INTRODUCTION
This work explores the general direct relationships that internal traffic has on yield, thus avoiding the complications of detailed soil compaction and complex soil interactions. To review the techniques, practices and systems being developed and promoted locally and internationally to reduce the impact of traffic on the ground.
OVERVIEW OF HARVESTING AND LOADING SYSTEMS USED IN
- Cane Cutting
- Mechanical Harvesting
- Infield Loading
- Infield Haulage
The choice of system depends on factors, such as labor cost and availability, growing conditions and topography (Meyer et al., 2005). High-capacity self-propelled loaders have been measured to load an average of 147 000 tons per year using two 9-hour shift operations (Meyer et al., 2001).
INFIELD VEHICLE MANAGEMENT SYSTEMS
Controlled Traffic
Field trials conducted by Braunack and McGarry (2006) showed that crop yields tended to be lower for random traffic than controlled traffic. In Columbia, Torres and Pantoja (2005) reported on controlled traffic being trained on a new crop configuration developed to better match that of the equipment's track widths.
Cropping System Configurations
The results showed that a row spacing of 0.9 m tended to give the highest, although this was more evident in plant crops than in ratoon crops. A 30% reduction in herbicide costs was also attributed to the change from single row spacing of 1.37 m.
SUGARCANE RESPONSE TO INFIELD TRAFFIC
Crop Growth and Development Responses to Infield Traffic
Short-term variable responses of cane stem populations have been shown to decrease in some cases (Jackson et al., 2000) or increase in others (Johnston and Wood, 1971; Fernandes et al., 1983; Braunack et al., 2006). In other cases, yield losses were significantly lower (Georges et al measured significantly smaller cane stem diameters and lower yields in their experiments.
Vehicle Characteristics Affecting Soil Properties
The tire-ground contact pressure or ground pressure is comparable to the tire pressure (Van Antwerpen et al., 2000). The first pass of a machine causes the greatest impact on a soil compared to subsequent passes under the same conditions (Maud, 1960; Fernandes et al., 1983; Van Antwerpen et al., 2008).
Modelling of Yield Response to Infield Traffic for Sugarcane
A database of machinery commonly used in Australian industry was included in the model to allow alternative traffic scenarios to be tested. Traffic intensities and traffic position parameters have not been precisely characterized for systems used in the South African sugar industry.
LINKING THE IMPACT OF INFIELD TRAFFIC TO SUGARCANE YIELD
Compaction Trial Results Database: Contrasting Row and Inter-Row Traffic 28
A preliminary high-level analysis of all data covering crop responses to inter- and intra-row turnover is summarized in Figure 5.1. The results of the categorization of interspecies and species traffic treatments are shown in Figure 5.3.
Investigating for Yield Response Trends of Sugarcane to Infield Traffic
From the trends it can be seen that cross-flow traffic in dry conditions is not affected by traffic intensity. Yield response trends indicate that high clay soils will respond best to control traffic practices. The lower clay soils appear to be susceptible to row and interrow traffic under high soil water conditions and are thus likely to be best managed by minimizing overall compaction through the use of light equipment with low soil contact pressure. and minimizing or avoiding.
Modelling Yield Response Trends of Sugarcane to Infield Traffic
From the database of yield responses and associated wheel passes, a model can be created from the relationships as defined in Figure 5.4, reverse-engineered to account for individual wheel pass effects. The trends are similar to those shown in Figure 5.4, scaled down to account for carryover effects. The model was tested against South African and international trial data as shown in Figure 5.7 and Figure 5.8 respectively.
METHODOLOGY
Field Data to Characterise Infield Traffic During Harvesting Operations
GNSS observation of the position of cane stacks or cane windows: to determine patterns of traffic movements in relation to cane stacking positions;. GNSS observation of the position of cane rows within the field: to determine the position of the rows, row spacing and inter-row areas within the field. Not every row was surveyed, but selected rows were observed and used to infer the position of an entire field of rows and areas between rows when processing the data in a CAD or GIS software package.
Determining the Position of Infield Vehicle Movements
- The use of GNSS to identify the position of wheel tracks infield
- Field marking instrument
- Field sketches of vehicle movements
The receiver antennas were placed over the wheel tracks to capture the movement of the loader position during the loading operations as shown in Figure 6.2. With wheel rotation, the tool will dispense a line of white agricultural chalk to mark the position of the wheel track. This line would then be visible for subsequent manual recording of the traffic movements that occurred during the loading operations.
Surveying Procedure
Both wheel positions of the loaders were simultaneously measured using GNSS receivers above the center line of each wheel track. It was necessary to accurately measure the wheel positions of the loaders, especially in the case of the non-swing loader as it dynamically rotates and rotates during the loading process.
Field Data Processing and Analysis
Thus, the entire field area was divided into two subareas consisting of rows and interrows, thus allowing for querying and analysis of the row and interrow area. The traffic layer was to be further separated by the position of the lane or inter-lane traffic components. This allowed the entire field to be categorized into lane traffic, lane traffic, or no traffic.
Summary of Systems Surveyed, Mapped and Analysed
In this way, each layer representing vehicle traffic within a given system was analyzed to determine the total area of traffic with lanes, traffic between lanes, and where traffic did not occur within the field. After determining the total row and interrow area of a field, the proportion of rows where traffic had occurred and the proportion of interrows that had been trafficked were determined. An estimated field-based yield loss for each vehicle in the system was determined by multiplying the 'point of impact yield losses', as determined from the literature synthesis in Chapter 5, by the proportion of lanes and interlanes trafficked within the field.
Description of Systems and Equipment Investigated in Survey A
Figure 6.12 shows an example of tractor-pulled double-stack self-loading trailers. A total of 10 loads with an average payload of 6.5 tonnes, consisting of 23 stacks of approximately 1.1 ha from the field, were transferred to the nearby loading zone. Figure 6.13 shows an example of the grab loader and box trailer used to remove sugar cane from the field from study A3.
Description of Systems and Equipment Investigated in Survey B
Three tractor-trailer units were used to transport the sugarcane from the field to a nearby transshipment zone. A total of 28 trips with an average payload of 4.2 t sugarcane per trip were removed from the field.
Description of Systems and Equipment Investigated in Survey C
Description of Systems and Equipment Investigated in Survey D
A no-turn loader was used to clear and store cane shafts approximately 15 m in the field from field edges to improve turning on the plows prior to loading. The track positions associated with the no-turn loader stockpiling operation were represented on the CAD after repeated observations of this stockpiling operation. In total, 170 t of sugarcane were removed from the field, which means about 55 t/ha of the crop.
Description of Systems and Equipment Investigated in Survey E
The area of the field was approximately 3.8 ha although the area used in the analysis was approximately 1.5 ha. Short row lengths with field narrowing were excluded from the analysis as the measured traffic patterns were inconsistent with the longer harvester runs commonly measured. To investigate the field impact of a mechanized helicopter harvesting system for local conditions while operating on a better management principle compared to alternative manual harvesting systems;.
Description of Systems and Equipment Investigated in Survey F
Defining Equipment Impact Ratings
To determine the impact of equipment on the systems studied, load transfer calculations were performed. Tire pressure for tractors was generally between 140 and 240 kPa; 160-300 kPa for loaders; 280-560 kPa for field trailers and approximately 600 kPa for road transport vehicles and trailer tires.
Estimating Field Production Yield Losses
Field-based yield loss estimates were calculated by imputing the yield loss contributions within the rows and the yield loss contributions between the rows for each vehicle entering the field. These contributions were added for each vehicle entering the field to determine an estimated system yield loss. The traffic 'footprint' examined does not take into account multiple passages over the same area and the additional yield loss that can be expected with multiple passages.
Estimating Field Production Yield Loss Economics
Similarly, yield losses at the 'point of influence' for interspecies turnover were applied to the proportion of interspecies field areas in which interspecies turnover occurred. Where traffic from different vehicle categories overlapped, the overlap area was considered to be dominated by the equipment with the highest impact, and the yield effect was based only on the traffic of the vehicle with the higher impact.
Mechanisation performances and costings
67 . applied to the proportions of the off-road areas that had vehicular traffic as derived from the field surveys. The next chapter focuses on the results obtained from the GIS analysis of the field surveys. It includes maps showing the volume of traffic occurring for each system examined, followed by the volume and proportions of lane and interlane traffic for each vehicle used in the system.
FIELD SURVEYS AND MAPPING RESULTS
- Infield Traffic System Maps
- Summary of the Extents of Infield Traffic for the Different Systems
- Comparison of Estimated Yield Losses Between Systems: General Analysis 76
- Comparison of Estimated Yield Losses Between Systems for High Soil
- Comparison of Estimated Economic Losses Between Systems
Yield loss estimates for this overall analysis are proportional to the amount of field traffic occurring across the field. This would indicate an approximate yield loss of 0.7% per lane traffic event for the low vehicle category. Synthesis of the literature for high soil moisture conditions, distinguishing between the characteristics of equipment entering the field, provided the following yield loss trend as shown in Table 7.4 (Refer to Figure 5.3, page 32).
SYSTEMS PERFORMANCE ANALYSES
The data presented are the basis for the assumptions used to perform the mechanization cost analyses. The performances of the various equipment compared to those reported in the literature study in Chapter 2 are as follows: the general performance levels for the self-loading trailers (A1 and A2) and the large skid steer loader (D) appear to be well matched; the small rotary loader (F) and non-rotary loaders (A3, B and C) performed better, and the forage harvester (E) operated at a lower discharge rate. This is based on modeled soil moisture content associated with long-term seasonal climate conditions.
ECONOMIC ANALYSIS: CASE SUDY
- Machinery system costs
- The value of a loss in cane production for a single harvesting event
- The compounding influence of annual yield losses on a whole crop cycle
- Yield loss cost taking seasonal factors into account
- Overall system costs
The use of the CF is intended to account for the annualized percentage loss of yield that occurs throughout the crop cycle. These results of the estimated crop loss during the harvest cycle (ECCYL) and the appropriate composite factors (CF) are shown in Table 9.4 for the harvesting systems examined. The value of the modeling approach allows testing of incremental return loss scenarios and sensitivities.
DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS
Discussion and Conclusions
The analysis of yield responses to field traffic indicated significant crop sensitivity to field traffic compared to field traffic treatments. The combination of equipment and management of the equipment on site must be carefully considered. Improvements in the management of the swing loader cutting and windrowing systems (D and F) are possible through the use of lower impact swing loaders that operate strictly according to the principles of controlled traffic.
Recommendations
The field research component of the study provided an overview of six typical operations covering the range of equipment and practices that exist in practice. An assessment of the severity of yield loss caused in areas of the field, such as field edges, where compaction and mud damage are likely to be widespread due to non-tracking configurations of long tractor-trailers with multiple trailers. This would require examination of field maps and typical extraction patterns to guide assessment of the extent and location of turnover within and between species to determine estimated yield loss for a defined system.
Effects of harvest turnover and soil water content on soil compaction and sugarcane regrowth. Effects of soil water content and compaction effort on soil compaction and sugarcane regrowth. The impact of sectorialization of agricultural workers on the South African sugar industry: A case study of the North and South Coasts of KwaZulu-Natal.
APPENDIX A: SOUTHERN AFRICAN DATA
APPENDIX B: INTERNATIONAL DATA
APPENDIX C: MECHANISATION SYSTEM COSTING ASSUMPTIONS
