The slippage is an essential criterion for evaluating the fuel consumption and the field performance of tractor. The objective of this research was to develop mathematical models using Design Expert software for modelling and predicting slippage of the CASE JX75T tractor (India manufacture) under operational field conditions. In this research, a chisel plough was used as a loading tool for the tractor under four levels of ploughing depths, with three levels of speed and two levels of cone index (CI) in silty clay soil texture. The experiments were carried out in the site of Basrah University. The results obtained from the fieldwork were analysed to evolve mathematical models and equations to predict and evaluate the performance of the tractor when the slippage occurred. According to the obtained results, the single effects of the parameters (CI, tillage depth, and forward speed) on the slippage were highly considerable (P<0.0001). Moreover, the interaction of the parameters were significant (p<0.05). The slippage of tractor increased by 187 and 116 % with increasing ploughing depth up to 25 cm and forward speed up to 1.53 m.s-1, respectively. On the other hand, tractor slippage reduced by 34% when CI increased up to 980 kPa. The data analysis showed that the developed model has passable imitation ability and excellently executed in confront of the actual data. This confirms the accuracy of the model for predicting tractor slippage under different fieldworks.

In this study a mathematical models were developed to simulate draft force for three types of plows (moldboard, chisel and disk plow). The study was carried out in the experimental field of Agricultural Machinery Department at University of Basrah, which had silty clay soil texture. Independent parameters included three levels of tillage depth (0.15, 0.20 and 0.25m), three forward speeds (0.54, 0.83 and 1.53 m/s) and two levels of cone index (550 and 980 kPa). Response Surface Method (RSM) was utilized to produce models and to analyze results. Acquired results were used to extract accurate model for draft force. The draft force increased by 114% when tillage depth increased from 15 to 25 cm. Increasing forward speed from 0.54 to 1.53 m/s led to increased draft force by 80%. The cone index had positive effect on draft force by 42% when increased cone index from 550 kPa to 980 kPa. The most influential factor in draft force is the tillage depth, followed by the forward speed and cone index. The highest draft requirements were recorded for moldboard plow, followed by chisel and disk plow. Models validation was acceptable ( R-Squared = 0.97) and the draft force could be predicted with reliability of about 95%.

Background: Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects during this operation. The aim of this work was to determine the best linear model using Artificial Neural Network (ANN), Imperialist Competitive Algorithm-ANN, regression, and Adaptive Neural Fuzzy Inference System (ANFIS) to predict the environmental indicators for land leveling and to determine a model to estimate the dependence degree of parameters on each other. Methods: New techniques such as ANN, ICA, GWO-ANN, PSO-ANN, sensitivity analysis, regression, and ANFIS that using them for optimizing energy consumption will lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand percent, and soil swelling index in energy consumption were investigated. The study was consisted of 350 samples which were collected from 175 regions in two depths. The grid size was set 20 m × 20 m from a 70-ha farmland in Karaj province of Iran. Results: The models that reveals the relationship between the land parameters and the energy indicators were extracted. As it was expected three parameters; density, soil compressibility factor and, embankment volume index had significant effect on fuel consumption. In comparison with ANN, all ICA-ANN models had higher accuracy in prediction according to their higher R 2 value and lower RMSE value. Statistical factors of RMSE and R 2 illustrate the superiority of ICA-ANN over other methods by values about 0.02 and 0.99, respectively. Results also revealed the superiority of integrated techniques over other methods for prediction of complicated problems such as land leveling energy estimation. Conclusion: Results were extracted and statistical analysis was performed, and RMSE as well as coefficient of determination, R 2 , of the models were determined as a criterion to compare selected models. According to the results,

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The primary objective of this paper was to develop an artificial neural network (ANN) simulation environment and mathematical models for predicting with high accuracy soil compression parameters. The experiments were conducted at the College of Agriculture - University of Basra, located at Garmat Ali, the soil was silty clay loam. The factors that were investigated are moisture content (14 and 24%), tillage depths (0, 15, 30, 45, and 50 cm) forward speeds (0.57, 0.94, and 1.34 m.s-1) and tire pressures (50, 100, and 150 kPa). ANN environment was developed with the back propagation algorithm using MATLAB software with various structures and training algorithms. Design Expert software utilized to evaluate the studied parameters and produce mathematical models. The results showed that all studied parameters had a significant effect on soil physical properties including bulk density and cone index. The effects of the studied factors on bulk density were depth > moisture content > forward speed, > tire pressure (6% 4%, 2.4%, 2%, respectively). Whereas, the order of the investigated factors based on their effects on cone index were depth > moisture content > tire pressure > forward speed (6%, 4%, 2.4% and 2%, respectively). The best model for predicting the bulk density under different field conditions was the 4-8-1 architecture. Levenberg-Marquardt (Trainlm) produced outstanding performance with an MSE of 0.00226 and R2 of 0.986. Moreover, this performance was occurring at an epoch of 100. For predicting cone index, the best performance was achieved by Levenberg-Marquardt (trainlm) in 85 epochs, giving minimum MSE equal to 0.005112 and greater (R2) equal to 0.967 during the training process. Thus, the optimal structure for predicting cone index was 4-7-1.