HARVEST HAUL MODEL THE COST OF HARVESTING PADDOCKS OF SUGARCANE ACROSS A SUGAR MILLING REGION By G.R. SANDELL 1 and D.B.

HARVEST HAUL MODEL THE COST OF HARVESTING PADDOCKS OF SUGARCANE ACROSS A SUGAR MILLING REGION By G.R. SANDELL 1 and D.B. PRESTWIDGE 2 1 BSES Limited, Mackay 2 CSIRO Sustainable Ecosystems, Brisbane gsandell@bses.org.au KEYWORDS: Sugarcane, Harvesting, Cost, Model. Abstract COMPETITIVENESS of the Australian sugar industry against declining terms of trade in the global market is an increasing issue for the industry. While advances have been made in individual sectors, future innovation is likely to be based on optimising the farming, harvesting, transport and milling value chain as a whole. Several mill regions are exploring opportunities to reduce the cost of harvesting and cane transport for the local industry. This required the development of a model, called the Harvest Haul Model, to quantify the performance of the harvesting sector on a regional scale. This model is integrated with other component models to provide a whole-of-system modelling capability to assess the regional impacts to the harvesting and growing sector from big picture changes in harvesting, such as the reduced number of groups. The Harvest Haul Model is a database application that estimates the cost of harvesting each paddock of sugarcane on a farm, in a harvesting group, or across a sugar milling region. The initial purpose for the Harvest Haul Model was to quantify the overall gains to a region by changing from standard harvesting practice to Harvest Best Practice (HBP) methods. HBP encourages harvesters to reduce fan speed and elevator pour rate, which reduces cane loss. The model was also developed to conduct scenario analyses looking at the effects on: harvesting and hauling of changing harvesting hours; harvesting group numbers, content and size; and also changing cane railway siding locations, which change haul-out distances. As gathering region-wide data is an enormous task, GIS methods were used to calculate estimates for row length and haul-out distance for each block of cane in the region. Surveys were conducted to estimate the type and value of capital equipment used in the region and some assumptions were made from previous research. Results of scenario analyses have shown that, while the region gains from the uptake of harvesting best practice, the harvesting sector returns decrease, emphasising a need to develop a more equitable payment method. The Harvest Haul Model was applied to the Mourilyan sugar mill region for quantifying the effect of introducing HBP and reducing the number of harvesting groups. Introduction Grower, harvester and milling representatives of the Mourilyan sugar region are currently investigating opportunities to reduce costs within the harvesting transport system. Researchers within CSIRO and BSES provided the modelling capability with participation from the local industry, who formulated a range of scenarios they saw as short and long term potential to have impact. These scenarios included: implementing harvest best practice (HBP) methods (Whiteing et

al., 2001); investigating the effects of changing harvesting hours; reducing harvesting group numbers, content and size; and changing the number and location of cane railway sidings. The Harvest Haul Model (HHModel) was developed to provide a capability for costing the region-wide performance of harvesting and hauling under a range of scenarios. The HHModel calculates the time performance and cost of harvesting each block (or paddock) of sugarcane in a milling region harvested by multiple harvesting groups. It can calculate a base-case scenario then compares scenarios against this. In the HHModel, costs are distributed by engine hours, rather than by tonnes of cane, to better reflect how costs are incurred. Depreciation is determined over 10 000 hours, rather than a number of years to account for any increases in annual machine usage. A blade factor can be added to a particular block to account for high levels of rock, which increase costs through accelerated blade wear. Improvements have been made to the front-end of the model so that it is now easier to process and report on scenarios. The HHModel accounts for changes in cane loss, extraneous matter (EM) and CCS under changes in operating practice. Cane loss is calculated using Norris s (pers. Com. 2000) equation: Cane loss [ / ] 0.0334 * 0.0048 *fan speed t ha = e (1) Extraneous matter is calculated using Ridge and Hobson s (2000) equation: 0.1 0.00395 * fanspeed + 0.101 * elevator pour rate (2) where fan speed is the rotational velocity of the primary extractor fan, expressed in revolutions per minute and elevator pour rate is the flow rate of material off the end of the elevator while the machine is continuously cutting expressed in tonnes per hour. The field or crop conditions, such as variety, lodging, and rain effect have the largest impact on cane quality. The model assumes average field conditions. Data acquisition The model requires specific data about each block such as area, tonnes of cane, row length, row width and haul-out distance to railway siding or road pick-up points. As gathering region-wide data is an enormous task, different strategies were used. Mill data were used to obtain delivered tonnage, CCS and area for each block. Surveys of harvesting groups were conducted to estimate the type and value of capital equipment used in the region and some assumptions were made from previous research for variables such as unload time for haul-outs and the time to turn at the end of rows. GIS methods were used to estimate row length and haul-out distance for each block of cane in the region. Row length was assumed to be the length of the longest side of the block polygon. To estimate the haul distance for each block, the siding nearest to the centroid of each farm was selected.

Distances from the centroid of individual blocks on the farm to this siding were then calculated as a straight-line distance and a multiplier of 2 was applied to account for travelling around block boundaries. Manual checking and adjustment was used where a creek or other boundary divided the farm. How the Harvest Haul Model works The HHModel evolved from the BSES Harvest Transport Model (Ridge and Hobson, 2000), which was developed to cost harvesting a single paddock. This was upgraded to run multiple blocks and groups to calculate regional harvester performance. To derive the cost of harvesting, the HHModel calculates the time performance of the harvest then applies costs on an hourly rate. To analyse scenarios, the model is run twice. The first run is the base case and outputs are copied to a standard Table. The second run models the scenario representing the changed harvesting practice and copies outputs to a scenario table. A financial model then aggregates block-level results to a regional level and subtracts the base case results from the scenario results to derive the net regional benefit or cost of the scenario. Yield is calculated from block tonnage and block area. Ground speed is calculated using crop yield and row width and the assumed elevator pour rate. If calculated ground speed is greater than the input maximum ground speed, the maximum ground speed is used and elevator pour rate is re-calculated with the maximum ground speed. The number of rows in the block is calculated by dividing block area by the row width and row length and is used to calculate turning time, backing time and cutting time. The time required to fill one haul-out is calculated. The time required to travel to the delivery point, unload and return is calculated. If the haul-out cannot return before the remaining haul-outs are filled, the harvester waits for the haul-outs. Conversely, if the haul-out can travel, unload and return before the remaining unit(s) are filled, the haul-out waits for the harvester. The total of turning, cutting, backing and waiting time is increased by input percentages to account for servicing, moving and repairs. From these data, harvest hours, engine hours and delivery rate are derived for the block. These block results are accumulated for the entire season for each group. The seasonal average delivery rate for each group is sent to the Transport Capacity Model (Higgins and Davies, 2004). This model returns the time each group will wait, on average, each day for mill deliveries. It is assumed that servicing occurs while waiting for mill deliveries. The portion of delivery waiting time that is greater than the servicing time is added to the seasonal harvest hours. Dividing seasonal harvest hours by the number of harvesting days within the season gives the harvest hours per day. Using a maximum shift length input, the model decides if one, two or three shifts are required and wage costs are calculated using award rates including overtime and oncosts.

Table 1 Data inputs required for each block, harvesting group, and mill region in the HHModel. Variable Units Data origin (or source) * Block variables Tonnes tonnes Mill data Area hectares Mill data CCS units Mill data Row length metres GIS methods Haul distance kilometres GIS methods Row width metres 1.53 Fan speed standard r/min 1100 Fan speed HBP r/min 980 Elevator pour rate - standard tonnes/hour 100 Elevator pour rate - HBP tonnes/hour 80 Maximum ground speed kilometres/hour 9.2 Backing speed kilometres/hour 0 # Time to turn at end of row seconds 60 No. haul-outs used number Time to unload at delivery point seconds 150 Group variables Rostered days on days Mill data Rostered days off days Mill data % time spent servicing % 8 % time spent moving % 4 % time spent repairing % 3 Cost of fuel $/litre 0.50 Harvester idle fuel burn rate litres/hour 11 Harvester working fuel burn rate litres/hour 52 Haul-out idle fuel burn rate litres/hour 7 Haul-out working fuel burn rate litres/hour 17 Vehicle daily fuel burn rate litres/day 9 Other equipment 1 fuel burn rate litres/day 0 Other equipment 2 fuel burn rate litres/day 0 Harvester wage rate $/hour 14.8955 Haul-out wage rate $/hour 14.4276 Maximum shift length hours 16 Wage on-costs % 30 Seasonal harvester repairs & maintenance $ Variable with group size Seasonal haul-out repairs & maintenance $ Variable with group size Seasonal overhead costs $ Variable with group size Seasonal cost of harvester blades $ Variable with group size Interest rate % 8 Capital equipment variables Capital equipment is classified as being either: a harvester, a haul-out, a vehicle, other equipment 1, or, other equipment 2. The following variables are defined for each. Current market value $ Variable with year model Salvage value $ Variable with year model Current engine hours $ Variable with year model Equity $ 0 Anticipated machine life hours 10 000 Mill area variables Season length days Mill data Sugar price AUD/t of sugar 270 Harvester pay rate $/tonne of cane 6.50 * Actual values used in the model for the Mourilyan region are shown where the variable is assumed. # It was assumed that all harvesting occurred two-way.

Fuel use is calculated for the harvester and the haul-outs using hourly fuel burn rates for working and idle periods. An allowance for daily fuel use in other equipment, such as service vehicles, is added to the total fuel use. Total seasonal repairs and maintenance costs are distributed by per engine hour. Total seasonal overhead costs, such as telephone, insurances and registrations, are distributed per engine hour. Depreciation is a non-cash cost distributed per hour and is calculated by subtracting equipment salvage value from market value then dividing by anticipated machine life. Capital ownership costs are calculated using an assumed interest rate and distributed per engine hour. Equity is used to split this cost between cash and non-cash cost. The financial model The purpose of the financial model is to provide stakeholders with a measure of the net financial cost/benefit to each sector from each scenario. Standard gross-margin and profit measures are calculated to allow consistent financial comparisons. Confidentiality requirements dictated that absolute levels of cost/benefit of each sector not be reported. Instead, incremental gains of the scenario over the base case are reported. The financial model subtracts the base case from each scenario to calculate the regional net cost/benefit of that scenario. The module calculates the sharing of proceeds between growers, harvesters and millers using the cane payment formula, an assumed sugar price and harvester pay rate. These cost/benefits include changes in cane loss and CCS and changes to harvester operating practice, such as fuel and wage cost/benefits. The financial model uses equations (1) and (2) to calculate cane loss and EM for HBP scenarios. Beginning with the actual tonnes and CCS delivered to the mill, the model calculates tonnes of sugar per hectare delivered to the mill. Next, the delivered tonnage is reduced by the EM and increased by the cane loss derived (using equations 1 and 2) for standard practice to calculate clean cane yield before harvest. This clean cane yield is then reduced by the cane loss and increased by the EM derived for HBP to derive HBP yield. The associated CCS value for HBP is calculated by reducing tonnes sugar per hectare by HBP cane loss then dividing by derived HBP yield. This process gives all required data to calculate grower income under standard practice and HBP. Mill income is similarly calculated using the payment equation with a CCS value of 4 units. Harvester income is calculated under standard practice and HBP and is combined with the cost of harvest (calculated in the model) to give harvester gross margin and net cash positions. Net industry position return is given by the sum of harvester gross margin, grower income less harvesting costs plus mill income. In our case studies, cost implications for the cane-transport system are calculated in the Transport Capacity Model. Optimising the number of haul-outs required in a block and for a group It is often the case that a harvesting group will own three or four haul-outs yet use less than this number to harvest the majority of the blocks in their contract. The extra haul-out(s) are used only as needed, such as where long haul distances or other factors exist. This allows the group to reduce labour and fuel costs in the majority of situations and still be productive in low efficiency situations.

Savings made by sustaining productivity in low efficiency blocks are offset by capital ownership costs of extra equipment. The HHModel is used to identify if extra haul-out capacity can be justified and in which blocks it would be utilised. For a group that owns 4 haul-outs (and hence the associated capital cost), the HHModel needs to be run for all blocks harvested by the group assuming the group owns 4 then 3 then 2 haul-outs. To do this, firstly assuming 4 haul-outs are owned, the HHModel is run 3 times with the number of haul-outs for each block set to 2, 3 or 4 for each run. The cheapest option (2, 3 or 4 haulouts) for each block is selected and then the HHModel is run again with each block set to the cheapest number of haul-outs. The block costs are aggregated to give total cost of harvest for each group for each of the model runs (ie. 2, 3 and 4 haul-outs, and the cheapest combinations of blocks). Secondly, this process is repeated assuming the group owns 3 haul-outs. The HHModel is run twice with the number of haul-outs set to 2 then 3. The cheapest option from these runs is selected (2 or 3), then the HHModel is run again with each block set to the cheapest number of haul-outs. The block costs are again aggregated to give total cost of harvest for each group for these model runs (ie. 2, and 3 haul-outs, and the cheapest combinations of blocks). Lastly, the HHModel is run assuming the group own 2 haul-outs, all blocks are run with 2 haul-outs, and the block costs aggregated for each Group. The optimal number of haul-outs for the group is selected by choosing the data set associated with the cheapest aggregate group total cost of harvest (ie. 2, 3 or 4). The optimum number of haul-outs for each block is selected by choosing the cheapest data set associated with this group scenario. Using the HHModel outputs to assess regional change in Mourilyan The Mourilyan case study uses a steering group in each region as an interactive group to affect regional change. The steering group is responsible for: producing a list of scenarios for analysis based on regional issues; participating in the review and development of the scenarios; and, deciding which scenario(s) are to be further developed for implementation measured by the greatest potential for economic benefit and for likelihood of success. The steering group listed a range of scenarios for analysis, including the two listed below: Scenario 1 HBP with the existing 17 groups and 171 sidings; sugar price $270 /tonne sugar. Scenario 2 HBP; 13 harvesters; harvesting times rostered over 18 hours 3 a.m. to 9 p.m.; harvesting 5 days per week; 100 sidings (a reduction in the current number of sidings).

The HHModel was used to measure the effects to the harvesting sector of these scenarios. Specifically, the effects of: implementing HBP; changing the number of harvesters in the region; reducing the number of sidings; increasing the day length of harvesting; and, changing the weekly roster of the harvesters. All scenarios assumed, among other things, the implementation of HBP. For scenarios that involved reducing the number of groups, it was necessary to reallocate: farms to the new groups; capital equipment in each group; how many haul-outs were required in each group; the new bin wait times calculated using Transport Capacity Model (Higgins and Davies, 2004). Farms were allocated to groups using a harvest group optimisation model. This model minimises distances of harvester migration and achieves variability in the CCS profile within a group. In this way, it is possible to achieve CCS maximisation (Higgins et al., 2002) within each group and this also spreads variety and soil type variation within group. Groups were optimised to achieve an approximately equal group size. Capital equipment selection followed the assumption of the steering committee that no new capital equipment would be purchased. Harvesters were allocated to groups in order of age; the newest harvesters were allocated first. Each group was also allocated a service vehicle. Allocation of haul-outs was more complex. Existing haul-outs ranged in size: 12, 10, 8, 6 and 4 tonne capacity. The process outlined above was used to calculate the effect of harvesting each block with the 12 tonne haul-outs. These units were allocated to the group that had the highest number of occurrences where 12 tonne haul-outs were cheapest. Next, the same process was used to allocate the 10 tonne haul-outs and so on until all groups were allocated with haul-outs. Figure 1 illustrates how the HHModel is integrated with the Transport Capacity Model. The outputs of this system of models are presented in Figure 2. All results are pre-tax profits for each sector or the supply chain and are net of the base case. Adoption outcomes for Mourilyan The Mourilyan region decided to attempt implementing harvest best practice pilots and an extended harvesting time window of 18 hours. Harvester start times are staggered between 3 a.m. and finishing at 9 p.m. The first challenge to implementing HBP was that harvesting contractors were reluctant to implement HBP as harvester gross margins decreased under HBP. Lack of proper communication channels to harvesting contractors was another barrier how does a contractor implement HBP? To begin to address these issues, an industry meeting was held that was attended by all contractors. It was noted at this meeting that most contractors were already achieving HBP in many instances. This could not be quantified due to the lack of previous season harvester baseline data.

Fig. 1 Integration of the HHModel and the Transport Capacity Model. 1 000 000 800 000 HBP - $270/t 13 groups over 18 hours with 100 sidings Net dollar benefits of the scenario less base case 600 000 400 000 200 000 0-200 000 Harvester gross margin Grower income net of harvesting cost Mill Industry -400 000 Fig. 2 Total dollar benefits of each scenario less the base case for each sector. The initial implementation of the plan was not as smooth as we would have liked. Due to excessive early season wet weather interruptions, mill crushing rate was progressively increased to 385 tonnes per hour with a target of 400 tonnes per hour; modelling used a 370 tonnes per hour crush rate.

Some operators recorded elevator hours on a weekly basis; elevator pour rate is given by the tonnes of cane delivered in that week, divided the hours of operation for the elevator. While this gives some broad scale information, these data are limited by the fact that they do not show the constraints faced by the harvester. Additionally, the BSES Record Keeping process and BigMate monitoring are being used to benchmark harvester performance. Results from the weekly elevator pour rates for four groups over five weeks showed that the majority of harvesters were achieving HBP pour rates 82% of the time. These harvesters had an elevator pour rate between 70 tonnes per hour and 95 tonnes per hour. However, it is likely that these pour rates are also linked to the moderate crop yields seen in Mourilyan during 2003. Additionally, 12% of the time, these harvesters had an elevator pour rate between 40 tonnes per hour and 55 tonnes per hour. Discussions with those harvesting contractors revealed that they were not able to achieve more adequate pour rates because they were consistently harvesting very low yielding crops. A weekly average elevator pour rate of 114 tonnes per hour was seen in 6% of cases. HBP pour rates are typically 80 tonnes per hour in cane in more difficult situations and up to 100 tonnes per hour in better conditions, such as dry erect cane. Other uses for the HHModel While the HHModel was used as a regional model, it also gives detailed information at the block level. At this level, costs are split into: depreciation (a non-cash cost), capital ownership noncash cost, capital ownership cash cost, wages, fuel, repairs and maintenance, overheads and blade costs. Other outputs include: field efficiency, shift length and fuel use in litres per tonne. This level of detail is being used in current scenarios involving exchanging block between groups. Information generated by the model can be grouped to provide information at a farm level or at a harvesting group level. The HHModel has also been used in other work by BSES to estimate the effect on shift length and other variables of taking more cane into a contract. It has also been used to produce quote estimates of the cost of harvest. The HHModel has been used to investigate improved efficiencies under dual row and 2 metre rows. A harvest planning process using the HHModel and the BSES Record Keeping process is under development. The model will be used to estimate the cost of harvesting during negotiations between harvesting contractors and growers prior to harvesting so that a harvest plan can be developed. The Harvesting Best Practice Manual pages 78 79 (Sandell and Agnew, 2002) provides a detailed description of the harvest planning process. The BSES Record Keeping process will record how the harvester actually performed in the block. The cane is then processed at the mill and the mill data on the actual tonnage and CCS will be returned. Using these measured data, the HHModel can be run again to calculate the actual cost of the harvest. Conclusions The HHModel was developed to provide scenario analysis on a regional level while still providing block level information. The model estimates the time performance and cost of harvest of each block of sugar cane then accumulates these data to a

regional level. Mill data, surveys, assumed values and GIS methods were used to collect the required data. The HHModel was used to estimate the effects of a range of scenarios on each sector in three different mill regions. These scenarios were presented to local steering committees that chose specific scenarios for implementation during the following season. While there were hurdles along the way, scenarios were successfully implemented by the local industry in Mourilyan. These hurdles included an increased crush rate, wet weather, a reluctance to change and having to adapt to a new system. The HHModel is also being used to conduct detailed block-level scenarios such as swapping blocks of cane between groups. The block-level ability of the model has also been used to provide price quote estimates for harvesting contractors. The HHModel is integral to the harvest planning process. This process estimates the cost of harvest and plans the harvest, prior to the harvesting season, at an agreed price and job quality. Future developments of the HHModel might include integration of the model with the BSES Record Keeping Process, which is used to measure the cost of harvest. Acknowledgements The authors acknowledge the contribution of the Mourilyan steering committee. This committee consists of representatives of farmers, Bundaberg Sugar, harvesting contractors, CANEGROWERS, Queensland Mechanical Cane Harvesters Association, Australian Sugar Milling Council Pty Ltd and other parties. The project was partly funded by the Commonwealth Government and the sugar industry through the Sugar Research and Development Corporation. REFERENCES Higgins, A.J. and Davies, I.R. (2004). Capacity planning in sugarcane transport: a case study at Mourilyan. Proc. Aust. Soc. Sugar Cane Technol., (CD-ROM), 26: (These Proceedings). Higgins, A.J., Haynes, M.A., Muchow, R.C. and Prestwidge, D.B. (2002). Optimised sugarcane supplies increases profitability for the Australian sugar industry. Australian Journal of Agricultural Research, in Press. Ridge, D.R. and Hobson, P.A. (2000). Analysis of field to factory options for the efficient gathering and utilisation of trash from green cane harvesting. Sugar Research and Development Corporation final report SD00011. Sandell, G.R. and Agnew, J.R. (2002). The Harvesting Best Practice Manual. Bureau of Sugar Experiments Stations Limited publication, 78 79. Whiteing, C., Norris, C.P. and Paton, D.C. (2001). Extraneous matter verses cane loss: finding a balance in chopper harvested green cane. Proc. Int. Soc. Sugar Cane Technol., 2: 276 281.