Soil science
P. Khosravani; M. Baghernejad; A.A. Moosavi; S.R. Fallah Shamsi
Abstract
IntroductionUnderstanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as ...
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IntroductionUnderstanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as an alternative and tool for DSM, which are mainly used for data mining and pattern recognition purposes, and are now widely used for regression and classification tasks in all fields of science. Hence, this study was undertaken to spatially model sand, silt, and clay particles utilizing machine learning models such as Random Forest (RF), Support Vector Regression (SVR), and the Co-Kriging geostatistical model. Additionally, auxiliary variables with high spatial resolution were incorporated into the analysis. This investigation was conducted in a section of the Marvdasht plain, located in Fars province. Materials and MethodsThe present study was conducted in a part of Marvdasht plain located between 35.82´41°52' to 1.07´57°52' east longitude and 35.02´48°29' to 14.72´2°30' north latitude, and 40 km north of Shiraz with an area of about 50,000 hectares. After determining the study area boundaries, the positions of 200 sampling points were determined using the R software and the conditioned Latin hypercube sampling method. In other words, for soil feature modeling, 200 samples were taken from two depths of zero to 30 and 30 to 60 centimeters in the study area. Then, the samples were transferred to the laboratory, dried, and passed through a 2 mm sieve. Finally, the soil texture components were measured by the hydrometer method. The environmental variables used in this study are a wide range of representatives of soil-forming factors that were prepared as much as possible from sources with minimum cost and high accessibility. In total, 75 environmental variables were prepared, and the raster format related to all environmental variables, including 39 elevation and altitude variables and 36 remote sensing measurement variables, was extracted. Finally, the factor-tuning inflation variance and Boruta algorithm were used to select the optimal variables. ResultsThe minimum amount of clay was measured at 10.21% and 10.45%, respectively, and the maximum amount was 32.65% and 36.35% at the surface and subsurface depths. The average amount of clay in all samples was 37.91% and 35.61%. The average amount of sand was measured at 25.65% and 26.02% at the surface and subsurface depths, respectively. The maximum amount of sand was observed in the northern and higher parts of the study area and was equal to 54.68% and the minimum amount was predicted in the low-lying areas of the study area. Low-lying areas and sedimentary plains in the central part of the study area contained high amounts of silt. Four depth variables valley depths (VD), texture (TE), topographic wetness index (TWI), and clay index (CI) related to geomorphometric parameters and the normalized difference vegetation index (NDVI) variable related to remote sensing indices were selected as optimal variables. The RF model with R2 of 54.0% and 36.0% for predicting sand, 48.0% and 64.0% for predicting silt, and 52.0% and 49.0% for predicting clay at both surface and subsurface depths performed better than the SVR and Co-Kriging models. The most effective variable in predicting the spatial distribution of soil particles was VD with relative importance of 60% and 65% for predicting sand at the surface and subsurface depths, 70% for predicting silt at the surface depth, and 70% and 65% for predicting clay at both surface and subsurface depths, respectively. Only TE and TWI variables were more important than VD for predicting silt at subsurface depth. These results show that topographic variables are effective in the spatial variation of soil particles. Unlike clay, the highest amount of sand in both depths was observed in the northern part and the highest part of the study area, and the lowest amount was predicted in the low-lying areas of the study area. ConclusionIn general, with the aim of this research, maps of the spatial distribution of soil texture components were prepared at both surface and subsurface depths using machine learning and geostatistical approaches along with environmental covariates in a part of Marvdasht plain. Among the selected environmental covariates, topographic attributes, especially the valley depth (VD), had the highest effect in justifying the spatial prediction of soil texture components. Also, the results of comparing the performance of machine learning models supported the higher efficiency of the RF model than other models. Therefore, the approach used in this study to prepare a map of soil texture components can be useful as a guide for mapping useful soil features in areas with similar climatic and topographic conditions.
Y. Ostovari; S.A.A. Mousavi; H. Mozaffari
Abstract
Introduction: Soil erosion is one of the most important and serious threats to food security and as a consequence of human life. In order to perform soil protection activities against soil erosion, knowledge about the amount of soil loss tolerable is very important. In fact, the soil loss tolerable is ...
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Introduction: Soil erosion is one of the most important and serious threats to food security and as a consequence of human life. In order to perform soil protection activities against soil erosion, knowledge about the amount of soil loss tolerable is very important. In fact, the soil loss tolerable is the potential for soil erosion, loss of productivity and lost production, and the final criterion for controlling soil erosion and degradation of land. Soil thickness methods, particularly Skidmore equation, based on their ability to estimate the tolerable amount of soil loss have been widely used. In the mathematical function developed by Skidmore based on soil thickness, the soil loss tolerable is calculated based on the soil's current depth, the lowest and maximum soil depth for sustained growth of crops, and the upper limit of tolerable erosion in accordance with the environment. Since the determination of soil loss tolerance by soil thickness method and the Skidmore equation requires time, cost and energy, the researchers have tried to estimate the soil tolerance is supported by regression methods using pedotransfer functions and easily available soil properties. Therefore, the present study was carried out with the aims of determining the tolerable tolerance of soil loss by thickness method and the development of regression pedotransfer functions for estimating this property in the upstream of the dam.
Materials and Methods: The study is place on Kamfiruz Watershed with an area of 422 km2, an average annual precipitation of 443 mm and an average annual temperature of 14 °C. It is closed to the Dorudzan Dam sub-basins and is considered as one of the five parts of Marvdasht plain in Fars province. For this work, 60 soil profiles were excavated by excavating machine. In addition to measuring the depth of soil, some physico-chemical soil properties were measured from the surface layer (0-30 cm) including; soil texture, organic matter, salinity, percentage calcium carbonate, mean weight diameter in the laboratory and filed. In order to develop regression models for estimating the tolerable soil loss, information from 60 soil profiles was divided into two data-sets. One set of the data with 42 samples (70% of whole samples) was used for developing the models and another set of the data with 18 soil samples (30% of whole samples) was used for validation. Multiple linear regression was used to develop the linear models. The same soil properties used in the multiple regression method were considered as inputs in the tree regression method to estimate the tolerable amount of loss.
Results and Discussion: The results showed that the minimum and maximum Z1 parameters (the lowest soil depth for stable growth of crops in the study area) were considered as 0.25 and 0.51 m based on the current depth of soil. Organic matter of the soils with the highest standardized coefficient (Beta = 0.44) and the highest correlation (-0.77) with soil loss tolerance was the most important soil properties for estimating the soil loss tolerance. In the regression model, only the coefficients of four characteristics of permeability, soil aggregate stability, pH and organic matter appeared among the soil grazing characteristics and entered into the model. Based on the evaluation statistic, tree regression method with the highest determination coefficient in both calibration data sets (R2 = 0.96) and validation (R2 = 0.78) and the lowest error value in the validation data (RMSE= 0.29 ton ha-1 year-1) and validation (RMSE = 0.125 ton ha-1 year-1) were more efficient than the multiple regression method in estimating the tolerable soil loss.
Conclusion: Soil loss tolerance was estimated using regression methods (multiple linear regression and regression tree) in Doroudzan Watershed, Fars province. The soil loss tolerable determined using Skidmore method, was 1.04 tons per hectare per year ranging from 0.29 to 2.25 ton ha-1 year-1. The soils of this area are slightly deep and their depth varies from 0.4 m in the marginal areas in the upstream parts of the catchment area of the dam and the slope of mountain up to 2 meters in the center of the plain with agricultural lands uses. In general, the tree regression method had a better performance than linear regression method for estimating the soil loss tolerance based on the statistical indices.
leila zare; abdolmajid ronaghi; Seyed Ali Akbar Moosavi; Reza Ghasemi
Abstract
Introduction: Vermicompost is one of the important bio-fertilizer which is the product of the process of composting different organic wastes such as manures and crop residues using different earthworms. Vermicomposts, especially those are derived from animal wastes,contain the large amounts of nutrients ...
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Introduction: Vermicompost is one of the important bio-fertilizer which is the product of the process of composting different organic wastes such as manures and crop residues using different earthworms. Vermicomposts, especially those are derived from animal wastes,contain the large amounts of nutrients compaired with the composts prepared from crop residues. Vermicomposts contain plant available form of nutrients such as nitrate nitrogen, exchangeable phosphorus and potassium, calcium and magnesium. Nowadays, the use of vermicompost in sustainable agriculture to improve the growth and quality of fruits and crops is very common. Drought occurs when the amount of moisture in soil and water resources and rainfall is less than what plants need for normal growth and function. Two thirds of farm lands in Iran have been located in arid and semi-arid regions with annual rainfall less than150 mm that has been distributed irregularly and unpredictable during growth season imposing water stress in most crops. It indicates the importance of water management and proposing different strategies for mitigating detrimental effect of water stress in croplands. Due to the fact that crops nutrient management under drought and water stress using organic fertilizers is an effective method in reaching to high yields in sustainable agriculture, the objective of the present study was to investigate the influence of vermicompost application on reducing the adverse effects of water stress on the growth and chemical composition of corn in a calcareous soil.
Materials and Methods: In order to study the influence of water stress and application of vermicompost on corn dry matter yield and nutrients concentration of corn shoot, a greenhouse factorial experiment (4×3) in completely randomized design with three replications was conducted in college of agriculture, Shiraz university, Shiraz, Iran. The factors consisted of four vermicompost levels (0, 10, 20 and30g kg-1soil equal to 0, 20, 40 and 60 Mg ha-1) and three moisture levels(100, 80and 60%of field capacity(FC)). The soil samples were collected (0-30 cm depth) from a calcareous soil (Fine, mixed, mesic, Typic, Calcixrepts), located at Bajgah, Shiraz, Iran. Soil samples were mixed thoroughly with different levels of vermicompost and transfred to plastic pots. Six corn seeds were planted in each pot and were thinned to three uniform plants, one week after germination. Eight weeks after germination, corn shoots were harvested, dried and recorded. Plant samples were grind using a portable grinder and transferred to the laboratory for chemical analysis. The collected data were statistically analysed using SAS software (9.1.3) package.
Results and Discussion: The results indicated that with increasing the levels of vermicompost, dry matter yield and concentrations of total nitrogen (TN), phosphorus (P), iron (Fe), copper(Cu) and zinc (Zn) in corn shoots were significantly increased. But, due to the antagonistic relationship between manganese (Mn) and Zn or Fe,concentrations of Mn were significantly decreased. However, the concentration of Mn was in the sufficiency range. The highest dry matter yield and concentrations of nitrogen and phosphorus in corn shoot was observed at 30 g kg-1 vermicompost treatment, with 19, 10 and 20 % increase (compared to the control), respectively. The application of 30 g kg-1 vermicompost increased the concentrations of Zn, Cu and Fe by 41%, 90% and 75%, respectively and concentration of Mn decreased by 11.88%, compared to the control. Increasing the levels of water stress increased significantly the concentration of nutrients in corn shoot due to the reduction of corn biomass. The highest increase in nutrient concentrations was observed at 60% FC moisture level. Nitrogen and phosphorus concentrations in corn shoots by 12.5and 22.5% and Zn, Cu, Fe and Mn by 25, 83, 43and29% were higher compared to those of control (100% FC), respectively. The interaction effects of water stress and vermicompost on the concentrations of shoot N and Cu were significant and both were incresead by simultanoeus application of vermicompost and levels of water stress. The applicaion of 30 g kg-1 vermicompost (about 60 ton ha-1) under 60% FC moisture level increased significantly dry matter yield and the concentrations of nitrogen, phosphorus, zinc, copper and iron in corn shoot by 29%,5.5%, 23, 110, 41 and 71 percent compared to the control, respectively. However, because of the antagonistic relationships,the iron or manganese concentrations were reduced, but were yet in the sufficiency range. The use of 30 g kg-1 vermicompost under 80% FC moisture level Also increased significantly the concentrations of nitrogen, phosphorus, zinc, iron and copper by 9, 23, 24, 59 and 43 percent compared to the control, respectively.
Conclusion: The applicaion of 30 g kg-1 vermicompost increased significantly dry matter yield and the concentration of nitrogen, phosphorus, zinc, copper and iron in corn shoot under water stress treatments. In conclusion, the application of vermicompost mitigated the detrimental effects of water stress on corn dry matter yield and concentration of nutrients due to the positive effects of compost on physical, chemical and biological properties of the calcareous soil.
shahrzad karami; mehdi zarei; jafar yasrebi; najafali karimian; s.Ali Akbar Moosavi
Abstract
Introduction: Heavy metals such as cadmium (Cd) are found naturally in soils, but their amount can be changed by human activities. The study of the uptake and accumulation of heavy metals by plants is done in order to prevent their threats on human and animal’s health.Cadmium is a toxic element for ...
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Introduction: Heavy metals such as cadmium (Cd) are found naturally in soils, but their amount can be changed by human activities. The study of the uptake and accumulation of heavy metals by plants is done in order to prevent their threats on human and animal’s health.Cadmium is a toxic element for living organisms. Cadmium competes with many of nutrients to be absorbed by the plant and interferes with their biological roles. Water stress affects the cell structure and the food is diverted from its normal metabolic pathway. It also reduces the availability and uptake of nutrients by the plant. One reason for the reduction of plant growth under drought stress is the accumulation of ethylene in plants. There are ways to mitigate the negative effects of drought stress that one of which is the use of Plant Growth Promoting Rhizobacteria(PGPRs) to increasing the availability of nutrients. Soil beneficial bacteria play an important role in the biological cycles and have been used to increase plant health and soil fertility over the past few decades.The aim of this study was to investigate theeffect of PGPRson the concentration and uptake of macro nutrients by corn in a Cd-contaminated calcareous soil under drought stress.
Materials and Methods: A greenhouse factorial experiment was conducted in a completely randomized design with three replications. The treatments were two levels of bacteria (with and without bacteria), four levels of Cd (5, 10, 20, and 40 mg kg-1), and three levels of drought stress (without stress, 80, and 65% of field capacity). The pots were filled with 3 kg of treated soil. Cd was treated as its sulfate salt in amounts of 5, 10, 20, and 40 mg kg-1. The soil was mixed uniformly with 150 mg N kg-1 as urea, 20 mg P kg-1 as Ca (H2PO4)2, 5 mg Fe kg-1 as Fe-EDDHA and 10, 10 and 2.5 mg Zn, Mn and Cu kg-1, respectively as their sulfate salt in order to meet plant needs for these nutrients. Six seeds of Zea mays (var. HIDO) were planted at each pot. Each seed of maize was inoculated with 2 mL (1×108 colony-forming units (cfu) mL-1) of Micrococcus yunnanensis (a gram positive bacterium with the ability of production of sidrophore and phosphate dissolving characteristic). Each pot was irrigated daily with distilled water to near field capacity by weight, until 15 days after corn planting. Then corn was thinned to 3 plants per pot and irrigation was started with different levels of drought stress (without stress (F.C), 80, and 65% of field capacity) by weight. At harvest (8 weeks after planting), the aerial parts of the plants was cut at the soil surface. The harvested plants were washed with distilled water, dried to a constant weight at 65C. Representative samples were dry-ashed and analyzed for macro nutrients.
Results and Discussion: The results indicated that the inoculation of bacteria increased shoot dry weight (DW) and total uptake of nitrogen (N), phosphorus (P), and potassium (K). Drought stress decreased DW, total uptake of N, P, and K, concentrations of N and K in corn shoots, and concentration of K in the soil. The application of biological fertilizers, such as plant growth promoting rhizobacteria, increase plant growth through increasing microorganism’s activities and population in the soil and so increase macro nutrients uptake by the plant. Phosphate solubilizing rhizobacteria increase plant growth and phosphate availability with production of organic acids and secretion of phosphatase enzymes or protons and conversion of non-soluble phosphates (either organic or inorganic phosphates) to the forms that are more available for the plants and improve their nutrition and increase their growth. Drought stress decreases Dry Matter Weight(DMW) through decreasing relative humidity of the air of plant growth environment and increases evaporation, transpiration, plant temperature and light intensity of the sun. It prevents normal development of roots, water uptake, and plant growth by reducing the moisture content of the soil. It also decreases uptake and availability of Phosphorus in arid soils because plant growth and root activity in arid soils are lower from those of wetlands and as phosphorus is immobile in the soil, its uptake by the plant will decrease. N concentration of plants will increase drought stress conditions through rapid accumulation of amino acids that had not been converted into protein. The combined effects of drought stress and inoculation of bacteria on decomposition of silicates, cause the release of nutrients such as potassium. Increasing levels of cadmium in both cases, with and without bacterial inoculation, decreased DW, N and K uptake by corn because of its toxicity and its competition and interactions with these nutrients.
Conclusion: The inoculation of bacteria mitigated the negative effects of drought stress and cadmium contamination by increasing dry weight of corn and increasing uptake of macronutrients by aerial parts. Drought stress in both cases (with and without bacterial inoculation) reduced shoot dry weight, total uptake of macro nutrients, N and K concentrations in corn shoots and post-harvest potassium concentration in the soil. Cadmium levels decreased shoot dry matter and N and K uptake by the plant. The use of bacteria was more effective at low cadmium and drought stress levels.
Ali Akbar Moosavi; Mohammad Omidifard
Abstract
Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. ...
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Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. Measurement of these hydraulic properties needs some special instruments, expert technician, and are time consuming and expensive and due to their high temporal and spatial variability, a large number of measurements are needed. Nowadays, prediction of these attributes using the readily available soil data using pedotransfer functions or using the limited measurement with applying the geostatistical approaches has been receiving high attention. The study aimed to determine the spatial variability and prediction of saturated (Ks) and near saturated (Kfs) hydraulic conductivity, the power of Gardner equation (α), sorptivity (S), hydraulic diffusivity (D) and matric flux potential (Фm) of a calcareous soil.
Material and Methods: The study was carried out on the soil series of Daneshkadeh located in the Bajgah Agricultural Experimental Station of Agricultural College, Shiraz University, Shiraz, Iran (1852 m above the mean sea level). This soil series with about 745 ha is a deep yellowish brow calcareous soil with textural classes of loam to clay. In the studied soil series 50 sampling locations with the sampling distances of 16, 8 , and 4 m were selected on the relatively regular sampling design. The saturated hydraulic conductivity (Ks), near saturated hydraulic conductivity (Kfs), the power of Gardner equation (α), sorptivity (S), hydraulic diffusivity (D) and matric flux potential (Фm) of the aforementioned sampling locations was determined using the Single Ring and Droplet methods. After, initial statistical processing, including a normality test of data, trend and stationary analysis of data, the semivariograms of each studied hydraulic attributes were calculated in various directions and their surface semivariograms were also prepared to determine the isotropic or anisotropic behavior of each studied soil attributes. Since all of studied soil hydraulic attributes were isotropic variables, therefore, the omnidirectional semivariograms were calculated and different theoretical models were fitted to them. The best fitted semivariogram models were determined using the determination coefficient, R2, and the residual sum of the square, RSS. The parameters of the best fitted models to the experimental semivariograms were also determined. The prediction of study hydraulic attributes was carried out using the parameters of semivariogram models by applying the ordinary Kriging approach. Predictions were also carried out using the Inverse Distance Weighing approach. The results of predictions were compared to each other using the Jackknifing evaluation approach and the suitable prediction method was determined and zoning was performed using the results of introducing prediction method. All of the semivariogram calculations and modeling, prediction of zoning of study hydraulic attributes were performed using the GS+ 5.1 software packages.
Results and Discussion: Results indicated that all of the studied soil hydraulic attributes belonged to the weak to moderated spatial correlation classes and the spherical model was the best fitted model for their semivariograms (except for Kfs and D that their best semivariogram models were exponential). The sill of all semivariograms ranged between 0.0003 to 0.419 for the S and Kfs, respectively. The nugget effects and the Range parameter of all semivariograms were located between 0.00015 to 0.108 for the S and Фm, and 211 to 6.4 m for Ks and D, respectively. Results also indicated that 3.5 and 50% of total variation of D and Ks was spatially structured and the other was random, respectively. The spatial correlation classes of near saturated soil hydraulic conductivity and soil hydraulic diffusivity were week, whereas, the spatial correlation classes of the other studied soil hydraulic attributes were moderate. Results revealed that the Inverse Distance Weighting method was the most suitable approach for the prediction of all studied soil hydraulic attributes in the present study. Comparison of the calculated statistical evaluation measures (i.e. Determination coefficient, R2, Mean residual error, MRE, mean square error, MSE, Normalized mean square error, NRMSE and geometric mean error ratio, GMER) and the final determined order of precision showed that the most and the least accurate predictions were obtained for Ks and Фm, respectively.
Conclusion: It is suggested in the cases that we need to map the hydraulic attributes or need their quantities in a large number; geostatistical prediction be performed using the limited measurements to reduce the needed time and costs.
M. Zahedifar; S.A.A. Moosavi; M. Rajabi
Abstract
Management and chemical quality of groundwater is very important in arid regions. Fasa plain (in Fars province) is an arid-semi arid region in Iran, that almost all of its residents are using groundwater in agricultural activities. Recent water shortages resulted in deepens water table, salinization ...
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Management and chemical quality of groundwater is very important in arid regions. Fasa plain (in Fars province) is an arid-semi arid region in Iran, that almost all of its residents are using groundwater in agricultural activities. Recent water shortages resulted in deepens water table, salinization and reduced groundwater quality in this area. Studying the spatial variability and zoning of the chemical quality attributes of water in order to optimum utilization and management of soil and water resources is one of the practical methods in conservation of these resources. Therefore, the spatial variability for some of groundwater quality attributes in 80 wells located in Fasa plain of Fars province including total hardness, total dissolved solids, electrical conductivity, pH, soluble cations (calcium, magnesium, sodium, and potassium) and anions (sulfate, chloride, and bicarbonate) concentration was studied and attributes were estimated by applying geostatistical methods. The suitable estimation method was determined and zoning of the studied area was done for each studied attributes. The spatial variability structure of studied attributes followed the spherical and exponential models having the range parameters of 6700 to 140600 m belonging to the moderate to strong spatial correlation classes. The Ordinary Point Kriging was determined as the suitable estimating method that used for preparing the maps of water quality zoning. The quality of groundwaters in the southern half of the studied area was lower than that of the northern half, therefore, the more sensitive management in utilization of water resources and in using of agricultural systems is needed in order to avoiding the deterioration of water quality and worsening of groundwater status that is directly related to the residents livelihood.
