Description of operations

The five main sequential field operations involved in potato (Solanum tuberosum L.) production are explained in details in the following:

1. Bed formation: Setting up perfectly formed beds is the first step towards successful establishment of a potato crop. The bed former uses shaped metal plates to lift up the soil and form it into one to more beds. This step is decisive, since the wheel tracks and the bed width are determined for all subsequent field operations of the season (Fig. 1a).

   Figure 1. The involved field operations in potato production: (a) bed forming; (b) stone separation; (c) planting; (d) spraying (photo source: gopixpic); (e) harvesting.

2. Stone separation: This operation is also a part of the seedbed preparation in stony and cloddy soils which can provide ideal growing conditions for fast emergence of the potatoes and reduction of the picking cost in the harvesting. A stone separator uses a digging share and separating web through which the fine soil falls into the bed while the oversize stones and clods are transferred laterally through a cross-conveyor to an adjacent furrow between already formed beds where separation is not performed. The conveyor can be adjusted either to the right or left when the tractor is at the end of the current bed. In successive operations the machine’s tires run on the rows of the processed stones and clods to bury them between alternate beds (Fig. 1b).
3. Planting: Potato planting starts immediately after the stone separation, normally by the use of automated planters. The planter is attached behind a tractor with the seed potatoes in a container, called the hopper. Special cups lift the seed potatoes from the hopper and place them with accuracy distance into the beds. The depth of sowing is about 5-10 cm and the distance between potato tubers along the rows are about 20-40 cm. Due to capacity constraints the hopper needs to be refilled occasionally. This is done by driving to the headland area where one or more reloading units are located, refill the hopper and return to the location of the field where the hopper ran empty. The time spent for reloading is part of the non-working time (Fig. 1c).
4. Spraying: Spraying with herbicides, pesticides, fungicides etc. are usually performed around 10 times during the entire season (Fig. 1d).
5. Harvesting: The most common harvest method is using a potato harvester with two or three rows diggers, depending on the bed type, which can dig out the potatoes from the bed. Soil and crop are transferred onto a series of webs where the loose soil is screened out. The potatoes are conveyed to a separation unit at the back part of the harvester. The potatoes then go on to a side elevator and into a trailer or bin located somewhere in the field (Fig. 1e).

Experimental field operations

Site description. The experiment was designed to record GPS data of the activities of all the machineries involved in the sequential in-field operations of the potato production in ten fields in Lolland, Denmark, from May to December 2014. Table 1 summarizes the information about the study fields’ shape, location and area.

Table 1. Experimental fields for case study.

Machinery and GPS recording equipment. The considered potato planting system consisted of 2.25 m wide beds which was the basic module width. Each bed consists of three rows. For each field crossing the bed former can produce two beds (one complete and two half beds). The stone separator, the harvester and the planter can only process one bed, while the sprayer can process 11 beds per crossing. Hence, the operating width w was 4.50 m for the bed former, 2.25 m for the stone separator, the harvester and the planter, and 24.75 m for the sprayer. The features of machineries that were used in the experimental field operations are presented in e 2. Two types of GPS receivers (Fig. 2) were used for recording the positions of the vehicles involved. An AgGPS 162 Smart Antenna DGPS receiver (Trimble^®, USA) with a Trimble® TMX-2050™ display (for storing the GPS data) was used for recording the trajectory of the bed former and the harvester, and three Aplicom A1 TRAX Data loggers (Aplicom^®, Finland) were used for recording the trajectory of the stone separator, planter and sprayer. The recording frequency was set to 1 Hz in all experimental operations. Geo-referenced data were recorded continually including the non-working activities, e.g. turning, machine repair, operator break time. It has to be noted that only the activities of in-field machines were recorded, so the activities of transport units, e.g. the tractor for transporting seed potato from the farm to the field in planting, and for transporting harvested potato in harvesting were not monitored in the experiment. In addition, in planting and harvesting, the service units temporarily were placed at one position, and then moved to another position to facilitate the planter/harvester when it was necessary. The positions of the service units were recorded by using the Aplicom data logger for later GPS analysis. It has to be mentioned that field 9 was not harvested at all due to the influence of the weather.

Definition of time elements and machinery performance measures

In order to classify time elements, e.g. time spent on effective working, turning and pausing, etc., a range of time element definitions were made as described in Table 3. There exists the following relationship between the time elements:

Table 2. Specifications of machineries involved in the potato production system.

Table 3. Time elements classification and definition.

Based on these time elements the field efficiency (FE, %) and the effective field capacity (EFC) for each operation in each field can be calculated, which is expressed as (Hunt, 2008):

Activities that contribute to delays, but take place outside the field, such as routinely maintenance, repair, and travel to and from the field, are not included in the estimation of FE.

The EFC of a machine can be calculated with two methods (Hanna, 2002). The first one is dividing the area completed by the hours of actual field time:

where is the area of the field (ha). The second method is using the estimation equation:

where S is the working speed (km/h). The constant 1000 in the formula ensures that the unit of EFC becomes ha/h.

Analysis tool for GPS recordings

Based on the concept introduced by Bochtis & Sorensen (2009), these five operations can be categorized into three groups according to the flow of material into or out of the field: Material neutral operations (MNO) (bed formation and stone separation), material input operations (MIO) (planting and spraying), and material output operation (MOO) (harvesting). In order to analyse the data recorded during the execution of these operations, a dedicated tool was developed using the MATLAB ^® suite (Mathworks, Natick, MA, USA). The input parameters of the tool include the coordinates of the field boundary and obstacle boundary (if any), the inner field boundary, i.e. the border between the headland and the main cropping area, and the coordinates of the machinery motion as well as the location of the service unit(s). The output consists of decomposed distance elements (e.g. effective working, turning, transporting, etc.) and the corresponding time elements.

The consecutively recorded data can be partitioned into line segments with sequential recorded data points by the field inner boundary. Those line segments that are located inside the main cropping area are considered as the on-the-tracks working motion trajectory while the line segments that are located in the headland area are considered non-working motion trajectory, such as turning, transporting, etc.

Due to the inherent inaccuracy of the speed measurements of GPS receivers the recorded position of a truly stopped machine may not be constant; consequently the machine is measured to have a slow movement. Therefore, to determine whether a machine is stopped or not, a threshold value v_>stop is applied in each data point. The value of the v_>stop parameter must be less than the usual operating speed and greater than the speed recorded due to the drift error. In this analysis v_>stop = 0.02 m/s was used. The effective working time on each track corresponds to the total number of data points that have a speed greater than 0.02 m/s, so the total effective working time in the main cropping area is the summation of the effective working time on the tracks.

The non-working motion trajectory in the headland area may consist of four activities: turning, transporting, refilling and unloading. In MNO operations only the turning activity occurs, while transporting occurs in both MIO and MOO operations. Finally, refilling and unloading occurs in MIO and MOO, respectively. To distinguish the turning, transporting, refilling/unloading activities in the headland area by the use of the recorded data points, circles were drawn with the radius of a given threshold value at the centres of the locations of the service units. If a machine stays inside the circle for a given threshold period of time, T_service, then the activity of the machine is categorized as being serviced and the transport time is the time on this motion trajectory minus the T_service. Otherwise, it can be considered as turning motion. The delay time in the headland area was calculated by isolating the sets of sequential points where the speed was lower than v_stop, 0.02 m/s. The value of T_service was set to 10 min and 1 min for reloading in planting and unloading in harvesting, respectively. Fig. 3 presents a flow diagram of the analysis.

Field shape index

In this study, operations were carried out in different complexity levels corresponding to field shape. The FE for irregular field shapes is expected to be less than for rectangular fields due to excessive turning time. In order to investigate the effects of field geometry on the FE a shape index, MBR (Moser et al., 2002), was used. MBR is defined as the ratio of the area of the field polygon and the area of the minimum bounding rectangle, and the index is used to describe the level of geometrical regularity of a field. The MBR is 1 for rectangles and approaches 0 when the shape becomes more irregular and odd. The calculated index values for the experimental fields are presented in Table 4. Furthermore, these index values were divided into two groups according the median value 0.78. These two groups were denoted as G1 (fields 1, 3, 5, 6 and 9 with MBR above the median) and G2 (fields 2, 4, 7, 8 and 10 with MBR below the median), respectively.

Table 4. Shape index values of MBR (minimal bounding rectangle) and index groups for the experimental fields.

Data recordings and analysis

In Fig. 4 the trajectory recordings of the bed former, stone separator, planter, sprayer and harvester in a selected field are presented. From the trajectories it is clear that the working width of the sprayer is much larger than the bed former, which is larger than the stone separator, the planter and the harvester. Fig. 4e gives the false impression that some of the tracks have not been harvested. The reason for this, however, is that the harvesting happens on the right-hand side of the tractor, where the GPS receiver is mounted, as shown in Fig. 1e. Therefore, the field is always subdivided into blocks to reduce the non-working turning distance and time, and the harvester starts its harvesting from the middle bed of each block. This fieldwork pattern creates the gaps between blocks as shown in Fig. 4e.

The GPS data analysis also revealed that a few track skip turns (loop turns: Ω-turn or Π-turn) were made. Often these turns were executed at higher speed and with shorter turning distance compared to the fishtail turns. For instance, in bed forming, the measured average speeds for T, Ω (skip 1 track), and Π (skip 2 tracks) (as illustrated in Fig. 5) turns were 1.08 m/s, 1.15 m/s, and 1.35 m/s, respectively. The measured average turning distance for these three types of turns were 30.1 m, 31.2 m, and 23.3 m, respectively.

Classification of time elements

Figs. 6a to 6e show the distribution of the average operational time elements and FE for each machine in each of the ten fields for bed forming, stone separation, planting, spraying and harvesting, respectively. The measured FE for bed forming ranged from 58.4% to 78.7% with an average of 71.3%; for stone separation, the FE ranged from 65.7% to 73.4% with an average of 68.5%; for planting, the measured FE ranged from 31.9% to 48.3% with an average of 40.3%; for spraying the measured FE ranged from 53.2% to 76.8% with an average of 69.7%; finally, for harvesting the measured FE ranged from 59.0% to 72.8% with an average of 67.4%. Note, that even though spraying is a MIO the sprayer did not need to reload (Fig. 6d), since the tank capacity was sufficient even for largest field, and that field 9 was never harvested (Fig. 6e).

Field capacity distribution

Figs. 7a to 7e show the distribution of the EFC (calculated by the first method) for each machine in each of the ten fields for bed forming, stone separation, planting, spraying and harvesting. The measured field capacity for bed forming ranged from 1.12 to 1.81 ha/h with an average of 1.46 ha/h; for stone separation, the measured field capacity was between 0.44 and 0.62 ha/h with an average of 0.53 ha/h. For planting, the measured field capacity ranged from 0.39 to 0.56 ha/h with an average of 0.47 ha/h. For spraying the measured field capacity ranged from 7.53 to 12.50 ha/h with an average of 10.21 ha/h. Finally, the measured field capacity for harvesting ranged from 0.37 to 0.62 ha/h with an average of 0.51 ha/h.

Comparison with ASABE norm data

The measured performance values of the machinery involved in the potato production were compared with the norms published by the Standard of the American Society of Agricultural and Biological Engineers (ASABE, 2011). The ASABE data give the FE and operating speed ranges with typical value for each machinery type. The selected values of FE from ASABE and calculated effective field capacity are presented in the Table 5. However, there are no specific ASABE data provided for bed forming and stone separation.

Table 5. Comparison of our measured values and the ASABE (2011) norms of field efficiency (FE), field capacity (EFC) and operating speed.

Effect of field shape on field efficiency

Large variations were found in the measured FE and field capacity for the five main operations in the ten experimental fields. It was found that the group G1 with higher MBR index values had higher average FE. As shown in Table 6, the group of most regular fields, G1, had 10.4%, 1.8%, 0.5%, 2.6%, 8.4% higher FE than G2 in bed forming, stone separation, planting, spraying, harvesting, respectively. In terms of the field capacity, the group with higher index values also had higher average field capacity, except in the case of planting where both groups had the same average field capacity of 0.47 ha/h. The G1 fields had 0.33, 0.05, 0.7, and 0.05 ha/h higher field capacity than G2 in bed forming, stone separation, spraying, harvesting, respectively.

Table 6. Comparison of field efficiency (FE) and capacity (EFC) between field groups G1 and G2.