Irrigation
S. JafarNodeh; A. Soltani; E. Soltani; A. Dadrasi; S. Rahban
Abstract
IntroductionAccurate knowledge of water balance components is necessary to optimize water consumption in agriculture. On the other hand, measuring water balance components is expensive and difficult. Therefore, the use of models that can simulate water balance values is important for water management ...
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IntroductionAccurate knowledge of water balance components is necessary to optimize water consumption in agriculture. On the other hand, measuring water balance components is expensive and difficult. Therefore, the use of models that can simulate water balance values is important for water management in agriculture and water used by plants. Crop simulation models have been turned into essential tools for studying plant production systems. In the SSM-iCrop2 models, it is presumed that diseases and weeds are optimally managed and will not affect growth and yield. Additionally, except in cases where the model accounts for specific nutrients such as nitrogen, it is generally assumed that nutrient deficiencies are eliminated through fertilization. Therefore, parameterized and evaluated models are designed to fit these conditions. These factors are present in the field and affect crop growth and yield as well as water use. However, in several cases it is required to estimate yield and water balance components and irrigation water volume under grower conditions. Naturally, models parameterized using experiments are unable to simulate these conditions. Therefore, a model must be prepared so that it can simulate the real conditions of farmers. In this study, the SSM-iCrop2 model has been calibrated for the real conditions of farmers, and the purpose of this study is to use the SSM-iCrop2 model in simulating water performance and water balance for farmers. Materials and MethodsIn this study, the SSM-iCrop2 model was calibrated for farmers conditions using variables such as yield and harvest index, which are available for farmers’fields or are cheap to measure. The effect of factors such as pests and diseases, weeds and unsuitable nutrients, density and sowing date entered the model along with the calibration of three parameters of radiation use efficiency, maximum leaf area and maximum harvest index for farmers’ fields. Calibration was done by comparing the performance of farmers against the performance simulated by the model and by changing the parameters of radiation use efficiency (IRUE), maximum leaf area (LAIMX) and maximum harvest index (HIMAX). This calibration was done at Hashem Abad station in Gorgan for irrigated rice (paddy) and wheat. The simulated actual yield was calibrated with the actual yield. Due to the acceptable simulation of actual yields after calibration, it was presumed that other estimates made by the model are also reliable. Results and DiscussionMeasurement of water balance and other estimates of the model from growth and yield formation in the grower fields is expensive, but a calibrated model can estimate them at a low cost. In this study, it was shown that with the model calibrated for farmers' conditions, not other easily measured information (such as the irrigation water volume) can be obtained, with the assumption that the model accurately captures this information as well as performance. To evaluate the simulated real performance model, it was compared with the actual performance of farmers (Agricultural Jihad Report) after calibration. In addition to phenology, the SSM model simulates traits related to growth and yield, evapotranspiration values, irrigation water volume, runoff, available soil water during planting and harvesting, cumulative drainage, etc. The output of the model shows the amount of irrigation water is needed for a certain amount of performance in a given place (with specified rainfall and transpiration). The irrigation water volume calculated by the model was compared with the results of field tests from previous studies conducted by researchers at agricultural research centers. It was found that the model's output and the observed values were in good agreement. The root mean square error for rice and wheat was 216.6 and 157.6 kg per hectare, respectively, and the coefficient of variation and correlation coefficient were 4 and 85% for rice and 3 and 94% for wheat, respectively. Then, the irrigation water volume estimated by the model was evaluated and validated with the measured irrigation water volume in different crops (in Golestan province for different years). Based on the results of the evaluation, the coefficient of variation and the correlation coefficient for the simulated irrigation water volume were 8.9 and 98%, respectively, compared with the observed value. This calibration was done for rice (paddy) and irrigated wheat in the fields of Gorgan town, and the simulation and running were done using the meteorological statistics recorded in Hashem Abad weather station, Gorgan. Noting the fact that the actual yield has been simulated with good accuracy after the calibration, it was assumed that the other estimates of the model are also reliable. Thus, the calibrated model estimates them with low cost and appropriate accuracy and can complement field experiments. ConclusionThis study discovered that the SSM_iCrop2 model, when calibrated for the conditions of farmers' fields, can accurately simulate both growth and yield traits as well as water balance characteristics. Notably, the model provides reliable estimates of irrigation water volume in farming scenarios, a crucial factor for agricultural planning and drought adaptation.
Irrigation
Z. Bigdeli; A. Majnooni-Heris; R. Delearhasannia; S. Karimi
Abstract
Introduction
Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface ...
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Introduction
Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area.
Materials and Methods
The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination.
Calculating average rainfall through the Thiessen Polygons method
Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to:
(1)
Random Forest Algorithm
Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks.
Support Vector Machine (SVM) algorithm
Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression.
Evaluation Criteria
To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria:
(2)
(3)
(4)
(5)
Results and Discussion
Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values.
As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively.
Conclusion
The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.
Soil science
Hamid Reza Matinfar; M. Jalali; Z. Dibaei
Abstract
Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques ...
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Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the ability of spectral and terrestrial data to model the amount of soil organic matter.Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. Data were categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle component analysis method (PCA) was used to select the features that have the greatest impact on quality.Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0.02 to 2.34% with an average of 0.56 and a coefficient of variation of 69.64% according to the Wilding standard was located in a high variability class (0.35). According to the average amount of soil organic carbon, it can be said that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed to land use change, land management, and other environmental elements in the study area. In other words, the low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil. Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing, fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that the random forest model with the values of coefficient of determination (R2), root mean square error (RMSE) and mean square error (MSE) of 0.74, 0.17, and 0.02, respectively, was the best performance ratio another study used to estimate the organic carbon content of surface soil in the study area.Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had different accuracy in estimating soil organic carbon and the random forest model was superior to the other models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the possibility of displaying results at different time and space scales. Due to the determination of soil organic carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as well as a suitable database and data for the implementation of any field operations, management of agricultural inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this study indicated the ability of remote sensing techniques and random forest learning model in simultaneous estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional laboratory methods in determining some soil characteristics, including organic carbon.
Irrigation
A,. Uossef gomrokchi; J. Baghani; F. Abbasi
Abstract
Introduction: One of the modeling methods researchers have considered in various sciences in recent years is artificial neural network modeling. In addition to the artificial neural network and regression models, today, the capabilities of data mining methods have been used to improve the output results ...
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Introduction: One of the modeling methods researchers have considered in various sciences in recent years is artificial neural network modeling. In addition to the artificial neural network and regression models, today, the capabilities of data mining methods have been used to improve the output results of prediction models and field information analysis. Tree models (decision trees) along with decision rules are one of the data mining methods. Tree models are a way of representing a set of rules that lead to a category or value. These models are made by sequentially separating data into separate groups, and the goal in this process is to increase the distance between groups in each separation. Research shows that plant yield is a function of various plant, climatic, and water, and soil management conditions. Therefore, calculating the amount of plant yield and related indices follows complex nonlinear relationships that also have special difficulty in modeling. Considering that the response of irrigated wheat to different inputs in different climates by field method is time-consuming, costly, and in some cases impossible, so the introduction of an efficient model that can predict yield and analyze yield sensitivity to various parameters is a great help. It will be to solve this problem. This study aimed to develop and evaluate the capability of three models of the neural network, tree, and multivariate linear regression in predicting wheat yield based on parameters affecting its yield in major wheat production hubs in the country. Materials and Methods: The information used in this study includes the volume of water consumption and yield of irrigated wheat and the committees related to these two indicators in irrigated wheat fields under the management of farmers (241 farms) in the provinces of Khuzestan, Fars, Golestan, Hamadan, Kermanshah, Khorasan Razavi, Ardabil, East Azerbaijan, West Azerbaijan, Semnan, south of Kerman and Qazvin, which were harvested in a field study in the 2016-17 growing season. According to the Ministry of Jihad for Agriculture statistics, these provinces have the highest area under irrigated wheat cultivation in the country and cover about 70% of the area under cultivation and production of this crop in the country. One of the most widely used monitored neural networks is the Perceptron multilayer network with error replication algorithm, which is suitable for a wide range of applications such as pattern recognition, interpolation, prediction, and process modeling. In the present study, in order to develop the neural network, the capabilities of R software with Neuralnet package have been used. After the normalization step, the data were randomized. This step aims to have a set of inputs and outputs in which the input-output categories do not have a special system. After the randomization of the data, the amount of information that should be used in the network training process is determined. This part of the data was considered for training (70%) and another part for network test (30%). Perceptron neural network activator functions in the implementation of network training and testing. The hyperbolic tangent activity function has been used to limit the range of output data from each neuron and the pattern-to-pattern training process. In the present study and the neural network modeling capability, the tree model method has been used to predict wheat yield. Tree modeling is one of the most powerful and common tools for classification and forecasting. The tree model, unlike the neural network model, produces the law. One of the advantages of the decision tree over the neural network is that it is resistant to input data noise. The tree model divides the data into different sections based on binary divisions. Each data partition can be re-subdivided into another binary, and a model fitted to each subdivision. In this research, the capabilities of WEKA software have been used to run a tree model. It is worth noting that after grouping, the prediction model is applied to the grouped data. Results and Discussion: In this study, the efficiency of three models of the artificial neural network, multivariate linear regression, and tree model to predict the performance of irrigated wheat in major production areas in the country was evaluated based on field information recorded in 241 farms. The results showed that the coefficient of explanation of the model in predicting the yield of wheat production in the model of artificial neural network and a multivariate linear regression model was 0.672 and 0.577, respectively, which was applied by grouping the data by tree method. The coefficient of explanation has been increased to 0.762. The output results of the tree model showed that the major wheat production areas in Iran in terms of water consumption could be divided into four independent groups. Finally, it can be concluded that the tree model, considering the purposeful grouping in the input data, can be used as a powerful tool in estimating irrigated wheat yield in major wheat production areas in Iran. Conclusion: In this study, the need to use data mining methods in analyzing field information and organizing large databases and the usefulness of data mining methods, especially the decision tree in estimating wheat crop yield, were investigated and compared with other forecasting methods. The general results of the research show that purposeful separation of input data into forecasting models can increase the output accuracy of forecasting models. However, it is not possible to provide a general approach to selecting or not selecting a forecasting model in different regions. In some studies, neural networks have shown a high ability to predict the performance of different products, but it is important to note that if there is sufficient data and correct understanding of the factors affecting the dependent variable, the accuracy of the models can be applied by data mining methods. It also improved the neural network. In a general approach, considering the accuracy of estimating the predicted models under study, these techniques can be used to estimate other late-finding characteristics of plants and soil.
E. Mehrabi Gohari; H.R. Matinfar; Ruhollah Taghizadeh-Mehrjardi; A. Jafari
Abstract
Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and ...
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Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and digital mapping of soil and on the other hand, soils are temporally and spatially variable, thus distinguish zoning and their monitoring with traditional sampling methods and laboratory analysis is very costly and time consuming. As a result, the development of methods for analyzing the soil and for required information has become very important. Visible and near infrared spectroscopy (VIS-NIR) is widely used to estimate soil physical properties and estimate soil texture. The present study aims to predict soil texture using spectral measurements and artificial neural network models and partial least squares regression.
Materials and Methods: The study area in southeastern Iran is approximately 70 km from Kerman. In the study area, based on the hypercube technique, 115 profiles were identified and then horizons were sampled. In this way, for each point of study, the necessary information, including the location of the profile on the ground, the type of geomorphic unit and the type of materiel, were recorded and taken from the horizons of each profile. In all soil samples, after drying and passing through 2 mm soil, the soil texture was measured by hypercube. Spectral radiometer was used to measure the spectral reflection of soil samples. The soil samples were air dried and sieved and then placed in a petri dish with an approximate diameter of 10 cm and transferred to the dark room for spectral analysis. Each specimen was tested four times (for each 90 degree sequential rotation) to remove the effects of a change in the radiation geometry. Soil samples were scanned, and absolute reflections at a spectral range of 2500-350 nm yielded 2150 spectral data points (SDPs) per soil sample with a spectral resolution of one nanometer. Finally, to construct a suitable model for forecasting the percentage of clay, sand, and silt, the least squares model was used with the number of factors 1 to 10 by Artificial Neural Network (ANN) modeling using JMP software Work.
Results and Discussion: The reflectance spectrum of the visible range - near infrared - was measured for specimens. Since preprocessing of spectral data has an effective role in improving the calibration, in order to perform spectral preprocessing, two first nodes of the first and the end of the spectra were first removed in the range of 350-400 and 2450-2500 nm. In addition, the interruption due to the change in the detector in the range of 900 to 1000 nm was also eliminated. Types of preprocessing methods were performed on spectral data. Then, using partial least squares regression analysis, the best model was produced when the first derivative was fitted to reflection values. The explanation coefficients for this low and unacceptable model were obtained. Therefore, using partial least squares regression analysis, the best wavelengths were selected to predict the percentage of clay, sand, soil, and extracted from the model. Then it was used as input in the neural network model. To determine the best combination, root error index and error coefficient were used. The results of artificial neural network showed that the number of neurons 9.8 and 10 had the best composition for predicting clay, sand and soil silt. The root-squared error results for clay, sand, and soil silt were 3.42, 6.94, and 4.383 respectively. Also, the results of the explanatory factor were 0.84, 0.83 and 0.81, respectively. After obtaining the optimal structure in the artificial neural network training phase described above, the trained network has been tested on the test data to determine the accuracy of this model to predict clay, sand and silt of surface soil. The root-squared error results for clay, sand and silt components were obtained at 5.54.9.14 and 7.01. Also, the results of the explanatory factor were 0.76.0.70 and 0.73 respectively. The best result of the prediction for partial least squares regression was obtained for the sand sample. The results indicate that the neural network performance is better than partial least squares regression, which is consistent with Mouazenet. al (2010) and also ViscarraRossel R. et. al (2009). Acceptable performance of the artificial-neural network can be attributed to the ability of this model for non-linear behavior of soil texture in visible spectroscopy. In this study, specific wavelengths, which Ben Finder et al. (2003) obtained in the study on the soils of Israel, were used. This conclusion confirms that various types of soil can be modeled using specific wavelengths. The advantage of this study is that, when using the artificial neural network, no pre-processing of reflection data is required before applying the model. Since the relationship between the percentage of soil particles (clay and gravel) and the reflection of the soil is not linear, the neural network method is very useful for analyzing the relationship between soils. Finally, the map of clay, sand and silt and map of soil texture was prepared by artificial neural network method in GIS environment.
Conclusion: The results of this study showed that the neural-dynamic network has a better performance than partial least squares regression. Calibration models designed and used in this study can be transported for use with other soils. When the partial least squares regression model was implemented, it had a very low accuracy (R2 ~ 0.1-0.3); on the contrary, the neural network-based method had high accuracy and less error. Note that although neural-dynamic modeling estimates higher precision results from soil texture, both approaches depend on wavelength selections, and so wavelengths should be selected before using any of the two models. To be finally, a meaningful relationship between the selected wavelengths and the percentage of clay, sand and silt in the present study indicates that soil texture is not only possible but also reliable by reflection spectroscopy.
S. Ashrafi-Saeidlou; A. Samadi; M.H. Rasouli-Sadaghiani; M. Barin; E. Sepehr
Abstract
Introduction: Potassium (K) is abundant in soil, however, only 1 to 2 % of Potassium is available to plants. Depending on soil type, 90 to 98% of soil K is in the structure of various minerals such as feldspar (orthoclase and microcline) and mica (biotite and muscovite). About 1 to 10 % of soil K, in ...
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Introduction: Potassium (K) is abundant in soil, however, only 1 to 2 % of Potassium is available to plants. Depending on soil type, 90 to 98% of soil K is in the structure of various minerals such as feldspar (orthoclase and microcline) and mica (biotite and muscovite). About 1 to 10 % of soil K, in the form of non-exchangeable K, is trapped between the layers of certain types of clay minerals. The concentration of soluble K, which is directly taken up by plants and microbes in the soil and is exposed to leaching, varies from 2 to 5 mg l-1 in agricultural soils. Imbalanced use of chemical fertilizers, a significant increase of crop yield (depletion of soil soluble K), and the removal of K in the soil system result in a large rate of K fixation in the soil. As a result, K deficiency has been reported in most plants. The annual increase in the price of K fertilizers and the destructive effects of them on the environment have made it necessary to find a solution for the use of indigenous K of soil. The use of biofertilizers containing beneficial microorganisms is one of these strategies. Although K solubilizing bacteria can be an alternative and reliable technology for dissolving insoluble forms of K, lack of awareness among farmers, the slow impact of K biofertilizers on yield, less willingness of researchers to develop K biofertilizers technology and deficiencies of technology in respect to carrier suitability and proper formulation, are the major reasons for why potassium solubilizing microorganisms and K biofertilizers draw low attention.
Material and Methods: The purpose of this study was modeling and evaluating the effects of different vermicompost, phlogopite and sulfur ratios on the solubility and release of K by Pseudomonas fluorescens and indicating the optimized levels of these variables for efficient biofertilizer preparation. 20 experiments were carried out using the response surface methodology (RSM) based on the central composite design and the effect of different values of vermicompost, phlogopite and sulfur variables, in the four coded levels (+α, +1, 0, -1 and -α), was evaluated on K dissolution. The applied vermicompost, phlogopite and sulfur in the experiment were ground and filtered through a 140 mesh sieve and their water holding capacity were determined. According to experimental design, different amounts of mentioned materials were combined and samples were sterilized in autoclave. The required amount of water along with 1 ml of bacterial inoculant were added to the samples. The samples were kept in incubator for 2 months. At the end of experiment, amount of soluble K were measured by the flame photometer.
Results: The analysis of variance (ANOVA) depicted the reliable performance of the central composite predictive model of K dissolution (R2= 0.949 and RMSE=0.8). Based on the results, the interaction of vermicompost with sulfur (p < 0.038) and the interaction of phlogopite with sulfur (p < 0.0083) were relatively high and significant. Sensitivity analysis of the central composite design revealed that the vermicompost (X1), phlogopite (X2) and sulfur (X3) had positive and negative impact on potassium dissolution, respectively. Therefore, when sulfur content increased to 91.70%, K dissolution decreased to around 31.61%. According to the prediction under optimized condition, maximum potassium dissolution was obtained at the presence of 41.78, 24.35 and 10.25% of vermicompost, phlogopite and sulfur, respectively.
Conclusion: The results indicated that the applied fertilizer composition (vermicompost + phlogopite + sulfur) had a desirable impact on Pseudomonas fluorescens solubilizing ability on a laboratory scale. Due to the fact that Iran soils are often calcareous, there are high amounts of insoluble and unavailable nutrients. Under these unsuitable conditions, the application of these nutrients chemical fertilizers cannot reduce deficiencies. Therefore, we must use the ability of efficient microorganisms to dissolve and mobilize soil native elements. A combination of 41.78% vermicompost, 24.35% phlogopite and 10.55% sulfur could create a proper potassium biofertilizer by providing favorable conditions for bacterial activity. Along with solubilizing activities of bacteria, the presence of sulfur reduces soil pH and thereby nutrients availability and stability increase in these soils. Because of its acidity, sulfur has a significant effect on nutrients dissolution such as phosphorus, nitrogen and potassium, and micronutrients. On the other hand, the presence of vermicompost in this fertilizer, while meeting the carbon and energy requirements of bacteria and acting as a suitable carrier, improves the physicochemical properties of the soil, increases the biodiversity of the microbial community and, as a result, promotes the soil quality and health. The evaluation of this fertilizer composition efficiency (using optimal amounts of materials) at the greenhouse and field scales is suggested.
Saeid Okhravi; Saeid Eslamian; nader fathianpour
Abstract
Introduction: Horizontal subsurface flow constructed wetlands have long been applied to improve water or wastewater quality. Previous studies on wetland systems have focused on trying to comprehend the processes leading to the removal of pollutants. Comparatively, there have been fewer studies dedicated ...
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Introduction: Horizontal subsurface flow constructed wetlands have long been applied to improve water or wastewater quality. Previous studies on wetland systems have focused on trying to comprehend the processes leading to the removal of pollutants. Comparatively, there have been fewer studies dedicated to the assessment of flow distribution on hydraulic behavior through the wetland. Researchers declared the aspect ratio (length to width ratio), inlet-outlet configuration, the size of the porous media and the loading rate of constructed wetland could influence hydraulic retention time (HRT). Su et al. (2009) have stated that in free water surface constructed wetlands, when the aspect ratio is greater than 5, the hydraulic efficiency will reach 0.9, or even higher. If the project site or field area cannot meet the theoretical standard, the recommended aspect ratio is higher than 1.88 to ensure some hydraulic efficiency greater than 0.7. The present study was an attempt to analyse, with the aid of 3D numerical simulation and tracer study, how flow distribution affected hydraulic behavior by using 3 different input flow layouts.
Materials and Methods: The treatment system consisted of a horizontal subsurface flow in a constructed wetland having an aspect ratio of 6.5 and the bed slope of one percent. The geometry of this system, which was 4 m wide × 26 m long × 1 m deep, was planted with Phragmites australis. Inlet configurations were selected as a variable parameter. Three different inlet flow configurations including midpoint-midpoint (A), corner- midpoint (B) and uniform-midpoint (C), with the same fixed outlet configurations, were studied. The average flow discharge in each configuration was 6.58, 6.52 and 6.4 m3/day, respectively. Dye tracer was used to draw retention time distribution curves in each configuration for assessing the internal dispersion, short-circuiting and hydraulic parameters such as effective volume rate which is derived by division of mean retention time per nominal retention time. The 3D model presented, which was built on the Comsol Multiphysics platform, was implemented for fluid flow to show internal hydraulic patterns in the system. Hence, the hydraulic model used the Darcy equation to simulate a stationary water flow through the bed. The simulations were verified by using real data obtained from the existing constructed wetland. It was mostly used to show pressure throughout the system for each configuration of the inlet and the outlet.
Results and Discussion: The mean retention time for each configuration was found to be 4.53, 3.24 and 4.65 days, respectively. A marked reduction of the mean hydraulic retention time signified leaving tracer concentration from the outlet rapidly, high short-circuiting and dead volume and finally defective treatment process influenced by changing the inlet to the corner. According to tracer breakthrough curve, the effective volumes for configurations A and C were 87.5%, as compared to 62.1% for the configuration B. The two-day difference of tpeak between configurations 2 and 1, and 3 was probably due to the establishment of preferential streamlines resulting in short-circuiting and areas of dead volume in the system. The value of tpeakis related to dispersion, in the sense that a retention time distribution curve with a small peak time generally contains low dispersion. Simulation results showed the pressure difference from the inlet to the outlet ranged from 12-14, 14-15 and 10-13 cm H2O for A, B and C layouts, respectively. It was shown that the maximum pressure gradient occurred at the outset of the influent wastewater at the inlet, and it was gradually reduced to the lowest values at the outlet ports. Consequence of surface pressure demonstrated uniform pressure from inlet toward outlet at configuration C. Simulated streamlines approved this result, while range of high and low pressure area at configuration B was the most. There was a strong association between tracer experiments and simulation works. One of the major findings of this study was the significant shorter hydraulic mean retention time of the corner inlet setup. There are many that may cause these effects, although short-circuiting may be the primary one. A large low-pressure zone appeared at the opposite corner that was neither inlet nor outlet in this configuration.
Conclusions: This paper investigated the hydraulic performance and short-circuiting effects on water flow due to three different inlet patterns (i.e. midpoint, corner, and uniform) in horizontal subsurface flow wetlands based on dye tracer measurements and numerical modeling. The results showed that the uniform inlet could provide the highest hydraulic efficiency (i.e. longest hydraulic retention time, HRT), in comparison to other two setups. The performance of the three different layouts was also investigated in terms of hydraulic parameters. Short-circuiting was influenced by lower hydraulic retention time, leading to inadequate treatment. Uniform-midpoint and midpoint-midpoint yielded the best effective volume as compared to the corner-midpoint. It was demonstrated that these two cases increased dispersion and used the whole capacity of the constructed wetland for the treatment process. The most important result of this paper was the evaluation of internal hydraulic pattern thorough the wetland, something not investigated in previous research works. Based on the simulation results, the spatial pressure distribution in wetland cells was depicted. Finally, it can be concluded that the best configuration of inlet-outlet layout based on both numerical simulations and physical experiments is uniform-midpoint. Meanwhile, midpoint-midpoint is preferable to corner-corner by all performance criteria.
farshad kiani; Behroz Behtari nejad; Ali Najafi nejad; Abdolreza Kaboli4
Abstract
Introduction: population growth, urbanization and land use changes cause negative effects in natural ecosystems and water resources. Soil erosion is one of the most important problems in agriculture and natural resources of Golestan province. Using low cost and accurate methods for planning and proper ...
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Introduction: population growth, urbanization and land use changes cause negative effects in natural ecosystems and water resources. Soil erosion is one of the most important problems in agriculture and natural resources of Golestan province. Using low cost and accurate methods for planning and proper management of land and water resources are essential for estimating consequences of soil erosion and providing appropriate solutions to reduce soil losses.
Materials and Methods: The study area is located in eastern part of Golestan province with an area of 1524 square Kilometers. The average annual precipitation of the region is 496 millimeters. In this watershed, rainfall decreases from south and south west to north and north east (due to the remoteness from the Caspian Sea), while evapotranspiration, temperature and the number of dry months increase in the same direction. Also the average annual temperature of the watershed and its relative humidity and evaporation are 17.8°C, 68.5 % and 1398.34 millimeters, respectively. Tamer watershed was divided into 15 sub-watersheds by adding an outlet in the site of Tamar gauging station. In this study, the SWAT model was used to simulate erosion and sedimentation. To compare the measured and simulated data and evaluation of the SWAT performance in terms of simulating flow and sediments, daily flow (cubic meters per second) and sediment (tons per day) data at the Tamar gauging station located in Tamar’s watershed outlet was collected from the studies of water resources organization (Tamab). Simulated values were generally consistent with the data observed during calibration and validation period. At this stage of calibration, the SUFI-2 model was used to optimize the parameter values. In this study, daily rainfall and temperature data recorded during an 8-year period by the stations within the watershed were imported into the model. The daily discharge data and daily sediment data of Tamar station recorded during 1999- 2006 were selected. Then model was run using runoff and sediment parameters, and ranges of parameters were adjusted at each iterations, and therefore SWAT model was calibrated using SUFI-2 model. After calibration, model must be validated and its ability to predict future events must be determined. Validation was performed using the runoff and sediment data recorded in Tamar gauging station from 2007 to 2010.
Results and Discussion: NS, R2, R-factor and P-factor were estimated for runoff calibration about 0.76, 0.77, 0.06 and 69 and for runoff evaluation 0.72, 0.75, 0.05 and 69 respectively. The same parameters were also measured for sediment calibration about 0.54, 0.62, 0.15, and 16 and sediment evaluation 0.55, 0.61, 0.35, and 12 respectively. The results showed that irrigated agriculture 24.95 and 15.56 t ha -1y-1 respectively, with average erosion and sediment ha of agriculture by an average of 20.23 and 12.33 t ha -1y-1 respectively erosion and sediment erosion and deposition are tons per hectare maximum value. Results also showed that the soil loss caused by erosion in this watershed is average 6.49 t ha -1y-1 in sediment and 10.28 t ha -1y-1 in erosion.
Conclusion: The assessment factors showed that model has successfully simulated the daily runoff discharge during calibration and validation phases with a Nash-Sutcliffe coefficient of 0.76 and 0.72. A Nash-Sutcliffe coefficient above 0.5 could be acceptable for sediment simulation. However, sediment load simulated for rainy seasons has been lower than actual value while this value has been higher than actual value during dry seasons. In most months of the year, model results are higher than measured values and this issue is more pronounced in the peak runoffs. This issue is due to limitations in spatial distribution of rainfall, so when a small area in watershed experience a severe rainfall, model considers the impact for the entire watershed and therefore overestimates the total runoff. The results showed that SWAT model can be a useful tool for the simulation of flow and sediment basins in the loess land.
Simulation results showed that land use changes have resulted in corresponding increases in surface runoff and sediment. Rates were highly variable both spatially and temporally, and the agricultural lands were most significantly affected. These land use changes have negative implications for the ecological health of the river system as and local communities.
Banafsheh Sheikhipour; Saman Javadi; Mohammad ebrahim Banihabib
Abstract
Introduction: Most part of Iran is located in an arid and semi-arid region, thus in most parts of a region; groundwater is the only water resource also Population growth, limitation of surface water resources and excessive water withdrawal from the aquifers, caused a sharp drop in groundwater level in ...
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Introduction: Most part of Iran is located in an arid and semi-arid region, thus in most parts of a region; groundwater is the only water resource also Population growth, limitation of surface water resources and excessive water withdrawal from the aquifers, caused a sharp drop in groundwater level in many plains of Iran such as Shahrekord plain, So it is necessary to have suitable management plans to improve the aquifer and evaluate some indicators to see the effects of the methods. In this research, many management plans were assessed for the case study.
Materials and Methods: A groundwater numerical flow model (GMS 10.2) was established by using the monthly data including hydraulic heads, depletion volume of the wells, springs and qanats, precipitation values in Shahrekord aquifer. The model was prepared and calibrated for both status of steady (October 2010) and unsteady flow (November 2010-October 2012), and verified for the following year (November 2012- October 2013). The final values of hydraulic conductivity and specific discharge were obtained by trial and error and PEST method. The water level fluctuation was predicted for three years later (until October 2016) by applying management scenarios of 5% and 10% reduction in water withdrawal, underground dam and artificial recharge. After that, two indicators of Sustainability Index and modified Water Exploitation Index (WEI+) were calculated to determine the effect of the scenarios. The Sustainability Index indicates the consumption ratio of natural resources to water demand. The optimal value of this Index is 1 and it may also have negative values. Low values of this index mean high usage of natural resources. The Water Exploitation Index shows to which extent the total water demand puts pressure on water resources. This index has positive values and its optimal value is close to zero. These two indicators were used for surface water resources in the past studies so in this article they were redefined for underground water resources.
Results and Discussion: The result of groundwater modeling shows that the hydraulic conductivity from 1 to 25 m/day and specific yield from 0.01 to 0.08 are varied also the result of prediction shows that the underground water level would be decreased about 1.34 meter per year in the next 3 years when it hadn’t any management plans in this area but after 5% and 10% reduction water withdrawal scenarios Decreasing of water level were, respectively, 1.33 and 0.71 meter for each year also, considering that there were more wells in the center of the aquifer, water level in this area increased more than other areas, after 5% and 10% Reduction scenarios. According to the results of the artificial recharge and underground dam storage prediction, groundwater head increased in upstream of underground dam and the area near the artificial recharge. Considering the results it was found that the current condition of the aquifer is inappropriate and the amount of withdrawal from the aquifer is more than its capacity. The amount of Water Exploitation Index for business as usual scenario equal to 1.068 and for underground dam, artificial recharge, 5% and 10% reduction water withdrawal, were, respectively, equal to 1.068, 1.061, 1.045 and 0.969. Also the amount of Sustainability Index for business as usual scenario equal to 0.071 and for the other scenario were 0.068, 0.071 and 0.114. , respectively.
Conclusion: Considering the values of the indicators, 10% reduction water withdrawal scenario improved both indicators and selected as the best scenario. After that, 5% reduction water withdrawal was in the second place, then the artificial recharge scenario and underground dam scenarios, respectively, were in the third and fourth place. The scenario of underground dam had any positive effect on these two indicate. Regarding the calculated values of the indicators, it can be seen that although management scenarios have improved these two indicators, the amounts obtained are also significantly different from their optimal values. Several management scenarios can be used simultaneously to bring the calculated index values closer to their optimal values. Used two indicators of sustainability and modified water exploitation can be used exploitation for other management scenarios and assess the performance of them for the other aquifers.
hossin shekofte; maryam doustaky; aezam maseodi
Abstract
Introduction: Soil quality is defined as the capacity of a soil to function within different land uses and ecosystem boundaries, sustain biological productivity, maintain environmental quality and promote plant, animal, and human health. Soil quality cannot be directly measured but can be evaluated on ...
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Introduction: Soil quality is defined as the capacity of a soil to function within different land uses and ecosystem boundaries, sustain biological productivity, maintain environmental quality and promote plant, animal, and human health. Soil quality cannot be directly measured but can be evaluated on the basis of several parameters; the type of parameter to be used depends on research scale and goals. Soil quality indicators (SQIs) are used to evaluate the effect of different management and types of land use on soil quality and can be achieved by easily-measured soil physicochemical properties. Soil quality indicators are measurable characteristics of the soil affecting the soil capacity for crop production or environmental performance. Air capacity (AC), relative field capacity (RFC) and plant available water (PAWC) are the most important indicators. Selection of appropriate input parameters is the first and most important step in predicting SQIs. Feature selection can be defined as the identification and selection of a subset of useful features among the primary data collected. One of the methods for choosing the features is the Pearson coefficient, which shows the correlation between the input variables and target variable. When the coefficient is close to one, there is a strong relationship between the input and the target variable. The features having a correlation coefficients of greater than or equal to 0.9 are considered important and less than that are considered non-important. Decision tree algorithm is one of the prediction approaches in statistics and data mining literature. This algorithm can select the property with the highest separation capability. Working with this algorithm and interpret its results is very straightforward. The aims of this study were to select the best set of input properties influencing SQIs using Pearson correlation coefficient and then model the effect of the input properties by decision tree and multiple linear regression.
Materials and Methods: In this study, the Pearson correlation coefficient was used for selecting effective soil properties influencing SQIs and these indices were modeled and predicted by the decision tree algorithm with selected input properties. For this purpose, 104 soil samples were collected from the soil surface (0-15 cm depth) of four land uses including a garden with 20 year-old walnut trees, pasture, agriculture and a mountain almond in a semi-arid area in Iran (Rabor region, 29 27′ N to 38 54′ N and 56 45′ E to 57 16′ E). A multiple linear regression (MLR) model was constructed as the benchmark for the comparison of performances. Sensitivity analysis of decision tree model was performed with input variables using StatSoft method. The predictive capabilities of the proposed models were evaluated by the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) between measured and predicted SQIs values.
Results and Discussion: The soil properties including porosity, bulk density, clay and sand content for air capacity, porosity and sand, clay and silt content for relative field capacity, and bulk density, electrical conductivity, porosity, and sand, clay and silt content for plant available water were selected as important input parameters. In addition, the values of r2 for the decision tree model for air capacity, relative field capacity and plant available water were 0.95, 0.84 and 0.85, respectively, while the r2 values for multiple linear regression for AC, RFC and PAWC were 0.63, 0.62 and 0.61, respectively. According to the evaluation indices, it appears that the conventional regression model was poor in predicting SQIs. Therefore, conventional regression techniques (i.e., multiple-linear regression) may not be reliable for predicting the SQIs. The results of sensitivity analysis for decision tree model showed that porosity and bulk density for air capacity, porosity for relative field capacity and bulk density for plant available water had the greatest influence.
Conclusion: This research work provided a basis for predicting soil physical quality indicators and identifying important parameters impacting these indicators in agricultural soils, grassland and forests in semi-arid regions which can be generalized to other areas. Further studies are needed to assess the effects of selected input variables under different conditions.
Aida Mehrazar; Jaber Soltani; omid Rahmati
Abstract
Introduction: Limited water resources and its salinity uptrend has caused reducing water and soil quality and consequently reducing the crop production. Thus, use of saline water is the management strategies to decrease drought and water crisis. Furthermore, simulation models are valuable tools for improving ...
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Introduction: Limited water resources and its salinity uptrend has caused reducing water and soil quality and consequently reducing the crop production. Thus, use of saline water is the management strategies to decrease drought and water crisis. Furthermore, simulation models are valuable tools for improving on-farm water management and study about the effects of water quality and quantity on crop yield.. The AquaCrop model has recently been developed by the FAO which has the ability to check the production process under different propositions. The initial version of the model was introduced for simulation of crop yield and soil water movement in 2007, that the effect of salinity on crop yield was not considered. Version 4 of the model was released in 2012 in which also considered the effects of salinity on crop yield and simulation of solute Transmission in soil profile.
Material and methods: In this project, evaluation of the AquaCrop model and its accuracy was studied in the simulating yield of maize under salt stress. This experiment was conducted in Karaj, on maize hybrid (Zea ma ys L) in a sandy soil for investigation of salinity stress on maize yield in 2011-2012. This experiment was conducted in form of randomized complete block design in four replications and five levels of salinity treatments including 0, 4.53, 9.06, 13.59 and 18.13 dS/m at the two times sampling. To evaluate the effect of different levels of salinity on the yield of maize was used Version 4 AquaCrop model and SAS ver 9.1 software .The model calibration was performed by comparing the results of the field studies and the results of simulations in the model. In calculating the yield under different scenarios of salt stress by using AquaCrop, the model needs climate data, soil data, vegetation data and information related to farm management. The effects of salinity on yield and some agronomic and physiological traits of hybrid maize (Shoot length, root length, dry weight and crop yield) under different levels of NaCl solution osmotic potential were also investigated by SAS ver 9.1 software. Data's mean comparisons were performed by Duncan's multiple range test. To assess the accuracy of AquaCrop Model for Simulation of the Maize Performance under Salt Stress used from Indicators RMSE, MAE, CRM, NSE, d and Er.
Results Discussion: The results of RMSE and MAE indices showed that AquaCrop model can simulate maize yield under the salinity stress. Accuracy decreased and crop yield prediction underestimated with increasing salinity from treatment 0 to 18.13 ds/m in the first and second harvest. The highest yield related to salinity treatment of 0 dS/m and the lowest yield related to salinity treatment 18.13 dS/m. yeild simulation error increased by increasing salinity, the highest and lowest error of yield simulation in model respectively related to salinity treatments 18.13 and 0 dS/m. The highest and lowest error was in the first harvest respectively 0.56 and 13.1 percent and in the second harvest respectively 0.42 and 21.79 percent, that in the comparison with the results of studies conducted by Steduto and colleagues on maize is not much different. The results comparison in the first and second harvest showed that soil salinity was increased by increasing irrigation number in second harvest, so the error in second harvest is greater than first harvest and the maximum error is related to treatment 18.13 ds/m in the second harvest 21.79 percent.The coefficient of determination R2 for the first and second harvest is respectively 0.850 and 0.834, that indicates a high correlation between yeild values of measured and predicted by the AquaCrop model. CRM index was negative and near zero in both harvest under Salinity different scenarios. According to CRM value, AquaCrop model was overestimated and the model was simulated maize yield under the salinity stress a little more than measured yield. The d statistic index value is close to unity, indicates that yield values in model is compatible with actual values. NSE index was calculated for the first and second harvest respectively 0.81 and 0.84, that is close to one and showed that the model has suitable performance in the yield simulation. Comparison of means by Duncan's multiple range test and analysis of variance in the software SAS ver 9.1 indicated Salinity has a very significant effect on all traits including shoot length, root length, dry weight and crop yield that all traits were decreased significantly by increasing salinity.
Conclusion: Comparison of the results of AquaCrop model and statistical analysis in software SAS ver 9.1 showed that maize yield was reduced with increasing salinity. According to index CRM, AquaCrop model was simulated maize yield under the salinity stress more than measured yield in farm. The results showed that the AquaCrop model simulated well maize yield in moderate and low stress, but accurately simulation slightly decreased in high stress. The results of this study was compared with other research and indicated that the error values of AquaCrop model in Karaj is not much different with the error values of other research.
azam gholamnia; mohammadhosein mobin; atefe jebali; hamid alipor
Abstract
Introduction: Solar radiation (Rs) energy received at the Earth's surface is measured usingclimatological variables in horizontal surface and is widely used in various fields. Domination of hot and dry climates especially in the central regions of Iran results from decreasing cloudiness and precipitation ...
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Introduction: Solar radiation (Rs) energy received at the Earth's surface is measured usingclimatological variables in horizontal surface and is widely used in various fields. Domination of hot and dry climates especially in the central regions of Iran results from decreasing cloudiness and precipitation and increasing sunshine hours, which shows the high potential of solar energy in Iran. There is a reasonable climatic field and solar radiation in most of regions and seasons which have provided an essential and suitable field to use and extend new and pure energy.
Materials and Methods: One of the common methods to estimate the solar energy received by the earthis usingtemperature variables in any place . An empirical model is proposed to estimate the solar energy as a function of other climatic variables (maximum temperature) recorded in 50 climatological, conventional stations; this model is helpful inextending the climatological solar-energy estimation in the study area. The mean values of both measured and estimated solar energy wereobjectively mapped to fill the observation gaps and reduce the noise associated with inhomogeneous statistics and estimation errors. This analysis and the solar irradiation estimation method wereapplied to 50 different climatologicalstations in Iran for monthly data during1980–2005. The main aim of this study wasto map and estimate the solar energy received in four provinces of Yazd, Esfahan, Kerman and Khorasan-e-Jonoubi.The data used in this analysis and its processing, as well as the formulation of an empirical model to estimate the climatological incident of solar energy as a function of other climatic variables, which is complemented with an objective mapping to obtain continuous solar-energy maps. Therefore, firstly the Rswasestimated using a valid model for 50 meteorological stations in which the amounts of solar radiation weren't recorded for arid and semi-arid areas in Iran. Then, the appropriate method was selected to interpolate by GS+ software and after that, the seasonal maps of the received solar energy over the ground surface were produced by GIS software. The best fitof the Gaussian model was determined in winter with the lowest residual error and the highest correlation 1.87 and 0.913respectively, in spring with the lowest RSS and highest R23.87 and 0.86 respectively and during summer with RSS and R2, 5.9 and 0.851 and the exponential model in autumn withthe RSS and R2, 3.61 and 0.88..
Results and Discussion: Naturally, some of the differences in the mean solar energy among the stations may be related to inter annual variability rather than to differences in the climatic, radiative regimes. If different periods for the climatological estimations are used, the resulting mean values can be representative of the regional climatic regime of solar energy. The results showed that 53% of Yazd province Received 26 Mj / m2.day, in summer.In winter, more than 80% of Yazd province received 15 Mj / m2.day radiation. Kerman compared to other provinces received high solar radiation, especially this feature wasmore pronounced in winter because in this season compared to Yazd, Kerman radiation didnot only showed greater range, but also about 40% of the province's total area received 16 Mj / m2.day radiation, whereas Yazd no radiation was received during this season. Because Kerman is located in the southeast of region and itreceived more solar radiation than other provinces.In this study, the amount of solar energy in surface of 4 provinces including Yazd, Esfahan, Kerman and South Khorasan in arid and semiarid regions of Iran was estimated by the geostatistic. Seasonal mean values of solar energy absorbed at the surface of 4 stationswascalculated. The results showed that Kerman with receiving 27.25 (Mj m-2. D-1) averagely has the most received solar energy and Esfahan with 21.54 (Mj m-2. D-1) during the summer has received the least solar energy. The limited records of solar energy used in thisanalysis madethe analysis of long-term variations impossible. This paper wasthe first stage of a more extensive study which involvedmonitoring the behavior of photocells under real environmental conditions, which allowedto obtain efficiency curves used in the mapping of actual photovoltaic potential inarid and semiarid regions of Central Iran. This analysis must be complemented by better, higher resolution estimates of the incident solar energy; a viable alternative for such a task is the use of satellite observations. However, a better photovoltaic prospection, with high quality data, is necessary.
shahab ahmadi doabi; Majid Afyuni; Mahin Karami; Safura Merati Fashi
Abstract
Introduction: Zinc (Zn) is an essential trace element for plants as well as for animals and humans. On the other hand, Zn is a heavy metal and its high concentration can cause some environmental problems. There are significant relationships between soils, plants and humans Zn status in a certain agro-ecosystem.Therefore, ...
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Introduction: Zinc (Zn) is an essential trace element for plants as well as for animals and humans. On the other hand, Zn is a heavy metal and its high concentration can cause some environmental problems. There are significant relationships between soils, plants and humans Zn status in a certain agro-ecosystem.Therefore, mass flux assessment of Zn in agro-ecosystem is important regarding to plant and human nutrition in one hand and environmental quality on the other hand. Therefore, assessing the Zn accumulation trend in agricultural soils is essential to prevent Zn deficiency as well as soil pollution by Zn.
Materials and Methods: This investigation was conducted in order to model Zn accumulation rate in agricultural soils of Kermanshah province using inputs and outputs fluxes mass balance. Mass Flux Assessment (MFA) model were applied for the modeling accumulation rate of Zn uses a random method of element balance with the combination of Latin Hyporcube method and Mont-Carlo simulation, in several agricultural ecosystems of some townships (Kermanshah, Songhor, Gilanegharb, Ghasreshrin, Shaneh, Sarpolezahab, Kangavar, Paveh and Javanrood). In this study, mass flux assessments were done at both provincial and township scales. Various routes of Zn considered in this study were livestock manure, mineral fertilizers, pesticides, atmosphere deposition, municipal waste compost (input) and uptake by plant (output). Agricultural information, including crop type, crop area and yield, kind and number of livestock, application rates of mineral fertilizers, compost, pesticides and atmospheric deposition rates and also a metal concentration in the plants, livestock manure, mineral fertilizers, compost and dust was used to quantify Zn fluxes and Zn accumulation rate. Given that the other sources of Zn input such as sewage sludge and output such as leaching are not important fluxes in the study area, the calculations performed here presented a good estimation of the average net effects of the dominating Zn inputs and outputs of the Zn status in agricultural soils of the study region.
Results and Discussion: The results showed that the maximum and minimum of the Zn accumulation rate were seen in agricultural soils of Paveh (1172 g ha-1yr-1 in average) and Kermanshah (-26 g ha-1yr-1 in average)respectively. The average net flux of Zn accumulation rate for Kermanshah province was also 1538 g ha-1yr-1. The negative Zn accumulation rate of Kermanshah soils implies depletion of this element that is due to higher uptake of Zn by plants, especially crops with high performance such as maize and sugar beet. The calculated accumulation rates were less than the critical accumulation rate (calculated for the next 200 years in the study area). The results showed the high range (difference between the simulated maximum and minimum) of the Zn accumulation rate in Paveh was 1307 g ha-1yr-1, and the lowest in Songhor was 175 g ha-1yr-1. The major part of the uncertainty in the Zn balance resulted from manure source. According to the calculated SRCAP (Standardized Regression Coefficients Aggregated in Percent) values, Zn input with manure and then Zn output with crop removal were the main sources of Zn net flux uncertainty at township and province 9 levels. The uncertainty associated with livestock manure fluxes explained 67-94% of the total uncertainty. This large contribution was mainly due to large uncertainty in the numbers of dominant livestock, in particular cattle and poultry, and in the Zn:P concentration ratios of their manures. The influence of crop removal on Zn net fluxes uncertainty ranged from 3-29% among the townships. Differences in contributions of individual crops to the total cultivated area and in the Zn concentration of dominant crops as well as uncertain crops yield data were the main reasons for this large variation among townships.
Conclusion:The most important routes of Zn entry into the agricultural soils were livestock manures (69-93%) and atmosphere deposition (9-28%) in township level, while in provincial scale, they were compost (61%), livestock manures (33%), and atmosphere deposition (5%) respectively. The uncertainty analysis results indicated that livestock manure was the most effective rout on Zn accumulations rate uncertainty (79% in province scale and 67-94% in township scale). The results also indicated that current agricultural management generally leads to accumulation of Zn in soils of the study area (with exception for Kermanshah township soils). This can cause some difficulties such as soil contamination or soil fertility loss by nutritional elements imbalance in future.
N. Zabet Pishkhani; S.M. Seyedian; A. Heshmat Pour; H. Rouhani
Abstract
Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy ...
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Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM). In this paper the ability of artificial intelligence methods including support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) were analyzed in monthly precipitation prediction.
Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS) refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model since it is less computationally exhaustive and more transparent than other models. A consequent membership function (MF) of the Sugeno model could be any arbitrary parameterized function of the crisp inputs, most like lya polynomial. Zero and first order polynomials were used as consequent MF in constant and linear Sugeno models, respectively. In addition, the defuzzification process in Sugeno fuzzy models is a simple weighted average calculation. The fuzzy space was divided via grid partitioning according to the number of antecedent MF, and each fuzzy region was covered with a fuzzy rule.
Results Discussion: The statistical results showed that in first structure determination coefficient values for both the training and test was not good performance in precipitation prediction so that ANFIS and SVM had determination coefficient of 0.67 and 0.33 in training phase and 0.45 and 0.40 in test phase. Also the error RMSE values showed that both models had failed to predict precipitation in first structure. The results of second structure in precipitation prediction showed that determination coefficient of ANFIS at training and testing was 0.93 and 0.87 respectively and RMSE was 7.06 and 9.28 respectively. MBE values showed that the ANFIS underestimated at training phase and overestimated at test phase. Determination coefficient of SVM at training and testing was 0.89 and 0.91 respectively and RMSE was 9.28 and 5.59 respectively. SVM underestimated precipitation at train phase and overestimated it at test phase. ANFIS and SVM modeled precipitation using precipitation gauging stations with reasonable accuracy. Determining coefficient in the test phase was almost the same for ANFIS and SVM but the RMSE error of SVM model was about 20% lower than the ANFIS. The coefficient of determination and error values indicated SVM had greater accuracy than ANFIS. ANFIS overestimated precipitation for less than 20 mm but for higher values of uniformly distributed around the 1:1. SVM underestimated precipitation for more than 90 mm precipitation due to the low number of data in the training phase, which made this model, did not train well. When meteorological parameters were introduced as input, minimum determination coefficient and maximum error in the test phase occurred while humidity parameters were removed. By removing any of the parameters of temperature, pressure and wind speed the error values and coefficient of determination in test phase was approximately equal.
Conclusion: The potential of the support vector machine (SVM) and neuoro fuzzy inference system (ANFIS) in monthly precipitation pattern were analyzed. In order to model, two data sets were used containing meteorological parameters (temperature, humidity, pressure and wind speed) and the stations precipitation. The results showed that the simulated precipitation using meteorological parameters by ANFIS and SVM had low accuracy. Precipitation forecasting using stations precipitation in the region had good accuracy by ANFIS and SVM. Comparing the results of this study showed the high efficiency of SVM in simulating precipitation. This method can be successfully used in modeling precipitation to increase efficiency of precipitation modelling.
M.M. Chari; B. Ghahraman; K. Davary; A. A. Khoshnood Yazdi
Abstract
Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these ...
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Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these methods is the use of fractal geometry. Determining the relationship between particle size distribution fractal dimension (DPSD) and fractal dimension retention curve (DSWRC) can be useful. However, the full information of many soil data is not available from the grading curve and only three components (clay, silt and sand) are measured.In recent decades, the use of fractal geometry as a useful tool and a bridge between the physical concept models and experimental parameters have been used.Due to the fact that both the solid phase of soil and soil pore space themselves are relatively similar, each of them can express different fractal characteristics of the soil .
Materials and Methods: This study aims to determine DPSD using data soon found in the soil and creates a relationship between DPSD and DSWRC .To do this selection, 54 samples from Northern Iran and the six classes loam, clay loam, clay loam, sandy clay, silty loam and sandy loam were classified. To get the fractal dimension (DSWRC) Tyler and Wheatcraft (27) retention curve equation was used.Alsothe fractal dimension particle size distribution (DPSD) using equation Tyler and Wheatcraft (28) is obtained.To determine the grading curve in the range of 1 to 1000 micron particle radius of the percentage amounts of clay, silt and sand soil, the method by Skaggs et al (24) using the following equation was used. DPSD developed using gradation curves (Dm1) and three points (sand, silt and clay) (Dm2), respectively. After determining the fractal dimension and fractal dimension retention curve gradation curve, regression relationship between fractal dimension is created.
Results and Discussion: The results showed that the fractal dimension of particle size distributions obtained with both methods were not significantly different from each other. DSWRCwas also using the suction-moisture . The results indicate that all three fractal dimensions related to soil texture and clay content of the soil increases. Linear regression relationships between Dm1 and Dm2 with DSWRC was created using 48 soil samples in order to determine the coefficient of 0.902 and 0.871 . Then, based on relationships obtained from the four methods (1- Dm1 = DSWRC, 2-regression equationswere obtained Dm1, 3- Dm2 = DSWRC and 4. The regression equation obtained Dm2. DSWRC expression was used to express DSWRC. Various models for the determination of soil moisture suction according to statistical indicators normalized root mean square error, mean error, relative error.And mean geometric modeling efficiency was evaluated. The results of all four fractalsare close to each other and in most soils it is consistent with the measured data. Models predict the ability to work well in sandy loam soil fractal models and the predicted measured moisture value is less than the estimated fractal dimension- less than its actual value is the moisture curve.
Conclusions: In this study, the work of Skaggs et al. (24) was used and it was amended by Fooladmand and Sepaskhah (8) grading curve using the percentage of developed sand, silt and clay . The fractal dimension of the particle size distribution was obtained.The fractal dimension particle size of the radius of the particle size of sand, silt and clay were used, respectively.In general, the study of fractals to simulate the effectiveness of retention curve proved successful. And soon it was found that the use of data, such as sand, silt and clay retention curve can be estimated with reasonable accuracy.
R. Zamani; F. Ahmadi; F. Radmanesh
Abstract
Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series ...
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Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming), the daily flow modeling has been at the time interval (1981-2012) in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang).The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.
B. Shabani; M. Mousavi Baygi; Mehdi Jabbari Nooghabi; B. Ghareman
Abstract
Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and ...
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Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and minimum monthly temperatures of Mashhad plain, the long- term data of Mashhad and Golmakan were used for the joint period from 1987 to 2008. The SARIMA(0,0,0)(0,1,1)12 model for maximum monthly temperature and the SARIMA(0,0,0)(2,1,1)12 model for minimum monthly temperature were determined as the final models using time series. High correlation coefficientsindicate acceptable adaptation of modeling and actual values in the calibration and validation of models. Finlay, predictions were performed based on models fitted for the next 10 years (2009-2018). Comparison of results for future period (2009-2018) and the base period (1987-2008) represents maximum temperature mean 1 °c increase and minimum temperature mean 1.4 °cincrease.
M.A. Mousavi Shalmani; A. Lakzian; A. Khorasani; V. Feiziasl; A. Mahmoudi; A. Borzuee; N. Pourmohammad
Abstract
In order to assessment of water quality and characterize seasonal variation in 18O and 2H in relation with different chemical and physiographical parameters and modelling of effective parameters, an study was conducted during 2010 to 2011 in 30 different ponds in the north of Iran. Samples were collected ...
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In order to assessment of water quality and characterize seasonal variation in 18O and 2H in relation with different chemical and physiographical parameters and modelling of effective parameters, an study was conducted during 2010 to 2011 in 30 different ponds in the north of Iran. Samples were collected at three different seasons and analysed for chemical and isotopic components. Data shows that highest amounts of δ18O and δ2H were recorded in the summer (-1.15‰ and -12.11‰) and the lowest amounts were seen in the winter (-7.50‰ and -47.32‰) respectively. Data also reveals that there is significant increase in d-excess during spring and summer in ponds 20, 21, 22, 24, 25 and 26. We can conclude that residual surface runoff (from upper lands) is an important source of water to transfer soluble salts in to these ponds. In this respect, high retention time may be the main reason for movements of light isotopes in to the ponds. This has led d-excess of pond 12 even greater in summer than winter. This could be an acceptable reason for ponds 25 and 26 (Siyahkal county) with highest amount of d-excess and lowest amounts of δ18O and δ2H. It seems light water pumped from groundwater wells with minor source of salt (originated from sea deep percolation) in to the ponds, could may be another reason for significant decrease in the heavy isotopes of water (18O and 2H) for ponds 2, 12, 14 and 25 from spring to summer. Overall conclusion of multiple linear regression test indicate that firstly from 30 variables (under investigation) only a few cases can be used for identifying of changes in 18O and 2H by applications. Secondly, among the variables (studied), phytoplankton content was a common factor for interpretation of 18O and 2H during spring and summer, and also total period (during a year). Thirdly, the use of water in the spring was recommended for sampling, for 18O and 2H interpretation compared with other seasons. This is because of function can be explained more by variables and there are more variables compare with other two seasons. Fourthly, potassium concentration in spring and total volume of water in summer would be most appropriate variables for interpretation of data during these seasons
nona sheikholeslami
Abstract
Evapotranspiration is one of the most important parameters that its understanding is necessary for estimating crop water requirement and design of irrigation systems. This phenomenon is greatly influenced by climatic parameters. In this study, the relative importance of variables affecting this phenomenon ...
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Evapotranspiration is one of the most important parameters that its understanding is necessary for estimating crop water requirement and design of irrigation systems. This phenomenon is greatly influenced by climatic parameters. In this study, the relative importance of variables affecting this phenomenon was evaluated and the reference evapotranspiration was estimated using principal component analysis and factor analysis. Daily scaled measurements for the period of 1991-2005 were obtained from synoptic stations located in Mashhad Khorasan Razavi provience, Iran. Mashhad has a semi-arid climate area. The measurements included the relative influence of temperature (T) (maximum, average and minimum), relative humidity (RH), sunshine hours (Rs), and the wind speed at a height of two meters above the ground (U2). The multiple linear regressions were used to estimate evapotranspiration. T-statistic with a significant level of 5% was used for the main components. The evapotranspiration was correlated more with T (minimum. maximum, and average), and relative humidity as than wind speed or sunshine. PC1 had more effect than PC2 (with coefficients of 0.694 and 0.556, respectively). MLR-PCA and MLR with coefficients of 0.903 and 0.897 (respectively) indicated higher ability for PCA method.
H. Kashi; H. Ghorbani; S. Emamgholizadeh; S.A.A. Hashemi
Abstract
With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed ...
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With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity as difficult and time consuming feature. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of two artificial neural networks, multi-layer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compared with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity was determined and most important parameters which could influence the soil cation exchange capacity were described.
S. Tajik; Sh. Ayoubi; F. Nourbakhash
Abstract
Enzymes are so crucial in the mineralization process of organic material. Information of the soil enzymes activity is used in determining of the soil microbial properties and they are also important in soil health and quality. Topographic attributes, soil properties and soil enzymes are associated together. ...
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Enzymes are so crucial in the mineralization process of organic material. Information of the soil enzymes activity is used in determining of the soil microbial properties and they are also important in soil health and quality. Topographic attributes, soil properties and soil enzymes are associated together. Hence, it is essential to know how these parameters affect on the soil enzymes activity. This study has been implemented in hilly region of Semiroum district located at southern Isfahan province, to develop a regression model between soil enzymes activity and soil and topographic characteristics. Mean annual temperature and precipitation in the studied area is 10.6°C and 350 mm, respectively. Soil sampling was done in a systematic randomly manner from the 0-10 cm surface layer. Topographic attributes were calculated by the digital elevation model with 10×10 m spatial resolution. Soil properties were determined by laboratory analysis. Multiple regression models between these parameters and soil enzymes activity were established and then the predictive models were validated using 20% of data. Results indicated soil parameters explained 33-63% of total variability of soil enzymes activity in the studied site. Topographic attributes explained 14- 15 %, and a combination of soil and topographic characteristics could explain 33-67% of total variability of soil enzymes activity. Therefore, the use of a combined data set of soil properties and topographic attributes could provide the powerful models for predicting of soil enzymes activity. These results confirmed that soil enzyme activity in the studied area is influenced by soil and topographic attributes synchronously. The results of validation ascertained that the predictors were unbiased and sufficiently accurate.
