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• Spatial mapping of mallee feedstock including age and planting arrangement to improve harvest management, reporting, mapping and data exchange. • Monitoring of harvest progress using GPS tracking on harvesters and transport units to record area and volume of biomass removal, consignment delivery and, based on weighbridge data at the processor, paddock yield. • Tracking of road haulage equipment based on GPS tracking to inform scheduling of deliveries and asset management. • Harvest haul modeling of specific supply areas to optimize placement of loading zones and layout of mallee plantings. • Refinement of mallee biomass production models to optimise farm layout and row spacing, given information on soil type, drainage and adjacent crop requirements to understand the implications of changes on both the farming and harvesting sectors. • Broaden the scope of the models so that they encompass the entire value chain right through from biomass production, to harvesting and transport, processing and product diversification options. 157
7. Supply Chain Modelling and Economic Considerations Chapter six has discussed broad supply chain planning and management issues relevant to the sugar and mallee industry. It was beyond the scope of this project to undertake a comprehensive value chain assessment for the mallee industry. A desk-top assessment of the logistics for mallee supply in Western Australia was undertaken to provide economic consideration of alternative harvest haul systems and in particular identification of key drivers and cost sensitivity. The basis for this assessment was the harvest-haul model discussed in section 6.5 (Sandell and Prestwich, 2004) 7.1 The Harvest-Haul Model The Harvest Haul model is a deterministic model that estimates the time and cost performance of harvest at a block level and aggregates results to the farm, group and regional levels. While the model was originally developed for use in the sugar cane industry it is applicable to and has been used for a variety of harvesting operations. Industry issues that the Harvest Haul Model has been used to investigate include: • Modelling new projects and conducting sensitivity analyses. • Modelling harvesting cost changes for farm re-configuration. • Comparison of current harvesting practices to Harvest Best Practice • Modelling harvesting cost changes for full trash collection for co-generation • Harvest group restructure or amalgamations • Siding or pad location re-arrangements • Haulout optimisation within a group to determine the cost effective number of haulouts The Harvest-Haul Model has been used extensively in the Australian sugar industry from Condong mill to Mossman mill and internationally for Fiji Sugar and Ramu Sugar in Papua New Guinea and is applicable to biomass harvesting operations. The model estimates the time performance of harvest and applies costs on an hourly basis. Block area, tonnes, row spacing, row length, maximum ground speed and a target elevator pour rate are used to estimate the time spent cutting. The time taken to turn at the end of the row is assumed. Time spent waiting for haul transport is estimated by assuming that the first haulout has just left the harvester. If this haulout can travel to the delivery point, unload and return in less time than the harvester can fill the remaining haul capacity then the haulout waits for the harvester. Alternatively, the harvester must wait for the haulout to return and this time is added to the total harvest time. Variables such as haul distance, haul speed, unloading time, haul capacity and number are used for this calculation. This basic cut-turn-wait time is then increased by 6% to account for servicing (regular and scheduled), 3% for repairs (unscheduled break-downs) and 3%, 6% and 9% for moving between fields as discussed later. 158
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Sustainable Biomass Supply Chain fo
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Foreword This report provides an as
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include business development (new p
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Acknowledgments The authors would l
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4.1 Product Options and Supply Chai
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Table 4.2 Assessment of current val
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Figure 3.4 Tracked rigid self-prope
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Executive Summary What the report i
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• Biomass production potential of
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• As the Industry expands, the mo
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Harvesting, transport and storage s
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Biomass processing, supply chain pl
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Key barriers to biomass industries
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Crop-Biomass Production Production
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Figure 1.1 The cropping and pasture
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Table 1.1 Mallee species used for p
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1.1.4 Growth Cycle Mallee System Ma
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northern New South Wales (where it
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Almost 80% of the industry now cuts
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Bark has relatively high ash but as
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Carbon sinks Planting of mallees to
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Table 1.5(a) State Land area devote
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out the fluctuations in farm income
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Mallee system Most of the mallee bi
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inconclusive result may have been d
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For a sustainable woody crop indust
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changed significantly since they we
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Figure 2.2 Biosystems Engineering p
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Whole-of-crop harvesting represents
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During the last decade the harvesti
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single row, as this would improve t
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2.2.2.2 Weight Sugar System The Aus
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Table 2.1 Harvester Comparison Tabl
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The quality of cut may be less impo
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ate than a 170 tonne/ha crop of sta
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Dry Leaf 6.1 - 3.5 17.0 58.9 53.2 T
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Bulk density will be a key consider
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Table 2.4 EM levels in cane supply
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L/T L/T 60 0.97 0.71 80 0.92 0.66 1
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Total 7.5-26 16.5 Mallee System The
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• waiting for mill delivery of em
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perhaps at 10 - 20 km intervals. Th
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transport arrangements, harvest gro
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contractors and growers, and the fa
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experience of the sugar industry wi
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Table 2.11 Alternative harvest paym
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onto the harvester. BR+F still send
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Repairs and maintenance 2.10 Capita
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Figure 2.14 and Table 2.14 describe
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Sichter et al. (2005)), harvest and
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Transport efficiencies may be possi
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3. Transport and Storage Systems Tr
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same time, the potential problems o
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Figure 3.3 Articulated self-propell
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• The harvester will not have any
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Figure 3.7 Gross mass compared with
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external factors dramatically impac
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Figure 3.11 The effect of haulout t
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3.3 Road Transport 3.3.1 Configurat
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Figure 3.15 shows an example of the
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Road distance one way < 20 km 70 km
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Haul distance is largely outside th
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3.6 Recommendations The nature of t
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• The strategy offered lower tota
Page 129 and 130: sugarcane billets are such that wit
Page 131 and 132: • Transfer the whole tree product
Page 133 and 134: Figure 4.5(b) Energy balance of con
Page 135 and 136: 2010). Table 4.2 presents a summary
Page 137 and 138: 4.4.2 Activated charcoal Activated
Page 139 and 140: Nett Product Value ($/t) $475.00 $
Page 141 and 142: Table 4.9 presents an estimation of
Page 143 and 144: • Oil from leaf @ $2/kg • Synth
Page 145 and 146: • The mallee oil would be extract
Page 147 and 148: 5. Industry and Business Structures
Page 149 and 150: is conducted into tariff levels on
Page 151 and 152: Bx is % brix in first expressed jui
Page 153 and 154: Hildebrand (2002) estimated that th
Page 155 and 156: also has a flow on effect to the sp
Page 157 and 158: Sugar Industry Illustrative Example
Page 159 and 160: with super size multi-lift bins ove
Page 161 and 162: farmer vs. harvester) is the next l
Page 163 and 164: 6. Supply Chain Planning and Manage
Page 165 and 166: Figure 6.1 Building blocks of the s
Page 167 and 168: weighed against the costs of operat
Page 169 and 170: • There is close contact between
Page 171 and 172: 6.4 Planning, Management Tools and
Page 173 and 174: • Harvest and transport logistics
Page 175 and 176: interface. The system allows users
Page 177 and 178: Case Study 6.2 - Model Application
Page 179: Relationships between sectors and p
Page 183 and 184: It was assumed that there was a fif
Page 185 and 186: Table 7.2 Scenario two capital equi
Page 187 and 188: Table 7.4 Fuel burn rates harvester
Page 189 and 190: Figure 7.5 Effect of capital equipm
Page 191 and 192: Figure 7.7 Effect of annual tonnes
Page 193 and 194: 30 1.8 7.0 12.3 17.5 50 1.1 4.3 7.6
Page 195 and 196: 8. Conclusions and Recommendations
Page 197 and 198: Biomass bulk density has a large im
Page 199 and 200: Vehicles used for infield haulout w
Page 201 and 202: This can be compared with harvest a
Page 203 and 204: While diversification can add value
Page 205 and 206: Appendix 1: Comparative Assessment
Page 207 and 208: and transport conditions. Billet le
Page 209 and 210: that cane production is the most pr
Page 211 and 212: on sugar only, other proceeds (mola
Page 213 and 214: References Agnew, J 2002. A Partici
Page 215 and 216: Enecon 2001. Integrated Tree Proces
Page 217 and 218: Keating, BA, Antony, G, Brennan, LE
Page 219 and 220: Ridge, DR and Linedale, AL, 1997. T
Page 221: Willcox, T, Hussey, B, Chapple, D a
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