فهرست مطالب محمدامیر دلاور

Barley (Hordeum vulgare L.) is among the most influential cereals, and a wide range of populations in several regions of the world (Sallam et al., 2019). However, there are some restrictions for accomplishment to the potential production, which the most consequential one among them, water shortage, a drastic obstacle in many regions of the world, in particular in arid and semi-arid areas. Water scarcity, results in plenty of disturbances in plant functions like cell division and elongation, water and nutrients relations, photosynthesis, enzymes activity, stomata movement, assimilate partitioning, respiration, oxidative damage, growth, and productivity, as several types of researches show that water shortage in the soil cause many disorders in plant tissues, which in turn leads to a punctual diminish in the photosynthesis rate (Todorova et al., 2022). Under circumstances of water shortage, the electron transport through PSII is prohibited, and the oxygen-evolving complex of PSII is damaged (Georgieva et al., 2005). In repercussion, depends on the times, duration of facing, and stress intensity, developmental stage, and kind of tissue, plant defenses against it differently.

An experiment as a factorial in a completely randomized block design in five replications at the experimental greenhouse of the University of Zanjan was carried out in two years 2021 and 2022. In this study two regimes of irrigation (D0, regular irrigation as the control, D1, Water Deficit for two weeks immediately after flowering), four levels of biochar (zero% as the control treatment, 0.25, 0.5 and 1 percent of soil weight) and three levels of spraying Methyl Jasmonate (zero μmol as the control treatment, 50 and 100 μmol) at the same time with flowering stage and starting withholding water, were executed.

Results demonstrated that utilized treatments had significant effects on studied parameters. Results illustrated that, despite the fact that proline content, carbohydrate and quantity of electrolyte leakage were elevated as a result of drought stress, and accumulation of photosynthetic pigments were dwindled, however, application of biochar in the soil and foliar spraying of methyl Jasmonate caused modulating the effects of drought stress. Lack of water can reduce the concentration of photosynthetic pigments, mainly chlorophylls and carotenoids, as it causes oxidation which leads to diminishing the biosynthesis of pigments (Pandey et al., 2012). It is claimed that using biochar ameliorates the reduction of pigments in stressed plants as a result of better surface area, higher water holding capacity, more cation exchange capacity, and useful absorptive features of biochar (Ibrahim et al., 2020). Moreover, the drought stress resulted in a substantial elevation in the concentration of proline (PC) which might be as a result of devastating impacts of drought on the performance of plasma membrane as well as the process which leads to dehydration of cytoplasm, which in turn results in boosting proline concentration in Barley plants under drought situation (Hafez et al., 2020). It is said that biochar reduces proline content, because of its impact on maintenance of plasma membrane in a stable situation, modulating the pressure of water in cell, and enhancing the ability of plants in conserving relative water content (Ahmed et al., 2016). Furthermore, methyl Jasmonate (MJ) may facilitate the strengthening of cell walls and effectively neutralize excessive increase in the level of ROS during further exposure to the stress factor, thereby preventing the damage of cellular membrane structures and changes in their permeability under stress conditions (Hayat and Ahmad., 2007).

The current study represents that Barley can tolerate drought stress through several mechanisms such as modulating some biochemical compounds. In this study drought was shown to have negative impacts on some of the studied physiological and biochemical parameters. Results demonstrated that the use of biochar and Methyl Jasmonate can improve the adaptation ability of Barley plants to water shortage, associated with enhancement in morpho-physiological and biochemical traits. Soil content of biochar at 0.25% w/w and Methyl Jasmonate leaf applies at 50 micro moles protected plants against drought stress. Importantly higher doses of biochar and Methyl Jasmonate had negative impacts, highlighting the need to better understand optimal doses, and mechanisms of action of these amendments.

Imbalanced data remains a widespread and significant challenge, particularly impacting machine learning algorithms. Therefore, addressing imbalanced data classification has emerged as a crucial research area within the field of data mining. This issue, often characterized by a limited number of instances in one class and a substantial number in other classes, poses substantial hurdles for machine learning algorithms. Consequently, data mining experts and machine learning professionals are actively working on refining methods and models for classifying imbalanced data with the aim of improving the accuracy of such classifications. The principal objective of this study is to precisely detect and categorize samples from the minority class, ultimately enhancing the precision of soil class classification. This research is conducted in a specific region, encompassing the southwestern territories of Zanjan province.

To achieve this objective, a total of 148 soil profiles were excavated using a regular grid pattern with an average spacing of 500 meters (and in some locations, up to 700 meters based on expert recommendations). After the samples were air-dried, they were transported to the laboratory. Physical and chemical analyses were conducted on all collected samples, including assessments of soil texture, soil pH, calcium carbonate equivalent, cation exchange capacity, electrical conductivity, organic carbon content, and gypsum content. Subsequently, the soil samples were meticulously classified and described up to the family level, following the comprehensive standards of the soil classification system. The most appropriate covariates were selected among 57 covariates including geomorphological and geological maps, digital elevation model (DEM), and data from Landsat 8 satellite images, using principal component analysis (PCA) and expert knowledge approaches for predicting soil classes selected. Saga-GIS and ENVI software were used to extract environmental covariates. Modeling of the soil-landscape relationship was performed using three algorithms, namely multinomial logistic regression (MNLR), random forest (RF), boosted regression tree (BRT) and ensemble model (after data balancing) in “R studio” software. To check the accuracy of the used model, the data was randomly divided into training and validation data. 80% of the data (118 profiles) were used for model training and 20% (30 profiles) were used as validation data for evaluation.

The results of the selection of covariates showed that 10 information covariates of geomorphological maps, geological information and features extracted from the digital elevation model (DEM), including Analytical hill shading (AHS), sunrise, valley depth (VD), LS Factor, Channel network distance (CND), Topographic wetness index (TWI) and Multi-resolution ridge top flatness (MRRTF) were selected as input variables. Based on the results of profile analysis, the soils of the region at the subgroup level were categorized into five classes, with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. The results of evaluation metrics such as overall accuracy and Kappa index were 65% and 0.32 for the RF algorithm, %60 and 0.35 for the boosted regression tree algorithm, 65% and 0.41 for the MNLR algorithm and after balancing the data with the ensemble model approach, it was 70% and 0.62 respectively. The results of two statistics of user’s accuracy and producer’s accuracy showed that among individual models, the multinomial logistic regression model has higher accuracy in predicting soil classes. Although the ensemble model has succeeded in predicting the soil minority classes well, due to the fact that the two weaker models of the RF and BRT are involved in the modeling, It showed lower values compared to the individual multinomial logistic regression model, in predicting some classes of the majority of soil, especially the two classes of Typic Haploxerepts and Typic Xerorthents.

In summary, the results have demonstrated that when learning algorithms are individually applied, they do not exhibit high accuracy in spatially predicting soil classes. However, when these algorithms are amalgamated into an ensemble model, they exhibit remarkable accuracy in spatial soil class prediction, outperforming individual models in terms of performance and accuracy. Moreover, the ensemble model substantially enhances prediction accuracy and reduces the occurrence of misclassifications, especially at the subgroup level. While each specific model excels in predicting a particular soil classification, the cumulative ensemble models consistently outperform individual models in terms of overall performance and accuracy, underscoring the effectiveness of ensemble modeling in improving spatial soil classification.

Investigating the effect of various sources of sulfurous compounds along with the inoculation of Thiobacillus bacteria on corn plants under stress conditions of lead and zinc metals, Factorial experiment of randomized complete blocks design was carried out in the Agricultural Research Greenhouse of Zanjan University in 2021. The treatments include elemental sulfur (0.75, 1.25 and 2 g/kg soil), sulfur with thiobacillus bacteria(biosulfur) (1, 2 and 3 g/kg soil) and potassium sulfate (0.5, 1 and 1.5 g/kg soil). The results showed traits were significant in the vegetative growth stage in all applied treatments. The treatments increased the amount of chlorophyll, enzymes compared to the control. Then decreased the leaf temperature, Increasing the length of leaf cells. also, it increases the leaf area and leaf production rate in plants. This increase was higher in sulfur treatment with bacteria. But the treatments of 2 grams of elemental sulfur and 1.5 grams of potassium sulfate caused stress in the plant and reduced the amount of traits compared to the control. Also, the plants in contaminated soil were stressed and the amount of the mentioned traits also decreased. However, with the application of treatments, the amount of carotenoid, total protein and peroxidase traits increased, which reduced the stress in plants and increased the amount of traits and growth indicators compared to control in contaminated soil. Sulfur treatment with bacteria, compared to other applied treatments, by activating the enzymatic and non-enzymatic defense system of the plant, causes the plant to tolerate stress and improve plant growth.

Investigations showed that the pollution of heavy metals, especially lead, in fields has caused tension and decreased yield. This issue has caused concern about the risk of consuming contaminated food for human health. The role of plant regulators in heavy metal stress conditions has been of interest in recent years. Lead and zinc (in high concentration) are toxic metals for plants, which are easily absorbed by the plant's root system. As a result, they cause damage to plant growth and development and prevent enzyme activity. Maize is economically important for harvesting seeds and fodder. However, in areas contaminated with metal stress, due to the absorption of metals by the plant, the yield of the cultivated plant in these areas decreases, for this purpose, an experiment aimed at the effect of concentration and different methods of salicylic acid hormone on growth, photosynthesis and anatomical characteristics. and physiological in corn under the stress of zinc and lead metals and also the possibility of reducing the risk of toxicity of these elements is investigated.

In order to investigate the mitigating effects of two methods of applying salicylic acid hormone on corn plants under heavy metal stress, the experiment was carried out as follows. The concentration of metals included: different concentrations of lead (0 and 250 mM) and zinc (0 and 2500 mM) and the treatments were with two methods of foliar spraying and priming in zero concentrations (distilled water), 750 and 1500 μmol salicylic acid and a group of seeds without stress and no treatment were considered as controls. By implementing a factorial design on morphological traits (height, plant temperature, temperature difference between the environment and leaves, and leafing ratio) and physiological traits (membrane stability index, chlorophyll fluorescence, proline, soluble sugars, carotenoid, protein and some enzymes) as well as the absorption of lead and zinc metals in the root tissue and the aerial part of the plant under hydroponic cultivation conditions. The obtained results were subjected to statistical analysis using SPSS statistical software. The mean comparison of experimental data was also done with Duncan's test. Excel 2003 software was used to draw the figures.

Morphological traits such as: plant temperature, difference between Environment and leaf temperature and physiological traits such as: proline, soluble sugars, carotenoid, protein and some enzymes increased and some other traits such as: height, and leafing ratio, index Membrane stability, chlorophyll fluorescence decreased compared to the control. These traits showed better results in seed priming treatment without stress. However, the use of foliar spray under lead-zinc and lead stress improved the stress effects by reducing the absorption of heavy elements by the root system.

SA activates the defense system of plants by synthesizing a number of proteins and increasing proline osmolytes and soluble sugars and enzymatic and non-enzymatic antioxidants such as carotenoids. As a result of reducing the oxidative stress and protecting the chloroplast membrane and the photosynthetic apparatus, the photosynthetic production is improved, resultly, the height and the proportion of the leaves increased. SA also prevents the absorption of heavy metals by the root system of the plant and its transfer to the aerial part of the plant, which reduces the signs of stress in the aerial part of the plant. By applying salicylic acid, some properties were improved by reducing the effects of stress. The use of foliar spraying improved the physiological and morphological traits during plant growth and worked better than the seed priming method. resultly, in farms with two metals lead and zinc, it is possible to tolerate the stress of these two metals by foliar spraying treatment.

Digital soil mapping (DSM), which uses machine learning (ML) algorithms and environmental covariates, is used worldwide for predicting soil properties and classes, due to being time-effective and cost-saving. This research was conducted to compare ML models and their efficiency in predicting the spatial distribution of soil subgroups in part of Zanjan Province. For this purpose, sampling was carried out through a regular pattern with 500 meters intervals, and 148 soil profile samples were randomly collected and classified. The soils of the region at the subgroup level were categorized in five classes, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge approaches. Fifty-seven environmental variables were extracted as representatives of soil forming factors. Modeling of the soil-landscape relationship was performed using three algorithms, namely, multinomial logistic regression (MNLR), random forest (RF), and boosted regression tree (BRT) in Rstudio software. The results of evaluation metrics such as overall accuracy and Kappa index were 65% and 0.32 for the RF algorithm, 60% and 0.35 for the BRT, and 65% and 0.41 for the MNLR. Referring to the importance of environmental variables, results showed that the four factors of valley depth, LS-Factor, channel network distance, and multiple resolution ridge top flatness index (MRRTF) were the most important variables in MNLR algorithm. Also, the results of two statistics of user’s accuracy (UA) and producer’s accuracy (PA) showed that the MNLR model performed better in the spatial prediction of soil at subgroup level. Soil classes with higher frequency had higher accuracy. The results of the prediction accuracy of soil classes showed that the model prediction is more accurate for the more frequent types in the observations.

Barley (Hordeum  vulgare L.) is among the most important cereal crops and  large portions of human  populations in many  parts of the world depend on them  as a source of food and animal feed. Drought is one of the most important environmental stresses which affects yield and yield components of plants significantly which plants respond and adapt to drought stress by means of various morphological, biochemical and physiological changes. Nowadays Biochar has been employed for scientific and commercial usage as a soil amendment for improving the productivity of crops. Results of researchs showes that Biochar makes a great contribution in crop plant's growth. For instance investigations showed that drought stressed chickpea plants exhibited considerable reduction in uptake of nitrogen and phosphorous which was ameliorated by Biochar treatments. Jasmonic acid (JA) and its methyl ester, methyl jasmonate (MeJA) are important cellular regulators involved in many growth, developmental and physiological processes. It was found that exogenous application of MeJA improved tolerance to drought, salinity, chilling (Rehman et al., 2018) and heavy metal  stresses in different plant species.

In order to investigation of the impacts of different levels of mineral biochar and methyl Jasmonate on growth, yield and yield components of spring barley under different levels of drought stress a factorial experiment in  randomized complete block design in pots in 5 replications was done in 2019-2020 at the experimental field of university of Zanjan. Reviewed factors included in three levels of drought stress (100% FC as the none stressed, 60% FC as the mild stress and 30% FC as the severe stress), biochar application before cultivation in three levels (without biochar, 1.5% and 3% of the soil weight) and foliar spraying of methyl Jasmonate in three levels (0, 75 and 150 μmol per liter Studied factors included  electrolyte leakage, proline and photosynthetic pigments concentration, water use efficiency, relative water content and economical yield.

Results showed a significant impact of used treats on most of the characteristics. All studied factors in this experiment including WUE, RWC, proline, EL, photosynthetic pigments and yield were affected by drought stress but 1.5% biochar  and 75 μmole per liter methyl Jasmonate resulted in enhancement of studied factors of stressed plants both in 60% of FC and in 30% of FC in comparison to untreated plants. But increasing in usage of these materials  didn’t have any positive impact on studied plants. The highest economical yield was obtained in normal irrigation circumstance with foliage application of 75 μmole per liter of methyl jasmonate and using 1.5% of biochar per soil weight and the lowest yield was obtained when we used 30% of field capacity in irrigation without using methyl jasmonate.

We can find out from these results that as a whole drought in middle and severe levels can be disastrous for both morphological and physiological characteristics of every plant which results in reduction in economical yield. But it's necessary to mention this point that using stress moderator  materials whether materials which are used in soil like Biochar or plant growth regulators like methyl Jasmonate which are known as the anti stress substances cause moderation in the catastrophic effects of drought stress which through enhancing some indexes like the relative water content and ameliorating of water use efficiency in comparison to untreated plants with these materials can diminish the severe impacts of stress and eventually through boosting economical yield can dwindle damages of drought. Nevertheless it's absolutely comprehensible from the results of the experiment that using too much of these materials not only do not have any positive effect on growth, physiological or biochemical indicators under drought stress but also can reduce these characteristics even to lower than their quantity in untreated plants.

Despite the great use of digital soil maps, the problems of imbalance in classification disrupt the classification performance of many machine learning algorithms, and for this reason, it has attracted the attention of many researchers. Therefore, the aim of this research is to improve the classification of unbalanced soil data using resampling pretreatment technique in three forecasting models including Random forest (RF), Boosted regression trees (BRT) and Multinomial logistic regression (MNLR) in a part of the lands of Zanjan province in Iran.Sampling was done based on a regular grid pattern with 500 meters intervals, and 148 soil surfaces were randomly studied and classified. The region's soils at the subgroup level were in five classes with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge methods AND a number of environmental variables including geomorphological map information, Geological information and features extracted from the DEM were selected as the most effective environmental variables for predicting soil classes and as input to the model. Extraction of environmental covariates was done in ENVI and SAGA_GIS software and modeling of soil-landscape relationship was done using the aforementioned algorithms in Rstudio software. The resampling technique was applied to the minority and majority soil classes prior to modeling.The results showed that using original data that have imbalanced classes for mapping resulted in loss of the minority classes and relatively low Kappa agreement values and overall accuracy for RF (ovrall=65%, k=0.32) and BRT models (ovrall=60%, k=0.35). However, after resampling the data, two overall accuracy and Kappa coefficient statistics increased in all models. In addition, the BRT model provided an acceptable estimate by maintaining the minority classes and the Kappa coefficient of 0.64 and the overall accuracy of 75% in the spatial prediction of soil subgroups. The producer accuracy (PA) and user accuracy (UA) results showed that the two classes of Gypsic Haploxerepts and Lithic Xerorthents, which were excluded when training using imbalanced datasets in RF and BRT algorithms, showed significant improvement after balancing the data. Results show that they were well predicted in RF algorithm (UA =100%, 78%) and BRT algorithm (UA= 60% and 70%) using treated data. Also, these minority classes showed Producer accuracy in RF algorithm (PA= 75%, 88%) and BRT algorithm (PA=100%, 78%) in compared to zero accuracy when training using imbalanced data. On the other hand, the validation results of the MNLR algorithm showed that despite maintaining the minority classes after balancing the data, the minority classes were predicted with less accuracy. Results showed that modeling using imbalanced distribution of class observation caused uncertain maps with minority classes being lost and relatively poor accuracies. After data treatment, with over- and under-sampling, all models showed significant improvement in maintaining the minority classes, in evaluations. Data resampling technique can be a useful solution for dealing with imbalanced class observations to produce more certain digital soil maps.

Despite the great use of digital soil maps, the problems of imbalance in classification disrupt the classification performance of many machine learning algorithms, and for this reason, it has attracted the attention of many researchers. Therefore, the aim of this research is to improve the classification of unbalanced soil data using resampling pretreatment technique in three forecasting models including Random forest (RF), Boosted regression trees (BRT) and Multinomial logistic regression (MNLR) in a part of the lands of Zanjan province in Iran.Sampling was done based on a regular grid pattern with 500 meters intervals, and 148 soil surfaces were randomly studied and classified. The region's soils at the subgroup level were in five classes with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge methods AND a number of environmental variables including geomorphological map information, Geological information and features extracted from the DEM were selected as the most effective environmental variables for predicting soil classes and as input to the model. Extraction of environmental covariates was done in ENVI and SAGA_GIS software and modeling of soil-landscape relationship was done using the aforementioned algorithms in Rstudio software. The resampling technique was applied to the minority and majority soil classes prior to modeling.The results showed that using original data that have imbalanced classes for mapping resulted in loss of the minority classes and relatively low Kappa agreement values and overall accuracy for RF (ovrall=65%, k=0.32) and BRT models (ovrall=60%, k=0.35). However, after resampling the data, two overall accuracy and Kappa coefficient statistics increased in all models. In addition, the BRT model provided an acceptable estimate by maintaining the minority classes and the Kappa coefficient of 0.64 and the overall accuracy of 75% in the spatial prediction of soil subgroups. The producer accuracy (PA) and user accuracy (UA) results showed that the two classes of Gypsic Haploxerepts and Lithic Xerorthents, which were excluded when training using imbalanced datasets in RF and BRT algorithms, showed significant improvement after balancing the data. Results show that they were well predicted in RF algorithm (UA =100%, 78%) and BRT algorithm (UA= 60% and 70%) using treated data. Also, these minority classes showed Producer accuracy in RF algorithm (PA= 75%, 88%) and BRT algorithm (PA=100%, 78%) in compared to zero accuracy when training using imbalanced data. On the other hand, the validation results of the MNLR algorithm showed that despite maintaining the minority classes after balancing the data, the minority classes were predicted with less accuracy. Results showed that modeling using imbalanced distribution of class observation caused uncertain maps with minority classes being lost and relatively poor accuracies. After data treatment, with over- and under-sampling, all models showed significant improvement in maintaining the minority classes, in evaluations. Data resampling technique can be a useful solution for dealing with imbalanced class observations to produce more certain digital soil maps.

Microplastics (MPs) have been reported as emerging contaminants. There was little information about their effect and behavior on soil properties. This study aimed to investigate the changes in exchangeable concentrations (extracted with 1 M ammonium acetate) and water-soluble (ratio of 1 to 2.5 soil to water) of sodium (Na), potassium (K), calcium (Ca), and magnesium (Mg) elements under the presence of Low-Density Polyethylene MPs particles in the soil. The experiment was performed as a factorial experiment based on a completely randomized design with three replications. Experimental factors included the amount of MPs particles (zero, 1, 2, and 4% w/w) and incubation time (3, 17, 31, 45, 90, and 180 days). The results showed that MPs particles affected exchangeable and water-soluble elements. MPs particles reduced the exchangeable amounts of Na, K, and Ca. The largest decrease was related to the level of 4% MPs. Briefly. In the level of 4%, the amount of exchangeable Na, K, and Ca decreased by 7.2, 5.7 and 2.6 %, respectively, in comparison with control soil (without MPs). On the other hand, water-soluble K and Ca under the influence of MPs particles (4% MPs level) decreased by 6.97 and 8.4 % respectively, as compared with control. MPs particles reduced water-soluble Na in the first (3 days) to fourth (45 days) incubation periods. Also, MPs particles reduced the amount of exchangeable and water-soluble Mg, but it was not significant. In summary, the presence of MPs particles in the soil, especially in higher amounts (ex: 4%), can affect the availability of cationic elements such as Na, K, Ca, and Mg.

 Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.

 The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.

 The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.

 The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.

The present study investigated and compared the effects of two types of crop residues, sugarcane bagasse (BG) and rice straw (RH), on some soil chemical properties and enzymatic activities related to the carbon and phosphorus cycle in sodic vertisols (control, C). The pot experiment was conducted in four replications using the factorial structure in complete randomized design in the greenhouse of Zanjan University in 2016. The factors of this experiment included two type of organic amendment (BG and RH), three application rates (L1=1.25%, L2=2.5% and L3=5% by weight) and four incubation times (two (M2), four (M4), eight (M8) and twelve (M12) months). Some of the most important chemical and biological properties were measured after the treatments. The results showed that values of soil organic carbon (SOC), carbon to nitrogen ratio (C:N), and available phosphorus (AP) were significantly (p< 0.001) affected by the type of organic amendments, their application rate, and incubation time. The highest and lowest SOC values were measured in the BGL3M2 and RHL1M12 treatments, respectively. Changes in SOC and total nitrogen (TN) were increasing by increasing the amount of organic amendments and decreasing by increasing incubation time. Total nitrogen in the RHL3M12 treatment increased 51.6% compared to the RHL1M12 treatment and decreased 8.5% (p<0.01) compared to the RHL3M2 treatment. AP in BGL3M12 treatment had a significant increase of 21.5% compared to BGL2M12 treatment. The highest alkaline and acid phosphatase activity was related to RHL3M12 (18.6 µg PNP g-1h-1) and BGL3M12 (7.1 µg PNP g-1h-1) treatments, respectively. RHL3M12 and BGL1M2 treatments showed the highest and lowest beta-glucosidase activity, respectively, and showed a significant difference of 87.5% and 70.3% with the control treatment. The highest and lowest levels of microbial biomass carbon (MBC) were related to RHL3M2 (71.5 mg kg-1) and BGL1M12 (28 mg kg-1) treatments, respectively.

Soil organic carbon is a key element in determining soil quality, health, and fertility. Due to the complexity of the structure and relationships of soil organic carbon pools, the use of models is beneficial in identifying the reaction of these pools to the change in ecosystem conditions. So, by using the RothC model, the effect of global warming and climate change on the amount of soil organic carbon pool of the agricultural ecosystem of southeastern of Mashhad was investigated. Therefore, the model was calibrated and validated using data measured in 2020 and available long-term data. Comparing the measured values of soil organic carbon and the simulated values by the model, the coefficient of determination (R2) was 0.89. Root means square error (RMSE): 3.45, mean difference (MD): 1.84, mean absolute error (MAE): 2.79, and model efficiency (EF) was 0.73, demonstrating the validity and suitability of the model. The modeling of the future climate changes of Mashhad showed a decrease in rainfall and an increase in temperature and evaporation, leading the amount of total soil organic carbon (TOC) would decrease by 1.13% compared to the current conditions. Considering the decomposition rate constant of the model's four active carbon pools, humus exhibited the slowest decomposition rate of 0.96%. At the same time, decomposable plant materials (DPM), resistant plant materials (RPM), and microbial biomass (BIO) were decreased by 1.18%, 2.21%, and 2.10%, respectively, compared to the current climate condition. Moreover, over time, the decomposition rate decreased due to the decay of active organic matter pools that are easily decomposed.

RothC is among the most successful models in terms of simulating the impacts of climate change and management activities in natural ecosystems on local and global scales. The current research was carried out to assess and validate RothC in studying SOC content from 1990 to 2020 and defining the scenarios of maintaining the current situations (scenario 1), climate change (scenario 2), manure fertilizing till 2100 (scenario 3), and climate change with manure fertilizing till 2100 (scenario 4) in grasslands, range, and croplands in Saral Research Center, Kurdistan Province, under rotations of wheat-uncultivation and wheat-pea. The parameters of the model were determined by using the measured data from the soils sampled at two years (2018 and 2019) from Saral Research Center and the local weather data, Next, the model was validated by comparison of the predicted values with the measured SOC data. Assessment of the measured and simulated data through validation for different land uses revealed that RothC could satisfactorily predict the changes in SOC contents under different fertilizing and climate change scenarios so that root mean square error (RMSE) and the simulation efficiency were calculated as 8.92% and 74.0%, respectively. The simulation results indicated that compared to scenario1, climate change in the studied land uses will cause a decrease in SOC contents till 2100. Manure fertilizing (scenario3) would be the best scenario so that by using this strategy the grassland, with 59.83 ton SOC per hectare until the end of the current century. According to the results predicted by the model, the highest SOCrate (32.9%) and the most change in SOC sequestration (ΔSOCs) (0.16 t/h) during the next 80 years were calculated in the range under scenario 3; whereas the lowest values (-32.0% and -0.11 t/h, respectively), were achieved in croplands under rotations of wheat-uncultivation under scenario2.

The optimum and sustainable use of soil is only possible with a correct and complete understanding of its properties. This study was conducted to investigate of should be omitted some physical, chemical, and micromorphology properties of soil formed on the should be added landscapes in the should be added semi-arid region of Lorestan province.

For this study 5 profiles, a long, a should be omitted transect in the direction of slop (plateau, gravelly colluvial fan and pediment plain) were identified in the area, and divided into different physiographic unit based on the type of parent material. At each, physiographic units a profil was excavated, dissected sampled. Physicochemical together with micromorphological observations performed and Soil Taxonomy and WRB were used for soil classification.

The results showed that should be omitted low amounts of organic carbon in the surface and depths of the soil due to the high rate of decomposition of these substances in these areas. The equivalent calcium carbonate in all profiles showed an increasing trend with depth. Thin section investigation showed that there was not much difference between the horizons in terms of the type of voids and the relative distribution of large particles (C/F). Planar, vugh and cannel voids were the most dominant voids of the profiles. Accumulation of calcite was seen in various forms such as coating, infilling, needle- shaped calcite, and nodules calcite which indicates that secondary calcite, and caused a should be omitted crystallite calcite of kind b-fabric. Soil structure in most the horizons of kind angular blocky and sub angular blocky. Also, lenticular plates gypsum was observed on the Bky horizon on the of the gypsum physiographic units plateau. According According to the existence of pedogenic gypsum in the soils of the region, and the important of the gypsum horizon in the soil classification system and its important effect on plant growth. New great groups, and subgroups of Xerepts (Gypsic Calcixerepts) are suggested to be included in soil taxonomy.

Results of the study showed raise in topography and parent material on the variability of soil properties in the area.

Soil physicochemical properties are important variables in runoff production and soil loss. This research was conducted to find the most important soil properties affecting runoff and soil loss in rainfed wheat furrows under fallow condition. To this end, three rainfed landswith 15% slope gradient were selected in south west of Kermanshah province. In each land, furrows with 5 m in length and 30 cm in width were created using sowing set. Runoff and sediment were measured using simulated flows with a discharge of 0.5 and 1.5 lit.min-1 for 60 minutes in three replications. The results showed that in the two flow discharges (0.5 and 1.5 lit.min-1) runoff and soil loss are negatively correlated with clay, sand, soil permeability (If), organic matter and clay to silt ratio (p<0.01), while positive correlations were found between them and silt (p< 0.01) and bulk density (p< 0.05). The results of multiple linear regression analysis showed that runoff production and soil loss by a 0.5 lit.min-1 flow discharge can be estimated using using the clay to silt ratio, organic matter and soil permeability (If) with a R2 of 0.94 and 0.81, respectively. Runoff production and soil loss by a 1.5 lit.min-1 flow discharge can be estimated using soil organic matter and soil permeability (If) with a R2 of 0.90 and 0.94, respectively.

The variety of factors affecting soil properties leads to temporal changes in the soil erosion process. This research was conducted to assess short-term changes in runoff and soil loss in rainfed wheat furrows under fallow conditions. To this end, three rainfed lands with 15% slope gradient were selected in south west of Kermanshah Province. In each land, furrows with five m in length and 30 cm in width were created using sowing set. Runoff and soil loss were measured using simulated flows with a discharge of 0.5, 1, 1.5 and 2 L.min-1 at intervals of five minutes to 60 minutes 60 minutes in three replications. Results showed that the lowest soil loss was recorded in flow discharge of 0.5 L.min-1 (2.66 g.m-2) and the highest of soil loss was produced in flow discharge of 2 5 L.min-1 (85.33 g.m-2). Also, the lowest runoff was recorded in flow discharge of 0.5 L.min-1 (0.47 L) and the highest of soil loss was produced in flow discharge of 2 5 L.min-1 (7.65 L). The effect of time on runoff and sediment variables was significant in all flow discharge (p<0.01). Runoff production was low at the beginning of the experiment and increased over time. The pattern of temporal changes in soil loss was different from runoff production, amount of soil loss at the beginning of the experiment was higher values ​​than the final test times, which associated with to supply of erodible soil particles in the rills in the beginning of the experiment. With starting the experiment to 25 minutes, the rate of soil loss changes drastically and then until the end of the experiment, it followed a uniform reduction pattern and in the final stages, it is almost constant. The results showed that rill erosion is strongly influenced by flow intensity and its value changes over time and these changes are independent of flow production and depended on the transmittance of soil particles transferable in the rill.

Microplastics are plastic particles smaller than 5 mm that are known as emerging contaminants. Most research about microplastics has been performed in aquatic ecosystems and there is limited information about the effects of these particles on soil biological and enzymatic properties. Therefore, the aim of this research was to evaluate the effect of Microplastic particles on some soil properties included basal and cumulative respiration, microbial biomass carbon as well as soil acid and alkaline phosphatase activity. For this purpose, Low-density polyethylene (LDPE) microplastic particles (diameter 0.5 - 1 mm) added to the soil (1, 2 and 4 % w/w). Incubation times for investigation of soil properties and enzymes activity were 87 and 45 days, respectively. The results showed that the microplastic particles increased the soil basal and cumulative respiration rate. Soil microbial biomass carbon increased during the 3th to 17th days of incubation, but decreased after that when compared to control treatment. LDPE Microplastic particles had a negative effect on the activity of acid and alkaline phosphatase and decreased them. The highest decline was related to the microplastic level of 4%. The rate of decrease in acid phosphatase activity was more than alkaline phosphatase activity. Briefly, the results of the present study showed that the microplastic particles can increase soil respiration, but it has a negative effect on phosphatase activity.

Soil organic carbon is one of the most important basis for assessing soil quality, health, and fertility. Therefore, accurate, rapid, and inexpensive measurement of soil organic carbon is essential to determine efficient management and conservation practices, which might increase carbon sequestration or preservation in soils. Soil carbon measurement methods are generally classified into two categories: ex-situ and new in situ methods. The first category includes traditional field-collected samples and chemical laboratory degradation of soil organic matter using wet and dry combustion methods. These methods are often expensive, time-consuming, and environmentally destructive. In many cases, common sampling and laboratory errors have resulted in these approaches to be less precise. In situ methods, including infrared reflectance spectroscopy, laser-induced breakdown spectroscopy, inelastic neutron scattering, and remote sensing, are considered more efficient, especially in large-scale national studies as utilizing such methods may increase the speed and accuracy of measuring and significantly reduce the cost of conventional field sampling and laboratory analysis. National quality monitoring of Iranian agricultural soils showed that in more than 60% of lands, soil organic carbon content is less than one percent and further studies have confirmed the declining trend of organic carbon storages in most parts of the country. On one hand, more than 80% of Iran's land area is located in an arid and semi-arid climate whose soils are low in organic carbon content, and on the other hand, the salinity and predominance of calcareous soils in vast areas of Iran have become controversial issues, which have made the attempts to find the correct method of measuring the organic carbon of these soils more complicated. Although the use of an automated carbon analyzer is considered as the standard method for comparing the ex-situ soil carbon measurements, the incomplete conversion of carbonate to CO2 under the combustion conditions in highly calcareous soils might happens which is problematic. Therefore, the use of wet combustion methods is more popular because it’s not affected by the presence of carbonates and the direct calculation of organic carbon. All soil organic carbon measurement methods have strengths and weaknesses, so its choice depends on the characteristics of the studied soils, the purpose of the project as well as the financial considerations, and the allocated budget. Considering the lack of comprehensive studies in this field, the present study aims to help to gain a better understanding of the results of the Walkley Black method, as the basic method of measuring soil organic carbon in Iran, and to assist with developing a standard and robust protocol for measuring the organic carbon of saline and calcareous soils of Iran, and finally to comparatively evaluate the soil carbon measurement methods by outlining their strengths and weaknesses.

The present study was intended to improve the chemical properties of a saline-sodic soil using the individual application of alfalfa residue and two biochars produced from sugarcane bagasse and walnut shell, at the weighting ratio of 5%; their concomitant application with gypsum, aluminum sulfate and the mixture of these two chemical amendments was considered. The experiment was conducted in three replications using the factorial experiment in a completely randomized design. After four months of incubation, the soil samples were measured for their main chemical properties. The results showed that alfalfa residues were the most effective treatment to reduce the soil pH; so the concomitant application of this organic amendment with gypsum lowered the soil pH from 9.13 in the control (untreated soil) to 7.24. It was also observed that the addition of gypsum and/or aluminum sulfate to the soil led to the increase of the soil electrolyte concentration and consequently, the increase of soil electrical conductivity to three times greater than control, through an increase of ions, like calcium and sulfate in the soil solution. Increasing the soluble sodium concentration by replacing exchangeable sodium by other similar ions showed that the studied treatments enhanced the sodium adsorption ratio (SAR), which could be regulated by washing. Concomitant application of the walnut-shell biochar with gypsum had the most increasing effect on the soil SAR, enhancing it from 22.6 in the control to 54.3. Potassium was released from organic amendments, improving the soil general conditions; addition of chemical amendments elevated soil exchangeable potassium contents; however, the elevated soil available phosphorus contents were less influenced by chemical amendments application. As the conclusion, it seems that the positive impacts of the applied chemical and organic amendments would supplement each other; as a result, the concurrent use of both treatments not only improves the bad soil chemical properties, but also enhances the soil fertility.

In the last few decades, thematic maps and models were usually assessed using Kappa index of agreement. The index gives us the relative observed agreement among raters (identical to accuracy), but lacks any useful information to make practical decision making about the model’svalidity easier. In other words, Kappa index does not provide an explanation about classification quality or an idea about increasing theaccuracyof the predicted map. Moreover, the index does not explain the causes of disagreement.Thus, giving indices of agreement without any interpretation will not be satisfactory. Today, new complementary methods are required to show the quantitative and spatialagreement and disagreement between two maps. It is necessary to show how a modeled map can be produced with better accuracy. The present study seeks to introduce and explain concepts of agreement and disagreement components with an example. Finally, these components are introduced as a useful method for the validation of digital maps.  

An area of 410 hectares which belongs toZanjanUniversity was used to express the findings of this study. The area is located 5 km before the beginning of Zanjan-Miyaneh Road at 48.4° eastern latitude and 36.68° northern longitude. A digital soil mapin which probability distribution of different soil classes is obtained using multinomial logistic regression algorithm and a reference soil mapproduced with the conventional methods are usedto explain the concepts and investigate the spatial and quantitative agreement and disagreement indices. Validation and calculation of quantitative and spatial agreement and disagreements are performed using IDRISI software (SELVA version). To simplify the process, two maps with a grid structure (3 x 3) are introduced as a reference map and a predicted map. The reference map is used for spatial and quantitative evaluation and validation of the predicted map cells. Each map contains 9 cells and each grid cell has a membership value of either white or gray categories.  

In the validation process of two maps, most researchers seek to find answers to two important questions: 1- How much agreement is there between the cells of each mapping class group? And 2- How much agreement is there between the map used in modeling and the reference map regarding the position of the cells in each class? The present study expresses agreement between the two soil maps using an index of (M (m)) which equals 60.69%. With an average level of quantitative and spatial information about different classes of the digital soil map (DSM), the H (m) index equals46.4%. Results indicate that if the produced map is modified or rearranged (provided that the level of quantitative information remains unchanged but the amount of spatial information increases), the agreement between the maps increases dramatically and reaches 87.17%. Quantitative and spatialagreement and disagreement between the digital and traditional soil maps also equal 61% (M(m) = 61%) and 39%, respectively. The DSM accuracy can be increased to 87% (P (m) = 87%) compared to thetraditional soil map through spatial modification of cells(without changing quantitative information).  

Evaluating the accuracy and validity of digital maps are considered to be an important and sensitive stepof research projects. Therefore, introducing more accurate indices is very important. According to the results of the present study, displayingquantitative and spatialagreement and disagreement in the form of a matrix and according to the different levels of quantitative and spatial information can be a new strategy to verify modeling methods. The method presented here not only introduces and interprets sources of (quantitative and spatial)error, but also provides information on the possible ways of reducing these errors. Thus, introducing the amount of error without any scientific interpretation cannot be useful for predicted maps. Unfortunately, researchers does not concur on how to report agreement and disagreement. However, it seems thatwhen it comes to explaining errors and finding a method to reduce such errors,the components of disagreement and its related parameters are more useful than agreement component and its indices. Therefore, it is recommended tointerpretdisagreement components before other components of agreement. The advantage of this method is that complex analyses can be reported in a simple form. Finally, this assessment and validation method is expected to be used in different studies as an appropriate and alternative method.

High concentrations of As in contaminated soils represent a potential risk for groundwater sources and threat the food chain. It has been found that the iron-containing compounds used in remediation of As contaminated soils have distinct effects on the solubility of As and can be used as adsorbents for As removal from aqueous and soil solutions. The objectives of this study were to determine As stabilization in soil, with iron-containing compounds and also to compare the fixation of magnetite nanoparticles, ferrous sulfate, ferrosilicon, magnesium ferrosilicon and iron oxide in fixation of arsenic in contaminated soils.

A factorial experiment was conducted using a completely randomized design with three replications. The experimental factors were the amendment types and levels. The modifiers used were magnetite nanoparticles, ferrous sulfate, ferrosilicon, magnesium ferrosilicon, Sfordi, and Golgohar iron soil containing 0, 0.1, 0.2 and 0.3% iron. The soil was artificially contaminated with As (20 mg/kg) using Na2HAsO4.7H2O salt and incubated for 1 month. At the end of incubation time, the modifiers were added to the As contaminated soils and after 3 months, the available fractions of arsenic, iron, zinc and copper were extracted using 0.1 M HCl and measured with ICP.

The results showed that the type and the amount of the modifiers had a significant effect on the available fraction of arsenic and iron in soil (extractable fraction with 0.1 M hydrochloric acid). The available fraction was reduced due to the addition of all  modifiers: Magnetite nanoparticles > iron sulfate > magnesium ferrosilicon > ferrosilicon > Esfordi iron soil and Golgohar iron soil, respectively. The highest decrease in the concentration of available arsenic occurred in the soils treated with 0.3% of modifier. Application of 0.3% levels of magnetite nanoparticles, iron sulfate, ferrosilicon, ferrosilicon magnesium, Golgohar iron soil and Esfordi iron soil stabilized 91, 63, 57, 32 and 48% of arsenic extractable with 0.1 M HCl, respectively. Application of 0.3% of magnetite nanoparticles reduced available arsenic more than other adsorbents. Among the studied  modifiers, magnetite nanoparticles showed more efficiency in chemical stabilization of arsenic in soil. The application of magnetite nanoparticles increased the Fe available fraction in soil. Golgohar iron soil, ferrosilicon, Esfordi iron soil, magnesium ferrosilicon, ferrous sulfate and Magnetite nanoparticles, increased the iron extractable with 0.1 M HCl of the soil, respectively. The highest Fe concentrations were observed in 0.3% of Gol Gohar soil, ferrosilicon, Esfordi soil and ferrosilicon. Increasing the modifiers decreased soil copper extractable with 0.1 M hydrochloric acid concentration and increased soil zinc extractable with 0.1 M hydrochloric acid concentration, which was not statistically significant.

Application of magnetite nanoparticles reduced arsenic concentration more than other adsorbents and showed more efficiency in chemical stabilization of soil arsenic. Other modifiers have also been able to stabilize the arsenic in the soil, suggesting the possibility of using iron-containing modifiers in arsenic-contaminated soils. The use of modifiers increased the iron concentration in the soil. Due to their reasonable price and availability, iron sulfate and magnesium ferrosilicon are recommended for soil arsenic stabilization. At 0.3% soil level, Gol Gohar and Esfordi iron soil were able to reduce 32% and 48% the arsenic concentration, respectively and are recommended for arsenic stabilization in contaminated soil. Golgohar, ferrosilicon, Esfordi and magnesium iron soils caused the highest increase in soil iron concentration. Due to the concentration of other soil elements and the price of modifiers, the level of 0.2% of iron sulfate, Gol Gohar and Esfordi iron soil, ferrosilicon and magnesium ferrosilicon is recommended for stabilization of arsenic in contaminated soil.

There are innumerable challenges for digital soil mapping in the world full of change. One of these challenges is the sampling method which plays an important role in providing appropriate information for digital soil mapping and increasing its efficiency. Sampling method efficiency, with considering the number of samples, space changes, and cost, is a way to identify a set of scattered sampling locations in geographical space that have good location coverage of features. A good space coverage of features ensures accurate estimation of regression parameters and it makes effective spatial interpolation. In soil evaluation, the number of samples collected is limited by time and cost. Also, lack of roads, dense vegetation, and rugged terrain are caused more restrictions when visiting the area. These deficiencies lead to the use of stronger sampling methods. Methods that can provide a good description of space changes of soil and its features to reduce the number of samples, time, and cost are needed. So that it supports the final quality of the maps. Here are checked several methods of statistical sampling and geometric that optimize the geometric sampling pattern in geographical space. The strengths and weaknesses these methods considering spatial coverage, simplicity, accuracy, and efficiency briefly expressed. The results showed in terms of accuracy and efficiency; classified random sampling has the highest accuracy and has been widely used. In terms of spatial coverage; spatial coverage sampling is the best method. Due to the simplicity in the design and implementation stages; Simple random sampling, network sampling, and spatial sampling are the simplest sampling methods. Among the sampling methods studied, the Latin conditional sampling method is the most common method. It is widely used and recommended, and stratified random sampling and spatial sampling are the most efficient methods that optimize the sampling pattern in geographical space.

The present study was conducted to investigate the effects of rice husk biomass (RH) and its biochar (RHBC) on some chemical properties and enzymatic activities involved in carbon and phosphorus cycling of a calcareous sandy soil (control, U) after two (M2), four (M4), eight (M8), and twelve (M12) months incubation. For this purpose, a factorial experiment with a randomized complete block design and four replications and two treatments (RH and RHBC) was performed. Three levels of organic amendments (L1= 1.25%, L2= 2.5% and L3= 5%) were added to the soil. The results showed that the amount of soil organic carbon (SOC) was significantly (p < 0.001) affected by the types of organic amendments, their application rate and incubation time. The highest and the lowest values of SOC were observed in RHBCL3M2 and RHL1M12 treatments with 96.3% and 73.2% increase as compared to the control treatment, respectively. The trend of changes in SOC and TN was similar and decreased with incubation time. The highest and the lowest activity of β-glucosidase were corresponded to the treatments of RHL3M12 (1.6 μgPNP.gr-1h-1) and RHBCL1M2 (0.6 μgPNP.gr-1h-1), respectively. The RHBCL3M8 and RHL1M2 treatments exhibited the highest (590.6 mg Glu.g-13h-1) and the lowest (109.6 mg Glu.g-13h-1) invertase enzyme activity, respectively. So that, the highest enzyme activity was observed in 8th month of incubation. The highest activity of alkaline and acid phosphatases activities were found in RHL3M12 treatment which were 78.1% and 81.7% higher than that in control treatment, respectively. In general, the addition of good quality biochars to the soil could be a very good source for compensation of soil organic carbon deficiency and thereby improving chemical and biochemical properties and soil health, because of its higher carbon content than its biomass. Therefore, based on the effects of rice biomass and its biochar on the increasing of SOC content as well as enzymatic activities, these organic amendments are suggested to improve the quality of sandy soils.

Soil quality (SQ) index is used as a quantitative tool for assessing the impact of land use and management systems on soil condition. The aim of this study was to quantify SQ under agricultural ecosystems in Zanjan province using multivariate analyses. 154 soil samples were collected from 77 farms in Zanjan province (27 sites under irrigated and 50 sites under rain-fed land use) at 0-30 and 30-60 cm depths. 24 soil physical, chemical and biological properties were measured. Principal component analysis was used to identify minimum data set (MDS) for assessing SQ. Two groups of MDS were determined by considering physical, chemical and biological properties separately (MDS1), and by using all measured soil properties (MDS2). The capability of linear or non-linear approaches to develop SQ index was investigated. Four SQ indices were calculated using MDS1 and MDS2, linear and non-linear scoring methods. Total nitrogen, available phosphorus, sodium, zinc and copper, microbial biomass carbon, microbial quotient index, aggregate stability and bulk density were determined as the MDS1. Soil organic carbon, available phosphorus, sodium, zinc and copper, microbial quotient index, aggregate stability and sorptivity were identified as the MDS2. Both methods had enough efficiency to identify MDS for assessing SQ. The linear SQ indices (p˂0.001) showed higher capability than non-linear indices (p˂0.01) to differentiate SQ between rain-fed and irrigated land uses. The SQ index calculated using linear function and MDS1 indicated SQ of irrigated land use (0.524) had a better condition compared to rainfed land use (0.433). The SQ index calculated using MDS2 also indicated a similar trend between irrigated (0.551) and rainfed land use (0.433).

Today, to reduce the risks of contaminants, new remediation techniques have been focused on low-cost and environmentally friendly manners. Given the frequency of access, inexpensiveness and good physical and chemical properties, biochar has a high potential for the remediation of water pollutants. In this paper, the efficiency of chitosan engineered biochar (Bc-Ch) and pristine biochar (Bg-Bc) prepared from sugarcane bagasse biomass (Bg) in the Cd2+ removal in aqueous solution was investigated. To this aim, the effects of contact time, adsorbent dosage and solution pH on cadmium removal were evaluated by adsorption isotherms and Kinetic models. The results indicated that the Langmuir isotherm and the pseudo-second-order kinetic model could be well fitted with the process of cadmium biosorption. The maximum adsorption capacities of Bc-Ch, Bg-Bc and Bg ,according to Langmuir model, were found to be 32/78 mg/g, 11/57 mg/g and 2/23 mg/g, respectively. For these absorbents, the pseudo-second-order kinetic model showed the best fit to the experimental adsorption data. This study, therefore, indicated that the chitosan engineered biochar could be used as an effective, low-cost, and environmentally-friendly sorbent to remediate heavy metals contamination in the environment.