seyed ali akbar moosavi

Water infiltration into soil is one of the most important soil physical processes for hydrological and agricultural applications. It plays a key role in hydrological studies, water resource management, soil conservation, irrigation systems, drainage systems, and soil erosion control in watersheds. There are various equations for determining how water infiltrates into the soil. Some of these (e.g., Philip and Green-Ampt equations) are based on the physical properties of the soil and the results of solving the relationships governing water flow in the soil. The others (e.g., Kostiakov, Kostiakov-Lewis, Horton, and US Soil Conservation Service equations) are empirical relationships obtained from analyzing the curve between infiltration rate and time without any physical background. Using these relationships avoids the waste of time and high cost required to measure infiltration in the field, especially on a large scale. The coefficients of these equations, like other soil characteristics, depend on the soil type and conditions and are subject to spatial and temporal variations. Therefore, this research aimed to study the spatial variability and model of the spatial dependence of the coefficients of different theoretical and empirical infiltration equations in the calcareous soils of Bajgah, Shiraz.
 

Infiltration tests were carried out at 50 points of the studied soil using the single-ring method. Different infiltration equations, including Horton, Kostiakov, Kostiakov-Lewis, US soil conservation service (SCS), Green-Ampt, and Philip equations were fitted to the measured data, and the coefficients of the equations were determined. Preliminary statistical checks included determining the summary statistic (measure of location, measure of spread, and shape parameters of data distribution), checking the normality of the distribution of the infiltration coefficients data, and performing necessary transformations if required. To check the spatial dependency of the data, the experimental semivariogram of the data was calculated. Various theoretical models, including spherical, exponential, and Gaussian models, were fitted and the best semivariogram model and its characteristics were determined using statistical criteria. Coefficients at unmeasured points were also estimated using the normal kriging method and the inverse distance weighting (IDW) method with different weight powers. The evaluation of the estimation methods was also carried out using the jack-knife method and the appropriate estimation method was identified. Estimation of the coefficients at points without data and zoning was done using the appropriate estimation method. The statistical and geostatistical analyses mentioned above were carried out using the software packages Excel and GS+.

The coefficient of variation (CV) of the studied infiltration equation coefficients varied between 12.5 and 478%, with the highest and lowest CV for the coefficients “A” of the Kostiakov-Lewis equation and “b'” of the SCS equation. The isotropic spherical model was the best-fitted model to the semivariogram of the coefficients of the Kostiakov (K and b), Horton (c, m, and a), Philip (“A”), and Kostiakov-Lewis (b') equations. Whereas, the isotropic exponential model was the best-fitted model to the coefficients of the SCS (a and b), Philip (“S”), and  Kostiakov-Lewis (K and A) equations. The range of variation (the radius of influence) of the coefficients of the infiltration equations varied from 1.96 to 211 m, respectively, for the “K” coefficient of the Kostiakov equation and the coefficients of the Kostiakov-Lewis, “a” of Horton, “S” of Philip, and “b”' of SCS equations. Among the coefficients studied, the highest and lowest nugget effect (C0) to threshold (C+C0) ratio was obtained as 0.648 and 0.5, respectively. The spatial correlation class of the infiltration equation coefficients was moderate, and the maximum and minimum radius of influence were 211 and 6.4 m, respectively, which corresponded to the “S” coefficient of Philip, the coefficients of Kostiakov-Lewis, the “a” coefficient of Horton, and the “b” coefficient of the SCS equations. The most precise and the least precise estimates were related to the “A” coefficient of Philip, “b” of Kostiakov, and “b'” of Kostiakov-Lewis equations, respectively.

In this study, spatial variations of the coefficient of various infiltration relations were investigated and modeled, and estimation and zoning were performed using the best model. Results showed that the spatial dependence class of the coefficient of infiltration relations in the study area is medium, and also, the maximum and minimum radius of influence of 211 and 6.4 m are related to the coefficient S of the Philip and the coefficients of the Kostiakov-Lewis and the coefficient a of Horton and the coefficient b of the US Soil Conservation Service equations, respectively. In other words, this study suggests geostatistical methods and limited measurements to estimate the coefficients of the infiltration equations with reasonable precision and to save time and cost when zoning or estimating these coefficients at large scales. However, due to the weak and unsuitable spatial structure, the IDW method outperformed the kriging method in some cases in the studied area and its use can lead to more precise estimates. Therefore, in cases where the spatial structure of the desired feature is weak and inappropriate, methods such as Kriging that rely on strong spatial correlation are unsuitable, and in these cases, other alternative estimation methods, such as IDW which does not depend on the presence of strong and appropriate spatial structure in the data should be used.

The number of environmental variables used in digital soil mapping has increased rapidly, which has made it a challenge to select and focus on the most important covariates. No environmental covariates have the same predictability in modeling, and some covariates may introduce noise that reduces the predictive power of the models used. On the other hand, it is beneficial to identify all environmental variables to obtain spatial information that can improve predictions. In this regard, the feature selection algorithms help reduce the dimensions of the predictive model by identifying the associated covariates. Therefore, this study aims to investigate different feature selection algorithms in the selection of auxiliary variables and evaluation their effect on the predictive model.

The area under study is a part of Darab city in the southeast of Fars province with an area of about 31000 hectares. In the study area 140 profiles were determined and excavated according to the diversity of geomorphological units and thus the type of soils. After excavating the profiles and checking the morphological characteristics of each soil profile, a sufficient amount of soil samples were collected from the genetic horizons and transported to the laboratory for further analysis. Some of the physical and chemical parameters of soils were tested using accepted techniques after air drying and passing through a 2 mm sieve. Finally, all profiles up to the great group level were classified using the U.S. Soil Taxonomy based on the data collected from field observations and the outcomes of laboratory analysis. Environmental variables include the parameters derived from the Digital Elevation Model, Landsat 8 images, geology and geomorphology maps of the study area. All parameters were derived using ArcGIS, SAGAGIS and ENVI softwares. In the present study, four different feature selection techniques including Variance Inflation Factor (VIF), Principal Component Analysis (PCA), Boruta and Recursive Feature Elimination (RFE), were used to identify an optimal set of covariates for predicting spatial classification of soil classes at the great group level. In addition, a Random Forest model (RF) with 10-fold cross-validation and the 5-repeat method, was used to compare different feature selection strategies in soil class mapping. The comparison of different feature selection techniques in estimating soil classes, was based on the evaluation criteria of accuracy and Kappa coefficient between observed and predicted values.

The results showed that the prediction accuracy increased by using variables selected with different feature selection methods compared to using all variables in the model. In addition, the improvement in predictive performance is different between the four types of feature selection. The VIF and PCA methods had the highest and lowest accuracy index and Kappa coefficient, respectively. The Boruta method, with the lowest number of variables, improved the model's performance after the VIF method. However, the Kappa coefficient showed poor agreement between predicted and observed values for all approaches. The imbalance of soil classes could be a reason for decreasing the accuracy index and Kappa coefficient. However, the random forest model, with and without feature selection methods, identified all soil great groups in the study area. Therefore, it can be concluded that the Random Forest algorithm is a very powerful technique for spatial prediction of soil classes in the study area. Although the performance of the model varied using different feature selection algorithms, the predicted soil maps had similar spatial patterns. Based on the prediction of model with the variables selected by the VIF, the resulting map indicates that Ustorthents soils are mainly located in high altitude regions with steep slopes. Haplustepts, Calciustepts, and Calciusterts great groups have developed in places with low to medium slopes. Haplosalids have developed downstream of the salt dome. Great groups of Ustifluvents were discovered in fluvial sedimentary plains. Endoaquepts were found in the floodplains, which had the smallest area on the predicted map.

Overall, the findings indicate that the feature selection methods can utilize significant dependencies among relevant covariates to predict soil classes and to improve modeling accuracy. In the current study, the environmental factors, obtained from the Digital Elevation Model, were selected as key variables, showing the importance of topography and morphology in the classification of soil types in the area. Although the selected variables improved the performance of the model, the prediction of soil classes was random. This could be attributed to the imbalance of soil classes.

Postoperative nausea and vomiting (PONV) is considered a common complication of anesthesia, which, particularly in eye surgery, may exert pressure on stitches and open or leak the surgical wound, leading to bleeding.

We aimed to study the effect of ginger on PONV and changes in vital signs after eye surgery.

In this triple-blind randomized controlled trial, 120 candidate patients for eye surgery were divided into group A (n = 40) and group B (n = 40). Patients in group A received the capsules of ginger 1 g while patients in group B received a placebo one hour before the procedure with 30 mL water. The incidence of nausea and the frequency of vomiting were evaluated at 0, 15, 30 minutes, and 2 hours after the operation. Also, the vital signs of the participants were recorded at certain times.

The results demonstrated a statistically significant difference in the frequency of nausea between group A and group B (P < 0.05). The severity of nausea was lower in group A than in group B immediately and 2 hours after recovery (P < 0.05). The incidence of vomiting was significantly lower in group A than in group B (P < 0.05). The vital signs were not significantly different between group A and group B (P > 0.05).

Ginger was effective in the prevention of PONV after eye surgery but had no impact on vital signs. Hence, ginger is proposed to use as a low-cost, prophylactic measure for PONV reduction.

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.
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.
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.
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.