Research Paper
Precised Equipment
Hojat Hejazipoor; Jafar Massah; Keyvan Asefpour Vakilian; Mohsen Soryani; Gholamreza Chegini
Abstract
One of the most important issues in spraying fields and greenhouses is reducing the use of pesticides, reducing the dangerous effects of spraying, protecting the environment, improving the quality of spraying and increasing people's health. Children have weaker immune systems and are unable to detoxify ...
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One of the most important issues in spraying fields and greenhouses is reducing the use of pesticides, reducing the dangerous effects of spraying, protecting the environment, improving the quality of spraying and increasing people's health. Children have weaker immune systems and are unable to detoxify toxic and harmful compounds. For this reason, the adverse effects of poisons on children's health are more important than adults, and the need to reduce the use of poisons and follow the principles of spraying to prevent children from developing cancer is twofold. In this study, the robot sprays by measuring the volume of plant mass and in order to reduce the consumption of poisons. The robot is mechanically designed to be able to move between rows of products and open its manipulator step by step and take deep pictures of each plant in front of it, then analyze the image of each section and observe the plant volume. Detect and spray the same section based on the calculated volume. The process of imaging, volume detection and spraying of the solution based on the estimated volume is repeated at each stage of manipulator opening until the height of the plant is completed and at the end the whole manipulator is retracted.Robot acts intelligently in detecting plant height and closes in the last section after imaging and spraying the solution. The manipulator is able to assess and spray plants up to 270 cm in height. The above robot consists of different parts including camera chamber and nozzle, nozzle and Kinect American camera version 1, manipulator and manipulator actuator mechanism, pump and solution tank, processor, Arduino and relay boards, cart and robot actuator system. To design the above robot, first the static forces applied to the manipulators were examined and then the kinematic calculations of the manipulator were performed. The result of the calculations showed the accuracy of the kinematic equations. After performing calculations to design the robot, examining the environmental conditions and considering the construction cost, the three-dimensional model of the robot was designed in Solidworks 2016 software and based on the above model, the construction work was done step by step. The robot is controlled by Matlab 2010 software. The entire robot working algorithm is coded in Matlab software. For this reason, the main part of controlling the robot is the laptop processor. The laptop controlled by the robot is located in the built-in place behind the robot and transmits all the robot commands to the set of operators through the Arduino board and the relay board. The input information is transmitted to the processor by the Kinect camera, and the processor makes the necessary decisions according to the coded program. Finally, the output commands from the processor are transferred to Arduino board and the relay board to start the actuators. ADM A10-4655M APU processor was used. Developer Toolkit Browser v1.8.0, KinectExplorer-D2D, and Kinect for Windows Software Development Kit (SDK) were used to connect the Kinect camera to a Windows laptop. Two coefficients α and β are needed to determine the plant volume in each section. α is the average plant volume of several plants that has been calculated manually and β is the correction factor multiplied by the amount of plant volume estimated by the robot so that the actual volume of sprayed solution is more in line with the plant needs and the opinion of relevant experts. The volume estimated by the robot in each section is the product of the volume factor multiplied by the average plant volume of the plant (α). The volume factor is the average observed plant width (M) divided by the distance between two consecutive plants in pixels (D). Multiply the volume of the plant observed in the section by multiplying the volume factor by the calculated volume (α) using the Scale Invariant method (independent of the distance from the camera to the object).To calculate the average plant volume manually, several plants should be selected randomly and the plant volume should be calculated by computational methods or flooding method. Then introduced the average volume of these few plants as α to the program. Therefore, the more accurately the manual volume is calculated, and the greater the number of selected plants, Finally, the value of α and the final volume of the plant will be calculated more accurately. The robot should be able to spray the right amount of solution depending on the type of plant and its conditions. Spraying the solution to the plant may not be scientifically justified by experts and specialists according to the type of plant, time of spraying, poison concentration and plant needs. Therefore, the correction factor β should be multiplied by the volume estimated by the robot to the actual volume. Spray the solution to the plant according to the needs of the plant and the opinion of experts. The results of the evaluation show that the robot is able to spray different amounts of solution in the detection of plants with different volumes and the amount of solution sprayed by the robot was proportional to the volume of plants. The average volume of solution sprayed by the robot is 27.1 cc and the average volume of solution sprayed by the worker is 33.1 cc. Also, the standard deviation of the average volume of solution sprayed by the robot and the worker is 2.94 and 3.11, respectively. In other words, the robot is able to spray more accurately and the amount of poison consumption in the robot is estimated less than the worker. It was mentioned that the evaluation of the robot is reported in order to reduce the consumption of acceptable poisons. The feature of being online includes collecting plant information and spraying the solution moments after data processing is one of the important features of the above research. Also, the ability of the robot in online and scale invariant (independent of the distance from the camera to the object) evaluation of the robot was considered acceptable and useful.
Research Paper
ُSaeid Hojati
Abstract
Introduction Khuzestan province in southwestern Iran is one of the most critical areas affected by dust storms due to the arid climate and the abundance of desert areas in its western and southern parts. Dust storms in these areas are among the most critical environmental issues. Air pollution, the development ...
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Introduction Khuzestan province in southwestern Iran is one of the most critical areas affected by dust storms due to the arid climate and the abundance of desert areas in its western and southern parts. Dust storms in these areas are among the most critical environmental issues. Air pollution, the development or increase of respiratory diseases, reduced soil fertility, damage to crops, and reduced solar radiation are among the most critical consequences of dust storms. Dust particles can absorb significant amounts of heavy metals, which facilitate their transport on a large scale due to their fine particle size distribution. Street dust is considered the major source of pollutants from a wide range of traffic, industrial emissions, pesticides, and mining activities. Although many studies have been conducted to identify the origin and pollution status of dust particles in the country, the assessment of pollution and source of street dust particles during dust storms, especially in Ahvaz city, has received less attention. .Thus, this study was conducted to: (1) identify the source of street dust in Ahvaz city, and (2): determine the level of contamination to Pb, Zn, and Cu.Materials and Methods Dust and soil samples were collected respectively at 69 and 23 points from streets and the surface soil (0-5 cm) in Ahvaz city in February 2015. To determine the particle size distribution pattern in the dust samples, they were first dispersed in 1 M sodium hydroxide and 10% sodium hexaphosphate solutions for 2 hours. Then, they were analyzed using A Malvern Hydro 2000g laser diffraction device. The ionic compositions of the dust and soil samples were also determined after extraction from 1 (dust/soil): 5 (water) suspension with an advanced Meterohm 861 model ion chromatography apparatus. The heavy metal contents of soil and dust particles were determined using inductively coupled plasma (ICP) spectroscopy. To determine the Pb, Fe, Cu, and Zn contents, 0.5 g of the dust or soil samples were digested with 60% nitric acid, and after 24 hours, the samples were heated for 0.5 hours at a temperature of 80 ° C. Then, they were filtered with Whatman 42 paper and finally were examined using an Agilent 7000 inductively coupled plasma (ICP) spectrometer. To assess the degree of street dust pollution in Ahvaz city, various indicators, including the single element pollution index and Nemerow integrated pollution index, were calculated. A pollution index is expressed as the ratio of the concentration of an element in soil or dust samples to the same component's baseline value in soil or dust sample. If this index is greater than 1, it indicates different levels of pollution.Results and Discussion The particle size distribution in the studied samples showed a bimodal pattern with more abundance of particles in the size of silt and fine sand. Accordingly, 57 to 89% of the particles were in the silt size, and 5 to 16% were in the size of fine sand. The results also indicated that the abundance of sodium, calcium, chloride, and sulfate ions was comparably higher than the local soils. Similarly, the average concentration of each heavy metal was higher than those of the local soils and the upper earth crust, which followed the order Zn> Cu> Pb. Accordingly, The average Pb, Cu, and Zn concentrations were 5.23, 6.37, and 6.89 times more than their corresponding values in the earth's upper crust. Accordingly, and based on the values obtained from the pollution index (PI), all the studied elements in the street dust of Ahvaz city could be categorized as highly polluted. The average of Nemrow integrated pollution index was found 7.26, which shows a high pollution level for street dust in Ahvaz cityConclusion It seems that dust particles collected from streets and sidewalks of the Ahvaz city are mainly originated from regional focal points in eastern and southeastern parts of the city. When Pb, Cu, and Zn concentrations in the street dust of Ahvaz city and those reported from different cities in Iran and other countries are compared, it is concluded that dust particles deposited over the streets and sidewalks in Ahvaz county have a higher degree of pollution. Therefore, Prompt actions are needed to lower the risk of these elements for the environment.
Research Paper
Mohsen Soleymani; Vahid Jahangiri Boltaghi; Mohammad Javad Sheikhdavoodi; Zabihollah Mahdavifar
Abstract
Introduction: Biogas, a product of anaerobic digestion of biomass resources, is one of the major renewable energies with the potential to replace fossil fuels. Anaerobic digestion is performed under specific conditions and according to a specific chemical process. Sugar cane is one of the most common ...
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Introduction: Biogas, a product of anaerobic digestion of biomass resources, is one of the major renewable energies with the potential to replace fossil fuels. Anaerobic digestion is performed under specific conditions and according to a specific chemical process. Sugar cane is one of the most common sources of sugar and bioethanol production in the world. In the ethanol distillation process, large quantities of vinasse are produced. The direct consumption of vinasse as fertilizer has many environmental problems. Anaerobic digestion of vinasse is a potential solution to such environmental problems. Factors affecting the performance of an anaerobic digester can be classified into three main categories: (1) raw material characteristics, (2) reactor design, and (3) operating conditions. Among the operating conditions, temperature and pH are the most important parameters, so in this study, these two parameters were investigated.Materials and Methods:The main raw material was vinasse. Some other additives were used to alter its chemical properties. To have a proper substrate composition, the ingredients before loading into the digesters were evaluated for their chemical and physical properties, including pH, concentration and C/N ratio. The bovine rumen contents of 10% of the final volume of input material were added to supply methanogenesis bacteria as well as to modulate the (C/N) ratio.The Total Solid Content (TS), Volatile Solid (VS) and Chemical Oxygen Demand (COD), were evaluated before and after digestion.A series of batch reactor were used to perform the experiment. The experiment was carried out in a split plot design in a completely randomized design. The main and sub-factor was respectively temperature (at four levels of 30, 35, 40 and 45 ° C) and pH (at four levels of 6.8, 7, 7.2 and 7.4), and the experiment was performed in three replication.To measure the volume of gas produced, a 50 ml water tank connected to the digester outlet as a U-tube was used. The amount of water movement in the U-shaped tube is an indicator of the volume of biogas produced. For better detection of water displacement, some color was dissolved in water. Passing the gas produced from the three-molar NaOH solution, its impurities (mainly carbon dioxide) were absorbed, and the resulting pure gas was re-measured using a U-shaped tube. Using the law of complete gases, the biogas volumetric index was converted to the standard gas volume and finally converted to values based on (ml/gVS) and the new values were analyzed by analysis of variance and mean comparison.Results and Discussion:Almost all main and interaction effects on all the factors studied were significant at the 1% probability level. The amount of gas produced increased with increasing temperature but with increasing pH, it first increased and then decreased. The amount of gas produced at 35, 40 and 45 °C was not significantly different. So because of economic and energy constraints, an operating temperature of 35 °C is recommended for anaerobic digestion of vinasse. The graph of the interaction of temperature and pH shows that at higher temperatures the rate of gas production increases with increasing pH. Although the highest gas volume was obtained at pH of 6.8 and 7.4, but the gas produced in the pH range of 7–7.2 was more pure. Therefore, the best combination of pH and temperature to produce the highest and purest gas is 7 and 35 °C, respectively. But since the vinasse produced in the alcohol factories has high temperature and therefore higher temperatures are possible, so 40 °C is also recommended.It was also clearly observed that the smaller the volume of gas produced, the greater its purity.The VS-R factor is also more sensitive to temperature changes than to pH changes. Thus, in anaerobic digestion of vinasse, pH control is more important than temperature control. VS-R performs best at pH 7. This factor was not significantly different at 35, 40 and 45 °C. Therefore, considering the cost of providing more heat at temperatures of 40 and 45 °C compared to 35 °C, 35 °C is the best temperature for manure production with the highest volatile organic matter removal.The COD-R process was similar at all pHs. COD-R at pH 7 was higher at all temperatures than at other pHs. It was also significantly higher at 40 and 45 °C, compared to other temperatures. So like other factors, the best pH and temperature based on this factor are 7 and 40 °C, respectively.Conclusion According to all factors studied, the best pH and operating temperature of anaerobic digestion of sugar cane vinasse is 7 and 35 ° C, respectively. Another important conclusion to be drawn from this study is that changes in all parameters studied are affected by pH changes rather than temperature changes. Therefore, sufficient care must be taken to ensure that pH variations in the anaerobic digestion medium be very low and around the range proposed (about 7).
Research Paper
Soil Chemistry and Pollution
Masoumeh Sadeghi Poor Sheijany; Fatemeh Shariati; Nafiseh Yaghmaeian Mahabadi; Hassan Karimzadegan
Abstract
Introduction One of the most important results of population growth, urbanization, and industrialization is the increase of urban waste. Accumulation of municipal solid waste produces toxic leachate that can transfer contaminants to the soil and alter its quality, especially in vulnerable forest ecosystems. ...
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Introduction One of the most important results of population growth, urbanization, and industrialization is the increase of urban waste. Accumulation of municipal solid waste produces toxic leachate that can transfer contaminants to the soil and alter its quality, especially in vulnerable forest ecosystems. This study was carried out to determine the properties of the soil of the Saravan municipal solid waste disposal site that is located in a part of the Hyrcanian forests, Rasht, Guilan province, which have been affected by the activity of the open dumpsite; Determining the minimum data set (MDS) and evaluating the quality adjacent soil to the dumpsite, the route affected by leachate and downstream lands, through soil quality indices such as simple integrated quality index (IQISA), weighted integrated quality index (IQIW) and Nemoro quality index using total data set (TDS) and MDS, and comparing them with each other.Materials and Methods Based on the distance from the disposal site, slope, height, and the route of leachate, from 32 sampling points with the same vegetation, a total of 32 composite samples were prepared in plots 10×10 from (five sub-samples from four heads and the middle by a polyethylene hand auger) a depth of 0-15 cm in June 2019.The soil properties including pH, clay, silt, sand, available phosphorus (Pava), copper (Cuav), zinc (Znav), and iron (Feav), total nitrogen (N), cation exchange capacity (CEC), electrical conductivity (EC), organic carbon (OC), basal respiration (BR), microbial biomass carbon (MBC), the metabolic quotient (qCO2) and enzymatic activities of Urease (UR) and alkaline phosphatase (ALP) were measured. One-way analysis of variance (ANOVA) and independent comparison tests was used to compare the results of the soil samples in areas exposed to dumpsite activities and control. Six properties were selected as MDS using principal component analysis (PCA). The models of the simple integrated quality index (IQIsa), weighted integrated quality index (IQIW), and the Nemoro index were used to determine soil quality. One-way ANOVA and Duncan’s multiple range tests were used to compare the mean soil quality indices in the areas around the disposal site, leachate-affected route, and downstream lands. The possible relationship between chemical, physical and biological properties was investigated by calculating Pearson’s correlation coefficients.Results and Discussion The results showed that the value of soil properties including Feav, EC, Pav, N, Znav, Cuav, OC, BR, MBC, the enzymatic activities of UR and ALP is significantly different from the control (p < 0.01). The properties of Pav, Cuav, EC, clay, silt and MBC were selected as MDS, which can describe 73% of changes in the soil quality. Evaluation of the soil quality through Nemoro index, using MDS and TDS (IV and III, respectively) at different distances from the dumpsite was the same as the control. The values of IQIsa and IQIw using MDS did not show any significant difference with the control in all routes exposed to the activity of the disposal site, except around the dumpsite. However, the degree of soil quality through the overall average IQIsa and IQIw, using MDS in all areas exposed to the dumpsite was the same as the control. The results of IQIsa and IQIw, using TDS were so different so that the values of IQIsa and IQIw, using TDS in the path of leachate and lands downstream of the disposal site showed a significant difference with the control (p < 0.01). Also, the quality degree through the overall mean value of IQIsa and IQIw, using TDS (III and IV, respectively), around the disposal site and the path of leachate were different from the control (II and III, respectively).The Saravan municipal waste disposal site is located in an area, with a Mediterranean climate, with high relative humidity and rainfall. It has increased the possibility of leachate production. On the other hand, with the leachate flowing along the sloping path of 15%, especially after each rainfall in the area, the soil is contaminated by leachate and transfer downstream. Also, Leachate is discharged from the disposal site downstream, into the river, which is used to irrigate agricultural land downstream of the dumpsite. The results of changes in IQIsa and IQIw by TDS can indicate the possible consequence of the leachate effect from the disposal site on the path to the soil of downstream farms.Conclusion According to the objectives of the research, it seems that soil properties including Feav, Pav, EC, N, BR, MBC, and the enzymatic activities of UR and AIP have been affected by the activity Saravan solid waste disposal site. Investigating the results of the quality indices using MDS and TDS showed that IQIsa and IQIw, using TDS can better represent the effect of waste disposal site activity on soil quality. Significant differences of the IQIsa and IQIw, in the leachate route and downstream agricultural lands with the control can probably be due to the effect of leachate and leaching of soil around the leachate route and its transfer downstream. Considering the same quality results in the area exposed to the activity of the disposal site with the control through the Nemoro index, using MDS, TDS, it can be concluded that Nemoro index does not have the required sensitivity to describe the effect of waste disposal activity on the quality adjacent soil. This study showed that the change of use of the forest area to waste disposal site affected its soil quality in the path of leachate and downstream lands. Therefore, to protect the areas of Hyrcanian forests in the Saravan region and to prevent the reduction of soil quality in the region, taking the necessary measures to separate the municipal solid waste from the origin, to establish leachate collection systems and treatment of leachate before flowing in the forest areas should be carried out.
Research Paper
Precision Agriculture
Alireza Dahmardeh; Ali Shahriari; Mohammad reza Pahlavan Rad; Asma Shabani; MARYAM GHOEBANI
Abstract
Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes ...
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Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes under the influence of environmental and managerial factors. Wheat is one of the key crops grown worldwide and is a source of nourishment for millions of people around the world. Therefore, studying this strategic crop is very importance. On the other hand, more than 70% of wheat and 84% of barley in Sistan and Baluchestan province were produced in Sistan plain and wheat has the highest area under cultivation among different crops, in this arid region. So, the aim of this study was modeling wheat yield with some soil characteristics and determination of the most important soil factors affecting wheat yield in the Sistan plain.Materials and Methods This research was done in the educational and research farm of University of Zabol. Topsoil (0-30 cm) sampling of 100 soil sample was done randomly. Clay, silt, sand abundances and soil texture class, soil pH, electrical conductivity, apparent electrical conductivity of soil, organic carbon, phosphorus, potassium and nitrogen were measured by conventional methods. Wheat plant samples were taken from a one m2 plot and the grain weight, 1000-grain weight and total weight were measured. Performance modeling was performed by three methods of multi-linear regression (MLR), multi-layer perceptron (MLP) and support vector machines (SVMs) by two kernels types linear(SVM-L) and radial basic function (SVM-RBF). It should be noted, before modeling, 80% of the data were selected for modeling (or training) and 20% for testing (or validation) of the models. These data (training and validation) were the same for all models. Coefficient of determination (R2) and the root mean square error (RMSE) were the criteria for comparing the models. Sensitivity analysis was used to determine the most important soil factors affecting wheat yield.Results and Discussion The results of soil properties analyses showed that the soil of this area is non-saline and alkaline soil, has a medium to coarse soil texture and the soil fertility conditions are poor to moderate. The results of comparing the models showed that the highest R2 and the lowest RMSE in estimating all three wheat yield indices were related to the MLP method (grain weight with R2= 0.61, 1000-grain weight with R2= 0.64 and total yield with R2= 0.76). After MLP, with less difference, the SVMs method with two kernels types of linear (grain weight with R2= 0.54, 1000-grain weight with R2= 0.44 and total yield with R2= 0.65) and radial basic function (grain weight with R2= 0.48, 1000-grain weight with R2= 0.58 and total yield with R2= 0.67) showed the better modeling and finally the MLR (grain weight with R2= 0.20, 1000-grain weight with R2= 0.27 and total yield with R2= 0.40) showed the lowest accuracy in modeling the yield components of wheat. The results of sensitivity analysis of wheat yield components showed that total soil nitrogen, clay, silt and soil organic matter had the highest on wheat yield components (grain weight: nitrogen, clay and organic matter; 1000-grain weight: nitrogen, silt and clay; and total yield: clay, organic matter and nitrogen) and soil pH had the least effect on it, maybe because of its low variation.Conclusion Due to harsh environmental conditions in the arid regions, the study of crops yield is very important for the optimal management of facilities and resources. Investigating the application of several wheat yield modeling methods using some soil characteristics in the arid region of Sistan showed that the perceptron neural network (MLP) performed better in predicting the yield components of wheat than other models. Also, some chemical and physical properties of soil that affect the soil fertility and water storage conditions in the soil (soil nitrogen, organic matter, clay and silt contents), were the most affecting factors on the yield of wheat in this arid region. It is important to note that attention to other soil properties as well as climatic parameters and studies and monitoring wheat yield for several years can can lead to more accurate modeling of this strategic crop and thus optimal farm management.
Research Paper
Parstoo Aslani; Masoud Davari; Mohammad Ali Mahmoodi; Farzad Hosseinpanahi; Naser Khaleghpanah
Abstract
Introduction Soil quality is one aspect of sustainable agroecosystem management. The application of zeolite minerals alone or in combination with other soil amendments (organic and inorganic fertilizers) can, directly or indirectly, affect soil quality indicators. Considering the unique characteristics ...
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Introduction Soil quality is one aspect of sustainable agroecosystem management. The application of zeolite minerals alone or in combination with other soil amendments (organic and inorganic fertilizers) can, directly or indirectly, affect soil quality indicators. Considering the unique characteristics of zeolites, such as the low-cost and abundance of its mines in Iran and the large area of wheat cultivation in Kurdistan province, the need to study the effect of zeolite application on soil properties and wheat yield becomes apparent. Although there is a lot of research on the impact of zeolite on improving soil properties and increasing the yield of various crops, few studies have been done on its residual effects. Therefore, in this study, we investigated the effect of zeolite and nitrogen (N) application on some basic soil properties, N efficiency, and wheat yield under field conditions after two years of zeolite application. Materials and MethodsBefore conducting the research, a composite soil sample from the soil surface (0 to 30 cm depth) was collected and analyzed to assess the farm's soil properties. The experiment was laid out in a split-plot based on a randomized complete block design with three replications at the University of Kurdistan research farm in Dehgolan. The main plots consisted of natural zeolite at four levels (0, 5, 10, and 15 ton. ha-1). Within each main plot, subplots were subjected to nitrogen applications at five levels (0, 50, 100, 150, and 200 kg. ha-1). Urea fertilizer was used to supply the required nitrogen. Zeolite was only utilized in 2018 and mixed into the surface layer of soil. The experiment was repeated in 2019 except for no addition of zeolite. The field was under potato cultivation in the first year of the experiment and followed by wheat crop in the second year. Wheat cultivation (Pishgam cultivar) was done in 2019 by grain seeders in plots with dimensions of 4.5 × 8.25 m. At the end of cultivation season, harvest was done from each plot, and some plant traits (grain protein, thousand-grain weight, spike number, grain number in spike, an economic yield of the plant, biological yield of plant, harvest index, and chlorophyll concentration) were measured. In order to investigate the effect of zeolite on basic soil properties, soil samples were collected from plots in the second year after harvest, and a number of physical and chemical properties of the soil were measured (dry bulk density (ρb), particle density (ρp), total porosity (f), saturated hydraulic conductivity (Ks), electrical conductivity (EC), soil reaction (pH), cation exchange capacity (CEC), and total soil nitrogen (TN)). Statistical analysis of data was performed using SAS 8.02 software.Results and DiscussionThe results from the second year indicated that the applications of zeolite or nitrogen alone or in combination with each other decreased dry bulk density and particle density of soil, but increased total porosity, saturated hydraulic conductivity, electrical conductivity, soil reaction, and cation exchange capacity. The porous structure of zeolite helps improve soil structure and increase porosity, thereby reducing the bulk density of the soil. Also, zeolites can affect the soil hydraulic conductivity due to channels in their structure. Zeolite is not acidic but marginally alkaline, and its use with fertilizers can help buffer soil pH levels. The very open structure of the zeolite and the similar pore network create a high specific surface area for the storage and exchange of nutrients. Therefore, different salts can be absorbed or desorbed from the zeolite structure. Desorption of salts from the zeolite can increase EC in the soil. The high cation exchange capacity and porosity of zeolite increase soil CEC, which increases the soil's ability to retain nutrients such as ammonium. The results also revealed that the grain protein, thousand-grain weight, spike number, grain number in spike, an economic yield of the plant, biological yield of plant and harvest index, with mean increasing about 37%, 6%, 30%, 15%, 43%, 26% and 7%, respectively, compared with the control, were significantly affected by zeolite and nitrogen applications, and also zeolite and nitrogen interaction. However, the chlorophyll concentration was not meaningfully influenced by them. Increased grain yield can be attributed to reduced nitrogen leaching and increased soil water holding capacity in the presence of zeolite, which improves nitrogen status and the availability of water for growth. Drought stress significantly affects grain yield, harvest index, thousand-grain weight, spike number, grain number in spike, and plant height. The use of zeolite can maintain soil moisture for a longer period and mitigate the adverse effects of drought stress on the crops.ConclusionThe improved agronomic traits and enhanced grain yield potentials induced by zeolite amendment were related to decreased drought stress in wheat crops and the increase in soil quality indicators and N uptake. The zeolite application probably enhanced NH4+–N retention in the topsoil and prevented NO3-–N from leaching into the subsoil. In general, the results showed that the combined application of zeolite and N can be a beneficial approach for increasing nitrogen fertilizer efficiency and improving the sustainability of agricultural systems.
Research Paper
Land Evaluation and Suitability
Behnam Kamkar; Parysa Alizadeh Dehkordi; Pooya Aalaee Bazkiaee; Omid Abdi
Abstract
Introduction: Understanding the suitability of the lands is very important in terms of the ability to cultivation a particular crop. Having information in this field helps us to act more intelligently in prioritizing land allocation for the cultivation of various crops. Also, adapting the current-grown ...
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Introduction: Understanding the suitability of the lands is very important in terms of the ability to cultivation a particular crop. Having information in this field helps us to act more intelligently in prioritizing land allocation for the cultivation of various crops. Also, adapting the current-grown lands under cultivation of a crop selected by the farmer to the final layer of land suitability can give us an overview of the right or wrong choice of land use. This information will help agricultural policymakers to replace crops when necessary and to replace crops that have been misallocated in disproportionately desirable lands with other crops or to improve their crop management. Therefore, this study was conducted to assess the land suitability of Golestan province agricultural lands for soybean cultivation and the degree of adaptation of current soybean-grown fields to the obtained suitability layers.Materials and Methods: This study was carried out in the agricultural lands of Golestan province with an area of 821 thousand hectares. First, the real lands under cultivation of soybean were separated using 1674 land samples taken from different crops and object-based image analysis (OBIA) method. To separate the lands under soybean cultivation in Golestan province, sentinel 2 satellite images with a spatial accuracy of 10 meters related to planting to harvesting time in 2018 were used. Then, the layers of soil, climate, and topography characteristics were provided to investigate land suitability for soybean cultivation. Climatic components including minimum, optimum, maximum temperatures, and rainfall were estimated using long-term statistics of synoptic stations in the province (maximum available statistics). Data of soil texture, nitrogen, organic matter, phosphorus and potassium, soil pH, and salinity were also received from the provincial agricultural and natural resources research center, and from the data, the soil properties map was obtained. The digital land elevation map (DEM) of the province with a spatial resolution of 20 meters was used to extract slope, elevation, and aspect maps. The process of interpolation of climatic and soil layers was performed using ordinary kriging method. The relative importance of each factor was determined through the Analytic Hierarchy Process (AHP). This was done by designing questionnaires based on AHP paired matrices and completing it by agricultural specialists. After extracting the weights from the questionnaires and preparing the classified raster layers, these layers were imported in GIS version 10.3. Combining and overlaying the layers was done by assigning AHP weight to each layer. Finally, a land suitability map was prepared for the cultivation of the soybean in the study area which, in turn, was used to determine the adaptation of current soybean fields with determined suitability classes.Results and Discussion: The accuracy of classification by object-oriented method using kappa coefficient and overall Accuracy coefficient (0.87 and 90%, respectively) shows the acceptable accuracy of soybean land separation in this study. In the study of land suitability for soybean cultivation, the results obtained from hierarchical analysis showed that the soil criterion had the greatest effect on the site selection of soybean cultivation with a coefficient of 0.52 with respect to both climate and topography factors. The results showed that most of the fields (about 87% of total) placed in suitable class and 13% placed in a relatively suitable class. In suitable areas for cultivation, despite having the best conditions for factors such as maximum temperature, average temperature, slope, aspect, height, soil texture, soil pH, phosphorus and soil salinity, soybean production is limited by factors such as precipitation (400 to 500 mm per year), minimum temperature (10 to 12 °C), phosphorus (8 to 10, 15 to 20 mg/kg soil). In these areas, maximum yield can be achieved by managing the mentioned factors and applying desirable agricultural management. In relatively suitable areas, limitations of nitrogen deficiency (less than 0.5 mg/kg soil), organic matter (less than 2%), salinity (above 6 dS/m), slope (more than 5%), restriction of soybean cultivation due to heavy soil texture (high percentage of soil clay), potassium (less than 100 mg/kg soil), phosphorus (more than 20 or less than 8 mg/kg soil), precipitation (less than 400 mm per year), minimum temperature (less than 10 °C), slope (more than 8%) and aspect (west and north) caused relatively high land restrictions for soybean cultivation. Compatibility analysis of the current soybean fields with the suitability maps indicated that about 99% of total cultivated lands are located in a suitable class, which demonstrates the proper selection of farm locations by the farmers. Conclusion: By considering the position of Golestan province in the production and area under soybean cultivation in the country, if it is possible to identify suitable soybean cultivation areas according to the environmental requirements of this product and identify the limitations created by the environment, more yield per area can be achieved, which will improve the agricultural economy and the level of income of the country.
