An experiment was conducted on a farm at the College of Agriculture – University of Baghdad in a silty clay loam soil. “New Holland" tractor was used with the following equipment (rotary harrow, spring spike tooth harrow, disk harrow, disk ridger, ditcher and land plane) as a machinery unit. Studied treatments were laid out using split plot with a randomized complete block design with three replicates. The experiment consisted of two factors, the first included harrow types: rotary, spring spike tooth and disk harrow which represented main plots. The second factor included special equipment types: disk ridger, ditcher and land plane which represented sub plots. Effective field capacity, fuel consumption, soil disturbed volume, Energy requirements, slippage percentage as well as equipment effective depth was determined in this experiment. Results obtained indicated that land plane highest rate of field capacity (1.40 ha / hour) and less depth treatment averaged (16.26 cm) and the lowest rate of energy requirements (72.53 kW. h / ha) and the highest rate of the soil disturbed volume (2276.70 m 3 / h), while ditcher recorded lowest rate of field capacity (0.65 ha / hour) and the highest rate of energy requirements (148.54 kW. h / ha) and the highest rate of fuel consumption (32.15 L / h). the highest rate of the depth of treatment (18.92 cm) and the lowest rate for the soil disturbed volume ( 1224.87 m 3 / h) and the highest slippage percentage (10.40%). Superiority in disk ridger recorded the lowest rate of fuel consumption (28.88 L / h) and the lowest slippage percentage (8.09%). Research indicated that disk ridger was the best in the fuel consumption rate and the lowest slippage percentage, while the land plane was the best in the field capacity rate and the lowest rate of energy requirements and the highest rate of the soil disturbed volume. To relate the changes in the soil disturbed volume (Sdv), fuel consumption (Fc), slip (S) and  energy requirements (Er) with special equipment (SE), and harrow equipment (Ɛ), a regression analysis was carried out, and the predictive regression equation was obtained as in the following table:  Predictive Regression equation R2 Fc = 0.001639 SE + 0.63  Ɛ  + 29.1610 0.87 S = - 0.00591 SE + 0.31166  Ɛ  + 8.664487 0.90