نشریه مهندسی زراعی
سال چهل و هفتم شماره 1 (بهار 1403)

One of the decision-making methods using quantitative data is multi-criteria decision-making, which helps the manager make rational decisions by considering different conflicting criteria. Planning for the optimal use of water and soil resources, in addition to their conservation, involves increasing production, income growth for farmers, and rural economic prosperity. Given the limited resources, the optimal cropping pattern should be appropriate and effective for each region. The selection of an appropriate cropping pattern and, if necessary, adjusting the cultivation based on regional and national needs and the relative advantage of products in different regions is of great importance. So far, many studies have been conducted to optimize cropping patterns. The main reasons for the low productivity in agricultural production are the inappropriate allocation of resources and production factors. Although farmers are faced with various options for agricultural activities and crop selection, their production factors are limited. By formulating and implementing an optimal cropping pattern in a region, it is possible to familiarize farmers with the available potentials while considering the constraints of production resources, reducing risk, ensuring system stability, improving income in production, and creating the groundwork for the growth and prosperity of agricultural regions. This leads to agricultural development, increased profitability, and profitability. Decision-making is one of the most important and fundamental tasks of management, and the quality of decision-making determines the achievement of organizational goals. However, the main focus of research has been on improving the productivity of crops based on water and soil resources, with little attention given to the subject of agricultural mechanization and the criteria of regional operators. Considering the criteria of farmers in decision-making processes increases the acceptance of programs and better collaboration in implementing them. Additionally, most studies have only used one decision-making method, but each decision-support method provides a unique outcome and may differ from the results of other methods. Therefore, in this study, multiple decision-making methods were evaluated and compared to determine the best method to be used. Based on the given explanation, the objective of this research is to prioritize the factors influencing the determination of suitable cropping patterns for agricultural products using four different decision-making methods and introduce the best method.

The research area is sited in Silakhour Plain, in Lorestan Province, between the cities of Doroud and Borujerd, at geographical coordinates 38 degrees 36 minutes north and 48 degrees 31 minutes east, in coordinate zone 39. In this study, Analytic Hierarchy Process (AHP), TOPSIS, VIKOR, and Simple Weighted Average methods were used for decision-making. The parameters investigated in this study included technical-agricultural, economic, macro-governmental, soil and climate, social, and production support factors. These factors included sub-factors such as the presence of mechanized planting and harvesting equipment, farming unit larger than one hectare, water requirements of plants, distance from the place of consumption, farming unit smaller than one hectare, crop profitability, required capital, suitable market for the product, customs and farmer habits, farmer education, crop cultivation experience, guaranteed purchase of the product, government incentive policies, physical characteristics of the soil in the region, chemical characteristics of the soil in the region, average temperature during the growing season, elevation of the region, average rainfall in the region, insurability of the product, sustainability of crop production, availability of seeds compatible with regional conditions, and prevalent pests in the region. To validate all judgments made in the analytic hierarchy process method, the inconsistency ratio was calculated using Expert Choice11 software. Based on this, the inconsistency ratio was calculated to be less than 0.1 in all steps of this method. If the consistency ratio is 0.1 or less, it indicates consistency in the comparisons and confirms the validity of the judgments. To reach a general consensus on the ranking of parameters, the method of merging average ranks was used.

In this study, 22 indicators were identified, including the presence of mechanized planting and harvesting equipment (0.061), operational unit larger than one hectare (0.021), crop water requirement (0.014), distance from consumption site (0.008), operational unit smaller than one hectare (0.006), product profitability (0.125), required cash capital (0.116), suitable market for the product (0.020), agricultural customs and habits (0.028), education of the farmer (0.006), crop cultivation experience (0.130), guaranteed purchase of the product (0.3), government incentive policy (0.122), physical characteristics of the soil in the region (0.075), chemical characteristics of the soil in the region (0.022), average temperature during the growth season (0.022), elevation of the region (0.008), average precipitation in the region (0.006), insurability of the product (0.013), stability of crop production (0.007), availability of seeds compatible with the regional conditions (.0004), and common pests in the region (0.0002). Among the mentioned parameters, cash capital (0.236), water requirement for cultivation (0.233), product profitability (0.098), and operational unit larger than one hectare (0.039) are considered the most important factors. Certain purchase of the product (0.3), product profitability (0.0125), government incentive policy for products (0.122), and required cash capital for cultivation (0.116) were identified as the most important factors influencing the cropping pattern, respectively, using the hierarchical weighted method. product profitability and required cash capital are among the influential factors in the design of cropping patterns for agricultural products. The results showed that the ranking of agricultural products for inclusion in the regional cropping pattern differs in each decision-making method. Although grains and sugar beets have high rankings in all groups, it is necessary to validate and finalize these methods with integrated approaches to reach a general conclusion. In a multi-criteria decision-making problem, multiple decision-making methods may be used because decision-makers do not limit themselves to one decision-making method, and they may obtain different results using different methods. In fact, in such situations where the results of different methods of multi-criteria decision-making are not the same, the question is which option should be chosen. To reach a general conclusion, it is necessary to validate and finalize these methods with integrated validation and finalization approaches. Among the integration methods,

Soil is a dynamic natural system and interface between land, air, water, and life, which performs vital services for human sustenance. The increasing population growth has led to the excessive use of this natural resource to provide food, clothing and other human needs. This has led farmers in different parts of the world to improper exploitation of inferior and marginal lands such as pastures and forests located on sloping lands. However, on the one hand, these lands have low potential and on the other hand, they have a high potential for erosion. Soil quality is usually introduced as the ability of the soil to interact with the ecosystem maintain the productivity of the quality of different parts of the environment and thus improve the health of plants, animals and humans. The quality of soil and its importance for the development of sustainable agriculture are more important nowadays. Land use change is one of the most important current problems of our country, especially in the Hyrcanian forest lands in the north of the Iran. The objectives of this study were to evaluate the effects of land use change on some soil quality indicators in north of Iran.

A part area in the south of Lahijan was selected including 3 land uses: Natural Forest (NF), Tea plantation (TP) and paddy rice (PR) cultivation. In each land use 10 soil samples were collected at 0-20 cm depth and transferred to the laboratory. Undisturbed soils samples by core was taken for measurement of bulk density. A part of sample passed through the 4 mm sieve for measurement of aggregate stability and three indices comprised mean weight diameter: MWD, geometric mean diameter: GMD and water stable aggregates: WSA were calculated. Other soil properties such as pH, Calcium carbonate equivalent (CCE), soil organic matter (SOM), particulate organic carbon (POC), and soil respiration also measured.

The statistical results in this study showed that due to the change of land use from forest to tea and rice cultivation, the amount of organic carbon decreased, while the amount of pH and calcium carbonate increased. As a result of changing the use of forest land to other two land uses, the indicators of stability of soil aggregates (MWD, GMD and WSA) have significantly decreased, and as a result, the bulk density of the soil has increased. The amount of MWD was 1.95 mm in the forest, 1.2 mm in the tea plantation, and 0.45 mm in the rice cultivation. The amount of particulate organic carbon as one of the indicators of soil quality in forest lands was observed in the maximum amount. In addition to the reduction of particulate organic matter, this change is also caused by the excessive traffic of machines. Soil microbial respiration was analyzed as a soil biological indicator. The results showed that the average microbial respiration in the natural forest was equal to 300 mg C/ day.g soil, and in the two other land uses of tea and rice cultivation, it was calculated as 200 and 120 mg C/ day.g soil, respectively. Positive and significant relationship between SOM and MWD confirmed that soil organic matter had high contribution for soil aggregate formation and its stability.

This research was conducted to investigate the impacts of land use changes in the north of Iran in Gilan province on some soil quality indicators. The results of this research showed that the soil’s chemical, physical, and biological characteristics have shown significant differences due to land use change. In forest soils, the highest amount of organic carbon, and the lowest amount of pH was observed, which is due to the high accumulation of organic matter and high leaching of cations. Due to the degradation of organic carbon in the other two uses, the bulk density and aggregate stability indicators have decreased. The intense cultivation operations in the other two uses, especially in the paddy fields, have destroyed the soil structure. Also, more organic carbon in forest soils has led to more microbial respiration. In total, all soil quality indicators have decreased with the change in land use in the study area. Therefore, land conversion and especially deforestation in the studied region should be avoided. In total, the results showed that land use change in the study area has caused land degradation and reduced the soil quality and soil health indicators, and it is necessary to consider it in land use planning, land improvement, and sustainable land management.

Soil organic carbon (SOC) is the largest source of terrestrial organic carbon and small changes in its components have many effects on global warming and carbon cycle. Soil organic matter (SOM) is considered as the most complex and least known component of soil, because it consists of plant, microbial and animal masses in various stages of decomposition and is a mixture of heterogeneous organic materials that are closely related with mineral components. Soil organic matter has beneficial effects on the chemical (buffering and changes in pH) and biological (precursor and supply of nutrients for microbes) properties of the soil and thus affects the fertility capacity of the soil. The quality and quantity of soil organic matter is the most important criterion for sustainable soil management. Total organic carbon (TOC) consists of labile and non-labile forms of SOC and have different degrees of sensitivity to different types of land use changes and management operations. The purpose of this research is to investigate the effect of changing land use on the chemical components of soil organic carbon and carbon recalcitrant index in Toshan Watershed, Golestan province

For this research, four major and dominant types of land use were considered in the study area, including forest, cropping land, garden and abandoned lands in the Toshan watershed in the northwest of Gorgan city of Golestan province. The soil organic carbon and total C of soils were measured. Furthermore, the soil carbon fractionation was performed by Young's method (using hydrolysis methods with HCl and Labile fraction). In this research, Acid hydrolysis method was used to separate the recalcitrant SOM pool. For this purpose, one gram of SOM sample was treated with 25 ml of 6 M hydrochloric acid solution at 105°C for 18 hours in a Pyrex tube in a hydrolysis package. After cooling, the remaining non-hydrolyzed materials were separated by centrifugation. Then, they were dried in an oven at a temperature of 60 degrees Celsius and considered as a part of resistant organic matter. The resistant part of the soil organic carbon was determined with the CHNS Analyzer device. The Labile fraction consists of water soluble carbon, microbial biomass carbon and mineralizable carbon are measured using the following methods and the labile part of carbon is calculated from their sum. Water-soluble organic carbon is extracted by adding 20 ml of distilled water to 10 grams of wet soil. The mixture will be shaken and centrifuged, filtered. Then they will be quickly analyzed by TOC Analyzer. Microbial biomass carbon will be determined by the chloroform fumigation-extraction method. Mineralizable carbon determined as follow. The amount of CO2 will be measured by titration of NaOH solutions with 0.1 M HCl in the presence of BaCl2. Cumulative amount of CO2-C emitted in 30 days of incubation is called Mineralizable carbon. The data were analyzed based on the factorial test in the form of a completely randomized design (CRD) with two levels of soil depth and four land uses with five replications. Correlation between traits was also estimated. Statistical analyzes were performed using SAS software. Therefore, it can be concluded that depending on the climatic conditions and the condition of the soil, the forest, in terms of natural cover, the correct management of agricultural lands (using modern methods of no-tillage or low-tillage) can be a potential practice. It is to store carbon in the soil as well as various soil components and increase soil formation, which will subsequently reduce the concentration of carbon dioxide in the atmosphere.

The results showed that the first depth of forest use has the highest amount of total carbon and soil organic carbon (6.12% and 3.5% respectively). Also, the highest amount of resistant organic carbon (HCl hydrolysis), water-soluble organic carbon, microbial biomass carbon, and microbial mineralizable carbon were observed in forest land use. The second depth (10-20 cm) of forest land use had the highest and the second depth (10-20 cm) of garden land use had the lowest organic carbon resistance index (82.1% and 50.17%, respectively). In all land uses, except for the forest, the soil organic carbon resistance index decreased with increasing sampling depth. Due to the fact that the carbon management index can be easily calculated, it can be a suitable index for quick assessment of soil quality.

The results showed that with the change of land use and cultivation, the soil organisms received more oxygen and the speed and intensity of respiration in the soil increased in the short term, which caused more decomposition of organic matter and with the decrease of organic matter in the long term, the quality of soil decreases after a while.

Mechanization is one of the main factors in the development of agriculture. Agricultural mechanization, as a basic approach in the production of agricultural products, provides goals such as timely performance of agricultural operations, reduction of production costs, reduction of labor intensity, quantitative and qualitative improvement of production and, in principle, the possibility of Economic production. There are inequalities in the development of agricultural mechanization, which is partly affected by natural factors, but human factors also play a significant role in its occurrence. Planning for the development of mechanization is one of the most important components in the development plan of the agricultural sector. The requirement for correct planning regarding agricultural mechanization depends on recognition of the existing situation. Knowing and evaluating the development indices of rice mechanization is necessary for the correct selection and optimal use of rice machines and timely and quality agricultural operations to be used as basic information in the calculation of rice mechanization projects and economic analyses. In this research, the indices of rice mechanization in the central and southern regions of Gilan province were studied with the aim of estimating the number of machines needed in rice cultivation.

Gilan province is one of the northern provinces of Iran, with an area of 14711 square kilometers which stands the second ranking (31% of total) in terms of area harvested. A study was conducted during the years 2020 and 2021 for determination of indices that govern the mechanization development in the central and southern regions of Gilan province. The studied areas were as rasht and khomam (in the central areas of Gilan province) with an area under rice cultivation of 62430 hectares and roudbar (in the southern areas of Gilan province) with an area under rice cultivation of 3375 hectares. The field method or field study was employed in terms of broad-based (holistic) and deep-based (depth-based) methods and its subset based on questionnaire for data collection in this research. Due to the lack of access to all villages of each city, one village was randomly selected and after checking their conditions, the relative homogeneity of the area was determined and the obtained information was generalized to other places. Collecting of data was done by completing the questionnaires through available statistical sources, field surveys and interviews with farmers. Data were collected from reliable authorities such as the Gilan agricultural jihad organization, agricultural jihad management of the cities, agricultural jihad centers, and the statistics of the Ministry of Agricultural Jihad. From the obtained data, the indices determining the state of mechanization, working days and farm productivity were calculated.

The results revealed that in the central and southern regions of Gilan, the degree of mechanization was 65.1 and 78.9 percent, the level of mechanization was 2.71 and 9.12, horsepower per hectare and the average capacity of mechanization was 415.74 and 782.10 horsepower in hour per hectare, respectively. On average, in the central and southern regions, there was one tractor for every 35 and 5 hectares, a tiller for every 5 and 11 hectares, a transplanter for every 46 and 31 hectares, and a combine harvester for every 88 and 56 hectares, respectively. According to the results, the number of machines in the tilling and spraying stages is more than the estimated number of machines in the studied areas. The number of available machines in the central areas was 77.1 and 55% more in tillage and 35.6 and 41.2 percent less in planting and 25.8 percent more in the southern areas in tillage and 79.7 percent and in 56.4 plantings and 2.3 percent less than the estimated number.

The degree of mechanization for tillage and transplanting operations in the central and southern regions of Gilan province demonstrated a good circumstance based on the sixth state plan of development. According to the expectations, by the end of the sixth development plan, the degree of mechanization in plant protection and harvesting operations, there is a need to reinforce and import more machines. The level of rice mechanization was higher in the south region than the central. From the above-mentioned reasons, the level of mechanization of rice in the southern region can be attributed to the multiple usage of the driving machines for paddy fields and other crops, the low area under rice cultivation and the large number of tillers and tractors, the lack of companies providing mechanized services, and little time available to farmers to carry out land preparation, transplanting, protection, and harvesting in these regions. The findings also showed that tractors and tillers, which were the most important sources of power supply, were not evenly distributed across the central and southern regions. In some cases, tractors and tillers were used in irrelevant tasks such as transportation and handling. According to the results, in the stages of tillage and spraying, the number of available machines is more than the estimated ones in the studied regions. According to the results, the number of machines available in the central areas in Tillage (Primary tillage, Secondary tillage, Puddling, Leveling) is 77.1% and Plant Protection (spraying and weeding) 55% more and in planting 35.6 and harvesting (Rice reaper, rice combine harvester, baler) 41.2 percent less than the estimated number. The number of machines available in the southern regions in tillage is 79.7% and harvesting 25.8% percent more and in planting 56.4 and Plant Protection 2.3% percent less than the estimated number. The comparison of the current conditions of these areas with the estimate shows that there is no proper planning in the supply and distribution of agricultural machines according to the cultivated areas. This shows the necessity of planning to establish more balance to create appropriate and homogeneous conditions for the distribution of agricultural machines in the studied regions.

Recovery of nutrients from plant residues is a sustainable and economical method in agriculture. Considering the important role of nutrients, it is essential to supply these elements in the soil and achieve the appropriate yield. The amount of nutrients in the plant residues after harvesting is very variable due to the difference between the species used. Each plant residue contains some nutrients that during the decomposition process, these nutrients can be available to the soil and crops in different amounts. In more detail, considering that the excessive use of chemical fertilizers has caused environmental problems and unused plant residues in the environment have created problems for the environment and farmers, therefore, to solve these problems, recovering important elements such as silicon from plant residues can be effective in improving the quality and quantity of many different products and plants. Rice straw, wheat straw and sugarcane bagasse are among the most common plant residues that have been studied in different studies to recover nutrients from them with different methods. In particular, rice straw is known as one of the most important plant residues that can be found in abundance in the north of Iran. Obviously, there is still a need for a better understanding of the amount of nutrients recovery from plant residues with different methods. And there is an effect of these elements on improving the condition of the soil. Considering that the excessive use of chemical fertilizers has caused environmental problems, as well as unused or underused plant residues in the environment have caused problems for the environment and farmers. The purpose of this study is to compare the residues of rice straw, wheat straw and sugarcane bagasse and the methods of recovering nutrients from these residues in order to add macro-nutrients (nitrogen, phosphorus and potassium) and micro-nutrients (iron, zinc, copper and manganese) into the soil. This research was carried out based on a factorial experiment in the form of a completely "randomized" design with three replications during 2022-2023. The treatments of plant residues in three levels (rice straw, wheat straw and sugarcane bagasse) and the methods of recovering elements from these residues in five levels (biochar, straw, digestion, ash and ash with acid) were examined. Soil samples, from a depth of 0-25 cm and with silty loam texture were randomly taken from the forest parts of Mazandaran province, Iran, characterized by a Mediterranean climate, Csa type, with an average annual rainfall of 676 mm, and average air temperature of 14 ℃, and then were air-dried. After preparing the samples, the characteristics of the treatments, macronutrients and micronutrients, including pH, electrical conductivity, total nitrogen, phosphorus, potassium, iron, zinc, manganese, copper, and silicon were measured. Analysis of variance (ANOVA) assessed the statistical significance of the differences in the studied variables among the different treatments. Tukey test was used for the post-hoc comparisons at a p-level < 0.01. Prior to the statistical analysis, QQ-plots were used to check the normality of sample distribution, and the data were square root-transformed whenever necessary. Moreover, the principal component analysis (PCA) was used to cluster the studied variables in groups related to the studied treatments. The results of analysis of variance showed the effect of plant residues and element recovery method on all studied characteristics including soil characteristics (pH, electrical conductivity and organic carbon), macronutrients (nitrogen, phosphorus and potassium) and micronutrients (silicon, manganese, copper, iron and zinc) were significant at the probability level of 1%. The results showed that the biochar treatment of rice straw had the maximum amount of pH (7.66), organic carbon (2.61%), nitrogen (0.24%), phosphorus (46 mg/g), potassium (781 mg/g) and silicon (261.33 mg/g) compared to other treatments. Also, the results of the compare means showed that sugarcane bagasse biochar treatment had the maximum amount of manganese (25.01 mg/kg), zinc (3.20 mg/kg), iron (48.27 mg/kg) and copper (2.20 mg/kg) compared to other treatments. The application of principal component analysis showed that three distinct groups (for rice straw/biochar, sugarcane bagasse/biochar and control treatments) were demonstrated, without clear overlap of the points related to these treatments and their element recovery methods. In general, this study confirmed that the treatment of rice straw residues and the method of recovering its elements through biochar play a significant role in increasing the quality and fertility of the soil and can be recommended to farmers.

These days, most of the disinfectants used in the food industry such as chlorinated compounds are dangerous and harmful. Common methods of removing all types of pollution have many disadvantages for human health and the environment. It is possible to help preserve the environment and human health by replacing these methods with new ones such as ultrasound technology. Ultrasonic waves are non-thermal technology that helps increase microbial safety and prolong the shelf life of heat-sensitive foods with nutritional value and functional characteristics. Ultrasonic waves are known as one of the most effective disinfection methods for all forms of microbial and fungal contamination. These waves reduce the resistance of microorganisms by physically damaging them. Therefore, this study deals with the effect of high-power ultrasound waves on the population of two type of pathogenic microorganisms in the washing effluent of tomatoes. The selected bacteria included one type of gram-positive bacteria (Staphylococcus. aureus) and one type of gram-negative bacteria (Escherichia coli) to compare the effect of ultrasound waves on the two different types of bacteria with different cell walls.

In this research, irradiation of high-power ultrasound waves were applied to the water after washing the tomatoes. In this washing effluent, the impacts of ultrasonic power (100, 300, 500 W), radiation time (300, 750, 1200 s), and water temperature (0, 30, 60 °C) were examined on the survival of the S. aureus and Ecoli. The data analysis was done for each experimental runs, using the response surface methodology (RSM), to find the best model for estimating the difference in bacterial population (CFU) before and after irradiation.

The lack of fit was not significant in the analysis of variance and also the value of the explanation coefficient in the model for S. aureus and Ecoli were 0.9721% and 0.9206% respectively. This indicated the appropriate accuracy of the quadratic model in estimating the number of S. aureus and Ecoli remaining in the water after washing tomatoes (for the mentioned independent variables). Gram-negative bacteria (E coli), are composed of an inner thin peptidoglycan cell wall, surrounded by an outer lipopolysaccharide membrane. Gram-positive bacteria (S. aureus), lack an outer membrane but are made up of a multi-layered and very complex structure layers of peptidoglycan many times thicker than is found in the Gram-negatives. In general, the application of ultrasound waves causes to destruction of the mentioned bacteria. The main disinfection effect of ultrasonic waves on the population of S. aureus was power, while for Ecoli the main variable was temperature (based on the highest coefficient of quadratic equations/ 99% confidence level). Through physical, chemical and mechanical effects caused by acoustic cavitation, ultrasound is able to affect the bacterial suspension without producing a side product. The antimicrobial effect of ultrasound is achieved by a combination of chemical effects such as the production of active free radicals and thermal effects such as the production of local hot spots. The observations showed that increasing the temperature first increased and then decreased the effectiveness of ultrasound waves in the inactivation of bactetria. The negative effect of increasing temperature can be related to the decrease in the intensity of bubble explosion.

According to the results of the experimental tests in the average time (750 s), with the simultaneous decrease in temperature (from 60 to 0 ºC) and increase in power (from 100 to 500 W), the destruction effect of ultrasound waves on S. aureus and Ecoli was increased. In the perturbation curves, the simultaneous effect of all three parameters (temperature, time and power), were investigated at the middle points (30 ºC, 750 s and 300 W). At these points, power changes were more effective in reducing S. aureus population, while temperature changes were more effective on the reduction of E coli. The population of S. aureus and E. coli decreased by increasing power of ultrasonic waves. Temperature and power had a synergistic effect, that is, the increase of both parameters led to the decrease of bacteria population. Finally, the tested variables were optimized by desirability in the RSM to minimize the population of microorganisms (S. aureus and E coli simultaneously), and parameters (in the range) obtained for the ultrasonic power, time, and temperature were 300 W, 1200 s, and 0 °C respectively.

Soil and water salinization is a worldwide problem, especially in irrigated areas, causing decrease in crop yield and the continuous loss of arable fields. Halophytes are the natural genetic source of salt tolerance traits and can be used for revegetation and remediation of salt-affected lands, and also as an alternative crop or biofuel. Due to the limited quality of water resources in the country and considering that the major regions of Iran's area are considered to be arid and semi-arid, it is important to cultivate plants with high tolerance to environmental stresses such as drought and salinity. The quinoa (Chenopodium quinoa Willd.) plant is important because of its ability to be cultivated in saline areas and irrigated with saline water. According to previous research, quinoa is an optional halophyte, and its irrigation is possible up to sea level salinity. Quinoa (Chenopodium quinoa Willd.) is one of the plants that has outstanding economic and agronomic advantages among the crops; it is particularly important in terms of forage production. There is no reliable and accurate information about the amount of water consumption by this plant in Iran. Considering the climatic characteristics and water shortages in the country, as well as the development plan for the cultivation of this plant due to its high nutritional value, attention to its water requirement becomes more important. For this reason, the importance of precise irrigation design and planning is needed in order to improve the performance of irrigation water usage in this region.

This research is conducted aim to determine the effects of different levels of moisture and salinity on the yield, some morphological traits, and some yield components of quinoa (Chenopodium quinoa Willd.) in field conditions during two growing seasons (2020-2022) in Yazd, Iran. The experiments were carried out in a factorial experiment in a randomized complete block design, which included two irrigation water salinity levels of 5 and 12 dS/m and four irrigation levels of 60, 80, 100, and 120% to provide the amount of allowable moisture depletion (MAD equal to 50%) in the root zone, in three replications. Experimental plots were designed with dimensions of 5×7 meters. Applying the amount of irrigation was done according to the determination of the field capacity levels and the permanent wilting point moisture measured (using a pressure plate device) before the start of the experiments. In this regard, according to this information, on the day of irrigation, the amount of soil moisture in each of the plots was measured at the root zone, and based on the treatments, the amount of water required was calculated, and irrigation was applied to the determined moisture level. Irrigation was carried out in the form of flooding, and the volume of irrigation water for each treatment was controlled by the volume contour and applied separately at each interval. At the end of the experiment, quinoa was harvested in a one-square-meter grid, and then plant height, panicle length and width, and stem diameter were measured. After the plant's drying, the weight of the seeds and the weight of the whole shoot were measured in different treatments.

The results showed that the different levels of salinity and soil moisture cause significant changes in biomass yield, seed yield, and harvest index. Also, the results indicated that changes in salinity levels and moisture levels caused significant differences in plant height, stem diameter and panicle length, panicle width, and 1000-seed weight (P<0.01), but their interaction was not significant. For two levels of salinity, the maximum biomass (9.28 tons/ha) was observed by supplying 100% of the depleted soil moisture based on MAD = 50%. According to the yield-water use function, the maximum seed yield for 5 and 12 dS/m irrigation water salinity was observed in treatments that supplied 115% and more than 120% of depleted soil moisture based on MAD = 50%, respectively. With the increase in salinity stress from 5 to 12 dS/m, biomass weight decreased by 23% and seed yield decreased by 17%. Based on the results, the average volume of applied water in fall cultivated quinoa under the 5 dS/m irrigation water salinity was 4900 m3/ha during the growth season (90 days).

In the autumn planting of the Titicaca variety of quinoa, with a planting period of about 90 days in arid and semi-arid regions like Yazd, water consumption is about 450 to 550 mm. But in conditions of moisture deficiency, it is possible to grow this plant. Because it has a lower yield reduction slope than other plants under drought and salt stress conditions. Furthermore, the results indicated that the salinity of the soil profile increased in deficit irrigation conditions (60% and 80% of depleted soil moisture based on MAD = 50%) due to the lack of leaching requirements.

In many countries, on average, more than 50% of people's food comes from grains, and nearly 70% of the cultivated area of one billion hectares of the world is dedicated to grains. A variety of weeds grow along with cereals in the fields, which can reduce crop yield due to competition for light, water and nutrients. To eliminate weeds accurately and with minimal problems, timely detection with high accuracy and speed is required. be done.
In the field of agriculture, it is controlling and eliminating weeds in grain fields. Weeds are one of the most important factors affecting the production of agricultural products, which are their most important competitors in conventional agriculture, they spray the entire field to eliminate weeds, while weeds appear scattered and patchy in the field. which shows the necessity of using precise agriculture to solve this type of heterogeneity. In addition to causing economic damage, the conventional method of fighting can cause pollution of the environment and even the human food chain. Research shows that the losses caused by pests, diseases and weeds can reach 40% of the global crop every year and it is predicted that this percentage will increase significantly in the coming years. Besides, according to the research of Goktoan et al., the annual cost of weeds for The Australian economy is estimated to be around $4 billion as a loss in agricultural income.

Among the new methods in this field is the use of machine vision technology and related methods such as deep learning object detection algorithms and convolutional neural networks (CNN). The steps related to the implementation of the project include preparing data for training and evaluating networks, using new object detection algorithms, using different convolutional neural networks with different characteristics to extract image features in algorithms, and using the Feature Pyramid Network (FPN) method in object detection algorithms. Was. The output of the networks was evaluated in terms of the number of detections, the exact location of detection and the time of detection in the field. ViTs is based on the Transformer architecture that was originally developed for NLP tasks. Transformers use self-awareness mechanisms that allow the model to capture complex relationships between elements in a sequence. In the case of ViTs, sequence elements are image patches. In using the transformer architecture for visual data, it is dividing the image into small and non-interfering parts. Each patch typically consists of a grid of pixels. These patches are considered the "words" of the image sequence. Spatial embeddings are added to image patches to provide spatial information to the model. Spatial embeddings are necessary because transformers do not have built-in notions of order or spatial relationships. ViTs use multi-series self-awareness mechanisms to capture relationships between different image patches, and the representation of each patch is updated by attention to other patches. Data separation is very important in data watch transformers for two reasons a) the model needs data to learn and b) we need data to measure the model because the model may not be able to extract the information correctly.

The best network in terms of positioning accuracy was the transform model (ViTs) with an average accuracy of 0.95. In addition to this, the network considered in this research managed to recognize 503 of the 535 target weeds, and this means that our network is able to recognize 95% of these weeds. The presented method has been able to reach the highest accuracy compared to other existing methods and has been able to detect existing grasses in a much shorter period of time. Compared to other methods, the reset50 algorithm has been able to detect more than 88%, although its execution time is about 2.5 times that of the proposed method.
In comparing the efficiency of algorithms, execution time is as important as accuracy. By making comparisons and considering 70% of the data as training data and 30% as test data, the presented algorithm has been able to detect the weeds in the field with an accuracy of over 90% in just 13 seconds.

Today, deep learning methods are much more efficient than other methods, so we can use the new methods available in deep learning in the field of agriculture.