Soil science
S. Rezaei; H. Bayat
Abstract
Introduction
Given the energy crisis in the world, increasing environmental pollution, clean, renewable energy and the reduction of environmental pollution are needed. Soil is the main source of agricultural production. Therefore, maintaining soil health and fertility is very important for sustainable ...
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Introduction
Given the energy crisis in the world, increasing environmental pollution, clean, renewable energy and the reduction of environmental pollution are needed. Soil is the main source of agricultural production. Therefore, maintaining soil health and fertility is very important for sustainable food production. Nanotechnology is a good way to reduce soil issues in agriculture, a promising method to improve soil properties and significant capacity to increase yield. Nanotechnology is one of the newest technologies that is used in all fields of science and research due to its high potential and unique features, including natural resources and soil protection. Nanoparticles have the ability to change some physical, mechanical and chemical properties of soil due to their very high specific surface area and activity. Nanoparticles increase the cation exchange capacity of soil and soil porosity. Among all nanoparticles, zinc oxide (ZnO) is one of the most widely used nanoparticles. Zinc oxide nanoparticles due to their high specific surface area can act as a bonding agent between particles and stabilize the soil structure by flocculating soil particles. Although many studies have used zinc oxide nanoparticles (ZnO) in the field of heavy metal contamination in soil, aqueous solutions and plants, the effect of one nanoparticle on soils with different textures has been less reported. Therefore, objective of this study was to investigate the effect of zinc oxide nanoparticles on some physical and chemical properties of soils with different textures.
Materials and Methods
In this study, three soil samples with different textures, including sandy loam, loam and clay were collected from three locations as Malayer, Abbasabad and Nahavand, in Hamedan province, respectively. Samples were taken from soil surface (0-20 cm depth). The soil samples were transferred to the Soil Physics Laboratory. After air drying, they were passed through a 4 mm sieve and mixed with specific weight percentages of zinc oxide (ZnO) nanoparticles (zero, 0.5, 1 and 3 % W/W) in three replications. After preparing the treated samples, the soils were homogeneously poured into plastic containers measuring 18 × 5.5 × 18 cm with a specific bulk density related to the field. The treated soils in plastic containers, were wetted and dried with municipal water for 120 consecutive incubation period. After 120 days from the start of incubation, the samples were taken from the containers. Some physical and chemical properties including pH, cation exchange capacity, organic matter, calcium carbonate and electrical conductivity were measured.
Results and Discussion
The results showed that the use of nanoparticles increased the cation exchange capacity in two textures of loamy and clay soils. The increment was significant compared to the control in loamy soil at two levels of 1 and 3% and in clay soil in all three levels of 0.5, 1 and 3%. Electrical conductivity increased and decreased (P <0.05) at 3% level for loamy soil and at 3% for sandy loam and clay soils, respectively. In contrast, the application of nanoparticles led to a decrease in pH and organic matter content (P <0.05) in sandy loam and clay soils, respectively. At the level of zero and 0.5%, the order of pH was: sandy loam> clay> loamy soil, with significant differences. But at the level of 1%, the order of pH was: sandy loamy> loamy> clay, with significant differences. At 3% level, the order of pH was: loamy> sandy loam> clay, with significant differences. At all levels of zero, 0.5, 1 and 3% of zinc oxide nanoparticles, the amount of organic matter was significantly in loamy> clay> sandy loam. Application of different levels of zinc oxide nanoparticles in clay soil reduced the percentage of calcium carbonate (P <0.05) (at the 3% by weight level), but had no effect on the amount of this variable in sandy loam and loamy soils. At all levels of zero, 0.5, 1 and 3%, the amount of soil calcium carbonate was significantly in the following order: clay> sandy loam> loam.
Conclusion
According to the results obtained in this study, it can be concluded that the use of nanoparticles can be a good solution to reduce the harmful environmental effects of chemical fertilizers. In addition to the positive effect of zinc oxide nanoparticles on physical and chemical parameters in different textures, the selection of the most optimal level of zinc oxide nanoparticles should be economically applicable. This requires further studies to determine the significant effects of nanoparticles on the physicochemical properties of the soils in different conditions to determine the optimal amount of nanoparticles, in order to save costs.
Fatemeh Rakhsh; Ahmad Golcchin
Abstract
Introduction: Mobilization and stabilization of organic matter in soils represent a set of complex processes involving the processing and decomposition of organic matter by diverse communities of soil fauna and microorganisms, as well as chemical-physical interactions with mineral particles of soil. ...
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Introduction: Mobilization and stabilization of organic matter in soils represent a set of complex processes involving the processing and decomposition of organic matter by diverse communities of soil fauna and microorganisms, as well as chemical-physical interactions with mineral particles of soil. Clay minerals have high effects on the soil organic matter dynamics. Clay minerals with the physical protection of organic matter play an important role in reducing the rate of decomposition of organic matter. The effects of soil texture on the soil organic matter dynamics have been investigated in many studies, but the effects of exchangeable cations and clay types on mineralization of organic nitrogen and microbial biomass nitrogen have not been given much attention. For this reason, the aim of this study was to evaluate the effects of types and clay contents and exchangeable cations on the mineralization of organic nitrogen and microbial biomass nitrogen.
Material and Methods: Appropriate amounts of homoionic Na-, Ca- and Al- clays from Georgia kaolinite, Illinois illite and Wyoming montmorillonite were mixed with pure sand to prepare artificial soils with different clay contents, exchangeable cations, and clay types. The artificial soils have zero, 5 and 10% clay from Georgia kaolinite, Illinois illite and Wyoming montmorillonite that their clay minerals saturated with Ca, Na and Al. Alfalfa plant residues were incorporated into the artificial soils and the soils were inoculated with microbes from a natural soil and incubated for 60 days and concentration of NH4-N and NO3-N were measured every 15 days. In the artificial soil samples, microbial biomass nitrogen was measured by the fumigation-extraction method in the end time of incubation period.
Results and Discussion: The results of this study showed that the percentage of mineralized nitrogen in the two-month incubation period, was higher in the pure sand than in soils containing 5% and 10% clay, indicating that clay contents influence the capacity of soils to protect and store organic nitrogen. Microbial biomass nitrogen increased as the amount of clay in the soil increased. The highest and lowest amounts of microbial biomass nitrogen measured in soils with 10% clay (9.26 mg per 50 g dry soil) and pure sand (4.31 mg per 50 g dry soil), respectively. There was a significant influence of exchangeable cations on the percentage of mineralized nitrogen and microbial biomass nitrogen. The microbial biomass nitrogen and the percentage of mineralized nitrogen were highest in Ca-soils and lowest in Al-soils. The percentage of mineralized organic nitrogen in two months of incubation period was highest in soils with Georgia kaolinite clay and lowest in soil with Wyoming montmorillonite clay. The amounts of microbial biomass nitrogen in soils with Wyoming montmorillonite clay were lower than soils with Georgia kaolinite and Illinois illite clays. The percentage of mineralized organic nitrogen increased as the incubation period increased. The results of this study indicated that organic nitrogen mineralization rates and microbial biomass nitrogen were affected by types and clay contents and exchangeable cations and interaction of organic matter with clays and is an important process as it slows soil organic matter decomposition.
Conclusions: Mixing the alfalfa residues with artificial soils and incubation samples allowed to study the effects of types and clay contents and exchangeable cations on the percentage of NH4+-N, NO3--N, mineralized nitrogen, and microbial biomass nitrogen. Soils with different clay contents have different surface areas and cation exchange capacities; therefore, it is concluded that organic nitrogen storage of soils is, partly, controlled by the surface areas, cation exchange capacity and physical protection provided by the soils. Nitrogen mineralization and the amounts of microbial biomass nitrogen were different in soils with different exchangeable cations. It is concluded that exchangeable cations exert their influence on microbial biomass and hence nitrogen dynamics by controlling the size and activity of the microbial population through modifying the physicochemical characteristics of microbial habitats. Since various clay minerals have different specific surface areas and cation exchange capacity and the physicochemical changes induced in the soil environment as a result of variations of exchangeable cations is much greater in soils with higher cation exchange capacity and specific surface area. It seems the effects of clay mineralogy on the dynamics of organic materials and microbial biomass, in part, arise from the type of exchangeable cations present on the exchange sites of the clay minerals.
Y. Ostovari; K. Asgari; H. R. Motaghian
Abstract
Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used ...
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Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used to develop PTFs is multiple linear regressions. In this method, all easily obtained soil properties are linearly related to certain soil properties. In addition to multiple linear regressions method, more complex techniques such as artificial neural networks and regression tree have been used to develop PTFs. The regression tree method is a well-known method for analyzing the environmental science which determines optimal separation point of independent variables.The purposes of this study were to evaluate and compare tree and multiple linear regressions in estimating cation exchange capacity with reliable soil properties.
Materials and Methods: For this work, 106 soil samples of Unsaturated Soil hydraulic database (UNSODA), which contain a wide range of soil texture classes, were used. The examples were divided into 2 sets including 81 and 25 soil samples for developing and validating multiple linear regression and tree regression, respectively. For estimating CEC with tree and multiple regressions, soil texture properties, organic matter, pH and bulk density were used. To develop multiple linear regressions and create the tree structure, at first, correlation between cation exchange capacity with other soil properties were evaluated; then, soil properties that had significant correlation were chosen to introduce software. As well, the suggested linear function and tree structure were compared with 2 famous pedotranser functions including Bell and Van-kolen and Breeuwsma et al., which have been used for estimating CEC.For investigating the performance of multiple linear regression and tree regression to estimate CEC 1:1 lines, determination coefficient (R2), mean error (ME), root mean square error) RMSE), and geometric mean error (GMER) were used. Statistica 8.0 software that was developed by ESRI was used to develop multiple linear regressions and generate tree structure.
Results and Discussion: The results showed for developing multiple linear regression model to estimate CEC among all inputs parameters (sand, silt, clay, organic matter, pH and bulk density) only just two parameters including organic (with r=0.70) and clay percentage (with r=0.59) had a significant coefficient, so organic and clay percentage appeared, and suggested multiple linear regression models based on this two parameters, with coefficient of 3.183 and 0.274, respectively, were developed. Also, only organic matter and clay percentage from inputs parameter in tree were shown. In tree structure most nods were divided into 2 Childs nods based on organic matter and only in the left side of tree structure in the second level clay percentage was appeared. Regression tree in two data sets (validation and development) based on R2, RMSE, ME and GMER had a high quality for CEC estimation than regression methods. Proposed linear regression model had high performance than Bell and Van-kolen and Breeuwsma et al. to estimate CEC.
Conclusions: The main aim of this study was to investigate the efficiency of multiple linear regression model and regression tree to predict cation exchange capacity (CEC) based on relationships between CEC and easily measurable soil properties. For this work, 106 soil samples of UNSODA data set were used. Results showed that just clay percentage and organic matter that had higher correlation with CEC appeared in suggested linear regression and tree structure. Based on 1:1 lines, R2 ,RMSE, ME and GMER, tree regression model had higher performance than all linear regression models (suggested function , Bell and Van-kolen and Breeuwsma et. al.) to estimate cation exchange capacity. As well, suggested function had more efficiency than Bell and Van-kolen and Breeuwsma to predict CEC.
A. Hezarjaribi; F. Nosrati Karizak; K. Abdollahnezhad
Abstract
Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose ...
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Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose of this research was to investigate CEC estimating using easily accessible parameters with Artificial Neural Network. In this study, the easily accessible parameters were sand, silt and clay contents, bulk density, particle density, organic matter (%OM), calcium carbonate equivalent (%CCE), pH, geometric mean diameter (dg) and geometric standard deviation of particle size (σg) in 69 points from a 1×2 km sampling grid. The results showed that Artificial Neural Network is a precise method to predict CEC that it can predict 82% of CEC variation. The most important influential factor on CEC was soil texture. The sensitivity analysis of the model developed by using of Artificial Neural Network represented that clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size, OM% and total porosity were the most sensitive parameters, respectively. The model with clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size as inputs data was selected as the base model to predict CEC at studied area.
H. Kashi; H. Ghorbani; S. Emamgholizadeh; S.A.A. Hashemi
Abstract
With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed ...
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With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity as difficult and time consuming feature. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of two artificial neural networks, multi-layer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compared with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity was determined and most important parameters which could influence the soil cation exchange capacity were described.
