Showing 11 results for Taghizadeh
R. Taghizadeh, R. Seyed Sharifi,
Volume 15, Issue 57 (fall 2011)
Abstract
In order to evaluate the effects of nitrogen fertilizer levels on grain yield and nitrogen use efficiency in corn cultivars, a split plot experiment based on randomized complete block design with three replications was conducted at the Research Farm of Islamic Azad University of Ardabil during 2006-2007 cropping seasons. Factors consisted of nitrogen fertilizer at four levels (0, 80, 160 and 240 kg/ha) and corn cultivars at three levels (SC-310, SC-404 and DC-370). The results showed that grain yield was significantly affected by nitrogen levels, corn cultivar and nitrogen level × corn cultivars. The highest grain yield was related to application of 240 kg/ha nitrogen with SC-404 cultivar. Nitrogen levels of 160 and 240 kg/ha had similar yields, but more yield than 80 kg/ha. With increasing of nitrogen levels, plant height, the number of grains per ear rows significantly increased. Comparisons of means showed that increasing the application of nitrogen fertilizer decreased nitrogen use efficiency. Nitrogen use efficiency decreased from 17.13 kg/kg with application of 80 kg/ha nitrogen fertilizer to 12.4 kg/kg in application of 240 kg/ha nitrogen fertilizer. Nitrogen use efficiency was affected by corn cultivar. Nitrogen use efficiency in SC-404 was higher than SC-301. In conclusion, in order to increase grain yield and nitrogen use efficiency, SC-404 hybrid should be applied with 160 kg N/ha in climatic conditions of Ardabil Plain.
S. Ayoubi, R. Taghizadeh, Z. Namazi, A. Zolfaghari, F. Roustaee Sadrabadi,
Volume 20, Issue 76 (Summer 2016)
Abstract
Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km2, in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.
Z. Savari, S. Hojati, R. Taghizadeh-Mehrjerdi,
Volume 20, Issue 77 (Fall 2016)
Abstract
Salinity and alkalinity decreases physical, chemical and biological quality of soils and as a result reduces crop yield. This study aims to evaluate spatial variability of soil salinity in Ahvaz using geostatistical approaches. Accordingly, 69 surface soil samples (0-10 cm) were collected and their electrical conductivities (EC) were measured in 1:1 soil: water extracts. The data were then analyzed using ordinary kriging (OK), log-normal kriging (LOK) and indicator kriging (IK) interpolation techniques to produce soil salinity maps. Finally, the quality control of soil maps was performed by calculation of root mean square error (RMSE) and coefficient of determination (R2). The results indicated that due to the lowest RMSE and the highest R2 values, the LOK interpolation method is the best approach in mapping soil salinity in Ahvaz. The results also illustrated that based on defined threshold values (4, 8, 16, and 32 dS m-1) the indicator kriging methods have been able to show risk of soil salinity in the area. Based on this, most of the area is covered by soils with salinity higher than 4 dS m-1. Evaluation of final soil maps showed that the highest concentrations of salts are related to the western and southwestern parts of Ahvaz city. In contrast, the lowest amounts of salinity were found in Eastern and Northern parts of the city.
E. Mehrabi Gohari, H. R. Matinfar, R. Taghizadeh,
Volume 21, Issue 3 (Fall 2017)
Abstract
Typical routine surveys of soils are relatively expensive in terms of time and cost and due to the fact that maps have been traditionally developed and considering their dependence on experts' opinions, updating maps is time consuming and sometimes not economical as well. While soil digital mapping, using soil various models - the Landscape, leads to simplification of the complexity found in natural soil systems and provides users with quick and inexpensive updates. In fact, the model represents a simplified form of the complex relationships between the soil and the land. This study aims to consider inferential model Soil-Land (SOLIM) in mapping and estimating soil classes in Aran area, Isfahan province. For this purpose, the SOLIM model inputs are digital geological and environmental layers of digital elevation model (DEM) including elevation, slope in percent, slop direction, curvature of the earth's surface, wetness indicator, flow direction, flow accumulation, and satellite images of Landsat 8. The seven subcategory of soil in the study area are input data of SOLIM model. Then fuzzy maps were prepared for seven types of soil and final maps of soil prediction were created by non-fuzzy action. Results showed that the SOLIM using environment variables has very high ability to separate soil types in greater detail and soils with different parent materials, geology, climate and vegetation can be separated from each other by this model with a high degree of accuracy. Comparing error matrix shows that the overall accuracy of the map derived from the model SOLIM is 92.36%.
Z. Mollaee, J. Zahiri, S. Jalili, M. R. Ansari, A. Taghizadeh,
Volume 22, Issue 2 (Summer 2018)
Abstract
Spectral Reflectance of suspended sediment concentration (SSC) remotely sensed by satellite images is an alternative and economically efficient method to measure SSC in inland waters such as rivers and lakes, coastal waters, and oceans. This paper retrieved SSC from satellite remote sensing imagery using radial basis function networks (RBF). In-situ measurement of SSC, water flow data, as well as MODIS band 1 and band ratio of band 2 to 1 were the inputs of the RBF. A multi-regression method was also used to make a relationship between the in-situ data and the water reflectance data retrieved from MODIS bands. The results showed that RBF had the best SSC prediction error (RMSE=0.19), as compared to the multi-regression and sediment rating curve methods, with the RMSE of 0.29 and 0.21, respectively.
A. M. Ghaeminia, M. A. Hakimzadeh, R. Taghizadeh-Mehrjardi, F. Dehghani,
Volume 23, Issue 4 (winter 2020)
Abstract
One of the reasons for soil salinization is the accumulation of salts in it by transmission through water and wind. In order to investigate the phenomenon of transfer of salts with dust in the arid regions of the north of Yazd- Ardakan plain, field samples were taken using 32 MDCO sediments traps with uniform dispersion in an area of 20,000 hectares at some stage in 4 seasons of 2017. After washing the sediment collector with a liter of distilled water in the field and transferring the samples to the laboratory, for the quantitative analysis of saline dust, similar to measuring the Water Electrical Conductivity (ECw), the Total Soluble Solids (TDS) were additionally measured through evaporation technique. The form and distribution of the dust particle size were additionally investigated using a Scanning Electron Microscope (SEM) tool. Within the qualitative examine of salts, effective cations and anions in salinity including Na+, K+, Ca++, Mg++, C1-, HCO-3 and SO-4 were measured The results confirmed that, in general, the fallout was 11.1 g.m-2 of soluble material with dust particles (13.28%) in the course of only 12 months. Particles with a diameter of 5 to 10 microns were the most frequent. Considering the high correlation between C1- and Na+ in the spring, autumn and winter, due to the high correlation between Ca++ and SO-4 in summer dust, sodium chloride (NaCl) and gypsum (CaSO4) 2H2O)), the most abundant composition of dust- containing salts could be expected in these seasons. By determining the percentage of solutes in the fallout dust, it was observed that the impact of the amount of the deposited salt from dust was slight and insignificant in the short time period; with the assumption of no change in the rate of subsidence, it was anticipated that it would explain the poor salinity in non- saline mass soils for up to 10 cm in 72 years. In general, the capability of airborne salt in increasing the soil salinity in the study area can be in long- term periods. Therefore, it is recommended to investigate other environmental effects of this phenomenon in order to identify its hazards.
A. Forghani, A. H. Forghani, M. Taghizadeh, B. Rabiei,
Volume 24, Issue 1 (Spring 2020)
Abstract
Soils pollution with heavy metals is due to the presence of various metals such as copper, nickel, cadmium, zinc, chromium and lead. Heavy metals have a negative effect on the biological parameters of soil, including size, activity and diversity of soil microbial population, as well as the enzymes involved in the deformation of such elements as P, N, C, and S. Thus, the activity of soil enzymes as a bioavailable agent is reflected as a cheap and fast method for the natural and anthropogenic distribution of heavy metals contamination. The purpose of this study was to investigate the effect of lead, humidity and their interaction on urease and phosphatase enzyme activity during a 10 week incubation period. Different levels of acetate lead (50,100, 150 and 200 mg/kg soil) were added to the plots containing two different moisture regimes (field capacity and flooding). The activity of urease and phosphatase (alkaline and acidity) was measured after 2,4,6,8 and 10 weeks of incubation. The results indicated different levels of lead had no significant effect on the activity of urease and acidity phosphatase. In contrast, high levels of lead significantly reduced the activity of alkaline phosphatase. Moreover, moisture served a different role in the activity of these enzymes, and it was related to the lead concentration and incubation time. Additionally, the function and interaction of lead, moisture and time were very influential on urease and phosphatase activity. Therefore, the above three characteristics are very important to study soil contamination for the polluted soils.
H. Fathizad, M. Tavakoli, M. A. Hakimzadeh Ardakani, R. Taghizadehmehrjardi, H. Sodaiezadeh,
Volume 24, Issue 4 (Winter 2021)
Abstract
The purpose of this research was to investigate the trend of annual changes in Yazd station's meteorological parameters including minimum and maximum average daily temperature and average daily precipitation (1961-2005), as well as the predicted annual mean of these parameters in the three upcoming thirty years of the 2040s, 2070s and 2100s, by the SDSM model, under RCP2.6, RCP4.5, RCP8.5, A2, and B2 scenarios. Accordingly, by using the coefficient of determination and the MAE, R2, RMSE indicators, we evaluated the data generated by the SDSM model in comparison with the observed data in the base period. The lowest value of R2 based on the calibration and validation of the mean values of observed and simulated SRES was obtained for precipitation (86 and 80%). In terms of the R2 evaluation index, the accuracy of the small-scaled results of the minimum and maximum average temperature values was more than that of the average precipitation; however, in terms of the MAE and RMSE evaluation indicators, the accuracy of the small-scaled results of the average precipitation was higher than that of the minimum and maximum average temperature values. Subsequently, HadCM3 large-scale climatological data was used to predict the future periods (2010-2100). The results indicated that the temperature was raised in all months and seasons and the precipitation was decreasing in most of them, thereby confirming that the climate was changing in the studied region.
Z. Savari, S. Hojati, R. Taghizadeh Mehrjerdi,
Volume 25, Issue 3 (Fall 2021)
Abstract
Soil salinity and its development are the main problems that should be prevented by correct management methods. Recognition of saline districts and the preparation of salinity maps are the first steps in this way. Nowadays, the application of auxiliary data in digital soil mapping is increasing due to the current associated problems in the preparation of traditional maps. The objectives of this study were to map soil salinity by the Regression Kriging (RK) method, to identify areas with high salinity, and to investigate the relationship between soil salinity and soil-forming factors in Khuzestan Province. For this purpose, 291 surface soil samples (0-10 cm) were randomly collected in April 2014. Auxiliary variables or soil-forming factors were included in the land parameters such as slope, watershed and wetness index, OLI and TIRS images of Landsat 8, and the category maps (soil, land use, and geological maps). Also, kriging approaches were used to compare the precision of different mapping methods. The results indicated that the Regression Kriging method has a higher precision compared with other methods so that the coefficient of determination, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were estimated as 0.84, 0.41, and 6.21, respectively. The Decision Tree Regression method could also create a good relationship between soil salinity and auxiliary variables. The results showed that some auxiliary variables were more effective on the prediction of soil salinity including 2, 4, 5, and 7 bands of Landsat 8, Brightness Index, Wetness Index, Multiresolution index of Valley Bottom Flatness (MrVBF), Channel Network Base Level (CNBL), NDVI, SAVI and soil map. A Digital map of soil salinity was prepared by the obtained rules, and then it was assimilated with the map of error of variance to prepare the final soil salinity map. Accordingly, soil salinity was found to have an increasing trend from north to south in Khuzestan Province which indicates a salinity problem in the south of the Province. The main reasons for the high salinity in the south and southwestern parts of the area could be attributed to the high water table levels, differences in topography, capillary movement of salt to the soil surface, the difference in the type of land uses, and also groundwater quality and irrigation water which is altered by the frequent application of wastewaters and animal manures.
M. R. Taghizadeh, A. Motamedi, M. Galoie, F. Kilanehei,
Volume 27, Issue 4 (Winter 2023)
Abstract
Understanding flow behavior over bedforms is one of the most complex topics in sedimentary engineering. Despite numerous studies that have been conducted on river beds, the understanding of the interaction between flow and bed in turbid and clear waters is still impoverished. The present study mainly focused on simulating clear and turbid flows using SSIIM software. This study modeled the flow through a 12-meter channel with nine consecutive dunes of 1-meter length and 4 cm height. Nine simulations were performed to investigate the effects of flow velocity and flow separation zone in clear and turbid water. Finally, the results were compared with the experimental results of previous researchers using the PIV. The modeling results showed that the length of the flow separation zone increases with increasing velocity, and the highest probability of flow separation occurs at the highest velocity. In turbid flow, flow separation is less than the same flow condition in clear flow, and as fluid density increases, the length of the flow separation zone decreases. This study demonstrates the acceptable functionality of the SSIIM software and its accuracy in estimating flow behavior with and without sediment.
A. Barikloo, S. Rezapour, P. Alamdari, R. Taghizadeh Mehrjardi,
Volume 27, Issue 4 (Winter 2023)
Abstract
Soil quality is one of the most crucial factors determining crop productivity and production stability. The soil's physical, chemical, biological, and ecological characteristics affect its quality. Numerous researchers have concentrated the evaluation on a small number of soil quality indicators because measuring all soil quality indicators would be time-consuming and expensive. This study looked at the spatial autocorrelation of soil quality in the southwest areas of the Urmia Plain to establish the minimal data set for quantitative assessment. To accomplish this, 120 composite soil samples were collected from a depth of 0 to 60 cm, and the soil quality index was then calculated using the IQI method in 4 modes: Total-Linear (IQIwL-TDS), Total-Nonlinear (IQIwNL-TDS), Minimum-Linear (IQIwL-MDS), and Minimum nonlinearity (IQIwNL-MDS). 22 physical and chemical characteristics were used to choose the data set. The characteristics of sand percentage, sodium absorption ratio, cation exchange capacity, Available phosphorus, active calcium carbonate, and nickel concentration were chosen as the minimum data set (MDS) using the decomposition method into principal components. The linear IQIMDS mode produced the greatest soil quality index result, whereas the non-linear IQIMDS mode produced the lowest. The non-linear mode of the IQI index has a greater correlation coefficient (R2=0.85) than the linear mode of the IQI index (R2=0.73), according to an analysis of the linear and non-linear correlation coefficient between the soil quality index with the total category and minimum data. The findings of computing the global Moran's index for study sets of IQI soil quality index data revealed that the soil quality data are not independent of each other and are spatially autocorrelated, distributed in clusters, and have spatial autocorrelation. Getis-ord GI statistics indicated that the eastern and southeastern parts of the research region comprise clusters with poor soil quality, salt marshes produced by Lake Urmia's drying up, and surrounding arid plains.
