stéphanie Sayen ◽  
Eric Marceau ◽  
Lorenzo Stievano ◽  
Emmanuel Guillon ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Environmental Fate ◽  
Organic Matter Content ◽  
Calcareous Soils ◽  
Matter Content ◽  
Soil Composition ◽  
Retention Capacity ◽  
Wide Range ◽  
Soil Components

Environmental context. The wide use of pesticides for pest and weed control contributes to their presence in underground and surface waters, which has led to a continuously growing interest in their environmental fate. Soils play a key role in the transfer of these compounds from the sprayer to the water as a result of their capacity to retain pesticides depending on the soil components. The knowledge of soil composition should enable one to predict pesticide behaviour in the environment. Abstract. Eight calcareous soils of Champagne vineyards (France) were studied to investigate the adsorption of the herbicide terbumeton (TER). A preliminary characterisation of the soil samples using X-ray diffraction (XRD), elemental and textural analyses, revealed a wide range of soil properties for the selected samples. The adsorption isotherms of TER were plotted for all samples. The determination of soil properties, which significantly correlated with the Kd distribution coefficient, allowed identification of organic matter and CaCO3 as the two main soil components that govern the retention of the herbicide. Organic matter was the predominant phase involved in the retention but its role was limited by the presence of calcite. Finally, the ratio of CaCO3 content to organic matter content was proposed as a useful parameter to predict the adsorption of terbumeton in chalky soils. The evolution of Kd as a function of this ratio was successfully described using an empirical model.

scholarly journals Composition of microbial PLFAs and correlations with topsoil characteristics in the rare active travertine spring-fed fen

2016 ◽  
Vol 35 (4) ◽  
pp. 295-308 ◽  
Author(s):  
Peter Hanajík ◽  
Milan Zvarík ◽  
Hannu Fritze ◽  
Ivan Šimkovic ◽  
Róbert Kanka
Keyword(s):  
Soil Properties ◽  
Total Nitrogen ◽  
Environmental Variables ◽  
Small Scale ◽  
Klason Lignin ◽  
Data Set ◽  
Carbonate Carbon ◽  
Total Plfa ◽  
Acid Soluble Lignin ◽  
Soluble Lignin

Abstract We studied soil PLFAs composition and specific soil properties among transect of small-scale fen in Stankovany, Slovakia. The aim of this study was to determine potential differences in the microbial community structure of the fen transect and reveal correlations among PLFAs and specific soil characteristics. PCA analyses of 43 PLFAs showed a separation of the samples along the axis largely influenced by i14:0, 16:1ω5, br17:0, 10Me16:0, cy17:0, cy17:1, br18:0 and 10Me17:0. We measured a high correlation of sample scores and distance from fen edge (Kendall’s test τ = 0.857, P < 0.01). Kendall’s test showed a negative correlation of PLFAs content (mol%) and distance from the fen border for Gram (+) bacteria, Actinomycetes, mid-chain branched saturated PLFAs and total PLFAs. The redundancy analysis of the PLFA data set for the eight samples using PLFAs as species and 21 environmental variables identified soil properties significantly associated with the PLFA variables, as tested by Monte Carlo permutation showing most significant environmental variables including dichlormethan extractables, water extractables, Klason lignin, acid-soluble lignin, holocellulose, total extractables, organic matter content, total PLFA amount, bacterial PLFA and total nitrogen negatively correlated to axis 1 and dry weight and carbonate carbon positively correlated to axis 1. The amounts of Klason lignin, acid-soluble lignin, holocellulose total extractables, total PLFA, bacterial PLFA and total nitrogen were significantly correlated positively to the distance from fen border while moisture and total carbonate carbon were correlated negatively.

scholarly journals Changes of Available Cadmium Over time and its Relationship with Soil Properties in Highly Calcareous Soils

2018 ◽  
Vol 22 (1) ◽  
pp. 127-142
Author(s):  
M. Hosseini ◽  
E. Adhami ◽  
H. R Owliaie ◽  
◽  
◽  
...  
Keyword(s):  
Soil Properties ◽  
Calcareous Soils ◽  
Over Time

scholarly journals Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

2020 ◽  
Vol 12 (21) ◽  
pp. 3609
Author(s):  
Xinchuan Li ◽  
Juhua Luo ◽  
Xiuliang Jin ◽  
Qiaoning He ◽  
Yun Niu
Keyword(s):  
Machine Learning ◽  
Soil Properties ◽  
Environmental Variables ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Soil Thickness ◽  
Extreme Gradient Boosting

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

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