Farm Machinery Cost and Size Management and Optimization for Wheat Production in Dongola Area, Northern State, Sudan

Machinery management has increased in importance in recent farming operations because of its direct relation to the success of mixing inputs to return a satisfactory profit. The study aimed to develop a computer system to be used as a decision-making tool for improving the efficiency of field operations mechanization (land preparation, planting and harvesting) management for wheat production under Northern state, Dongola area. The data collected included machine purchase prices (SDG), expected working days, working hours per day, repair and maintenance (R Machinery performance unit to estimate effective field capacity (ha/h), and unit for field operations costs in (SDG/h and SDG/ ha). Principles of operation research (OR) and linear programming (LP) mathematical modeling techniques were employed to formulate the main objective functions and optimization. T-test was used to analyze the collected data. The results showed strong positive correlation between predicted and actual field capacity (R2 = 0.963). For land preparation, actual field capacity was significantly (P ≤ 0.05) higher (2.1ha/h) than the predicted one (1.71ha/h), while for the planting operation, the predicted effective field capacity of BALDAN drill was higher by 28% than the actual field capacity. On the other hand, the predicted field capacity of FOTTON harvesting machine was higher by 19% than the actual. Moreover, for land preparation operation cost, the system predicted the lowest total operation costs (155038.9 SDG) for offset disc harrow (2.70m) compared to the actual total operation costs while for the planting operations costs, Agro-master drill gave the lowest predicted total operation cost (171353.52 SDG) compared the actual total operation costs. Furthermore, FOTTON 4Lz2 recorded lower predicted harvesting operation cost (174841.3 SDG) compared to the actual operation cost. After optimization it was found that, the best combination options, for land preparation it was 21 tillage implements of size 1.9 m with 34 implements, size 3.65 m, while for planting operation it was four implements, size 3.30m, with 36 implements, size 4.00m and for harvesting operation it was two combine harvesters, size 4.20m with 43 combine harvester of size 4.30m to cover the 50000 ha field area. It was concluded that the developed program is valid to estimate these parameters with a high level of confidence and accuracy.