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.Methodology 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.