Irrigation
M.T. Sattari; S. Javidan
Abstract
Introduction
Surface and underground waters are one of the world's most important problems and environmental concerns. In the last few decades, due to the rapid growth of the population, the water needs have increased, followed by the input load to the water. In order to classify the quality of underground ...
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Introduction
Surface and underground waters are one of the world's most important problems and environmental concerns. In the last few decades, due to the rapid growth of the population, the water needs have increased, followed by the input load to the water. In order to classify the quality of underground water and water level according to the type of consumption, there are many methods, one of the most used methods is the use of quality indicators. Considering the facilities available in water quality monitoring stations and the need to save time and money, using alternative methods of modern data mining methods can be good for predicting and classifying water quality. The process of water extraction for domestic use, agricultural production, mineral industrial production, electricity production, and ester methods can lead to the deterioration of water quality and quantity, which affects the aquatic ecosystem, that is, the set of organisms that live and interact. Therefore, it is very important to evaluate the quality of surface water in water-environmental management and in monitoring the concentration of pollutants in rivers. The aim of the current research was to estimate the numerical values of the drinking water quality index (WQI) using the tree method and investigate the effect of wavelet transformation, the Bagging method, and principal component analysis.
Materials and Methods
In this research, to calculate the WQI index from the quality parameters of the Bagh Kalaye hydrometric station including total hardness (TH), alkalinity (pH), electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca), sodium (Na), Magnesium (Mg), potassium (K), chlorine (Cl), carbonate (CO3), bicarbonate (HCO3) and sulfate (SO4) were used in the statistical period of 23 years (1998-2020). Quantitative values calculated with the WQI index were considered as target outputs. By using the relief and correlation method, the types of input combinations were determined. The random tree method was used to estimate the numerical values of the WQI index. Then, the capability of the combined approach of wavelet, principal component analysis, and Bagging method with random tree base algorithm was evaluated. To compare the values obtained from the data mining methods with the values calculated from the WQI index, the evaluation criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and modified Wilmot coefficient (Dr) were used.
Results and Discussion
The use of the wavelet transform method and the Bagging method has improved the modeling results. Considering that the Bagging classification method with the random tree base algorithm is a combination of the results of several random trees, so using this method has increased the accuracy of the RT model. So, in general, it was concluded that the use of wavelet transformation and classification methods increases accuracy and reduces errors. The best scenario with the highest accuracy and the lowest error was related to scenario 10 of the W-B-RT model with Total Hardness, Electrical Conductivity, Total Dissolved Solid, Sulphate, Calcium, Bicarbonate, Magnesium, Chlorine, Sodium, and potassium parameters. The results showed that the effect impact of pH in estimating the numerical value of the WQI index is considered lower than other parameters. When the principal component analysis method was used, by reducing the value of the eigenvalue from F1 to F12, the value of the factor also decreased; As a result,so F1, F2, and F3 factors were selected as the basic components. Considering 3 main factors, modeling was done employed and R=0.98, RMSE=2.17, MAE=1.52, and Dr=0.97 were obtained. In general, the results showed that the PCA method, despite reducing the dimension of the input vectors and simplifying it, can improve the accuracy and speed of the model and is introduced as the best method for estimating the numerical value of the WQI index.
Conclusion
The results obtained from the present research showed that the use of wavelet transform, Bagging and PCA methods had a positive effect on improving the results and increasing higherthe accuracy. In estimating the numerical values of WQI index, PCA-B-RT method considering 3 main factors, with correlation coefficient equal to 0.98, root mean square error equal to 2.17, average absolute value error equal to 1.52 and tThe modified Wilmot coefficient equal to 0.97 had the highest accuracy. Considering that all the methods used in the estimation of quantitative values had acceptable accuracy, therefore, in case of lack of data and lack of access to all chemical parameters, it is possible to obtain appropriate and acceptable results by using a limited number of parameters and data mining methods achieved.
Irrigation
M. Mohammadi Ghaleni; H. Kardan Moghaddam
Abstract
IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools ...
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IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools have been found to simplify the evaluation of water quality data, and the water quality index (WQI) is one of these widely used tools. In summary, the WQI can be defined as a number obtained from the combination of several quality parameters based on standards for its extraction. The aim of this study was to develop and introduce the new Surface water Drinking Water Quality Index (SDWQI) adopt the water quality parameters measured on hydrometric stations of Iran. In developing this index, criteria such as the availability of required parameters in most rivers and simple and accurate methods have been considered. Also, the ability to calculate with the minimum general parameters of water quality, simple calculations and in terms of the international standard WHO for drinking is one of the advantages of the introduced index.Materials and MethodsFor this purpose, 12 water quality parameters including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total Hardness (TH), pH, Chloride (Cl-), Sulfate (SO42-), Carbonate (CO32-), Bicarbonate (HCO3-), Magnesium (Mg2+), Sodium (Na+), Calcium (Ca2+) and Potassium (K+) have been used from Rudbar and Astaneh hydrometric stations located on Sefidroud river. Then initial preprocessing on data e.g. correlation analysis, and multivariate statistical methods including cluster analysis (CA) and principal components analysis (PCA) are used to selecting and weighting of water quality parameters using the “clustering” and “factoextra” packages in R 4.1.1. In order to develop the SDWQI were performed four steps including, parameter selection, sub-indexing, weighting and aggregation of the index. Also, in order to evaluate the index of the present research, the results of the SDWQI have been compared with the WHO drinking water quality index and Schoeller drinking water quality classification.Results and DiscussionCorrelation analysis between water quality parameters shows a significant correlation between TDS, EC and TH parameters and also with Cl-, Ca2+ and Mg2+ parameters at the level of 1% in both Astaneh and Rudbar stations. On the other hand, the lowest values of Pearson correlation coefficient are related to pH and CO32- parameters with other quality parameters. The results of CA indicate that most of the water quality parameters are located in separate clusters. So only the parameters TDS, EC, Cl- and Na+ in both Rudbar and Astaneh stations are in the same cluster. The weights of the parameters showed that TDS and K+ are assigned with the highest and lowest weights equal to 0.163 and 0.031 based on PCA method. Also, PCA results show that first and second principal components covered 59.3% and 67.6% of the total variance of measured water quality parameters in Rudbar and Astaneh stations, respectively. Water quality classification results indicate that (40.5%, 16.4% and 23.7%) and (90.1%, 73.1% and 57.3%) of data in Rudbar and Astaneh stations, respectively, fell into the excellent and good categories for drinking purposes based on Schoeller classification, WHOWQI and SDWQI.ConclusionGenerally, the comparison of the SDWQI with the WHO index and the Schoeller classification shows the rigidity of the new index in the classification of water quality for drinking purposes. Each water quality index developed in order to evaluate the uncertainty of results, should be tested for data with different characteristics in terms of the range of variation with different limit values (minimum and maximum). The index developed in the present study is no exception to this rule and in order to better evaluate the results, it is suggested that to be evaluated and analyzed with data from other hydrometric stations. Another important points that should be considered in using any water quality index, including the present research index, is to examine the allowable limits of water quality parameters that are not considered in these indicators. The results of the study indicated that, two most important steps in the development of a quality index that have a great impact on its results are sub-indexing and weighting of parameters. According to the results, two ideas recommended for future research. One, choosing an appropriate method such as non-deterministic (fuzzy) and intelligent (machine learning) methods to sub-index the parameters and two, to weigh the parameters more effectively, multivariate statistical methods such as clustering, factor analysis and principal component analysis should be used.
Soil science
F. Maghami Moghim; A.R. Karimi; M. Bagheri Bodaghabadi; H. Emami
Abstract
Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the ...
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Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the natural resources. However, farmers and stakeholders need to be aware that through their management practices, they affect soil quality and, with the short-term goal of production and greater profitability, lead to soil degradation. They can both use the land economically and improve and maintain soil quality by balancing production inputs and refining their management approaches. There are different management systems of productivity in agricultural lands in Neyshabour plain in northeastern Iran. In addition to the water and soil limitations in the study area, the prevalence of the smallholder system and the unwillingness of farmers to integrate smallholder, has further increased the destruction of soils in the study area. The objective of this study was to assess the changes in soil quality index in surface soil and profile (0-100 cm) and calculate the correlation between soil quality index and alfalfa and rapeseed yield in rangeland and agricultural areas managed by smallholders, total owners, and Binalood Company in the study area.Materials and Methods A total of 21 soil profiles were described in the total owner, smallholder and Binalood company management system and sampled from the alfalfa and rapeseed lands. Questionnaires were prepared with the help of farmers and experts in the study area based on Analytic Hierarchical analysis (AHP) method. The physical and chemical characteristics of the soil samples were determined. The important soil characteristics affecting plant growth were determined by interviewing farmers and experts study area. Soil quality index in the minimum data set (MDS) was calculated by two methods of principal component analysis (PCA) and expert opinion (EO), by additive and weighted methods in surface soil and profile. To achieve a single value for each soil properties in the soil profile, two methods of weighted mean and weighted factor were used. To evaluate the accuracy of the assessment, the correlation between soil quality index and alfalfa and rapeseed yield was investigated of the various management system.Result and DiscussionThe results showed that the highest additive and weighted soil quality index at both surface and soil profile in both PCA and EO methods were in rangeland. It was due to lack of cultivation and maintaining organic matter comparing to agricultural land. The total owner management system due to its economic power and the use of appropriate and scientific methods comparing to smallholder management system, showed the highest additive and weighted soil quality index. In all management system, the EO-calculated weight index by weighted factor method had the highest value due to assigning the suitable weight for soil characteristics. The correlation analyses soil quality indices with canola and alfalfa indicated that the EO soil quality calculated by weighted factor for the soil profile were more correlated than surface soil in total owner system and the Binalood company. Weight coefficient method due to the application of different weights to each layer based on their importance, showed a higher soil quality index in both EO and PCA sets than the weighted average method. The reason for better EO performance probably is that the PCA is a reducing the dimensions, meanwhile, the minimum data selection in the EO method is based on regional experts which are familiar with cause-and-effect relationship of the soil properties. Due to the relatively good correlation of the yield of the studied products, with the soil quality index, an appropriate management needs to maintain and improve soil quality, especially in the smallholder system, as well as meeting the nutritional needs of these products.Conclusion Soil quality assessment in this study indicated that calculation of the soil quality index only considering the surface soil properties may not provide complete information for the farmers and land managers. Then inclusion of both surface and profile soil properties with farmers' knowledge and study area experts are essential for sustainable soil management. On the other hand, the differences in the management system also affected the soil quality index. Although the smallholder management system due to low input, especially chemical fertilizers, water and agricultural implements, had a high potential concerning environmental issues, but in terms of production, total owner and Binalood company management systems because of their high economic strength had the higher soil quality index. The farmers and stakeholders of the total owner management systems should be considered despite the proper management, however due to high inputs of fertilizer and water, especially in the Binalood company, the production may not be sustainable. Therefore, for further studies, calculating the water consumption in the desired management systems is recommended.
Irrigation
A. Mosaedi; E. Ramezanipour; M. Mesdaghi; M. Tajbakhshian
Abstract
Introduction: Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. ...
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Introduction: Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. Therefore, finding the proper methods in sediment yield study in watersheds is essential in planning and management of land and water resources. Climatic characteristics, physiography, geology, and hydrology of basins are the most effective factors in producing and transporting sediments according to several sources, but the role and impact of some factors are more pronounced than the others in different areas. As a result, the objective of this study was to investigate and identify the most important climatic, physiographic, geological, and hydrological factors in several watersheds of the northeastern part of Iran, by applying Gamma Test (GT) and principal component analysis (PCA) techniques.Materials and Methods: In this study, the data of discharge flow and suspended sediment concentration, and daily flow discharge recorded in 15 hydrometric stations in Mashhad and Neyshbour restricts and required maps were provided from the Regional Water Company of Khorasan Razavi, Iran. After drawing statistical bar graph period of suspended sediment, daily discharge, annual precipitation, and relatively adequate data, stations with the longest period and with the lowest deficit data were selected to determine the common statistical periods. Therefore, in this study, the time period of 1983-1984 to 2011-2012 was selected, and the run test was applied to control data quality and homogeneity. Then, the most effective factors of sediment yield were determined by principal component analysis (PCA) and Gamma Test (GT).Results and Discussion: The results of the principal component analysis showed that 90 percent of the first five components justify the changes. Among the factors, area and gross gradient of the mainstream from the first component, the average annual flow rate of mainstream, meandering waterways of the mainstream from second component, and drainage density of third component were identified as the most important influencing factors on suspended sediment production. Ninety superior combinations of 1500 proposed combinations were obtained by Gamma Test to evaluate the effects of each parameter on suspended sediment yield. To determine the order of importance of the entered parameters, first, Gamma Test was performed on all 12 parameters. Gamma values of all cases for each proposed combination were compared. The results showed that the impact of these statistics was lowered by eliminating high gamma parameters and the removal of low values. The data analysis revealed that the low levels of gamma and high accuracy of ratio to find the desired outputs from entries. By lowering the gradient, the complexity of the model was lowered and more suitable model was provided. As a result, high levels of gradient represented the complexity of the final model. The results of the percentage values of each of the 12 variables were considered among the superior equations for estimating the suspended sediment composition. In this regard, the mean annual discharge, main channel length, area, average annual rainfall, and percentage of the outcrop of erosion sensitive rocks with a total of 63 percent of the proposed equations were the most important factors affecting the sediment yield in the study area. The average height parameter of area, the average and gross slope of the mainstream had the lowest presence among the optimized compounds.Conclusion: Based on the results of the principal component analysis, the two factors of basin area and gross slope of the mainstream were selected as the most important factors affecting the amount of annual suspended sediment load, respectively. Based on the results of the Gamma Test, 12 main variables affecting suspended sediment load were identified and the effect of each of them on the production and transport of suspended sediment was determined. Based on the comparison of the results of the two methods of PCA and GT, it can be concluded that if the purpose of research or study is to prepare a model with the highest accuracy in estimating suspended sediment load, the 12-variable model of GT includes factors related to physiographical, geological, climatic and hydrological factors are suggested. However, if the preparation of a model with appropriate accuracy and a limited number of input variables is considered, a 5-variable model derived from the PCA method is proposed. At the same time, if the purpose is to prepare a model with the least input variables and their easy access and calculation and initial estimation of suspended sediments, a bivariate model (based on basin area and gross slope of the mainstream factors) resulting from PCA is proposed. According to the results of the present study, it can be concluded that the study of more parameters has provided grounds for evaluating their importance in sediment yield. Finally, due to the correlation of many parameters with each other, a limited number of parameters that have a more important role in suspended sediment estimation, were selected. Another finding of this study is the increase in the accuracy of the sediment model’s preparation due to achieving more important and effective parameters in sediment yield and identifying them in order to investigate the best sediment management measures in watersheds. It is suggested that similar research should be done in other watersheds with different conditions in terms of climatic conditions, topography, geology, and so on.
Soil science
M. Zangiabadi; manoochehr gorji; P. Keshavarz
Abstract
Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring ...
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Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring soil physical changes. In this regard, this study was conducted to determine the most important soil physical properties and calculate the SPQI of medium to coarse-textured soils of Khorasan-Razavi province.
Materials and Methods: Torogh Agricultural and Natural Resources Research and Education Station of Khorasan-Razavi province is located in south-east of Mashhad city (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil texture variability in this research station is one of its outstanding features. The soil textures are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam. More than 90% of agricultural soils in Khorasan-Razavi province are classified in these five texture classes. Using the available data, 30 points with different soil textures and OC contents were selected. The soil samples were collected from 0-30 cm soil depth at each point. Intact soil cores (5 cm diameter by 5.3 cm length) were used for sandbox measurements, and disturbed soil samples were used to determine other properties. Required laboratory analysis and field measurements were conducted using standard methods. In this research, 35 soil physical properties as total data set (TDS) including: soil moisture release curve (SMRC) parameters, particle size distribution and five size classes of sand particles, soil bulk and particle density, dry aggregates mean weight diameter (MWD) and stability index (SI), S-index, soil porosity and air capacity, location and shape parameters of soil pore size distribution (SPSD) curves, relative field capacity (RFC), plant available water measured in matric pressure heads of 100 and 330 hPa for the field capacity (PAW100 and PAW330), least limiting water range measured in matric pressure heads of 100 and 330 hPa for the field capacity (LLWR100 and LLWR330), integral water capacity (IWC) and integral energy (EI) of different soil water ranges were measured and calculated for 30 soil samples. The most important soil physical properties were selected using principal component analysis (PCA) method by JMP (9.02) software. Selected physical properties as minimum data set (MDS) were weighted and scored using PCA results and scoring functions, respectively. In this study, three types of linear scoring functions were used. The soil physical quality index (SPQI) was calculated by two scoring and two weighting methods for each soil sample and the differences between these four SPQIs were tested by sensitivity index.
Results and Discussion: Principal component analysis results showed that among 35 soil physical properties (TDS) which were studied at this research, six properties of mean pore diameter (dmean), PAW100, total porosity (PORT), EI LLWR330, SI and PAW330 accounted for about 90% of the variance between soil samples. Weight of the selected properties (MDS) was calculated by the ratio of variation in the data set explained by the PC that contributed the selected property to the total percentage of variation explained by all PCs with eigenvalue ˃ 1. In this research, the parameters of PAW100, total porosity (PORT), SI and PAW330 were scored using scoring function of more is better, EI LLWR330 was scored using scoring function of less is better and dmean was scored using scoring function of optimum by two scoring methods with score ranges of 0.1-1 and 0-1. Considering unweighted and weighted MDS and two ranges of scores, four SPQIs were calculated for each soil sample. The results showed that SPQIs which were calculated by the MDS derived from PCA method and scoring weighted MDS at the range of 0-1, had the highest sensitivity index and could represent the differences between the studied soil samples better than other SPQIs. By this method, maximum and minimum SPQI values for the studied soils were 0.82 and 0.12, respectively. SPQI is a relative comparison criterion to quantify the soil physical quality which could be applied only for the studied soils with specific characteristics.
Conclusion: The results of this research showed that minimum data set (MDS) explained about 90% of the variance between soil samples. Combining MDS into a numerical value called soil physical quality index (SPQI) could be used as a physical comparison criterion for the studied soils. From the SPQI based on the MDS indicator method, soil quality was evaluated quantitatively. Soil samples with grade I, II, III, and IV accounted for 10%, 36.7%, 30%, and 23.3% of the soil samples, respectively.
Rasoul Mirkhani; A.R. Vaezi; hamed rezaei
Abstract
Introduction: Awareness of the physical, chemical and biological quality of soil in agriculture and natural resources is essential for optimal land management and achieving maximum economic productivity. Soil has various functions, including crop production ability, carbon storage, water retention, nutrient ...
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Introduction: Awareness of the physical, chemical and biological quality of soil in agriculture and natural resources is essential for optimal land management and achieving maximum economic productivity. Soil has various functions, including crop production ability, carbon storage, water retention, nutrient cycling, water filtering and etc. Thereby, the quality of soils can be taken into consideration depended on the purpose of their use. The soil quality indices are often regional; therefore, a set of indices cannot be used consistently to determine soil quality in all areas. In this study, the Nemero Soil Quality Index (NQI), the Weighted Additive Soil Quality Index (SQIw), and the Additive Soil Quality Index (SQIa) were determined using the total data set (TDS) and minimum data set (MDS) and the impact of properties affecting the soil quality and the yield of irrigated wheat were investigated, in Nazarabad region. Materials and Methods: This study was carried out in 26000 hectares of Nazarabad agricultural lands, known as an area with irrigated farms in western Alborz province. The Nazarabad area was sub-divided into a network consisting of 95 squires of 1650 m × 1650 m. The surface soil (0-30 cm) was sampled from the farms located in the middle of each squire (9+5 soil samples from 95 farms) and the irrigated wheat was sampled from 32 farms. Then, soil physical properties including sand, silt, and clay percentages, soil structural stability (MWD), bulk density (BD), particle density, soil porosity (F), field capacity (FC) and permanent wilting point (PWP), available water (AW), saturated hydraulic conductivity (Ks) and soil chemical properties including salinity (EC), pH, organic matter (OM), equivalent calcium carbonate (TNV), available phosphorus )p < sub>ava(, available potassium )Kava(, sodium absorption ratio (SAR) and soil microbial respiration (SMR) were measured. Effective properties on soil quality were selected using SPSS 24 by principal component analysis method (PCA). For this purpose, components with Eigen values greater than one were selected and in each component, properties with high loading coefficient up to 10% lower than the highest loading coefficient were selected MDS affecting soil quality. Then, the Nemero Soil Quality Index (NQI), the Weighted Additive Soil Quality Index (SQIw) and Additive Soil Quality Index (SQIa) were determined using TDS and MDS. For validating soil quality indices, the correlation between the yield of irrigated wheat and NQI, IQIa and IQIw indices were determined in MDS and TDS. Results and Discussion: The results showed that the correlation between the soil quality indices (NQI, SQIw and SQIa( using total data set and MDS were significant (p <0.01). In addition, a significant correlation was observed between methods of MDS and TDS in IQIw (r=0.76), IQIa (r=0.73) and NQI (r=0.68) indices. According to the results, there was a significant correlation (p <0.01) between the yield of irrigated wheat and IQIw (r=0.68), IQIa (r=0.67) and NQI (r=0.62) using MDS method; and using total data set method this correlation was 0.61, 0.58 and 0.58, respectively. The results indicated that using NQI, SQIw and SQIa indices based on MDS, 42, 57 and 57% of the study area were in very high quality category and 29, 25 and 24% were in high quality category, respectively. However, using NQI, SQIw and SQIa indices based on TDS, 16, 16 and 18% of the study area were in very high quality class and 42, 39 and 45% were in high quality class, respectively. Conclusion: The results showed that in Nazarabad region, the yield of irrigated wheat was affected by texture, p < sub>ava, B, SAR, Bd and TNV. There was no significant difference between IQIw and IQIa and NQI indices. In addition, the correlation between soil quality indices based on MDS and total data set was significant, and the correlation between the yield of irrigated wheat and the soil quality indices was stronger while using MDS rather than the use of TDS. Therefore, the use of MDS is more appropriate due to better results and fewer properties and less cost. According to the results obtained from Nazarabad region using NQI and SQIw indices, nearly 82% and 72% of the area are in the very high and high quality class, about 6% and 8% are in the moderate quality class and about 7% are in very low and low quality class, respectively. The studied area is less restricted in terms of physical properties such as soil texture and bulk density. Consequently, due to the high quality of soils in Nazarabad region, it is possible to improve the yield of wheat by proper management.
Saman Hajinamaki; Hojat Emami; Amir Fotovat
Abstract
Introduction: Water scarcity is one of the important issues in agriculture, especially in arid and semi-arid regions of Iran. Therefore, the challenge for the agriculture in these areas is to find new sources of water for irrigation. One of the ways that has become more common in recent years is the ...
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Introduction: Water scarcity is one of the important issues in agriculture, especially in arid and semi-arid regions of Iran. Therefore, the challenge for the agriculture in these areas is to find new sources of water for irrigation. One of the ways that has become more common in recent years is the reuse of wastewater as a secondary source and replaces drinking water. The effects of irrigation with wastewater on physical, chemical and biological properties of soil have been studied by many researchers, which most of them are based on the direct use of untreated wastewater in agricultural land irrigation. In fact, a large amount of wastewater used in the agriculture is indirectly entered into the rivers, and used in the agriculture lands. Irrigation with wastewater may have effects on soil properties such as pH, EC, nutrient content, sodicity, pollutants and etc.
Materials and Methods: In order to determine the effect of irrigation by wastewater on soil properties in May 2015, several points of the Kashafrood River in the north of Mashhad were selected. The studied points were located between 59˚36ʹ- 59˚41ʹ E and 36˚19ʹ- 36˚22ʹ N geographical position. The wastewater is refined in Parkandabad station, and used for irrigation. The samples were taken from a depth of 0-30 cm in each point and three replications were regarded for them. Sampling distance was one kilometer from each other. In general, 15 points were irrigated with wastewater were selected. 12 physical, chemical and biological properties including pH, soil texture, bulk density (BD), dispersible clay (DC), mean weight diameter of aggregates (MWD), sodium adsorption ratio (SAR), organic carbon (OC), available phosphorous (P), available potassium (k), total nitrogen (TN), microbial biomass and base respiration (BR) were measured as a total data set (TDS). According to Liu and Chen the main component with an Eigen value greater than one using the PCA method were chosen as minimum data set (MDS). Within each PC, highly weighted properties were defined as those with absolute values within 10% of the highest weighted loading. When more than one variable was retained in a PC, each was considered important and was retained in the MDS if they were not correlated (r < 0.60). Among well-correlated variables within a PC, the variable having the highest correlation sum was selected for the MDS. Data analysis were performed using SPSS Statistics22 software.
Results and Discussion: The results showed that irrigation with wastewater increased biomass and BR, OC, SAR, K and stability index of soil structure. The parameters of K, TN, pH and MWD have been increased compared to the control, but were not statistically significant. Using PCA, five PCs were obtained, which PC1 and PC2 with Eigen value of 50.6 % were the most important components. The parameters of OC, SAR, TN, pH, BD, MWD, BR and K were chosen as MDS due to be changed as a result of irrigation with wastewater. Then, the correlations between these parameters in two groups of irrigated soils with wastewater and control were investigated. Organic carbon in both soil groups had the highest correlation with the SI. The SAR in both soil groups was negatively correlated with nitrogen and phosphorus. Nitrogen in irrigated soils with control was positively correlated with the SI and OC. The MWD was not correlated with any parameter. PH had shown positive correlation with microbial biomass and OC was positively correlated with BR, TN and SAR in soil controls. Potassium in the irrigated soils with wastewater had the negative and significant correlation with OC, SI, BD and MWD. Microbial respiration had a high positive correlation with SI, OC and TN in irrigated soils, which is due to carbon and nitrogen in the wastewater and causes an increase in its amount compared with the control.
Conclusion: The results showed that irrigation with wastewater caused a significant increase in parameters SI, SAR, P, BR, MBC and organic carbon in irrigated soil with wastewater and pH, MWD, TN and K had no a significant difference. On the other hand, the principal component analysis of the two groups of irrigated soils with wastewater and control had two distinct groups indicating that the irrigation with wastewater had a significant impact on the soil properties. According to the principal components analysis, eight parameters including OC, SAR, TN, MWD, BD, pH, BR and K were selected as the most important parameters to study the effects of irrigation by wastewater.
F. Farsadnia; B. Ghahreman
Abstract
Introduction: Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self Organizing feature Map (SOM) method has been applied in some studies. ...
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Introduction: Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces.
Materials and Methods: SOM approximates the probability density function of input data through an unsupervised learning algorithm, and is not only an effective method for clustering, but also for the visualization and abstraction of complex data. The algorithm has properties of neighborhood preservation and local resolution of the input space proportional to the data distribution. A SOM consists of two layers: an input layer formed by a set of nodes and an output layer formed by nodes arranged in a two-dimensional grid. In this study we used SOM for visualization and clustering of watersheds based on physiographical data in North and Razavi Khorasan provinces. In the next step, SOM weight vectors were used to classify the units by Ward’s Agglomerative hierarchical clustering (Ward) methods. Ward’s algorithm is a frequently used technique for regionalization studies in hydrology and climatology. It is based on the assumption that if two clusters are merged, the resulting loss of information, or change in the value of objective function, will depend only on the relationship between the two merged clusters and not on the relationships with any other clusters. After the formation of clusters by SOM and Ward, the most frequently applied tests of regional homogeneity based on the theory of L-moments are used to compare and modify the clusters which are formed by clustering algorithms and find the best clustering method to achieve hydrologically homogeneous regions. Two statistical measures are used to form a homogeneous region, (i) discordancy measure and (ii) heterogeneity measure. The discordancy measure, Di, is used to find out unusual sites from the pooling group (i.e., the sites whose at-site sample L moments are markedly different from the other sites). Generally, any site with Di>3 is considered as discordant. The homogeneity of the region is evaluated using homogeneity measures which are based on sample L-moments (LCv, LCs and LCk), respectively. The homogeneity measures are based on the simulation of 500 homogeneous regions with population parameters equal to the regional average sample l-moment ratios. The value of the H-statistic indicates that the region under consideration is acceptably homogeneous when H
A. Afshari; H. Khademi; P. Alamdari
Abstract
Introduction: Soil forms a thin layer over the surface of the earth that performs many essential life processes . Soil has always been important to humans and their health, providing a resource that can be used for shelter and food production. Higher heavy metals concentration within soils has beenshown ...
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Introduction: Soil forms a thin layer over the surface of the earth that performs many essential life processes . Soil has always been important to humans and their health, providing a resource that can be used for shelter and food production. Higher heavy metals concentration within soils has beenshown to be toxic for human bodies, since they might be broken out easily via dust or direct contact through trophic levels. In addition long term heavy metals recalcitrance in soil results in low potential of soil productivity . Heavy metals interact complicatedly in soil. The present study was conducted in large scale by analyzing elements Mn, Co, Ni, Zn, Pb, Cd and Cu in soils in central lands of Zanjan province. The main objectives of present research were to investigate heavy metals diffusion and total contamination status in soil and determination of their possible origin using multivariate analysis.
Materials and Methods: This research was conducted in central lands located in Zanjan province (northwest of Iran). In terms of the main land uses, study area may involve farmlands, rangelands, urbanized and industrial lands. Study sites are totally covered 2000 km2 in coordinates of 36.20 to 36.41 N latitude and 48.19 to 48.53 E longitude. Sampling was conducted based on gridding method in randomized manner in August 2011. Urban lands affected by much anthropogenic activities and farm and rangelands together were placed in grids of 1.5×x 1.5 and 3×3 km2 respectively. Totally number of 241 soil samples (0-10 cm depth) was taken. Soil specimen's digestions were conducted using nitric acid 5 normal. Total elements concentration of Pb, Zn, Ni, Mn, Cu, Cr, Fe and Co were measured using Atomic adsorption device Perkin-Elmer: AA 200 and Cd concentration was determined by Atomic adsorption device equipped with graphic furnace model Rayleigh: WF-1E. Mean separation analysis of parameters (Pearson and spearman) was conducted using Duncan test in probability level of 5%. Principle component analysis (PCA) and hierarchical cluster analysis (HCA) were used to classify metals group. Achieving a simple structure and better results interpretation, data rotation in varimax type was conducted in PCA algorithm. Before cluster analysis, data were standardized and subsequently exposed to cluster analysis and plotting dendrogram, Euclidean approach was applied.
Results and Discussion Multivariate analysis (PCA, CA and CM) have been shown as an efficient tool to identify heavy metals origins, helping us in better data comprehension and interpretation. Results obtained on multivariate analysis approaches might are promising to distinguish polluted area and heavy metals potential origin, in turns indicating soil environmental quality.
PCA is known as an efficient method to determine anthropogenic impacts on a spatial scale and it may be essential to specify heavy metals contamination degree in respect to anthropogenic and litogenic contribution. As it illustrated, heavy metals are categorized in three-component model framework, accounting for 67% of total data variations. In rotated component matrix the first PC (PC1, 30% of variance) involves Ni, Cr, Co, Mn and Fe, while the second PC (PC2, 19% of variance) involves Zn and Pb and eventually the third one (PC3, 18% of variance) covers Cu and Cd among others. PC1 can be introduced as geological component because of its less coefficient of variations than others, skewedness less than 1 and normalized data status. It denotes lithogenic distribution of these metals in area. Furthermore,as above mentioned, the average heavy metalconcentrations werefound to be less than calculated background threshold. Because of their increased concentration in soil, high coefficient of variations and very high concentration than background threshold level as well as positive skewedness in heavy metals, PC2 and PC3 can be defined to antropogenical components. Atmospheric precipitation (deposition) serves as one of the main anthropogenic source of heavy metals pollution in the soil samples near theintense transportation traffic roads, accumulation site of factories solidwaters, energy generation process, metal melting, construction and traffic breaking out in whole area. Our results are in line with enormous findings on farming operations as the main factor that cause changes in Cd concentration in soils. although Pb, Cu, Zn and Cd have been shown to have anthropogenic origin inputs, however in respect to PCA analysis, the main origins for Lead and Zn may be municipal and industrial operations specially Pb processing factory as well as industrial complexes. At the same time, Cu and Cd stems from farming operations as well as municipal activities. The main municipal input origins for elements Pb, Cu and Cd include all components used in automobile industry, batteries, engines oils, fossil fuels and construction materials (like cement).
Cluster analysis is used to classifying those samples having common and similar characteristics as well as evaluating information obtained from PCA analysis. Cluster analysis gave the same groups. So two majororigins can be identified. First (CI) involves prior interpreted lithogenic elements (Cr, Co, Mn and Fe), while two later clusters (C2, C3) contain anthropogenic elements (Pb, Cu, Zn and Cd). Many researchers believed that Mn, Cr, Ni and Fe are controlled by parent material. In contrast, they attributed any increases of Pb, Cu, Cd and Zn accumulation to anthropogenical operations. Cluster analysis gives the same results as derived from PCA, enabling us to identify two major origins on which heavy metals break out hence, multivariate analysis results confirms the presence of two different heavy metals origins within soils.
Conclusion: The aim of this research was to provide some preliminary information on heavy metals (Pb,Zn,Cd,Cu,Ni,Co,Cr,FeandMn) contamination status in soils in Zanjan province. Metal contamination cannot be assessed by common metal concentration test, hence useful and promising tools were applied to evaluate heavy metals pollution. The highest level of heavy metals pollution and accumulation was related to Cd, Pb and followed by then Cu. Multivariate analysis showed that Fe, Mn, Cr, Co and Ni are controlled by parent materials, while Pb, Cu and Zn originate from anthropogenic sources. In the second class, anthropogenic activity did not seem to significantly affect their accumulation which is strongly supported the lithogenicaly origin of these heavy metals in this study.
amir ranjbar; H. Emami; Ali reza Karimi; R. Khorassani
Abstract
Introduction: Saffron is one of the most important economic plants in the Khorasan province. Awareness of soil quality in agricultural lands is essential for the best management of lands and for obtaining maximum economic benefit. In general, plant growth is a function of environmental factors especially ...
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Introduction: Saffron is one of the most important economic plants in the Khorasan province. Awareness of soil quality in agricultural lands is essential for the best management of lands and for obtaining maximum economic benefit. In general, plant growth is a function of environmental factors especially chemical and physical properties of soil (20). It has been demonstrated that there was a positive and high correlation between soil organic matter and saffron yield. Increasing the yield of saffron due to organic matter is probably due to soil nutrient, especially phosphorous and nitrogen and also improvement of soil physical quality (6, 28, 29). The yield of saffron in soils with high nitrogen as a result of vegetative growth is high (8). Shahandeh (6) found that most of the variation of saffron yield depends on soil properties. Due to the economic importance of saffron and the role of soil properties on saffron yield, this research was conducted to find the relationship between saffron yield and some soil physical and chemical properties, and to determine the contribution of soil properties that have the greatest impact on saffron yield in the Ghayenat area.
Materials and Methods: This research was performed in 30 saffron fields (30 soil samples) of the Ghayenat area (longitude 59° 10΄ 10.37˝ - 59° 11΄ 38.41˝ and latitude 33° 43΄ 35.08˝ - 33΄ 44΄ 02.78˝), which is located in the Khrasan province of Iran. In this research, 21 soil properties were regarded as the total data set (TDS). Then the principal component analysis (PCA) was used to determine the most important soil properties affecting saffron yield as a minimum data set (MDS) and the stepwise regression to estimate saffron yield. To estimate the yield of saffron in stepwise regression method, saffron yield was considered as a dependent variable and soil physical and chemical properties were considered to be independent variables.
Results and Discussion: According to the PCA method, among the 21 studied properties, 7 out of them including calcium, iron, zinc contents, sand, calcium carbonate equivalent percent, mean weight diameter of aggregates (MWD) and manganese (Mn) had the higher Eigenvalues. Therefore, the above properties were introduced as the most important soil properties in saffron fields. Calcium carbonate had the negative effect on the availability of micro-nutrients (26). Christensen et al. (15) found that by increasing the calcium carbonate in soil due to high pH and formation of insoluble components, the uptake of micro-nutrients is especially limited.
The results of stepwise regression method (equation 1) showed that soil acidity (pH), zinc content, bulk density, MWD, iron content, salinity (EC), organic carbon and available potassium in soil were the most important properties that affect the yield of saffron, so that the determination coefficient (R2) of the regression model was high (Table 2) and it can explain 74% of the variation of saffron yield.
Y = 6924.51 – 1187.31 pH – 89.65 EC + 71.6 Fe – 826.02 Zn + 471.55 OC, + 5490.96 K + 1353.56 BD + 752.82 MWD (1)
where Y: saffron yield (kgha-1), pH: soil acidity, EC: electoral conductivity (dSm-1), Fe: iron concentration (mgkg-1), Zn: zinc concentration (mgkg-1), OC: organic carbon (%), K: soil potassium (%), BD: soil bulk density (Mgm-3), and MWD: mean weight diameter of aggregates (MM).
Based on the absolute values of standard ß in the regression model (Table 3), pH value and then after Zn concentration had the most effect on saffron yield. In general, responses of different plants to soil pH is varied, and saffron grows satisfactory in pH = 7.8 (5). Soil pH influences the uptake of soil nutrients by plants (15), so that this parameter had the most effect on saffron yield and by increasing the soil pH, the yield of saffron decreases. According to the regression model, Zn concentration was the second parameter in saffron yield. Zn has the important role in structure of plant enzymes (30). After these 2 parameters, Bd, MWD, Fe concentration, EC, Organic carbon and K concentration in soil had more effect on saffron yield (Table 3).
Conclusion: According to both PCA and regression methods, the concentration of iron and zinc and MWD were determined as the important and effective soil properties on saffron yield in the Ghayenat area. In addition, soil pH in stepwise regression method and calcium carbonate in PCA method were determined as the effective properties on saffron yield. Therefore, it is suggested that the parameters of Zn, Fe, and MWD along with soil pH and calcium carbonate which were regarded individually in two methods, were considered as the most soil properties in saffron yield.
M. Ghaemi; A. Astaraei; M. Nassiri Mahalati; S.H. Sanaeinejad; H. Emami
Abstract
Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to ...
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Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to identify intercorrelations among soil properties (chemical, physical and biological) using principal component analysis (PCA) and their relationships with maize yield variability in field. Site variables (18) and maize yield were measured in selected parts of Astan Quds agricultural fields in Mashhad city. The principal component analysis was used to reduce the site variables numbers and remove multicollinearity among variables. The first four PCs with eigenvalues>1 accounted for > 67% of variability in measured soil properties. Soil properties were grouped in four PCs as: (PC1) Soil highly descriptive fertility potential, (PC2) Soil moderately descriptive fertility potential, (PC3) Soil permeability potential, (PC4) Soil aggregation potential. The results showed that the factor of soil highly descriptive fertility potential explained 43% of variance and accounted for 77% of the yield variability in the field. Principal component analysis allows explaining a major part of crop yield variability by removing the multicollinearity.
salman naimi marandi; shamsollah Ayoubi; B. Azimzadeh
Abstract
Soil pollution by heavy metals is an important environment issue throughout the world. Heavy metals’ origin, accumulation, and distribution in soil have been the focus of much attention by many researchers. The objective of this study was to recognize the sources of some heavy metals in surface soils ...
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Soil pollution by heavy metals is an important environment issue throughout the world. Heavy metals’ origin, accumulation, and distribution in soil have been the focus of much attention by many researchers. The objective of this study was to recognize the sources of some heavy metals in surface soils in Zob-Ahan industrial district, Isfahan province, using multivariate geostatistical techniques. A total of 202 surface (0–30 cm) soil samples were collected. Total lead (Pb), zinc (Zn), manganese (Mn), iron (Fe), copper (Cu), nickel (Ni), cobalt (Co) and chromium (Cr) contents of the samples were determined. A multivariate geostatistical analysis was performed to identify the common source of heavy metals. The results of principal component analysis led to the identification of three components. So, these components were explained 31, 27, and 16 % of total variance of heavy metal concentration, respectively. The distribution of scores of each components were shown that the quantities of Fe, Mn, Pb and Zn were found to be associated with anthropogenic activities, corresponding to the first factor was termed the “anthropogenic component”. The quantities of Co were found to be associated with parent rocks, corresponding to the second factor was termed the “lithologic component”. Also, the third factor was mainly attributed to Cu, Ni and Cr which also comprised the first and third factors, indicating a mixed source both from lithologic and anthropogenic inputs.
