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Further detail on modelling approaches in the sugar industry is given below. Three case studies provide examples of the application of these models in various sugarcane mill supply regions. Further details on the case studies are provided by SRDC (2006b). Sugarcane production modelling Sugarcane production modeling has focused on many applications, including simulation of long term and seasonal sugarcane yields, comparing yield with and without a trash blanket, analysis of impact of soil type, irrigation and nutrient management on yield and the environment, assessment of field and row layout. The APSIM-Sugarcane model has been the basis of this work. (Keating et al (1999) and Thorburn et al (2004). Harvest and infield haulage Estimates of costs of harvest and haulage to loading zones or sidings were based on the Harvest-Haul model (Sandell and Prestwidge 2004). The model interacts with the Transport model by suppling harvester delivery rates and accepting time harvesters spent waiting for bin deliveries to the pad or siding. Much work has focussed on optimising the number and location of cane delivery pads in mill supply regions (Prestwidge et al 2006). The model requires inputs for (1) the block being harvested (crop yield, block area, row length, distance to siding, allocation to siding, allocation to harvester group), and (2) the harvesting equipment (capital equipment type, size, specifications and value) in the region. GIS was used to estimate these parameters. Chapter 7 provides an assessment of Mallee harvest and infield haulage costs based on this model. Transport systems A road transport model has been developed by Higgins (2006) and has been used widely to for capacity planning, road and rail transport schedule optimisation and siding and pad location and optimisation (Pinkney and Everitt, 1997). Milling/ Factory The sugar mill model has been developed to estimate raw sugar, molasses and electricity end-products from cane supply components. A particular focus has been prediction of sugar recovery based on differing proportions of cane and trash supplied to the factory. The model is configured to include the main infrastructure of the factory, including, where appropriate, trash separation, bagasse storage, bagasse handling and electricity generation (Hobson and Wright, 2002). Asset Management and Electronic Consignment Tracking cane from the field to the mill in real time provides information on the volume of cane harvested, where it came from, how fast it is being cut and its route and timing of delivery. Systems have been successfully developed which provide GPS tracking on harvester, haulout and transport units, RFID tags on bins for delivery, ZigBee modems for local communication and use of NextG networks for communication of information to a central server at the mill where data is integrated into a GIS system to display real time harvest progress (Marrero et al 2010). Benefits include paperless tracking of assets and harvest/transport scheduling. Spatial data to improve harvest management, data recording and reporting Crossley and Markley (2011) have developed a system AgDat which integrates data and information to improve harvest management, data recording, reporting and data exchange. Field data (eg varieties, inputs, surveys) can be collected and loaded in the field and combined with data collected from loggers (eg GPS referenced harvest progress and performance) which is integrated and interpolated on a database and exchange network and made available to users through a desktop or web based 151
interface. The system allows users to manage the production and harvest progress and interpret harvest areas from GPS tracking automatically. Farmers, extension services and advisors can record data on a spatial basis and maps can be generated or viewed. Harvest and transport integration using elements of this system has provided significant benefit to the Mackay region as indicated in a Case Study below. Yield Monitoring and prediction A key to supply chain management is having information on yield at a paddock and sub paddock level both prior to harvest and post-harvest. In the sugar industry post-harvest paddock level yield information is available after delivery of the consignment based on the mill weigh-bridge records for each bin which can be assigned to a field. Within field variation is not generally available and existing sugarcane yield monitors are not of adequate accuracy and require further development and testing (Jensen et al 2010). Yield forecasts prior to harvest are generally based on grower and cane productivity officer assessments of standing cane. Satellite imagery of area harvested as well as data derived from GPS harvester records are increasingly being used to establish areas yet to be harvested and hence tonnages remaining at stages through the season. Information on standing cane yield has also been derived by correlating canopy reflectance derived from airborne or satellite imagery with crop condition and biomass. While individual field estimates have been variable aggregation across a mill area or sub region has been shown to offer potential for production forecasting. Benchmarking The sugar logistics improvement program (SLIP) conducted in South Africa provides useful standards and benchmarks whereby participants can compare the performance of their delivery chain to that of others in the region. This alerts them of areas of opportunity to improve utilization and reduce delivery times. 152
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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
Page 123 and 124: Haul distance is largely outside th
Page 125 and 126: 3.6 Recommendations The nature of t
Page 127 and 128: • 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: • Harvest and transport logistics
Page 177 and 178: Case Study 6.2 - Model Application
Page 179 and 180: Relationships between sectors and p
Page 181 and 182: 7. Supply Chain Modelling and Econo
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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