Multi-linear Repair and Maintenance Cost Models for Tractors in Saudi Arabia

The relationship between the cumulative costs for repair, maintenance, the annual operating hours, tractor power and price, tractor age (years), and the rate of annual cumulative costs per kW-h was studied. Complete and accurate data recorded by Hail agricultural development company (HADCO) for 39 tractors were used in this study. The analysis showed that the average annual operating hours reduced from 1558 to 1338 as the tractor age (years) increased. Also the average annual operating hours increases with the tractor power except for huge tractors where their use was limited for high draft operations only. Based on the effect of different factors on the repair and maintenance costs, multi-linear models were developed to estimate tractor repairs and maintenance costs represented by the following function. ATC = f(N,P,AHRY,kw) Where: [ATC: accumulated repair and maintenance costs, N, P, AHRY, kW tractor age (years), tractor price, accumulated operating hours and tractor power respectively. The multi-linear models compared with exponential models which were developed in previous study. The comparison showed that there is no difference between the two models for tractors with power less than 80 kW and age of 6 years or less. But for other cases the difference from the actual costs ranged between 68.8 to 8% when the exponential models used, while the estimated cost was less than 2% of the actual costs when multi-linear models used for the same tractors

Farm tractors breakdown classificati

Work orders for 40 tractors, collected by Hail Agricultural Development Company (HADCO), Saudi Arabia, were used in this study. Data for more than 1670 work orders were classified as repairs or maintenance. The relationship between tractor age and power on repair and maintenance costs was also investigated. A cost ratio (CR) term was developed by dividing the repair or maintenance cost by the tractor purchase price. This CR was used in all variables affected the repairs and maintenance costs. 51% of the work orders were classified as repair works while 49% were classified as maintenance jobs. The highest CRs were listed for both repair and maintenance. The 64 WJOs for transmission repairs had the highest CR, while electrical system repairs WJOs were 197 and had the lowest CR. That is because the costs of some parts were much affecting the CR and not the number of work orders. Similar results were obtained when calculating the classified maintenance CR. The repair CR was higher than that of the maintenance. Both repair CR and maintenance CR were directly related to tractor working life (age) and tractor power

An Instrumentation System for Measuring Field Performance of Agricultural Tractors

An instrumentation system is designed to measure the field performance parameters of the most common agricultural tractors and implements. The package is designed such that it can be shifted from one to another easily. Transducers such as load cell, PTa (Power Take Off) torque transducer, magnetic pickups, fuel flow sensor, and PRTs (Platinum Resistance Thermometers) are used to measure drawbar pull, PTa torque and speed, tractor speed and slip, fuel flow, engine speed, and temperatures of fuel and engine. Signals from the transducers are scanned and recorded using a datalogger

Repair and Maintenance Cost Models for Agricultural Equipment in Saudi Arabia

Repair and maintenance cost models were developed in both exponential and multi linear equations. The accumulated cost per list price was calculated by two different methods; the first by using the accumulated working hours, while the second by using the percentage of machine age (years) to its expected wear out life. When the published repair and maintenance models were applied to Hail Agricultural Development Company case, it was found that the expected costs were higher than Hail Agricultural Development Company actual costs. That could be due to the unsuitable adoption conditions of such models. It is recommended that each area or country should develop its own models according to its operational, economic, and field conditions. Grouping machinery into different categories according to its power, age (life), or cutting width would provide better models to predict repair and maintenance costs than general models. In most cases, multi linear models employing many variables in the equation gave better cost prediction with higher confidence and less variation than exponential models

Repair and Maintenance Cost Models of Seed Drill

Repair and maintenance costs represent a major part of the operating costs of farm machinery. Usually the estimation of these costs associated with the working age of the implement or the worked area by developing a mathematical model contains all cost elements. This model mainly uses for determining the elements which had the highest potential of reducing the total operating cost. Due to the differences in the operating conditions and also the differences in the periodical maintenance of farm machinery from one place to another, it makes necessary that, each area develop its own cost models. The objective of this paper is to develop a model to estimate repair and maintenance costs for seed drills. The field data collected by HADCO (Hail agricultural development company) for 42 seed drills will be used for this purpose. The data covered the period of 1987-1993. The recorded data included: the implement age in years, the working width in meters, the cost of spare parts used for each implement and the labor cost. Also, the worked area by each implement during the previous period in hectares. The results showed that, annual repair and maintenance costs decreased by increasing the average worked area using seed drill until 390 ha/year and then increasing. The mean annual repair and maintenance cost was 5925 SRlyear and was 15.27 SRlha. Labor cost accounted 8% while spare parts represent 92% of annual R&M costs. Two models were developed to estimate accumulated R&M costs, the first one was a function of accumulated area divided by 1000, and the second was a function of machine age in years