Soil science
A.R. Vaezi; R. Bigdeli
Abstract
Introduction
Rill erosion is one of the main factors of soil degradation, especially in rainfed lands in semi-arid regions. These soils have relatively lower organic matter content with weakly-aggregated units, which increases their susceptibility to water erosion processes. Conventional tillage ...
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Introduction
Rill erosion is one of the main factors of soil degradation, especially in rainfed lands in semi-arid regions. These soils have relatively lower organic matter content with weakly-aggregated units, which increases their susceptibility to water erosion processes. Conventional tillage systems are adversely affect on soil structure and surface soil cover in rainfed lands. Raindrop energy and flow shear stress are the main erosive factors in the slope lands. The raindrop impact destroys soil structure and changes it to erodible unites; micro-aggregates and single particles, and so makes them to more detachment. A few studies have been done on the role of raindrop impact to soil erosion by water. Nevertheless, there is no sufficient information on the effect of raindrop impact on soil loss in the rills particularly in semi-arid regions. Therefore, this study was conducted to investigate the role of raindrop impact on soil loss from rills in various soil textures under different rainfall intensities.
Materials and Methods
A laboratory experiment was performed on two soil textures (clay loam and sandy loam) under four rainfall intensities (30, 50, 72 and 83 mm.h-1) in two rainfall conditions (under raindrops impact and without raindrops impact). Soil samples (0-30 cm) were taken from a semi-arid region in Zanjan province in 2020. The experiments were set up in an erosion flume with 100 cm long and 60 cm width and 15 cm depth which were exposed to simulated rainfalls for 30 min duration. Runoff and soil loss were measured at three rills under slope gradient 10% in the two rainfall conditions for each rainfall intensity. Soil loss from rills was determined as the mass of sediment collected from rill outlet per rill surface area (g.m-2). Under raindrop impact, the soil was exposed directly to raindrop impact and under without raindrop impact, a metal mesh sheet was used to eliminate raindrops impact to soil surface. The role of raindrops impact to runoff and soil loss was computed from the difference of runoff and soil loss under raindrops impact and without raindrops impacts. A t-test was used to assess the role of raindrops impact between the two rainfall conditions for the soils and rainfall intensities.
Results and Discussion
Results indicated that runoff production and soil loss were significantly affected by the soil texture and rainfall intensity. Runoff and soil loss under raindrops impact increased in the soils with increasing rainfall intensity. Clay loam showed more runoff production and soil loss than sandy loam which was associated to lower aggregate stability and hydraulic conductivity. Runoff and soil loss in the two soils and four rainfall intensities were significantly affected by raindrops impact. Runoff production and soil loss except to 72 mm.h-1 rainfall intensity were very higher under raindrop impact than without raindrop impact. It seems under 72 mm.h-1 rainfall intensity, raindrops impact varied the rill’s morphology and prevent more runoff production. Runoff production in clay loam and sandy loam under raindrop impact were increased by 44 and 36 percent, respectively (p< 0.01). Soil loss resulted by raindrop impact in clay loam and sandy loam increased by 53 and 62 percent, respectively (p< 0.01). Raindrops impact had more importance in soil loss rather than runoff production. This result is related to the role of raindrops impact in destroying aggregates and producing more erodible soil particles and closing soil macrospores and declining water infiltration. The role of raindrop impact in runoff production and soil loss varied among the rainfall intensities. A slight reduction in the role of raindrop impact in runoff and soil loss was occurred with increasing rainfall intensity, especially in sandy loam.
Conclusion
The role of raindrop impact in runoff production and soil loss was significantly affected by soil type and rainfall intensity. Raindrops impact has more important in runoff and soil loss in the soils having higher aggregate stability and more hydraulic conductivity. The role of raindrop impact in runoff and soil loss in these soils declines with increasing rainfall intensity. In general, maintain soil surface cover is essential to control raindrops impact and decrease runoff and soil loss in semi-arid areas. The importance of soil surface cover is most obvious under different rainfalls in weakly-aggregated soils which are dominant in many slope lands. Also, soil surface cover has important role in controlling runoff and soil loss under heavy rainfalls in soils with more water-stable aggregates. Prevention from intensive tillage and using conservation tillage systems such as minimum tillage are effective strategies in controlling raindrop impact in rainfed lands in semi-arid regions.
Saghar Fahandej saadi; Masoud Noshadi
Abstract
Introduction: Although the soil salinity as an effective factor on soil and water management is typically assessed by measuring the soil electrical conductivity (ECe), this conventional laboratory method is time-consuming and costly. Therefore, near-infrared spectroscopy (NIR) as a fast, cheap and non-destructive ...
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Introduction: Although the soil salinity as an effective factor on soil and water management is typically assessed by measuring the soil electrical conductivity (ECe), this conventional laboratory method is time-consuming and costly. Therefore, near-infrared spectroscopy (NIR) as a fast, cheap and non-destructive method to assess soil salinity level can be considered as a valuable alternative method. Reviews of literature on the application of NIR spectroscopy for soil salinity prediction have shown that there is no sufficient information about the effect of soil texture on results accuracy; therefore, in this study the soil salinity was predicted under different soil salinity levels and various soil textures. The effect of different pre-processing methods was also investigated to improve the predicted soil salinity.
Materials and Methods: Twenty three surface soil samples were collected from different places in Fars province, then; some soil properties such as percentage of particles size and ECe were measured. These samples were artificially salted by adding the water in different salinity levels to the soil samples. The ECe of these soils were between 2.1 to 307.5 dS/m and then all samples dried to reach the field capacity level. Soil reflectance spectra were obtained in 350-2500 nm wavelength range. The absorbance and derivative of reflectance spectra were calculated based on the reflectance spectra. In order to determine the effect of smoothing technique, as a pre-processing method, 4 various methods (moving average, Gaussian, median and Savitzky-Golay filters) in 12 different segment sizes (3,5,7,9,11,13,15,17,19,21,23 and 25) were applied and the processed spectra introduced to Partial Least Square Regression (PLSR) model to predict soil salinity in two calibration and validation steps. At the first step, the soil salinity was predicted for all samples using of reflectance, absorbance and derivative of reflectance spectra under 4 pre-processing methods and 12 segment sizes. According to the R2 and RMSE indices, the best type of spectra, the effect of various pre-processing methods and the best segment size in prediction of soil salinity were determined as absorbance spectra, moving average and Savitzky-Golay filters for segment size of 25 and 15, respectively. In the second step, the effect of soil texture on prediction accuracy was investigated. For this purpose, soil samples were divided into the coarse and fine textures and soil salinity was predicted for each of these groups using different pre-processing methods and different segment sizes.
Results and Discussion: In prediction of soil salinity by absorbance, reflectance and derivative of reflectance spectra, the R2 values in validation step were 0.742, 0.706 and 0.670; and RMSE values were 29.92, 31.96 and 33.9 (dS.m-1), respectively. The absorbance spectra were the best spectra type in prediction of soil salinity. Therefore, in next step, absorbance spectra were used only for predicting the salinity in fine and coarse soil textures. Results showed that the prediction in coarse texture was better than that of the fine texture (R2= 0.836 and R2=0.756, respectively). It was also revealed that the highest R2 occurred in coarse texture and the accuracy of prediction was reduced in fine textures. The results showed that the performance of different pre-processing methods is related to the spectrum type. Although the pre-processing methods had no positive effect in using of reflectance spectra, but it improved the predicted values which were obtained using of absorbance and derivative of reflectance spectra. The best results were occurred when the absorbance spectra were used. Moving average method increased the accuracy of prediction more than the other pre-processing methods, and according to the results this method, for the segment size of 25, was the best technique in soil salinity prediction.
Conclusion: According to the R2 and RMSE indices, the prediction of soil salinity by absorbance spectra was more accurate than the prediction using reflectance and derivative of reflectance spectra (R2= 0.742, 0.706 and 0.670, respectively). Although the predicted soil salinity in coarse soils were more accurate than that in fine soils. Using of absorbance spectra to predict the soil salinity in all soil textures was efficient. The results showed that using of pre-processing methods improved the soil salinity prediction by absorbance and derivative of reflectance spectra, and the moving average and Savitzky-Golay filter were the best pre-processing methods.
F. Afrasiabi; H. Khodaverdiloo; F. Asadzadeh
Abstract
Introduction Particle size distribution (PSD) is one of the most fundamental features of soil physics that is widely used as the most common input for predicting several key soil attributes. The mathematically representing the PSD provides several benefits to soil mechanics, physics, and hydrodynamics ...
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Introduction Particle size distribution (PSD) is one of the most fundamental features of soil physics that is widely used as the most common input for predicting several key soil attributes. The mathematically representing the PSD provides several benefits to soil mechanics, physics, and hydrodynamics as well as helps to convert PSD data of various particle size classification systems to the desired one. Consequently, the correct and consistent descriptions of soil PSD by using mathematical functions is necessary.
The PSD models have often been evaluated in terms of their general performance for predicting the entire PSD curve. However, a given model may be feasible and globally perform well to generate the whole PSD curve but locally may fail to predict some specific points on the curve. To our knowledge, as well as, PSD models have not been widely tested for salt-affected soils with different levels of salinity/sodicity.
The aim of this study was to determine the error structure of several more accurate PSD models in selected soil samples with different levels of salinity and sodicity.
Materials and Methods Twenty four locations neighboring the western edge of threatened hypersaline Lake Urmia were sampled in this study. The locations were selected considering the available soil maps so that soils with wide range of salinity/sodicity were sampled. Selected physical and chemical properties of the soil samples were determined by standard methods. The performance of six PSD models including Modified Logistic Growth (MLG), Fredlund type models with three (Fred-4p) and four (Fred-3p) parameters, Anderson (AD), ONL, and Weibull (Wei), which have been reported as the most accurate PSD models by previous studies, was evaluated by using different efficiency criteria that offer various performances depending on the range of particle sizes.
An iterative nonlinear optimization procedure was used to fit the observed cumulative PSD data of the soils to the PSD models. Since every statistical criterion evaluates a part and some (and not all) aspects of the correspondence between measured and predicted values, we suggest that an effective assessment of model performance should include a suitable combination of criteria. Furthermore, dependence of the models performance to the range of soil particle sizes was examined.
Results and Discussion The soils differed widely in their EC (range = 85dS/m and CV = 159%), ESP (range = 67 % and CV = 71 %), and PSD (CV of clay and silt particles, 48 and 55 %, respectively). Soil textural class of the soils was differed widely from sandy loam to clay. All the soils were calcareous and alkaline.
The results showed that according to the efficiency criteria, including R2 (coefficient of determination), RMSE (Root Mean Square Error) and Er (Relative Error), all of the models have high efficiency, so that, the lowest average value of R2 in models was 0.992 and the maximum value of RMSE and Er was 0.028 and 0.045, respectively.
Prediction error of the models was dependent on the diameter for which we predict the cumulative fraction and decreases with increasing of the soil particles diameter. The performance of the models showed a significant quadratic polynomial relationship with sand content of the samples, so that, the studied models had the lowest performance in soils containing 30 to 45 percent sand.
The point-to-point error structure of model represents a decrease in systematic error in estimating coarse soil particles, while the models over-estimated the relative frequency of the fine soil particles. In addition, the values of relative error were also lower for coarse particles of soil, so that, the Wei model (for example) had the lowest Er value for 100 to 500 μm diameter soil particles. The relatively high correlations between parameters of Fred-3p, MLG and ONL models show insights to reduce the number of their parameters.
Furthermore, parameters a and c of MLG model, parameters μ and α of ONL model and parameter α and m of Fred-3p model had a statistically significant correlations. The relatively high correlations between parameters of the PSD models show insights to reduce the number of their parameters which increases their applicability.
Conclusion The studied models generally performed well to predict the whole PSD curve, but their performance was particle size dependent. This implies that, one should consider the range of sizes of soil particles for which the model are used. A model might be accurate enough for predicting some ranges of particle diameter or the whole PSD, but not for particular range of particle sizes. Using such models might lead to large errors in predicting the specific PSD range of interest.
zahra dianat maharluei; ali akbar moosavi
Abstract
Introduction: In arid and semi-arid soils, low organic matter is one of the barriers to achieving optimal performance. The soils with more organic matter have a better structure and are more resistant to erosive factors such as water and wind. Soil organic matter has a particular importance and has significant ...
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Introduction: In arid and semi-arid soils, low organic matter is one of the barriers to achieving optimal performance. The soils with more organic matter have a better structure and are more resistant to erosive factors such as water and wind. Soil organic matter has a particular importance and has significant impact on the stability of soil aggregates, the extension of plant root system, carbon and water cycles and soil resistance to erosion. This substance acts as a cementing agent and plays an important role in soil flocculation and formation of resistant aggregates.Also, the addition of organic matter to the soil increases soil porosity and decreases soil bulk density.
Materials and Methods: In this research, the effect of the two types of organic matter (compost and the ripe fruit waste of fig) on some soil physical properties was studied. A factorial experiment based on completely randomized design, including the four levels of compost and the ripe fruit waste of fig (0, 1, 2 and 4 by weight %) and three soil types (loamy sand, loam and silty clay loam) with three replications was carried out. The soil samples were collected from the three territories of Fars Province: loamy sand soil from Shiraz, loamy soil from Maharlu and Silty clay loam soil from Zarghan area. The soil samples were air dried and passed through a 2 mm sieve. The physical properties including the bulk density, particle density, porosity, moisture content and soil crust strength was measured. In this research, the soil texture by hydrometer method, Electrical conductivity of the soil saturated paste extract by electrical conductivity meter, saturated paste pH by pH meter, seedling emergence test, soil crust strength by a pocket penetrometer (HUMBOLDT MFG.CO.) bulk density by cylindrical sample and particle density by pycnometer method were measured. The fig fruit treatments were prepared by thoroughly mixing the dried powder of ripe fig fruit passed through a 2 mm sieve (with the rates of 0, 1, 2, and 4 % by dry weight) with the air dried soils. Also, the compost treatments were prepared by thoroughly mixing the dried powder of compost passed through a 2 mm sieve (with the rates of 0, 1, 2, and 4 % by dry weight) with the air dried soils. The test measurement PVC cylinders with an inner diameter of 12.5 cm and a height of 20 cm were prepared. The bottom ends of the cylinders were closed with a screened PVC plate. These cylinders were uniformly filled with the treated soils and irrigated a few times to make a homogeneous soil column. About 3 cm of the top end of the cylinders were left empty.
Results and Discussion: The results showed that all the rates of the ripe fruit waste of fig and the compost treatments significantly decreased crust strength of all soils compared to control at 1% probability level. The results also showed nearly the greater effect of all the treatments on crust strength of loamy sand soil compared to the other soils. All the rates of the ripe fruit waste of fig and compost treatments significantly increased the moisture content of all the soils compared to control at 1% probability level. Moreover, the greater effect of all the treatments on the moisture content of silty clay loam soil compared to other soils was generally observed. All the rates of the ripe fruit waste of fig and compost treatments decreased the bulk density and particle density of all the soils compared to control. Tthe greatest impact was observed in the compost treatments at the level of 4% by dry weight and silty clay loam texture. Also, all the rates of the ripe fruit waste of fig and compost treatments increased the porosity of all the soils compared to control, and the greatest impact belonged to the compost treatments at the level of 4% by dry weight andsilty clay loam texture.
Conclusion: The results showed that the use of the ripe fruit waste of fig and compost in the soil increased moisture content and decreased crust strength significantly compared to the control. Also, the ripe fruit waste of fis and compost in the soil increased porosity and decreased bulk density and particle density compared to the control, but this increase and decrease were not significant.Reduction in crust strength caused by the ripe fruit waste of fig application was more than compost application. However, the effect of compost application on the soil bulk density, particle density, porosity and moisture content was more than the ripe fruit waste of fig application.
F. Sohrab; N. Abbasi; A. Mahdipour
Abstract
Introduction: Soil structural stability affects the profitability and sustainability of agricultural systems. Particle size distribution (PSD) and aggregate stability are the important characteristics of soil. Aggregate stability has a significant impact on the development of the root system, water and ...
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Introduction: Soil structural stability affects the profitability and sustainability of agricultural systems. Particle size distribution (PSD) and aggregate stability are the important characteristics of soil. Aggregate stability has a significant impact on the development of the root system, water and carbon cycle and soil resistance against soil erosion. Soil aggregate stability, defined as the ability of the aggregates to remain intact when subject to a given stress, is an important soil property that affects the movement and storage of water, aeration, erosion, biological activity and growth of crops. Dry soil aggregate stability (Mean Weight Diameter (MWD), Geometric Mean Diameter (GMD)) and Wet Aggregate Stability (WAS) are important indices for evaluating soil aggregate stability.To improve soil physical properties, including modifying aggregate, using various additives (organic, inorganic and chemicals), zeolites are among what has been studied.According to traditional definition, zeolites are hydratealuminosilicates of alkaline and alkaline-earth minerals. Their structure is made up of a framework of[SiO4]−4 and [AlO4]−5 tetrahedron linked to each other's cornersby sharing oxygen atoms. The substitution of Si+4 by Al+3 intetrahedral sites results inmore negative charges and a high cation exchange capacity.Zeolites, as natural cation exchangers, are suitable substitutes to remove toxic cations. Among the natural zeolites,Clinoptilolite seems to be the most efficient ion exchanger and ion-selective material forremoving and stabilizing heavy metals.Due to theexisting insufficient technical information on the effects of using different levels of zeolite on physical properties of different types of soils in Iran, the aim of this research was to assess the effects of two different types of zeolite (Clinoptilolite natural zeolite, Z4, and Synthetic zeolite, A4) on aggregate stability indicesof soil.
Materials and Methods: In this study at first, after preparation of two different types of soil with light and medium texture and doing identification tests such as determination of gradation and hydrometer tests and Atterberg limits, zeolite in four levels, 0 (control), 1%, 5%, and 10%w/w, was mixed with two soil textures (sandy loam and silty loam) in three replications. Then, each treatment was saturated for 48 hours in each month, during 6 months. Dry soil aggregate stability (Mean Weight Diameter (MWD), Geometric Mean Diameter (GMD), and Wet Aggregate Stability (WAS)), were determined. The experiment was carried out using factorial method in a randomized complete design.
Results and Discussion:The results showed that, in sandy loam texture, there was no significant difference between two types of zeolites, their level of using and their interaction on MWD (p
M. Karimzadeh; A. Alizadeh; M. Mohammady Arya
Abstract
One of the important factors that limits the maintenance and expansion of agriculture in irrigated lands of arid areas is the water shortage. Reuse of the municipal waste water effluent as one of the uncommon water resources especially around the big cities has received a lot of attention. One of the ...
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One of the important factors that limits the maintenance and expansion of agriculture in irrigated lands of arid areas is the water shortage. Reuse of the municipal waste water effluent as one of the uncommon water resources especially around the big cities has received a lot of attention. One of the most important physical properties of the soil affected by using wastewater is the saturated soil hydraulic conductivity (Ks). In order to investigate the effect of wastewater on Ks, the farms with sand, silty loam and clay were selected from the area around Parkand Abad (2) refinery in Mashhad that has been irrigated during the past 5 years with wastewater. Undistirbed sample was selected and saturated with water , wastewater and mixture of them was used to determine the amount of ks (with constant head method) and the of soil in laboratory. The results showed that the farms with wastewater with total suspended solids of 60 mg per litere floating in water limits the ks in different textures. The reduction in soil with clay texture as about 9 Percent and in silty loam and sand was about 4.5 and 2 Percent respectively. Using water as the liquid of experiment didn’t have any effect on increasing the amount Ks so that leaching the soil under the irrigation with wastewater increased the soil saturation up to 3 percent That shows no effect of leaching in improving the water direction. The most change of pb was observed in clay soil about 11 percent and the least in sand texture soil about 0.6 percent that with respect to the amount of floating materials in wastewater (60 mg) per liter the use of wastewater has been effective in blocking the soil openings. It seems that the floating material in waste water soil aggregation and the duration of continuous use of wastewater are effective factors in changing the physical properties of soil such as Conductivity of water saturated soil.
S. M. Hosseni; A. Mosaedi; K. Naseri; A. Golkarian
Abstract
Rill erosion due to run off on hill slopes is a kind of water erosion which causes the highest soil loss in world-wide scale. Since the length of hill slope is one of the most effective factors in erosion, in this research, the variation of width, depth, cross-section, and frequency of rills were evaluated ...
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Rill erosion due to run off on hill slopes is a kind of water erosion which causes the highest soil loss in world-wide scale. Since the length of hill slope is one of the most effective factors in erosion, in this research, the variation of width, depth, cross-section, and frequency of rills were evaluated on the length of hill slops. In addition, soil components were evaluated due to the variation of hill slope length. Some hill slopes with pronounced rills were chosen in Ahmad-Abad location and on each slope, fifty-meter –transect was selected with the distances of 10, 20, 30, 40 and 50 meters and the features of rills were measured. As the routine models of linear regression have not been fitted to the observed data, the incomplete gamma function was used to obtain logical relation between hill slope length and mentioned parameters. Therefore, this model were fitted well to all parameters, except to the frequency of rills and the mean amounts of clay (p
