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Modern Farm Machinery

Operational Analysis of Farm Machinery
Author: Redmond R. Shamshiri
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1. Introduction
Mechanical power was adopted for farm use in the late 1800’s and is a vital element in today’s modern agriculture. As the primary source of power in field, farm machinery should be used to the best possible advantage. This is an important issue as agriculture moves towards mechanization. With growth in average farm size, faster and higher capacity machines are demanded to accomplish farm tasks in a shorter time. Since larger machines are more expensive, their time lost such as field adjustments, loading seed and fertilizer and row ends turnings becomes more critical and more costly during annual operational hours. Yield variability in a farm that is presented in the yield map depends not only on agricultural and biological variables (such as soil nutrition and property) but also machine operator’s performance. Farm machinery indexes play important roles in agricultural mechanization and technical management especially during the busy pick periods when timing becomes an issue. Shortening machine operation time while keeping work quality is desired to produce optimum output. It is therefore important to know about important principles of GPS for agricultural application.

Operational analysis is an approach to increase machine capacity and obtain efficient machine utilization. Driver’s performance and field condition affect total operation costs, such as fuel, lubricants and repairs, especially in larger machinery which have higher hourly costs. Another issue that is important in any farm operation and may affect farm machinery is timeliness. This parameter refers to the ability of manager to complete a farm activity at such a time that crop return (quantity and quality) is optimized. Better management strategies to improve planning and scheduling such as motion-and-time study management will reduce peak machinery demand and maintain a more stable machine force on the farm, leading to increasing yield and profitability. Insufficient machine capacity may prevent completion of a field operation and create economic penalties. In some cases, the quality of field crops, including grains and hays, or horticultural crops, including vegetables and fruits are affected by the dates of planting and harvesting which represents a hidden cost associated with farm machinery. Therefore, obtaining accurate time record of all activities for a specific machine operation is always the first step in operation analysis. GPS receivers and data loggers can easily generate and store time and position information. The second step is to divide the time recorded into primary and support functions. For example, in a planting operation, placing seed in the ground is the primary function. Support functions include turning, adjustments, and adding seed, chemical and fertilizer. Each component of operation is expressed as a percent of total field time. GPS mounted equipment and computer algorithms can provide managers with essential information for analyzing machine performance, including effective operation time. The third step is to provide details analysis of the information obtained in the steps one and two. This includes the examination of each segment of the operation to determine if the time may appear to be excessive when compared to average values from reasonably efficient operations. Computer programs such as GIS software can be used to visualize this analysis and make
a decision for those segments which show the greatest possibility for improving the efficiency of the total operation.

2. Track-and-record of farm machinery
Increasing machine productivity can be achieved through optimizing effective field capacity (Hanna, 2001) which at the end, translates into lower unit cost of production. Two parameters play an important role in effective field machine capacity. First, machine management which refers to the mechanical condition of the machine and indicate where, when and how the machine is used on field. Second, physical condition of the field which includes field size and shape, topography, terrace layout, row length and arrangement, row-end turning space and field surface. Since a particular machine has a fixed theoretical field capacity, therefore, new technology such as GPS/GIS and wireless communication for real-time data increase machine productivity not in terms of acres per hour, but by utilizing machine and operator’s time more effectively. Track-and-record of machinery location in field using GPS is the first step in precision analysis of farm machinery operation. Processing such raw data provides useful information and document changes in machine field speed and field time that can help growers to create decision support systems for a better farm and machinery management. For example, precise determinations of time lost using GPS data along with accurate measurements and records of field speed provide an integrated tool to calculate field efficiency and machine capacity as well as visualizing driver’s performance. These results can be used to make decision on machine size and selection.

3. Field efficiency, field machine index and scheduling efficiency
Effective time of machine operation is total field time minus time lost. The percentage of machine’s time lost should be considered in the operational analysis. Field efficiency is the ratio between the productivity of a machine under field condition and theoretical productivity. This parameter accounts for time loss of operation, management policy and field characteristic. Time loses can be influenced by row end turning, machine adjustment, lubrication and refueling, material handling (seed, fertilizer, chemicals, water, harvested material, etc) and equipment cleaning. Field efficiency is not constant for a particular machine and varies with the size and shape of the field, crop yield, field pattern and other conditions. It can be increased by reducing time lost, such as row end turning. Turning time greatly influences machine capacity. Once the information about different points of a field is known, parameters such as distance between points, travel speed and the surrounded area between points can be calculated. In addition to that, having a GPS receiver mounted on a particular machine like a grain combine, citrus mechanical harvester, chemical sprayers, etc, and collecting the PVT and other relevant operational data such as the harvested mass or the amount of applied chemical, it will be possible to determine additional parameters that are used in analyzing farm machinery management or in creating yield map (Lotz, 1997, Parsons, 2000), soil map, land field boundary map, etc.

Field machine index (FMI) is an indication of how well a specific field is adapted for the use of machinery on it. This index includes the influence of row-end turning conditions and row length on actual field production time and total row end turning time. In the other words, FMI is the ratio of the productive machine time to the sum of productive machine time plus the row-end turning time. Time loses refers to the time used for support functions, such as making adjustment and fueling. The maximum possible value for FMI is 100 percent. The higher field machine index, the better field adapted to machine use. Three basic items of information are needed to determine FMI, namely, total field time (Tf), total support function time (Ts) and total turning time (Tr). All of these items can be calculated accurately from raw GPS data. The FMI can be calculated as follows:

FMI=(Tf-Ts-Tr)/(Tf-Ts )×100 (Eq.1)

FMI is useful in predicting machine capacity and for determining machinery needs and hours of use. An interesting point is that FMI for a specific machine on a particular field is almost the same for other machines used on that same field. For example, if FMI is low for one machine operation, it turns out to be low for other operations on the same field. Effective field capacity is a function of field speed (S), machine working width (W), field efficiency (η_f) and unit yield of the field and is expressed by area capacity (C_a) and material capacity (C_m), they are given by the following equations in ASABE:

Ca=(S.W.ηf)/10 : (Eq.2)
Ca=((S.W.ηf)/10) * Y : (Eq.3)

Field speed and field efficiency can be determined directly from GPS data. Instantaneous yield is defined as the harvested mass per unit area and can be calculated as:

Yield(kg/m^2 )=Mass/Area=Mass/(Distance.Width)


Yield=(Flow rate.Time)/(Speed.Time.Width)=(Flow rate)/(S.W)

The flow rate is measured using mass flow sensors such as impact force sensor (load-cell based), plate displacement sensors (potentiometer devices), radiometric systems or image processing applications. Calculating field efficiency from raw GPS data require computer algorithms to determine machine time loses, which is a result of row end turnings, machine adjustment (unclogging of spray nozzles), lubrication and refueling, material handling (seed, fertilizer, chemicals, water, harvested material, etc) and equipment cleaning. Time losses that are either proportional or non-proportional to the area should be determined from collected GPS data and filtered out from the effective harvesting time in order to calculate field efficiency according to the following equations:

η_f=Tt/(Te Th Ta ) : (Eq.4)

where Tt is the theoretical time to perform an operation, Te is the Effective operating time, T_h is the Time losses (not proportional to area) and Ta is the Time losses (proportional to area). These results can also be used to determine scheduling efficiency which is the ratio of effective operating time to the total workday hours and indicates the ability of farm manager to utilize working hours and employees. This is also a useful parameter for making a decision on machinery size selection (Eq.5).

Ci=A/(B.G) (Eq.5)

where C_i is the required machine capacity (ha/h), A is the area (ha), B is the number of days to finish the operation and G is the expected time available for field work each day (h/day).

Other parameters of interest that GPS data can be used directly or indirectly in their calculation are the unit price of machinery (Eq.6), fuel consumption cost, labor cost, annual timeliness cost for an operation (Eq.7) and optimum machine capacity (Eq.8).

K_p=(10P_w)/(S.η_f ) : (Eq.6)
W=(K_3 A^2 Y.V)/(Z.G.C_i ) : (Eq.7)
M_oc=√(100A/(C_o K_p ) (L_c T_fc (K_3 A.Y.V)/(Z.G)) ) : (Eq.8)

where K_p is the unit price function (dollars/ha-h), P_w is the price per unit width of increased of machine (dollars/m), W is the annual timeliness cost (dollars), K_3 is the timeliness coefficient obtained from ASAE D497, clause 8, V is the value per yield (dollars/ton), Z is equal to 4 if the operation can be balanced evenly about the optimum time, and a value of 2 if the operation either commence or terminates at the optimum time, G is the expected time available for field work each day (h), M_oc is the optimum capacity of a machine (ha/h), C_o is the ownership cost percentage (%), L_c is the labor cost (dollars/ha) and T_fc is the machine ownership cost (dollars/ha)

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