Vibration Evaluation of the Driver’s Seat of MF 285 Tractor in Conducting the Tillage Operations

The purpose of this study was to investigate the effect of the tractor speed on the tractors’ seat vibration and to assess the level of comfort of the driver during the agricultural operation using moldboard and disc plows. Experiments were conducted on sandy loamy soil in a factorial experiment based on randomized complete block design with three replications. The effect of the type of tillage implement using the moldboard and disc plows, the velocity of advance in four levels of 4.5, 6 and 8.8 km/h and vibration was measured and evaluated at three directions of longitudinal (x), lateral (y) and vertical (z). The results of analysis of variance showed that the main effects of type of tillage implement, velocity and vibration direction on the acceleration values entered into the driver\u27s seat were significant at 1% probability level. With increasing forward velocity of the tractor-implement system the vibration on driver’s seat increased. In order to reduce the possibility of less daily vibration, it is suggested that a moldboard plow be used at a forward speed of 4.5 km/h and a disc driven at a speed of 6 km/h

Agroclimatic Zoning for Cultivation of Saffron Using AHP Approach in SARAB

According to agricultural experts, saffron is one of the crops that can be a good solution to the problem of drought in a water crisis situation. A plant which uses low water consumption and high economic incomes is a good alternative to wheat, a water-consuming crop. Saffron does not need water at all in the summer, and in the fall and winter, rain will irrigate this crop and require very little water during the year. This study was conducted to select the optimum location of saffron cultivation and its comparative study in Sarab with regard to the role of important factors in locating. For this purpose, climatic criteria including (mean temperature, maximum temperature, minimum temperature, sunshine hours and precipitation), geology criteria (soil), topography criteria (elevation, slope) and socio-economic criteria (land use) were used. Due to the diversity of information, the AHP approach was used for the spatial analyzes of the criteria required for saffron cultivation, and then the layers were overlaid. To determine the potential of different areas of Sarab for saffron cultivation, after investigating the data normality, geo-statistical models were applied to the data. Then, based on AHP model, effective factors were evaluated. At the end, the final result is presented as a zoning map of suitable locations for saffron cultivation. Results revealed that the eastern and western parts of the city (46.5%) had high potential for saffron cultivation. In the northern and southern parts of Sarab, due to the high slope and consequently high erosion, and also the presence of volcanic structures in these areas, it was caused the presence of more volcanic rocks. Thus they got low potential for cultivating saffro

Investigating Nano-Coated Surfaces in Improvement Wear Resistance of Tillage Tools

The blade in the tillage operation has the most interaction with soil particles. It causes the wear and tear of this piece and reduces its expected life span. Tillage tool wear is of great importance to farmers in terms of economic aspects, reduced quality tillage operations, and increased power consumption. The aim of this study is to investigate wearing of five materials including st37 steel (SST37) plate, galvanized steel (GAS), and fiberglass (GFRP), and two coatings (i.e., titanium nano-nitride (nano-TiN) and tantalum carbide (nano-TaC)) by sputtering in the plasma medium of the layer based on conventional steel. These pieces were tested in three types of light, medium, and heavy soil textures. To assess tool wear, a rotating soilbin was developed. The level of operation of the blades was evaluated in three steps of 500 m and a total of 1500 m with a benchmark of weight loss due to deterioration in the assessment of laboratory operations. There was a significant difference between different treatments. The important point of this research is that the nano-coated blades, owing to their superior properties and the smooth surfaces, showed the least wear. Their abrasion resistance is about 7 times that of the uncoated steel, 5.5 times that of galvanized steel. Fiberglass with reinforced polymer fibers showed a good performance against ordinary and galvanized steel against abrasion

Energy use pattern in production of Sugar Beet in west Azerbaijan province of Iran

With regard to the limitation of energy resources, especially non-renewable sources and increasing trend of energy consumption in agriculture, energy management in this sector is important. The purpose of this study was assessing energy productivity, input and output energy, energy efficiency and output-input energy ratio of sugar beet production in west Azerbaijan province of Iran. To achieve these objectives, statistical data about cultivation area, sugar beet yield in 2010 were acquired from the agricultural research center of west Azerbaijan province. Also data about cultivation methods, implements and machinery in use were obtained from sugar beet farmers by questionnaire. According to the results, total energy consumption in sugar beet production was 52268.72 MJ/ha, output energy was 722400 MJ/ha, energy output-input ratio was 13.8, net energy was 670131.28 MJ/ha and energy productivity was 0.82 Kg/MJ. The major energy consumers were chemical fertilizers with 34% of total input energy, irrigation (22%), implements and irrigation equipment manufacturing (12.84%) and spraying (7%), respectively. Approximately 29.48% of total input energy used in sugar beet production was direct energy and the remaining of 70.42% was indirect

The Effect of Tractor Driving System Type on its Slip and Rolling Resistance and its Modelling Using Anfis

Pulling force required for operations such as tillage is a result of the interaction between the tractor’s wheel drive and soil surface limited by various factors, such as the rolling resistance and slip of the wheel drive. In this research, the traction performance of tractors with different driving systems (four-wheel drive, rear wheel drive, and front wheel drive) was investigated. Test parameters included different tractor forward speeds (1.26, 3.96, and 6.78 km·h−1), tire inflation pressures (170, 200, and 230 kPa), ballast weights (0, 150, and 300 kg), and aforementioned driving systems, as well as required drafts (2, 6, and 10 kN). For each experiment, two indices of slip and rolling resistance were measured. The results of this study showed that the four-wheel-driving system indicated a low slip at similar pulling forces. In order to achieve a low slip, the four-wheel driving system did not necessarily need to add the ballast weight or to reduce the inflation pressure. The four-wheel driving system showed lower rolling resistance than the other two systems. Slip and rolling resistance of wheels were predicted using an adaptive neuro-fuzzy inference system (ANFIS). It was found that ANFIS had a high potential for predicting the slip (R2 = 0.997) and rolling resistance (R2 = 0.9893)

Prediction of the tractor tire contact area, contact volume and rolling resistance using regression model and artificial neural network

A novel method to estimate the contact area and contact volume was developed with molding the tire footprint by liquid plaster and converting these molds to three-dimensional models using a 3D scanner. A 12.4-28, 6 ply tractor tire was operated under three levels of vertical load, three levels of inflation pressure and three levels of soil moisture content. To analyses the obtained data regression and Artificial Neural Network (ANN) models were used and the accuracy of predicted results were compared with measured data. A multi-layer perceptron feed-forward ANN with back propagation (BP) learning algorithm was employed. Two hidden layers were used in network architecture and the best number of neuron for each hidden layer was selected with attention to minimum RMSE criterion. The results showed that tire contact volume is a better parameter than tire contact area to predict rolling resistance. The comparison of the results of regression and ANN models to predict the contact area, contact volume and rolling resistance showed that ANN predictions had a closer agreement with the measured data than the regression model predictions

Studying the Effect of Balancer on Engine Vibration of Massey Ferguson 285 Tractor

The mechanical vibration causes health issues to drivers, such as backache, spinal cord injury, etc. In this regard, a tractor engine plays important role. Tractors without chassis are equipped with a balancer unit reducing the secondary engine vibrating force and decreasing the engine and tractor vibration. The paper presented investigates the effects of balancer on secondary vibration. In this research, the root mean square (RMS) of vibration was computed for specific periods of engine work. Effects of rotational speed and engine load on engine vibration in two modes with and without balancer were investigated. The results showed that, at full engine load, increasing the engine speed resulted in increasing the vibration in both observed modes. Balancer utilization reduced the vibration by 22.3% on average. At fixed rotational speed, increasing load caused an increase in vibration in both observed modes. At 1400 rpm rotational speed and 125 Nm torque, balancer utilization managed to reduce the RMS of secondary vibration by 38.9%. Furthermore, at 250 Nm, RMS vibrations were reduced by 21.3% in comparison to no balancer mode. At full load, variable rotational speed, the balancer significantly reduced vibration by 29% on average. The balancer proved to be more efficient at lower torques

Investigating the Effect of Tractor’s Tire Parameters on Soil Compaction Using Statistical and Adaptive Neuro-Fuzzy Inference System (ANFIS) Methods

Many factors contribute to soil compaction. One of these factors is the pressure applied by tires and tillage tools. The aim of this study was to study soil compaction under two sizes of tractor tire, considering the effect of tire pressure and traffic on different depths of soil. Additionally, to predict soil density under the tire, an adaptive neuro-fuzzy inference system (ANFIS) was used. An ITM70 tractor equipped with a lister was used. Standard cylindrical cores were used and soil samples were taken at four depths of the soil inside the tire tracks. Tests were conducted based on a randomized complete-block design with three replications. We tested two types of narrow and normal tire using three inflation pressures, at traffic levels of 1, 3 and 5 passes and four depths of 10, 20, 30 and 40 cm. A grid partition structure and four types of membership function, namely triangular, trapezoid, Gaussian and General bell were used to model soil compaction. Analysis of variance showed that tire size was significant on soil density change, and also, the binary effect of tire size on depth and traffic were significant at 1%. The main effects of tire pressure, traffic and depth were significant on soil compaction at 1% level of significance for both tire types. The inputs of the ANFIS model included tire type, depth of soil, number of tire passes and tire inflation pressure. To evaluate the performance of the model, the relative error (ε) and the coefficient of explanation (R2) were used, which were 1.05 and 0.9949, respectively. It was found that the narrow tire was more effective on soil compaction such that the narrow tire significantly increased soil density in the surface and subsurface layers

Investigating the Effect of Tractor’s Tire Parameters on Soil Compaction Using Statistical and Adaptive Neuro-Fuzzy Inference System (ANFIS) Methods

Many factors contribute to soil compaction. One of these factors is the pressure applied by tires and tillage tools. The aim of this study was to study soil compaction under two sizes of tractor tire, considering the effect of tire pressure and traffic on different depths of soil. Additionally, to predict soil density under the tire, an adaptive neuro-fuzzy inference system (ANFIS) was used. An ITM70 tractor equipped with a lister was used. Standard cylindrical cores were used and soil samples were taken at four depths of the soil inside the tire tracks. Tests were conducted based on a randomized complete-block design with three replications. We tested two types of narrow and normal tire using three inflation pressures, at traffic levels of 1, 3 and 5 passes and four depths of 10, 20, 30 and 40 cm. A grid partition structure and four types of membership function, namely triangular, trapezoid, Gaussian and General bell were used to model soil compaction. Analysis of variance showed that tire size was significant on soil density change, and also, the binary effect of tire size on depth and traffic were significant at 1%. The main effects of tire pressure, traffic and depth were significant on soil compaction at 1% level of significance for both tire types. The inputs of the ANFIS model included tire type, depth of soil, number of tire passes and tire inflation pressure. To evaluate the performance of the model, the relative error (ε) and the coefficient of explanation (R2) were used, which were 1.05 and 0.9949, respectively. It was found that the narrow tire was more effective on soil compaction such that the narrow tire significantly increased soil density in the surface and subsurface layers

Prediction compost criteria of organic wastes with Biochar additive in in-vessel composting machine using ANFIS and ANN methods

In-vessel composting machine with the agitating system, circulating aeration system, and heating system on vegetable and food waste with coco peat additives and biochar obtained from coco peat was investigated. The composting process was tested at 55 °C, at three fresh inlet air rates of 20%, 30%, and 50%, three initial carbon-to-nitrogen (C/N) ratios of 18, 22, 26, and the addition of coco peat biochar of 5%, 10% w.b. (wet basis). To predict compost evaluation indicators of Electrical conductivity (EC), pH, C/N & GI, artificial neural network (ANN), and neural-fuzzy inference systems were used. The evaluation of the output parameters of compost showed high efficiency of the process. The amount of EC, acidity, and GI increased for all treatments, and the C/N ratio decreased. Also, the initial C/N ratio of 22 and fresh inlet air (FIA) of 30% were considered as the optimal setting conditions of the device. Treatment containing 5% biochar in the C/N of 22 resulted in the highest germination index of 93.55%. The best values of the coefficient of determination for the output parameters of the compost production process (EC, pH, C/N & GI) in the artificial neural network were 0.9252, 0.9863, 0.9691, and 0.9909 respectively. Moreover, the best values of the coefficient of determination in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N and GI were 0.999, 0.999, 0.994, and 0.992, respectively. Also, the lowest values of MAE and RMSE in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N, and GI were 0.0308, 0.0001, 0.2420, and 0.003 for MAE; and 0.0021, 3.66E−05, 0.1908 and 0.0041 for RMSE, respectively