نشریه مهندسی زراعی
سال چهل و ششم شماره 3 (پاییز 1402)

Today, water consumption has increased dramatically as a result of technological advancement, extraordinary industrial development and urbanization, which has caused the production of large amounts of toxic waste. Zinc (Zn) is an essential element for plants and humans, however, excessive concentrations of zinc can cause problems for humans such as abdominal pain, nausea and vomiting. Zinc is the most toxic pollutant that enters the aquatic system through industrial wastewater. World health organization (WHO) has recommended limit concentration of zinc in drinking water of 5.0 mg L-1. Adsorption is one of the most efficient ways to remove heavy metals from the environment. Clay minerals are one of the good adsorbents for the adsorption of heavy metals due to their large surface areas, high ion exchange capacity and layered structure. Some factors such as temperature, pH, size of adsorbent, type of adsorbent and amount of adsorbent are considered as important factors in controlling the behavior of heavy metals in aqueous solution. The temperature of the solution can increase or decrease the adsorption of elements, which indicates the exothermic or exothermic nature of the surface adsorption reaction. Therefore, this research was carried out with the aim of investigating the effect of temperature on the kinetics and thermodynamics of Zn removal using sepiolite and kaolinite minerals.

In this research, two clay minerals (kaolinite and sepiolite) in a size of 25-53 µm were used as zinc metal adsorbents. Sepiolite mineral was collected from mines in Fariman region of Razavi Khorasan province and kaolinite was collected from Lalejin in Hamadan province. The kinetics and thermodynamics of Zn absorption from aqueous solutions by sepiolite and kaolinite were investigated. For kinetic studies, 0.1 g of sepiolite and kaolinite adsorbent was poured into a centrifuge tube and 20 ml of Zn solution with a concentration of 50 mg L-1 of zinc nitrate background solution was added to it and at different times (5, 10, 15, 20, 30, 60, 120, 240, 480, 720, 1440 and 2880 minutes) was stirred. The experiment of adsorption kinetics was performed at pH 5 and at a temperature of 25 ± 1 °C. The thermodynamics of zinc adsorption was investigated at temperatures of 25, 35 and 45 ℃. The adsorption behavior of zinc metal by sepiolite and kaolinite minerals was evaluated at different times with pseudo-first-order, pseudo-second-order, Ilovich and intraparticle diffusion kinetic models through non-linear regression and using Solver software. Then, the thermodynamic parameters of adsorption process including: the activation energy (Ea), gibbs free energy (ΔG), entropy (ΔS) and enthalpy (ΔH) were determined.

The results of this research showed that by increasing the contact time and decreasing the temperature of the solution from 45 to 25 ℃, the amount of Zn adsorption by both minerals increased. Also, the equilibrium time was determined to be 720 minutes. The results showed that the adsorption efficiency decreases with increasing temperature and the highest removal percentage was observed at 25 ℃. Based on the results obtained from the fitting of kinetic models with experimental data, the pseudo-second order model with the highest explanatory coefficient (R2=0.99) was selected as the best model. Adsorption capacity (qe) of Zn estimated from the pseudo-second order model for sepiolite and kaolinite at 25℃ compared to 45℃ decreased by 44.30 and 38.19%, respectively. Also, the amount of Zn adsorption capacity for sepiolite mineral was higher than kaolinite. The activation energy (-9.79 to -23.81 kJ mol-1) revealed the physical adsorption of Zn by sepiolite and kaolinite. The activation energy of Zn adsorption onto the sepiolite (-23.81 kJ mol-1) and Kaolinite (-9.79 kJ mol-1) indicated that Zn was more strongly sorbed by sepiolite than kaolinite.

the results obtained showed that sepiolite and kaolinite can be used an adsorbed to remove Zn from aqueous solution with good efficiency and low cost, while sepiolite had higher Zn adsorption capacity compared to kaolinite. Adsorption of Zn decreased with increasing temperature. The optimal temperature in this study for maximum adsorption of Zn by sepiolite and kaolinite was 40℃. Thermodynamic parameters including changes in Gibbs free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) showed that zinc adsorption process by the studied minerals is an exothermic and spontaneous reaction. As a conclusion, sepiolite has a high potential for remove of Zn from wastewater.

 Soil quality index is used as a quantitative tool for assessing the impact of land use and management practices on soil condition. Soil quality is a sensitive indicator for revealing the dynamics of soil conditions, and it may vary with different land use and ecological restoration measures. The land use affects the physical and chemical properties, biological processes, and land productivity, which lead to the change in soil quality. Land use change and agricultural development can lead to degradation, erosion and reduction of surface and subsurface soil quality. In most of the conducted studies, the surface soil quality has been evaluated; but these studies provide incomplete information because subsurface soil have the greatest impact on soil function and crop. In spite of various soil quality assessment methods developed in former researches, there are fewer attempts for selecting suitable and sensitive soil quality index, especially in different land uses. In this study, soil quality indicators were evaluated using multivariate analysis in three different land uses to select the most suitable and appropriate soil quality index in Tootkabon area of Guilan province.

The study area is located in Tootkabon in Guilan province (latitude 36º 53' 21" N, longitude 49º 33' 44" E). Parent material is limestone and geomorphologic units that are comprised of hill land and plateau. In order to achieve the objectives of the research, 20 composite soil samples were taken from two depths of 0 to 15 and 15 to 30 cm from each of the land use, including forest, cropland and rangeland (60 soil samples in total) with the same parent material. The three land uses were located next to each other and at a close distance. In this research, using the principal component analysis (PCA) method, among 12 physical, chemical and biological soil indicators as total data set (TDS), clay percent, mean weight diameter, organic matter and available phosphorus were determined as the minimum data set. Then, the soil quality was evaluated by integrated quality index (IQI) and Nemoro quality index (NQI) using two linear and non-linear scoring methods (LS and NLS) and two soil indicator selection approaches, a total data set (TDS) and a minimum data set (MDS). Finally, to prioritize the soil quality indices based on sensitivity index (SI) and efficiency ratio (ER), the ranks of both criteria were summed and then made appropriate decision. All soil parameters were tested using one-way analysis of variance and the differences among means were analyzed using Duncan's significant difference test at the probability level of 0.05.

The results of the present study showed that some soil properties including clay percentage, mean weight diameter, organic matter and available phosphorus had the greatest effect on soil quality in the study area. Most of the soil properties in rangeland and forest had a higher stratification ratio compared to cropland. The soil quality indices calculated using linear function for MDS indicated soil quality of forest and cropland were higher than rangeland. Maximum SI belonged to IQI-LS-TDS and IQI-LS-MDS with values of 1.56 and 1.40, respectively. Efficiency ratios (ER) were calculated to specify the power of each soil quality index being as representative index for whole soil parameters set. IQI-LS-MDS and IQI-NLS-MDS have the highest value of ER (75.0 %), it is obviously deducted that these developed soil quality indices correlate with much indicators than other indices. It has more efficiency ratio and therefore represents the soil overall condition highly. Finally prioritizing according to ranks of SI and ER showed that IQI-LS-MDS is the most suitable approach in soil quality assessment of study area.

Minimum data set selection using principal component analysis as a multivariate statistical method could adequately represent total data set method. Therefore, it seems to be an appropriate approach for choosing more effective indicators with respect to saving time and money in the developing countries The linear soil quality indices showed higher capability than non-linear indices to differentiate soil quality among different land uses. Overall results of the prioritization soil quality indices imply that the IQI-LS-MDS has the most efficiency and sensitivity for variation in land uses, so it is suggested to use this quality index for further and comprehensive soil quality assessments plans.

Agricultural residues and wastes are the main source of biomass for use in bioenergy production and animal and poultry feed production industries. These biomasses in their original form have a large volume and low energy (per unit volume) and require a lot of space and extensive movement. Therefore, one of the methods of optimal use of these biomasses is to transform them into pellets, which have more mass and energy per unit volume and enable their easier use and transportation. Currently, biomass has the fourth place in energy supply after oil, natural gas and coal and provides approximately 14% of the world's energy needs. The use of biomass, especially in European Union countries, as an attractive source for replacing fossil fuels is developing and expanding. The use of biomass as fuel has significantly reduced the amount of environmental pollutants, so that the amount of CO2 absorbed from the atmosphere during biomass growth is similar to the amount produced during combustion, followed by a net cycle of production.

 The raw materials for making pellets were prepared from spruce wood sawdust (collected from a sawmill in Sari) as well as corn stalk and soybean residues in the fields of Dasht Naz in Sari. The desired materials were transferred to the laboratory in the necessary amount and kept at ambient temperature until the experiments. The samples were first crushed into 20 mm sizes and then powdered using a grain mill (Mehr Tehiz company, Iran) and passed through 18 mesh sieves in the range of 1 mm to make pellets. A palletization mechanism was used to compress the pellet. This system was designed and built in biosystem mechanics of Sari University of Agricultural Sciences and Natural Resources. The material was placed inside a steel mold with a cylinder inner diameter of 8.05 mm and a height of 150 mm with a blocked end. A piston with a diameter of 8 mm connected to the driving arm of the tension-compression test machine was used to compress the material. Loading by a piston with a quasi-static speed of 5 mm per minute is compressed to a pressure of 1300 N.

In this research, the mechanical and thermal properties of pellets made from the combination of spruce sawdust and corn and soybean residues were evaluated. In the present study, the effect of four combinations of agricultural and forest materials at two moisture levels (12% and 18% based on fresh weight) on the indices of density, compressibility, Hausner ratio, strength and calorific value of the produced pellets were investigated and evaluated. it placed. The results showed that the pellet density at 18% humidity was lower than the density at 12% humidity. The highest density related to the combination of 60% spruce wood sawdust-40% corn stalks was obtained with a value of about 149 kg/m3 and the lowest value related to 100% soybean stalks was about 110 kg/m3. Also, the ratio of Hanser and CI in the combined pellets that have a higher percentage of sawdust and also in the combination of sawdust with corn stalks are within the permissible range. The highest pellet strength was 23.8 N/cm corresponding to 100% sawdust at 18% humidity and the lowest was 15.4 N/cm corresponding to 100% soybean stalk at 12% humidity. The calorific value of the pellets is in the range of 14.37 to 18.52 MJ/kg, which is the minimum value for the pellet made from 100% soybean stalk at 18% humidity and the maximum value for the pellet made from 100% fir wood sawdust and It was obtained at a humidity of 12%. Therefore, the use of agricultural wastes and their proper combination is a good option for the production of biofuels due to their density and strength.

 The type of biological waste and moisture percentage affect the physical and mechanical properties of the produced pellets. In general, the combination of spruce wood sawdust with corn stalks and soybean improved the mechanical and thermal properties of the pellet. Hanser's ratio and compressibility in the combined pellets that have a higher percentage of sawdust and also in the combination of sawdust with corn stalks are within the standard range. Also, in the compositions that have a higher proportion of spruce wood sawdust and lower moisture, the density and strength factors of the pellet increase. The highest and lowest calorific values were obtained in a higher ratio of sawdust and a higher ratio of corn, respectively. Therefore, it is possible to make pellets from the waste of garden and agricultural products that have good density and strength and high calorific value.

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.

The quality of irrigation water has an important effect on the growth and concentrations of nutrients. The application of boron-rich irrigation water is a global issue and the most important boron pollution source in the environment. Poor water quality unavoidably leads to decreased growth of plants. One of the problems of irrigation in tropical regions is the high concentration of boron element in water and its concentration in irrigation water increases every year. In dry areas where agriculture takes place, boron is often found in high concentrations along with saline soils and salty waters. Boron stress occurs widely and limits plant growth and crop productivity worldwide. Boron is in the form of boric acid in the soil solution and it is washed from the soil in heavy rains, but it is not washed enough when it rains, and by accumulating in the soil, it poisons the plant and prevents its growth. Therefore, in arid and semi-arid areas, irrigation with groundwater that has a high boron content reduces crop growth. Therefore, this experiment aimed to evaluate the effect of activated carbon on nutrient concentrations by tomatoes, cucumbers, and eggplants under the boron stress of irrigation water.

To evaluate the effects of activated carbon on the concentrations and translocation of boron in the plant a factorial experiment with a completely randomized design and three replications was performed in the greenhouse conditions. Treatments included three plants (tomato, cucumber, and eggplant), three levels of boron concentration in irrigation water (0.03, 2.5, and 5 mg l-1) from a boric acid source, and four levels of activated carbon (0, 1, 2, and 3% soil). To prepare seedlings, first, a sufficient number of healthy seeds were selected and for better germination, they were placed in wet napkins for one day and night. Then the seeds were planted in seedling trays with coco peat substrate. In this stage, watering was done once every two days until finally, after 30 days and when the seedlings reached the four-leaf stage and the true leaves appeared, the plants were ready to be transferred to the pots. For cultivation, each of the plastic pots was filled with 3 kg of sampled soil, which was mixed with a proportion of activated carbon according to the type of treatment. Then, in the middle of each pot, several seedlings of the same size were planted. Then the pots were placed in the greenhouse according to the plan. The experiment was conducted with 36 experimental treatments in three replications and a total of 108 experimental units. The soil used was prepared with geographical coordinates (longitude 57˚ 37ʹ and latitude 28˚ 42ʹ) and depth of 0-30 cm and was classified according to the American classification system Sand, mixed, hyperthermic typical Torriorthents. During the growing period, the plants were irrigated daily according to the farm capacity (FC). The day temperature of 25 – 30 °C, the night temperature of 15 – 20 °C, and the relative humidity was 50 – 70%.

The results indicate that the main effects of boron and activated carbon levels had a significant effect (p < 0.01) on the concentration of iron, zinc, manganese, and copper in the aerial parts of the plant. With the amount of boron increased in the treatments, the amount of copper and iron in the aerial parts increased while the amount of manganese and zinc decreased. Regarding the effect of activated carbon, the results showed that by increasing the amount of activated carbon in the treatments, the amount of copper, manganese, and zinc decreased. In contrast, the amount of iron has increased. The highest concentration of iron in the aerial parts (219.6 mg kg-1) belonged to the level of 3% of activated carbon. Also, with the increase in activated carbon in the treatments, concentrations and accumulation of boron in the aerial parts decreased. The highest concentration of boron in the aerial parts (31.77 mg kg-1) was obtained in the cucumber and the level of 0% activated carbon, and the lowest concentration (5.75 mg kg-1) was obtained in eggplant and the level of 3% activated carbon.

It is concluded that the use of activated carbon under boron stress conditions can reduce the concentrations and toxicity of boron in plants.

Biogas is a natural and cost-effective source of energy that leaves significant impacts on the environment and industries, widely produced and utilized in many countries. This gas is generated through the anaerobic digestion of organic materials, including animal manure, food waste, and sewage. Microorganisms play a crucial role in the biogas production process by feeding on biomass. The digestion carried out by these microorganisms produces methane, constituting approximately 50-70% of biogas, which is flammable and used for cooking, cooling and heating, electricity generation, methanol and steam production, waste management, and mechanical power. Given these benefits, biogas production holds special significance, and extensive research has been conducted globally in this field, yielding valuable results. In the present study, we aim to investigate and evaluate the influence of lentil skin as a biomass on the quantity and constituents of produced biogas.

This research was conducted in the Biosystems Mechanics Workshop of the Faculty of Agriculture, Ilam University. The objective of this study was to investigate the effect of lentil skin on biogas production and analyze its constituent components. The workflow typically comprised four stages. In the first stage, fresh lentil skins were broken down into smaller pieces and stored in a suitable environment to be used as digester feedstock for the experiment. Shredding organic waste aids in the digestion process. The second stage involved providing optimal conditions for microbes, which require warmth. Accordingly, the temperature was maintained at an average of 28-30 degrees Celsius during the experiment.
The third stage involved the actual digestion process, where anaerobic digestion took place in large tanks, resulting in real biogas production. For this purpose, materials were combined in predetermined proportions (1:1) and loaded into the digesters. In each stage, 5 kilograms of lentil skin were combined with 5 kilograms of water and added to the digester. The experiment was conducted in three repetitions, employing fixed digesters, digesters with agitation every three days, and digesters with daily agitation as influencing factors. The quantity of biogas production and its components were examined over a 30-day period. Gas sampling occurred every 10 days, while pH and gas pressure were measured every 72 hours. In the final stage, the gas underwent purification by removing impurities and carbon dioxide. The amount of gases produced from lentil skin was measured using a chromatograph with a TCD detector. This instrument employs chromatography-based separation. It's worth noting that 9 gas capsules specifically designed for automobiles were used to construct the digesters. The construction stages of the digesters included cleaning, coloring, and installing connections. Moreover, to create uniform temperature and concentration conditions inside the tank, inlet and outlet connections were carefully designed and installed. A safety valve was also installed to ensure the safety of the digesters.

The obtained results, including loading conditions, pH levels, and internal pressure within the digester during the experiment, and the quantity and components of biogas, were examined across all samples. Statistical methods, including Analysis of Variance (ANOVA) and Duncan's mean comparison test, were employed for data analysis. The results indicated that digester agitation directly influences the pH levels, with the highest pH observed in digesters with daily agitation, displaying the most significant fluctuations. Furthermore, digester agitation has a direct impact on the biogas production levels, enhancing structural effects within the digester. However, frequent agitation repetition has a negligible effect on the amount of biogas produced. The average methane production rates in this process were 34.06% mol for digesters with daily agitation, 23.09% mol for digesters with agitation every three days, and 17.32% mol for fixed digesters.

  Currently, a significant portion of the world's energy demand is met through fossil fuels, the combustion of which releases carbon dioxide and various pollutants, including sulfur and nitrogen oxides, which are highly harmful. Consequently, in recent years, there has been a growing inclination towards utilizing various renewable energy sources. One crucial energy source that also provides a solution for waste reduction is biogas. Given the increasing importance of sustainable energy development and the need for waste management, anaerobic digestion technology and biogas production have rapidly grown. Therefore, the findings of this research underscore the importance of exploring innovative methods and utilizing diverse biological resources in managing and optimizing the biogas production process.