Agricultural mechanization
Mehdi Anafche; nasim monjezi
Abstract
Introduction: Considering the high use of agricultural machines in sugarcane Agro-industry company; management of maintenance and repairs is necessary. The application of agricultural mechanization and the management of machines in agriculture is responsible for improving the way of working and correct ...
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Introduction: Considering the high use of agricultural machines in sugarcane Agro-industry company; management of maintenance and repairs is necessary. The application of agricultural mechanization and the management of machines in agriculture is responsible for improving the way of working and correct use of machines. Today, with the mechanization of agricultural systems, the timely performance of activities requires correct machine planning. For proper planning, one must know the reliability of the machines. Increasing efficiency and reliability of machines is an economic necessity. Any scheduled program to increase the reliability of machines can be effective in reducing time loss due to repairs and cause repairs to be completed on time. Therefore, in this research, according to the nature of the project of annual repairs of MF 399 tractors, in which the activities and the time of their completion are possible, the graphic evaluation and review technique (GRET) method was used. GERT's method is one of the possible network programming methods. In GERT's method, the occurrence of activities is considered possible and a percentage of probability is assigned for the occurrence of each activity.Materials and Methods: This research was conducted in the Dehkhoda Sugarcane Agro-industry Company in the crop year of 2023-2024. Dehkhoda Sugarcane Agro-industry Company has 42 Messi Ferguson 399 double differential tractors. In this research, in order to schedule annual repairs, statistics and data related to the repairs of 22 MF 399 tractors were collected and the data of other machines were used to compare the actual repair time with the calculated schedule. The 13 repair items of MF 399 tractors used in the company include: engine, clutch, gearbox, differential and rear axle, brake, rear hub, body, front axle, hydraulics, electricity and battery, wheels, service and greasing. The work breakdown structure diagram and network model of annual MF 399 tractor repairs were drawn. To extract the time distribution density function of each activity, first the available information was collected for each activity, then the time distribution density function was determined for each activity. The time to perform the activities is fuzzy and they are expressed by triangular fuzzy numbers. In this research, after scheduling, the results obtained from GERT's method were compared with the actual time spent to perform annual MF 399 tractor repairs and the appropriateness of the estimates was determined. For this purpose, in order to determine the actual time of annual MF 399 tractor repairs, among the tractors available in the mechanical equipment department of Dehkhoda Sugarcane Agro-industry Company, in the crop year 2023-2024, a sample was randomly selected and the actual duration of repairs was determined. Then the actual time to complete the operation was compared with the expected time.Results and Discussion: The time to complete the annual repairs of MF 399 tractors was estimated to be 225.93 hours using the classic GERT method. But in GERT's Fuzzy method, the time calculated for annual MF 399 tractor repairs was (295.30, 232.07, 168.73) hours. The actual duration of annual repairs for 20 tractors (out of a total of 42 tractors) in Dehkhoda Sugarcane Agro-industry Company, which were repaired during the 2023-2024 crop year, according to the information available in the technical office of the company's mechanical equipment, was determined. 29.12% of the real times for completing MF 399 tractor repairs were estimated outside the fuzzy time. Therefore, the manager of the repair unit can reduce the cost of time loss and manage the time to complete the operation in the optimal time frame by using correct planning and reducing the reasons for the delay in the operation.Conclusion: Therefore, GERT F Fuzzy's method provides the possibility to the manager of the repair unit to set the completion time in the estimated range appropriately, taking into account other effective factors involved in the operation, so that there is no disruption in the implementation of activities and on the other hand, the costs to minimize when the work is not done. It also provides the possibility to create cuts at different times and control the operation process and modify it if needed. In the real world, due to existing uncertainties, there is not enough confidence in the duration of the annual maintenance of tractors, but nevertheless, the results obtained from GERT's method in this research are close to the actual time of completion of operations and make the estimates more appropriate.
Agricultural mechanization
Roohollah Yousefi; Alireza Allameh
Abstract
Introduction: 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 ...
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Introduction: 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.Materials and Methods: 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.Results and Discussion: 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.Conclusion: 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. Keywords: Field Efficiency, Mechanization Index, Number of Machines, Rice, time opportunity, Working days.
Agricultural mechanization
Majid Namdari; Shahin Rafiee; Soleiman Hosseinpour
Abstract
Introduction Considering the essential role of the agricultural sector in Iran's economy, it is very important to investigate and identify optimal production methods from an economic point of view. The purpose of this study is to calculate the economic indicators of sugar beet production, use of the ...
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Introduction Considering the essential role of the agricultural sector in Iran's economy, it is very important to investigate and identify optimal production methods from an economic point of view. The purpose of this study is to calculate the economic indicators of sugar beet production, use of the Data Envelopment Analysis (DEA) method to identify the efficient units, and use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method to predict the benefit-cost index based on the consumption of production inputs in Hamedan province.Materials and Methods In this study, 88 farmers were studied. Data were collected from Hamadan province, Iran. Inputs included labor, machinery, diesel fuel, electricity, seeds, chemicals, farmyard manure, chemical fertilizers, and irrigation water. The indices of gross revenue, net income, gross income, economical productivity and benefit-cost ratio were calculated using information obtained from farmers. Then technical, pure technical, scale and cross efficiencies were calculated using CCR and BCC models for farmers. The benefit-to-cost ratio was considered as the economic index criterion in modeling with ANFIS. In this modeling, value of various inputs used for sugar beet production were selected as input variables. Various membership functions such as Triangular, Trapezoidal, Gaussian, Logarithmic and Gbell functions were tested. Also, different configurations were examined to provide the best configuration that predicts the model. In order to measure the accuracy of ANFIS models for estimating the observed values some quality parameters including the coefficient of determination (R2), root mean square error (RMSE) the mean relative error (RME) between the observed and the predicted values were applied to evaluate the performance of different models with different configurations.Results and Discussion The results showed that most of the production costs were in the category of variable costs. Variable costs account for 84% and fixed costs account for 16% of the total costs of sugar beet production. Cost of labor, water consumption, and land rent have the largest share of costs among all fixed and variable costs. The indexes of gross income, net income and benefit-cost ratio were obtained as 1188.99 $ha-1, 694.28 $ha-1 and 1.34, respectively. The results of data envelopment analysis showed that from the total of 88 farmers, considered for the analysis, 19 and 55 farmers were found to be technically and pure technically efficient, respectively. In other words, the farmers who are identified with the BCC model are more efficient than the farmers who are identified with the CCR model. Average technical efficiency, net technical efficiency, and scale efficiency were calculated as 0.73, 0.94 and 0.77, respectively.Data envelopment analysis indicates that farmers should focus on increasing the degree of mechanization of production by reducing the cost of human labor. The saving percentage of total input costs in the CCR model is higher than the BCC model. Optimization of input consumption in sugar beet production decreased the cost by 51.64% in the CCR model and by 28.27% in the BBC model. To predict the economic performance using inputs in sugar beet production, the three-layer arrangement with seven parameters obtained the best results. The modeled ANFIS is able to predict economic performance values with R2 of 0.96. This prediction is acceptable due to its high coefficient of determination and can be used in modeling.Conclusion Considering the high share of variable costs compared to fixed costs, it can be concluded that by applying appropriate management methods, the total costs of sugar beet production in Hamadan province can be significantly reduced. By mechanizing farms, the variable costs of farms can be reduced significantly. If the cultivated land does not have a problem with weeds, the use of conventional seeds can also reduce production costs. The DEA results showed that based on the CCR model, about 78.4% of farmers produce outside the efficiency and by providing management solutions taken from efficient DMUs (the recommendations of this study), they can reduce consumption costs by keeping product yield constant. The results of multi-level ANFIS implementation showed that the three-level ANFIS structure including four ANFIS models in the first level, two ANFIS models in the second level and a final model in the third level have the best performance for benefit-cost ratio prediction. It is proposed that implementation of multi-level ANFIS is a useful tool in helping to predict the economic indices of agricultural production systems.