Field traffic planning for reduced soil compaction

Transcription

1 Field traffic planning for reduced soil compaction D.D. Bochtis 1, C.G. Sørensen 1, O. Green 1 1 University of Aarhus, Faculty of Agricultural Sciences, Department of Biosystems Engineering, Blichers Allé 20, 8830 Tjele, Denmark of corresponding author: Dionysis.Bochtis@agrsci.dk Summary The mechanical impacts of field machinery on soil structure should be effectively controled in order to reduce the risk of soil compaction. In this paper, a route planning method for field machines for reduced soil compaction is presented. The resulting optimal traffic pattern consists of sequences of field-work tracks that do not follow any pre-determined standard motif. In contrast, the track sequence is a result of an optimisation under the criterion of the minimisation of a soil compuction risk measure. The resulting routes are compared with the traditional AB-patterns and the optimal under the criterion of non-working travelled distance B- patterns, in terms of two soil compaction risk factors and the non-working travelled distance for the case of an organic fertilising application unit. Key word: route planning, E38, machinery management Introduction Soil compaction induced by in-field traffic has adverse effects on bulk density, mechanical impedance, porosity and hydraulic conductivity which may reduce root penetration, water extraction and plant growth. Topsoil compaction is mainly influenced by the magnitude of contact stresses and without any mechanical tillage, topsoil compaction effects may last for up to 5 years (Lamandé et al., 2008). Subsoil compaction, on the other hand, is mostly related to the magnitude of wheel load. In the upper subsoil (25-40 cm), the natural recovery from compaction is very slow if it occurs, while the compaction below 40 cm is considered as persistent (Alakukku et al., 2003) leading to long-term yield reduction (Hamza & Anderson, 2005). The continuous increase in the weight of field machines and the necessity to use these machines in un-favourable soil conditions has increased the potential for soil compaction. In last decade, the use of large field machines has increased dramatically. Combine harvesters of more than 30 Mg and organic fertilising tanks of 35 Mg (e.g., in Western Europe) is a common occurrence in field operations. Fully loaded, the weight of two-axle sugar beet harvesters is about Mg and of three-axle harvesters up to 50 Mg (van der Linden and Vandergeten, 1999). Current results from research studies indicate that it is important to effectively control the mechanical impacts of agricultural machinery on soil structure in order to reduce the risk of soil compaction (Keller et al., 2007). A number of preventive strategies have been reported by Alakukku et al. (2003) and Chamen et al., (2003) including, the control of wheel/track loads and use of low tyre inflation pressures in order to adjust the machines and equipment used on fields in critical conditions to be aligned with the actual strength of the subsoil, the establishment of recommendations for wheel load ground contact pressure combinations in different soil conditions, or regulations based on quantitative guidelines for machinery, working at the most appropriate soil moisture content, on-land ploughing, reduced loosening of both topsoil and subsoil, the use of low ground pressure equipment, etc. However, there is a lack of research on the development of operational planning tools for the optimisation of the field traffic, in terms of route planning, under the criterion of the minimisation of risk on soil compaction.

2 Traffic planning for agricultural vehicles Traffic planning for agricultural vehicles has been addressed as two distinct problems in the related literature. The first problem regards the generation of the traffic lines (field-work tracks) and is more related to the representation of the field as a geometrical entity. The second problem regards the optimisation of the routing or motion of the vehicles within this geometrically defined world. Regarding the first problem, a number of methods for the representation of the field as a geometrical entity for field operations planning have been introduced. Oksanen and Visala (2009) introduced an algorithmic field decomposition method based on trapezoidal split of a complexshaped field plot into simple shaped subfields and using a heuristic search approach, the optimum driving direction in each sub-field was selected among a set of possible driving directions. De Bruin et al. (2009) proposed a method for optimising the spatial configuration of field tracks within agricultural fields while creating space for field margins. The optimisation approach involved an exhaustive search over a discrete set of track orientations and positional shifts that are derived from measured field geometry. Also, Hofstee et al. (2009) developed a tool for determining the optimum path for field operations in convex fields. Hameed et al presented a method for real-time generation of field geometrical representation for operational planning. The representation regards single or complex field shapes where generated tracks can be straight or curved. Regarding the second problem, the route planning for agricultural field operations, advanced methods based on combinatorial optimisation have recently been introduced. Bochtis (2008) introduced a new type of algorithmically-computed optimal fieldwork patterns (B-patterns) based on an approach according to which the field coverage is expressed as the traversal of a weighted graph, and the problem of finding optimal traversal sequences is shown to be equivalent to finding the shortest tours in the graph. The weight of the graph arcs could be based on any optimisation criterion, such as, total or non-working travelling distance, total or non-productive operational time, a soil compaction measure etc. Contrary to any traditional field-work pattern, B-patterns do not follow the repetition of standard motifs but they are the unique result of the optimisation approach on the specific combination of the mobile unit kinematics and dimensions, the operating width, the field shape, and the optimisation/s criterion/s. The implementation of B-patterns in autonomous (Bochtis, et al., 2009), or conventional agricultural machines supported by auto-steering systems (Bochtis and Vougioukas, 2008) showed that, under the criterion of the minimised non-working distance, it can be reduced significantly, by up to 50%. Using the well-known combinatorial optimisation problem, the vehicle routing problem for the majority of field operation, including the machinery system (single or multiple-machinery system), operational characteristic (deterministic, stochastic, and dynamic) and the facility units characteristics (single or multiple, mobile or stationary), can be carried out implementing the approach of the B-patterns (Bochtis and Sørensen, 2009). In this paper, a route planning method for field machines for reduced soil compaction is presented. The resulting optimal traffic consists of sequences of field-work tracks that do not follow any predetermined standard motif. In contrast, the track sequence is a result of an optimisation under a minimisation criterion. The planning approach The planning system presented here regards the case of organic fertiliser application, an operations that is usually executed with heavy machines (up to 35 Mg in total). The input of the system regards field-specific data and operation-specific data, specifically:

3 Field-specific data. These data include the field polygon, that is the ordered set of points (e.g. in UTM coordinates) where their sequential (e.g. pair-wise) connections constitute the field boundary, the driving direction in the field and the EM38 recordings (or any other related to the compaction spatial distributed measure). Operation-specific data. These data include the operating width, tank capacity, application rate etc. Initially, a 2-dimensional coordinate system is assigned to the field (e.g., the transformation of the UTM system), having as the y-axis a parallel to the driving direction. According to the headland pattern, a field is covered by a set of parallel field-work tracks. The parameters for the description of these tracks are generated (i.e., the set of points which defines each track). In a first stage, a reference orthogonal grid is generated. Each cell of the grid is a square with vertex equal to operating width. In a next step, the risk grid is generated. This grid is the results of the interpolation of the EM38 recordings on the reference grid of the field. It provides the carrying capacity map of the field which reflects the capability of the soil to support the weight of a machine. The interpolation method is the reverse distance method plus a weight factor correlated to the number of the samples (the later has to do with the reliability of the mean value). The next step is to provide a measure of trafficability of each track. Each track is represented by a board of cells (a subset of grid cells). There are two methods available for this purpose. According to the first one, this measure represents the average of the normalised EM38 recordings (or of any other soil sensitivity available measure). The second method is a threshold based method where the trafficability of a track is determined by the number of the cells that belongs to the track board with a normalised EM38 value above a defined threshold. In the presented case, the first method has been implemented. The experimental field The case study is based on an experimental field located at the Organic Research Station Rugballegaard, Horsens, Denmark [ N E]. The experimental field has a total area of 145,000 m 2. Based on 17 samples, the soil texture of the field for the depths of cm showed to be 2,1 % soil organic matter (SOM), 17,7 % clay (<2µm), 14,7 % silt (2-20µm), 40 % fine sand (20-200µm) and 25,4 % coarse sand (>200µm). As part of another more general field experiment, regarding the evaluation of the impact of the traffic indices on the soil compaction and (in the future) to the yield losses system (Green et al., 2011), a specialised experimental design was followed. Based on an algorithmic approach, a number of plots have been created where on each one of these different combinations of traffic treatments will be carried out. Each plot measures 9 x 1.5 m resulting in 739 plots in total. The plots were placed in pairs where the distance between the two plots centre was 3 m. The EM38 measurements in the centre of each plot were recorded (Figure 1a). The specific field area used for the presented study has an area of 20,736 m 2. The operating width of the application unit was 9m, resulting in 16 field work tracks on the test field area The tank capacity was 25 m 3 of organic fertiliser, while the application rate was m 3 /m 2 (volume of organic fertiliser per field area). The operational system capable for the execution of the route plans generated by the planning system is the pro-module itec Pro (Intelligent Total Equipment Control) and the GreenStar 3 System installed on an 8345/7730 John Deere tractor. The selection of this system was based on the advanced capabilities of the itec Pro on the coordination of the vehicle and implement functions with end turns (automated headland turnings).

4 (a) (b) Figure 1: The EM38 based conductivity map of the experimental field (a), and the particular field area for the presented case study. Figure 2: The system for supporting the execution of the advanced route plans.

5 Results The results for the implementation of three different routes are presented here. These routes are: 1. AB -pattern. This pattern is the most common used pattern followed in field operations and supported by all the commercial available auto-steering and navigation-adding system. Although the specific pattern is the most preferable in terms of the driver s convenience usually leads to a result far from optimal in terms of field efficiency, compaction, etc. 2. Bcom -pattern. The optimal () field-work pattern that results for the implementation of the presented planning system under the minimisation of the soil compaction (com) potential. 3. Bdis -pattern. The B-patterns type that result using as the optimisation criterion the nonworking distance (com) travelled by the machine during the traversal of the field tracks. According to the parameters of the specific field operation, two routes (or equivalently, two reloadings of the tank) in total are required for a complete field area coverage. For the specific case the resulted sequence for the two types of the optimal patterns are: Bcom -pattern R1=< > / R2=< > Bdis -pattern R1=< > / R2=< > The routes for the conventional AB -pattern are: R1=< > / R2=< >. The distribution of the load of the tanker while travelling on the field tracks is presented in Figure 3. Table 1 gives the outcome from the implementation of these types of routes. Table 1. Caption should be on the top of the table <Arial 11, italic; caption should end with a Factor 1 Factor 2 Non-working distance AB -pattern m Bcom -pattern ,317 m B -pattern m dis Factor 1 is the mean (for all cells of the grid) of the absolute value of the summation of the normalized EM38 measurement in a cell and the corresponding (mean) load at that cell plus 1 (in order for the factor to be also normalized). This factor derives from the logic that, ideally, heavy loads should be allocated at low values of EM38, while oppositely; light loads should be allocated at high values of EM38 values. Factor 2 is derived from a more complicated process. If the normalized EM38 value of a cell is below a predetermined threshold value, then the normalized value of Factor 2 equals zero. This outcome derives from the logic that in locations with low values of EM38, and consequently low soil sensitivity to damages, any machine load is acceptable (both light and heavy loads). If the normalized EM38 value is higher than the threshold value then two options occurs; if the normalized value of the load is also lower than the threshold value, the value of Factor 2 is again zero; in the opposite case where the normalized value of the load is higher than the threshold value, Factor 2 equals twice the difference between the threshold value and the normalized load. The total value of Factor 2 (characterising the whole field area) results from the summation of the values in all cells (actually in the cells that it not equal to zero, or equivalently, in the cells where a possibility for soil damage exists) divided for the number of cells of non-zero Factor 2. It has to be noted that the above mentioned factors do not constitute the optimisation criterions but rather they provide a measure of the optimality of the resulted route that can be connected in the

6 future (after the evaluation of a series of field trials) with potential yield losses as a result of a selected route. (a) (b) (c) Figure 3: The load distribution on the field tracks for the three different followed fieldwork patterns. Figure 4 presents the distribution of the above mentioned factors on the field area. It is clear that, for both factors, the B com -pattern provides significant reduction of the soil compaction risk on sensitive field areas. On the other hand, Bdis -pattern although that they provide the optimal field operation in terms of the non-working distance (and consequently of the non-productive time and fuel consumption) they impose soil compaction risk in more areas than from the conventional AB - pattern. Based on the previous results, the steps that will follow on this on-going research are: - The connection of the route planning effects on the yield - The correlation of soil compaction effects with economical cost measures - The implementation of a multiple-criterion approach (i.e. the combined problem of soil compaction risk and operational cost reduction)

7 Figure 4: The distribution of the soil compaction risk factors for the three fieldwork patterns. References Alakukku, L., Weisskopf, P., Chamen, W.C.T.,Tijink, F.G.J., van der Linden, J.P., Pires, S., Spoor, G., (2003). Prevention strategies for field traffic-induced subsoil compaction: a review Part 1. Machine/soil interaction. Soil and Tillage Research, 73, Bochtis, D. (2008). Planning and control of a fleet of agricultural machines for optimal management of field operations. Ph.D. Thesis. AUTh, Faculty of Agriculture, Department of Agricultural Engineering, Greece. Bochtis, D. D., & Sørensen, C. G. (2009). The vehicle routing problem in field logistics part I. Biosystems Engineering, 104(4), Bochtis, D. D., & Vougioukas, S. G. (2008). Minimising the nonworking distance travelled by machines operating in a headland field pattern. Biosystems Engineering, 101(1), Bochtis, D. D., Vougioukas, S. G., & Griepentrog, H. W. (2009). A mission planner for an autonomous tractor. Transactions of the ASABE, 52(5),

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