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.
Mina Touzandejani; Alireza Soffianian; Norollah Mirghafari; Mohsen Soleimani
Abstract
Introduction: All living organisms, such as plants, animals and humans depends on the water and life may exist in a place where water is available. Groundwater is the main source of drinking water for more than 5.1 billion people around the world, especially in arid and semi- arid regions such as Iran. ...
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Introduction: All living organisms, such as plants, animals and humans depends on the water and life may exist in a place where water is available. Groundwater is the main source of drinking water for more than 5.1 billion people around the world, especially in arid and semi- arid regions such as Iran. Currently, groundwater provided about 60 percent of the worlds drinking water and 77.8 percent of the Iran's drinking water. In recent years, it has been found that groundwater quality is also important as much as its quantity. Nowadays, pollution of groundwater resources from pollutants, especially heavy metals reduces the quality of these resources. Heavy metals are one of the most important environmental pollutant that its entering into the water is raised by agricultural activities, industrial and urban development. Among the heavy metals, arsenic is a toxic and carcinogenic metalloids which are widely distributed in the environment and it has a twentieth abundance of elements in the Earth's crust with an average of 1.8 mg kg-1. Arsenic has been classified in the first group of cancer-causing compounds. It has different effects such as horny skin, liver, skin and bladder cancer, mental disorders, damage to neurons, blood pressure, lower IQ and reducing white blood cells and red blood. The Maximum permissible arsenic in drinking water is 10 micrograms per liter which has been identified by the World Health Organization and America Environmental Protection Agency. According to national standards of Iran, limitation of arsenic in drinking water is 10 micrograms per liter. So far, numerous studies were done to evaluate the environmental contamination of heavy metals, especially arsenic using geostatistical methods. The aim of this study was to evaluate the quality of groundwater in terms of Arsenic pollution.
Materials and Methods: study area is Hamedan - Bahar aquifer with an area of 800 square kilometers that is located on the northern slopes of Alvand Mountains. The central part of Hamadan city, Lalejin, Saleh Abad and Bahar city is located in the study area. To conduct this study, concentrations of arsenic was investigated in 94 groundwater points. To determine the spatial distribution of arsenic, different geostatistical methods were used. Then the results of this methods were compared using cross validation technique and MAE & MBE index and the most suitable method was chosen for this purpose. Eventually RBF method by multiquadric model was used. Moreover Contamination probability map was developed using indicator kriging models.
Results and Discussion: Arsenic concentrations were in the range between 5 – 79.5 micrograms per liter. Also The average concentration was 12.4 micrograms per liter. While the threshold for arsenic in water defined 10 micrograms per liter by the World Health Organization (WHO). So an average of arsenic in ground water is higher than limits of international standard. The spatial correlation analysis showed that the concentrations of arsenic in groundwater have no strong spatial dependency. So, for zoning this variable, between the nonparametric methods, radial basis function (RBF) by Multiquadric model was used. This method had lowest MAE and MBE index for arsenic in groundwater. The highest concentration of arsenic was in the industrial zone in the north of Hamadan (Hamedan, Tehran road). In general Excessive concentrations of arsenic are visible in the three areas : The first area is between Hamedan and Tehran Road Industrial Estate, that the high rate of abnormalities was found in this area (79.5 μg/L). Also the suburbs of Saleh-Abad and the Bahar city has high arsenic concentration. In these areas, groundwater levels were high and pollutants can penetrate more easily. The results of the contamination map using an indicator kriging method showed that 21.18% of aquifer moderately contaminated and about 10.9% of the aquifer area have a high contamination possibility. Polluted groundwater is matched with agricultural land especially the potato fields.
Conclusion: The results showed that the average concentration of arsenic in groundwater of Hamedan-Bahar basin is more than WHO and Iran department of environmental guidelines. The highest concentration of arsenic in agricultural lands and consequently in groundwater resources is due to the existence of polluting industries, the geological structure of the area where arsenic concentration naturally is high, cultivation of potatoes and other crops in the region and indiscriminate use of pesticides and chemical fertilizers in agriculture.
Yaser Safari; Mohammad Amir Delavar; Zahra Noori
Abstract
Introduction: Land suitability evaluation (LSE) may be considered as a worldwide accepted procedure to achieve optimum utilization of the available land resources for sustainable agriculture. The common LSE procedures, like the widely accepted “A framework for land evaluation” presented by FAO, however, ...
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Introduction: Land suitability evaluation (LSE) may be considered as a worldwide accepted procedure to achieve optimum utilization of the available land resources for sustainable agriculture. The common LSE procedures, like the widely accepted “A framework for land evaluation” presented by FAO, however, do not consider variability of soil quality parameters; whereas, the soil quality and its suitability for different uses are influenced by highly variable land management strategies. Therefore, assessing the spatial variability pattern of environmental variables and their accumulative effects on land suitability for specific crops, is the key for achieving to thoughtful land use planning for sustainable use. The present study was done aimed to evaluation of spatial variability of land suitability for irrigated wheat in Zanjan plain using accumulated limitation scores and geostatistics.
Materials and Methods: The study area is located in the southern part of Zanjan city, northwestern Iran, between the latitudes 36° 33′ and 36° 40′ N and the longitudes 48° 23′ and 48° 37′ E, covering an area of about 7000 ha. A total of 85 sampling locations were designed using a systematic sampling grid with an interval of 1000 m and consequently, soil samples at all sampling sites were collected from the depths of 0–25, 25-50, 50–75 and 75–100 cm. The soil samples were taken to the laboratory, where they were air-dried and then passed through a 2 mm sieve. Prepared samples were subsequently analyzed for required soil properties in LSE (Sys et al., 1993) using standard methods. Besides, required climatic data for LSE were obtained from Znajan Synoptic Meteorological Station for a 50 years period (1961– 2011). Then, the limitation degrees for all of the important properties for wheat cropping were determined (Sys et al., 1993). Afterwards, the determined limitation degrees were converted to limitation scores using standard tables presented by Zhang (1989). Then, accumulated limitation scores were calculated for all locations and using an exponential equation, land-suitability membership scores were achieved. Finally, these scores were interpolated using ordinary kriging method in ArcGIS software (ver. 10.2; ESRI) and the final suitability map was produced.
Results and Discussion: The results showed that the climatic conditions for irrigated wheat was relatively good; so that the region received just 1 limitation score arisen from the mean temperature of the growing cycle. On the other hand, among the studied soil properties, the content of coarse fragments made some serious limitations for wheat farming in the studied area; so that more than half of sampling points showed moderate to very severe limitations in respect of this property. This high observed limitation of coarse fragments may be attributed to the youthfulness of studied soils; because according to Soil Taxonomy, the studied soils are mainly classified as Entisols, which are poorly developed and immature soils maintaining their rock structure to some extent. Other studied soil properties, like soil texture and calcium carbonate equivalent content, made no or slight limitations for wheat farming in the studied soils. Accumulated effects of limiting properties led to elevated limitation scores in some sampling locations, especially in northwestern parts of the area and consequently, their suitability classes were decreased. Attributing the specific land suitability classes to each sampling location based on the calculated limitation scores revealed some sharp variability in suitability classes thorough the relatively small distances, which seems to be less compatible with the widely accepted generality of soil continuity. Totally, the spatial distribution map of land-suitability membership scores showed appreciable variability thorough the area. This may suggest that the studied soil properties have relatively high short-range variations, which is originated from the soil substantial characteristics or management practices. Comparison of the interpolated suitability map with the point map revealed that the spatial variability pattern of land suitability for irrigated wheat was more gradual and more obvious in interpolated map.
Conclusions: Compared with common conventional land suitability procedures, continuous pattern of land suitability variation based on the fuzzy viewpoint to the soil variability, lead to more compatible results with the continuous nature of environmental variables. However, due to the long and short-range variations of various soil properties thorough the studied area, appreciable variations in land suitability for wheat farming was observed. Controlling this highly variable suitability of studied lands for irrigated wheat farming needs precise and thoughtful management strategies.
Mina Kiyani; Mohammad hasan Salehi; jahangard mohammadi; Abdolrahman Mohammadkhani
Abstract
Introduction: The spatial variability of soil properties and its importance in production is a matter-of-debate. Insight about the variability of soil properties as well as the yield of orchards is necessary to achieve higher productivity and better management. Orange is one of the most important export ...
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Introduction: The spatial variability of soil properties and its importance in production is a matter-of-debate. Insight about the variability of soil properties as well as the yield of orchards is necessary to achieve higher productivity and better management. Orange is one of the most important export products in our country and to sustainable production of this product, it is necessary to identify the factors affecting its growth. This study was performed to examine the statistical and geo-statistical relationship of some soil properties with the quantitative, qualitative and vegetative properties of Valencia orange in Kazerun area, Fars province.
Materials and methods: The study area contained 1 hectare (Valencia orange crop) of 205-hectare orchards of Rashnabad on the west of Kazerun, Fars province which is 860 meters above the sea level. 120 soil samples were collected from two depths of 0-40 cm and 40-80 cm (according to the root distribution) in order to investigate the statistical and geo-statistical relationship of some soil properties with the properties of Valencia orange. The sampling in the shade and with a minimum distance of one meter from the trunk of the tested tree was performed (It should be noted that orange trees have been planted as row planting with a distance of 5 meters from each other). In addition to the soil samples were collected for statistical studies from the depths 0-40 and 40-80 cm, the combined sampling of two trees that had less distance to the selected points was performed to measure the performance and quality of orange. It should be noted that all the Valencia trees, their age (about eight years) and management approach were similar. Soil samples were then transferred to the laboratory and air dried, the separately packed and passed through 2 mm sieve.Then, different soil and orange properties including soil texture, pH, EC, %OM, %CaCO3, solution potassium and available phosphorous, iron, zinc and manganese, branch length and branch diameter, trunk perimeter, trunk diameter and tree height, total soluble solids, acid percentage, Vitamin C, number of fruits,orange yield,average fruit weight and average fruit size were determined and the data set were analyzed using Statistica 6.0 software.Variograms of the data were drawn using variowin 2.2 and after determining the best fitted model, kriging maps of soil and fruit were prepared using Surfer9 software.
Results and Discussion: The results of correlation coefficient showed the significant and positive relationship between organic matter and available manganese of topsoil with total yield and number of fruits. According to the results of fitness of standard models to the empirical exponent change, all the properties had spatial structure. Soil properties including the percentage of clay, the percentage of organic matter, soluble potassium, phosphorous, available zinc and manganese in both depths in the eastern and south-eastern direction of the study area were higher than that of the others. These maps had the same spatial distribution pattern in terms of orange properties including the diameter and length of the current year branch, performance, number of fruits, average fruit size, acid percentage and total soluble solids.
Conclusion: The variability coefficient of soil and fruit properties did not show a consistent trend in the study. According to the correlation coefficients, in a few cases, a positive significance correlation was observed as an example, it can be referred to the positive significant correlation of orange yield with the organic matter and manganese in the depth of 0-40 cm. All the studied variables have spatial structure. Among the studied variables, the percentage of organic matter, clay particles percentage, soluble potassium, phosphorous, and available manganese in both depths showed the same spatial distribution pattern as that of the vegetative, qualitative and yield properties of orange including the total performance, fruit number, fruit size, the diameter and length of the current year branch. The proximity of the ranges of soil and fruit properties supports this result and is in line with the results of correlation coefficient. The results also showed that the spatial distribution and pattern of soil and crop variables may be different in a short distance with the same management. The study of the effect of NPK fertilizers on vegetative, qualitative and quantitative properties of orange in the region orchards is recommended. It is also suggested to study the effect of climatic factors on the orange qualitative properties.
M. Hashemi; Ahmad Gholamalizadeh Ahangar; Abolfazl Bameri; F. Sarani; A. Hejazizadeh
Abstract
Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, ...
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Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, particularly in agricultural lands for careful planning and land management.
Materials and Methods: To this end, in winter 1391, 189 undisturbed soil samples (0-30 cm depth) in a regular lattice with a spacing of 500 m were gathered from the surface of Miankangi land, Sistan plain, and their physical and chemical properties were studied. The land area of the region is about 4,500 hectares; the average elevation of studied area is 489.2 meters above sea level with different land uses. Soil texture was measured by the hydrometer methods (11), Also EC and pH (39), calcium carbonate equivalent (37) and the saturation percentage of soils were determined. Kriging, Co-Kriging, Inverse Distance Weighting and Local Polynomial Interpolation techniques were evaluated to produce a soil characteristics map of the study area zoning and to select the best geostatistical methods. Cross-validation techniques and Root Mean Square Error (RMSE) were used.
Results and Discussion: Normalized test results showed that all of the soil properties except calcium carbonate and soil clay content had normal distribution. In addition, the results of correlation test showed that the soil saturation percentage was positively correlated with silt content (r=0.43 and p
sh. jorkesh; Mohammad hasan Salehi; I. Esfandiarpour
Abstract
One of the most important soil contaminants are heavy metals. Chemical analysis of the samples can be used to evaluate the contamination but these methods are expensive and time consuming. Thus, for rapid evaluation, other techniques such as magnetic susceptibility are considered. The aim of this study ...
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One of the most important soil contaminants are heavy metals. Chemical analysis of the samples can be used to evaluate the contamination but these methods are expensive and time consuming. Thus, for rapid evaluation, other techniques such as magnetic susceptibility are considered. The aim of this study was to compare the spatial distribution of magnetic susceptibilityand cadmium, lead, nickel and copper in soil series of Isfahan, Khomeinishahr and Zayanderood in Lenjan at region, Isfahan province. Estimation of heavy metals via pedotransfer functions using magnetic susceptibility was also investigated. Total concentration of Cd, Pb, Ni and Cu in soil samples was determines and the magnetic susceptibility of the samples was also measured. Results showed magnetic susceptibility does not have high accuracy for estimation of heavy metals contents in the soils of this region. On the other hand, similar trends of continuous maps for heavy metals and magnetic susceptibility suggest that magnetic susceptibility can be a good indicator for trend of soil contamination in this area.
