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.
Amin Nasiri; Hossein Mobli; Shahin Rafiee; Keramat Rezaei
Volume 36, Issue 2 , March 2014, , Pages 37-48
Abstract
Thyme is one of important medicinal plants that have been used since the past. This plant has many properties in the treatment of diseases, especially infectious diseases, thyme and its components are used in various industries such as pharmaceutical, food, cosmetics and health. In order to maintain ...
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Thyme is one of important medicinal plants that have been used since the past. This plant has many properties in the treatment of diseases, especially infectious diseases, thyme and its components are used in various industries such as pharmaceutical, food, cosmetics and health. In order to maintain the quality and quantity of essential oil extraction of plant drying process has a great role in the processing of medicinal plants. An important aspect of the drying technology with the aim of selecting the most appropriate drying method is mathematical modeling of the process. Therefore in this study, thin layer drying behavior of thyme (Thymus vulgaris L.)was experimentally investigated in a convective type dryer and the mathematical modeling performed by using adaptive neuro-fuzzy inference system (ANFIS). The drying experiments were conducted at inlet drying air temperatures of the 40, 50 and 60⁰C, at three drying air velocities of 1, 1.5 and 2 m/s. For kinetic model simulation of thin-layer drying of thyme, four ANFIS models were used, and to generate the fuzzy inference system model, the two partitioning techniques, grid partitioning and subtractive clustering, were used. Results indicated that ANFIS model could satisfactorily describe the drying curve of thyme. Also, comparison of the results of the two partitioning techniques showed that subtractive clustering technique was found to be the most suitable for fuzzy inference system generation for predicting moisture ratio of the thin layer drying of thyme.
Mohammad Ebrahimi; Seyed Saeid Mohtasebi; Shahin Rafiee; Amin Nasiri; Soleiman Hosseinpour
Volume 36, Issue 2 , March 2014, , Pages 81-92
Abstract
This study was investigated the effective parameters on the banana slices shrinkage during drying, using the response surface technique. In this study, the banana slices were dried using a thin-layer dryer made based on a computer vision system. Therefore, the shrinkage of the slices was determined using ...
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This study was investigated the effective parameters on the banana slices shrinkage during drying, using the response surface technique. In this study, the banana slices were dried using a thin-layer dryer made based on a computer vision system. Therefore, the shrinkage of the slices was determined using an image processing technique in the MATLAB environment. The response surface technique, central composite diagram (CCD) with four parameters, was used to investigate the effect of drying time, drying temperature, slice thickness and air velocity during the drying process (as the process parameters) on the shrinkage (as the process response). The second-order model was selected to describe the shrinkage as a function of the independent parameters (time, temperature, slice thickness and air velocity) due to RMSE=0.033 and R2=0.951. The results showed that the drying time, drying temperature, slice thickness and air velocity had the most effect on the banana slices shrinkage, respectively.
