DECISION SUPPORT SYSTEMS FOR SUGARCANE PRODUCTION MANAGERS

Transcription

1 REFEREED PAPER DECISION SUPPORT SYSTEMS FOR SUGARCANE PRODUCTION MANAGERS LYNE PWL Agricultural Engineering, School of Engineering, University of KwaZulu-Natal, P/Bag X01, Scottsville, 3209, South Africa Abstract Sugarcane production systems which include all the elements of producing sugarcane are complex and require managers to consider multiple interrelated and interdependent factors that all impact on their sustainability. In turn, decision making becomes a complex process and, to facilitate the challenges faced by managers, Decision Support Systems (DSSs) are tools that have been developed to enable managers to consider various operational scenarios and quantify their impact. There are many DSSs available that consider different elements in the supply chain and these enable each element to be examined. There are also some holistic systems which consider segments or the whole of the supply chain and the interrelationships between the elements within the segment. The systems enable one to simulate, explore and evaluate changes to the systems, such as varieties, irrigation, harvest scheduling, or transport. There are also tools that enable one to manage risk and uncertainties, a vital aspect of an agricultural operation where there are factors such as weather, pests, sugar price and policy which are out of the control of many stakeholders. They enable one to carry out a sensitivity analysis to ensure that one is getting the best return on a financial or time investment. The main purpose of a DSS is to improve sustainability, and one important component is the economics of the system. This paper reports on a range of DSSs, that are more fully described in the new IFC Cane Industry Sugarcane manual, which covers all aspects of sugarcane production and discusses their features and benefits. See link: alcanesugarindustry.pdf?mod=ajperes Keywords: decision support system, programmes, sugarcane, scenarios, management, information Introduction This paper deals with the systems that support all managers involved with decision making in sugarcane production. A sugarcane production system is complex (Bezuidenhout and Bodhanya, 2010) and requires that all the stakeholders consider the multiple interrelated and interdependent 206

2 factors impacting on the sustainability (triple bottom line) of their business. In turn, decision making becomes a complex process and, to facilitate the challenges faced by managers, Decision Support Systems (DSSs), or, by a different name Decision Support Programs (DSPs), are tools which have been developed to enable one to consider various scenarios and quantify their impact. There are a great many DSSs available which consider different elements in the supply chain and these usually enable each element to be optimised for a particular set of circumstances. This is where the manager of an element of the supply chain would maximise particular items such as herbicide efficacy or vehicle fuel consumption. There are also a few holistic systems which consider segments or the whole supply chain and the interrelationships between the elements within the segment. These systems enable one to simulate, explore and evaluate the elements or sequential elements, such as harvest scheduling, irrigation or transport. There are also tools that enable one to manage risk and uncertainties, where there are factors such as weather, pests, sugar price and policy which are out of the control of many stakeholders. A few examples will be given and these will all be discussed, beginning with crop establishment and ending at the mill gate. Research institutions in each country are usually the custodians of these systems; however, there are some which are commercially available and examples will be given of the various types. While it is important for each stakeholder in the supply chain to maximise their own sustainability, it is critical that the overall supply chain operates at maximum effectiveness and this will require some compromise with the individual elements or stakeholders. When managing an element such as harvesting, it is important to consider the whole production system as a Supply Chain or an Integrated Production System. For example, a harvesting DSS may indicate that it is economic to burn at harvest; however, this will have many negative impacts on the system as a whole and these need to be considered. The most important factor or metric for the sugarcane production enterprise as a whole is a steady supply of good quality fresh cane to the mill. This will ensure that the mill runs effectively and maximises the sustainability of the whole sugarcane system, and all stakeholders will benefit from this. Each person in the supply chain should bear this in mind and collaborate with all others to ensure that each person, stakeholder or element benefits and results in a winwin situation. This is not an easy process, as each and every situation is unique and needs to be considered differently. A DSS will help to consider various options. The main purpose of a DSS is to support stakeholders in improving the sustainability of their system, and the economics component is therefore important. The focus should be on the elements of the system that will make the biggest impact on costs. Very simply, these are made up of overheads which include capital and running or operating costs. Overhead costs are the costs involved in owning capital items and here the aim is to reduce the investment in equipment to the minimum by maximising its utilisation. Because the logistics may not be running effectively there could be too many harvesters, too many infield units and too many tractors, and if there are frequent no cane stops the mill will have to have a larger 207

3 capacity than necessary. For example, in South Africa, and for many different reasons, some mills were running three times as many haulage vehicles as were needed (Giles et al., 2005). One habit that sugarcane production systems seem to develop is that whenever there is a capacity problem, more equipment is purchased, when the real problem is management and here a DSS can play a valuable role in quantifying and demonstrating alternative solutions. Where running costs are concerned, one can usually only fine tune the system and plan and manage elements such as crop establishment, crop maintenance, harvesting and transport more effectively. One should always prioritise improvements by starting with the one that has the largest impact on the system as a whole. In all of these, the DSS plays a valuable role in informing managers. Decision Support Systems A DSS is usually a software package based on a mathematical model of a particular system, such as an irrigation system. The system is modelled and all the relevant parameters and variables are included in such a way that one can vary the value of the parameters and variables and observe the impact, the response or the performance of the system. For example, with an irrigation scheduling DSS, one could examine how various irrigation practices affect issues such as water use, electricity consumption or yield. One would populate the system with known or assumed information; the DSS would then simulate the process and provide an output in tabular or graphic form. One can then change the value of the input parameters and variables and examine the impact on the process. A valuable aspect of this is to carry out a sensitivity analysis to determine which parameters have the most effect, and therefore which parameters to focus on and manage carefully. Another valuable aspect is the ability to carry out what if? exercises. Models enable one to ensure that the correct questions are being asked and that the real issues are being addressed. The models used in a DSS are usually empirical, mechanistic, stochastic or a mix of the three. An empirical model is based on experimental observations and would only apply to conditions similar to those where the measurements were taken. A mechanistic model attempts to emulate the actual physical process taking place and can therefore usually be used to emulate a wider set of conditions. Stochastic is where some randomness is introduced into the model to simulate a random breakdown or weather condition. These factors all need to be considered when using a DSS. Most models can be used as a DSS, as they enable one to explore many aspects of the system being modelled. This means that one will often find a model that can be used as a DSS when it is not publicised as such. Because of local factors such as agroclimatic conditions or varietal response, the DSS is not always generic, but is often specific to a region. One would therefore contact the local research institutions or consultants to obtain the systems relevant to that particular region and industry. Experience shows that although researchers and scientists throughout the world (Newman et al., 2000; van den Berg, 2005 spend an enormous amount of time and energy developing DSSs, they do not get used very effectively by practitioners. Jakku et al. (2007) attempted a participatory approach to improve the adoption and use of DSSs. However, there is still a low adoption rate and all stakeholders should make use of them more often. Every effort should be made by 208

4 managers to use DSSs because they provide a valuable tool to manage the complexity of sugarcane production and, because no two systems are alike, there is no single recipe or silver bullet. Some of the reasons for non-adoption include: Entrenched culture. Do not see the importance. Investment does not pay. Hard to implement. Complexity and uncertainty. Science versus farmer perspectives. Inappropriate extension model. There are always special circumstances, mainly driven by the relationships of the stakeholders (personal, financial, political) which will make mill areas different, and this makes DSSs important and valuable tools to explore unique solutions and will go a long way to improve the sustainability of the area. There will always be a number of compromise solutions to result in a win-win situation for all members of a single enterprise. Crop models A substantial effort has been directed at the development of crop growth models. Some of the more popular models are QCANE (O Leary, 1999) and APSIM-Sugarcane (Keating et al., 2003) in Australia, MOSICAS (Bernardes et al., 2007) in Brazil, CANEGRO (Singles and Bezuidenhout 2002) in South Africa and DSSAT v4.5 (2011) (DSS for Agrotechnology Transfer) in the USA, which uses the CANEGRO model. The crop models integrate the effects of soil, crop phenotype, weather and management options that allow users to ask what if? questions and simulate results by conducting, in minutes on a desktop computer, experiments which would normally consume a significant part of an agronomist s career. The DSSAT system, used by researchers in over 100 countries, combines crop, soil and weather databases which can be used by the crop models, including CANEGRO. The user can then simulate multi-year outcomes of crop management strategies for sugarcane at any location in the world. Version 4.5 is able to carry out seasonal and sequence analyses that assess the economic risks and environmental impacts associated with irrigation, nutrient management, climate change, soil carbon sequestration, climate variability and precision management. van den Berg and Smith (2005) carried out a thorough discussion of the use of growth models to support DSSs. Models such as these use up to 100 variables, including daily weather data and management information for establishment, maintenance and harvesting. They can be used for detailed what if? analysis to support both tactical and operational decisions. Some are complex and can only be used by experts, and others have been simplified and can be used by growers. 209

5 Crop production In addition to the growth models there are many systems to assist managers with varieties, nutrition, pest control and irrigation. CROP-9-DSS is an expert system which facilitates a wide range of management issues including nutrition, pests and irrigation (Ganesan, 2005). Tarumoto (2009) recognised that the sugar industry is a complex system and developed a simulation model to examine the effect of new cultivars and crushing season. The model considers: change in sucrose by week disposition of cultivars and planting date harvesting schedule estimated sugar production costs. The model can be useful for discussions between growers and miller regarding cultivars and season dates. CanePro SQR CanePro (2011) is a commercially available holistic planning program and an agricultural management tool. It is more a planning tool than a DSS. The user populates the system with all the input information required and it then provides powerful query facilities. It does have growth model and irrigation scheduling facilities and can therefore be used effectively as a DSS. CanePro should be used in conjunction with DSSs for other elements within the CanePro systems. For example, once a system is planned with CanePro, one could use the system to select an irrigation system, and/or the optimum number of vehicles for harvesting and haulage. The advantage of having a tool such as CanePro over many other independent systems (typically user-created spreadsheets) is that all agronomic data is centralised in one database. CanePro is very useful as a management tool for improved agricultural decision making with powerful graphical reporting features and tools for harvest and replant planning, seasonal quality and age trends, irrigation management, labour and vehicle activity capture. A centralised database gives management an integrated picture of the entire agricultural operation, it enables benchmarking, identification of problem areas and activity costing. This tool is used by many growers and estates internationally. Cultivars Work has been carried out to develop tools to assist in the selection of appropriate cultivars and Ramburan et al. (2010) showed that a DSS could be very effective in matching an appropriate cultivar to a particular agroclimatic zone. This tool was developed as a simple DSS to assist managers to select cultivars which would be appropriate for a particular location. The system characterises cultivars according to commercially important factors, especially soil, climate and pest and disease parameters (Ramburan et al. 2010). It accounts for genotype-by-environment (GxE) interaction, where the environment is characterised by a comprehensive number of factors 210

6 that interact with the cultivar. The system has been validated and found to agree very well with expert opinion. There will be further development of this system. Nutrition The various crop models can be used as DSSs to analyse the impact of various nutritional regimes. The South African Sugarcane Research Institute (SASRI) developed a Crop Nutrition Pack which some growers and extension staff (personal communication 1 ) consider valuable in helping to manage nutritional requirements. Herbicides Bezuidenhout et al. (2002) described a system that was developed as an information management tool for weed specialists and provide technology transfer for decision support. The system standardises record keeping for experiment results, related insights and observations to improve sustainability and uniformity in weed research programs. The computer based system is able to consider more factors than in previously developed manual field guides and therefore provides more comprehensive advice under complex scenarios. The system is an improvement over herbicide guide booklets and is anticipated to expand in the future (Bezuidenhout et al., 2002). This simplifies the difficulty in providing advice for the wide range of herbicides available, particularly taking into account the weed spectrum, time of year, soil clay content and organic matter. In addition, certain herbicides can cause phytotoxic effects and, by combining results into a single system, this provides a valuable support system. Pesticide CAB International (2011) has a Crop Protection Compendium which is recognised as the world s most comprehensive site for crop protection information. It has datasheets on over 3500 pests, diseases, natural enemies and crops and also has information on more than other pest species. The Crop Protection Compendium is an encyclopedic, mixed-media tool that draws together scientific information on all aspects of crop protection. It features extensive global coverage of pests, diseases, weeds and their natural enemies, the crops that are their hosts and the countries in which they occur (CAB International, 2011). The features include fast and easy navigation between text, images, maps and databases, making it a valuable support tool for researchers and crop managers, for managing all kinds of pests. This makes it a one stop shop for pest control internationally and not just in South Africa. Irrigation Irrigation has attracted considerable research and there are a multitude of systems available for irrigation scheduling. All make use of local weather data and long term records to establish the amount of water available from rainfall. The systems enable one to examine the impact of different irrigation installations, different water application strategies and different methods of estimating or measuring crop water use on water requirements, electricity use, yield, costs and economic margins. Most of the crop growth models also have irrigation management systems built into the DSS. Despite this, most irrigation systems are poorly managed and over-irrigation is far too common. The use of the DSS needs to be encouraged. The following are examples of available systems. 1 D McElligot, South African Sugarcane Research Institute, Mount Edgecombe, 4300, South Africa [2010]. 211

7 SAshed is a water conservation and demand management tool for irrigated sugarcane, developed and described by Lecler (2004). This is a spreadsheet based management tool for irrigation scheduling and cane yield forecasting. It aims to ensure optimum water use and accounts for runoff, drainage, effective rainfall and evaporation under conditions of excess, adequate or deficient soil water. A decision support tool named Irriecon v2 was developed by the South African Cane Growers Association (SACGA) and SASRI (Armitage et al., 2008). This is an economic analysis tool which can be used to evaluate the viability of an irrigation system and enables one to carry out what if? exercises with different irrigation schemes and different water and electricity use strategies. It can be used to determine capital, operating and marginal costs of various irrigation scenarios. It takes into account factors including fertilisers, herbicides, planting, harvesting and haulage operations. SIRMOD lll is a package developed in the USA to simulate the hydraulics of surface irrigation systems at field level, to enable one to select sizing and operational parameters to maximise application efficiency. Support of the SIRMOD III software can be obtained by written questions or comments directed to the author (Walker, 2010). Schmidt (2001) discussed the use of DSSs to evaluate the impact of sugarcane production and irrigation strategies on water resources and profitability. He illustrated the use of a number of DSSs, developed at or used by SASRI in a case study for the Mhlathuze catchment. The work showed that the DSSs were useful in a case study to assess the effectiveness, water requirements and limitations of different irrigation strategies in a particular region. This again demonstrates the value of DSSs in a production enterprise. Harvesting If harvesting is not managed effectively, much of the value of the crop can be quickly lost. Value can be lost by not base cutting or topping effectively, damaging and not collecting the stalks, introducing delays between burning, cutting and crushing, delays that increase season length and including extraneous matter in the delivery. There are a number of systems available to assist management of these tasks effectively. Harvesting schedule Stray (2010) and Stray et al. (2010) discussed a computerised sugarcane harvest scheduling DSS known as a tactical harvest scheduling problem (THSP). The system enabled the setting and evaluation of a seasonal harvesting schedule in the light of all the factors affecting a harvesting program. The system considered multiple complex factors including the effects of various pests, lodging, varying degrees of frost, accidental fires and partial harvesting. He proved that the system was very effective in managing the impact of these various factors to ensure that maximum revenue was recovered by the producers, but did not consider the impact on the mill. In particular, the program would be valuable in assisting managers with limited knowledge and experience. Gajendra and Pathak (1994) developed a DSS to assist managers of chopper harvesting systems in Thailand. A chopper harvesting operation has many factors to consider. The system calculates 212

8 the various harvesting costs and productivity, and shows the transport requirements for different scenarios. Higgins et al. (2004) considered the complex issues of synchronising the harvesting operation with cane transport, and developed a tool to maximise efficiency and economic return. Cane supply Le Gal et al. (2003) and Le Gal et al. (2008) reported on a simulation tool called MAGI, developed to analyse the sucrose yield from a cane supply area. MAGI was used at a single mill as a decision support tool to enable the miller and growers to design and assess new ways of scheduling the cane supply to optimise the sucrose yield from the area by changing delivery allocation rules and season duration. The mill area was divided into regions according to variations in cane quality that were related to agroclimatic differences. The benefit varied between seasons as the weather conditions varied and, by simulating different seasons, these authors showed that although the benefits varied, a modified cane supply strategy did result in a consistently improved sucrose yield. An analysis showed that the modified cane supply did have an impact on cane payment and some growers were disadvantaged because of different delivery strategies. A consequence of the strategy was therefore, that the cane payment system would need to be revised to ensure that some growers were not disadvantaged by the system and that the gains were shared amongst all growers (Le Gal et al., 2006). Trashing DSP Despite the many benefits of green cane harvesting (trashing), sugarcane is often burnt prior to harvest to increase the productivity of cutters and reduce the cost of the harvesting process. Without tangible evidence, it is difficult to argue that the benefits of green cane harvesting often outweigh the extra cost of harvesting and the conditions under which this occurs. It is therefore difficult to persuade operators to trash cane (harvest green cane), and a trashing DSS was thus developed to explore all the factors affecting the harvesting process and determine the preferred option. van Antwerpen et al. (2008) discussed the use of a trashing DSP developed by Wynne and van Antwerpen (2004) to investigate the economics of trashing cane at harvest instead of burning. This is a well thought out and comprehensive DSS which accounts for all the factors that impact the system. Most of the factors are relatively easy to model; however, although there is general agreement that one of the many benefits of surface residue (a result of trashing) is improved soil health, this is one aspect which is difficult to model and quantify. This system works with approximately 500 variables to create a setup that represents a particular farm and enables one to explore different what if? scenarios. The variables include the sugarcane price, the cost of fertilisers and herbicides and labor requirements. Variables selected for the sensitivity analysis were cane age at harvest, yield in tons cane per hectare per month, the number of seasons that trashing was practiced, weather conditions, sise of the farm and the value of the trash. The model can be run to determine the viability of carrying out trashing and it shows the conditions where trashing would be the wise and economic choice to make. One would, however, have to consider the impact on the mill. With increasing concerns about environmental issues, this DSS is likely to become extremely important for researchers in the future and useful in assisting growers to select an appropriate practice. 213

9 Loading and transport These are expensive components of the production operation and involve the harvesting, loading, transport and mill receiving components of the supply chain and the many stakeholders in the system. Transport also has a large impact on cane quality and the consistent supply of cane to the mill. This segment of the supply chain requires a great deal of collaboration and there are many internal and external factors which influence the effectiveness of the operation. It is therefore a segment which can derive a significant benefit from a DSS where the various scenarios can be tested and the outcomes quantified. Like irrigation, cane delivery and transport scheduling has generated a great deal of attention from researchers in an effort to reduce the cost of transport and to ensure a consistent supply of cane to the mill. As mentioned previously, a consistent supply of fresh cane to the mill is one of the more important parameters of the supply chain. Although the transport or supply scheduling system probably has the most impact on the supply chain as a whole, there are many tools to support most aspects of the transport operation. Vehicle scheduling In many sugar industries the cane transport system is over-fleeted, with too many interdependent people and no formal structure in place to manage the system. This results in a cyclic pattern of delivery which varies from vehicle queues at the mills to no-cane stops, both of these being detrimental to the system. One requires more vehicles than necessary, the mill cannot operate effectively because of a varying cane supply and the season length has to be extended. All of these reduce profitability, and in most cases a system such as scheduling is necessary to achieve regular delivery. Giles et al. (2005) carried out a case study at a mill area in South Africa and found that, with a formal system in place, there was a potential to significantly reduce the fleet and save in the order of R15 million per year. There are many tools available to analyse and solve the problem. Two possible options are Asicam (Weintraub et al. 1996) used in the Chilean forestry industry and FREDD (Agtrix, 2011) developed in the New South Wales sugar industry. Both of these systems enable one to explore the opportunities available and can be classified as DSSs. The Agtrix (2011) traffic scheduling system FREDD, is both a DSS and an operational tool which enables a continuous supply of sugarcane to the mill resulting from improved fleet management. The just in time transport scheduling software aims to reduce transport costs by minimising the number of trucks needed to maintain supply. Because FREDD enables one to ask what if? questions to discuss and develop a plan and also operates in real time, the system enables clients to respond immediately to changes in: crushing rate average payload traffic conditions truck or plant breakdowns and other delays. When combined with Agtrix (2011) telemetry in the harvesting fleet, FREDD can inform the transport or mill where cane is ready to load, while the ETA Calculator can accurately predict projected supply. The Tableland mill in Queensland uses FREDD and provides the benchmark for a transport system. They use 10 trucks to move t of cane in 26 weeks at an average distance of 25 km. Each transport unit hauls nearly t, which is substantially more than 214

10 most other sugar mills (personal communication 2 ). This is the kind of benefit one can achieve with the use of a support system. Giles et al. (2009) reported that the introduction of the scheduling system at four mills in South Africa had been a resounding success, with returns on investment which were greater than 10:1. The DSS capabilities of FREDD played a significant part in demonstrating to the role players the benefits of being able to model the system and then consider various scenarios. Higgins and Muchow (2003), Higgins et al. (2004) and Higgins and Davies (2005) carried out studies in Australia and developed models and systems to provide solutions for capacity planning in sugarcane transport, looking at alternative cane supply arrangements and a framework for integrating the complex harvesting process with the transport system. All of these systems improved performance and reduced the cost of the transportation operation. Hoekstra (1973) used a Monte Carlo simulation to study the impact of random variables on the number of vehicles required to deliver loose cane to the mill. These included variables such as mill stoppages, the number of vehicles loaded at the time of the breakdown, the pattern of arrival times of the vehicles, the crushing times of the cane and the number of loads of cane at the offloader. The results showed that the variables did not have a serious effect on the number of vehicles required. Vehicle selection There are support systems available to analyse transport requirements and select appropriate vehicles. Hellberg Transport Management (2011) markets the TransSolve software suite, which simplifies the process of selecting the correct vehicle for an application and predicting the costs of running the vehicle. TransSolve is an example of a powerful tool that can be used to customise the transport operation and carry out what if? exercises to ensure an optimum solution. This is a commercial tool which has an extensive and international range of customers including farmers, hauliers, millers, dealers and consultants. The loading module is a graphical design tool that enables the user to configure a vehicle, including accessories and trailers, to calculate correct load distribution, remaining within maximum dimensions and masses. The package dynamically simulates a specific vehicle or combination over any route to predict fuel consumption, trip time and productivity, typically to within 5% accuracy. TransSolve also combines information from the other modules with user inputs to accurately determine total operating cost, per unit, of virtually any operational scenario. One can simulate different vehicles, different options such as gear ratios or braking systems and different routes (the short, steep route or the longer, less hilly route). Roberts et al. (2009) used TransSolve to carry out a desktop study to demonstrate and compare the haulage costs of different vehicle configurations and combinations and the risks involved, such as the effect of changing parameters. However, numerous other opportunities also exist, such as innovations to reduce tare and thereby increase payload on the haulage cost. The information obtained from this study can be used in the future to help role players decide on the vehicle configuration which would best suit their needs and shows the value of using a DSS to assist management decisions. 2 C Norris, Mount Edgecombe, South Africa [2010]. 215

11 Zone placement Bezuidenhout et al. (2004) developed a model and used it in a case study to show the value of assisting managers to quantify and correct their on-farm road and zone layout to minimise their transport costs. New loading zones were suggested which reduced the cost of the transport operation. Bezuidenhout and Meyer (2005) reported on the development of a user-friendly DSS based on the above model. The DSS is a spreadsheet and includes menus, navigation bars and a help facility. By specifying a road s current traffic profile, the system will determine whether, and up to where, a road needs to be upgraded and where loading zones should be located. Shortcuts The cost of transport is directly related to the distance traveled and it was felt that in the sugarcane industry there were opportunities to build alternative road routes to reduce the distance required to haul the cane. Route planning involves the determination of a path between any two or numerous points in space, based on design objectives, such as minimum construction cost, maximum travel speed, safety and minimum environmental impact. Because of the significant costs of transport in the sugar industry, Harris et al. (2008) developed a model, named FastTrack, to investigate route planning opportunities in this industry. FastTrack looks at all the factors which influence the cost of road infrastructure and transport costs and, with the use of GIS tools, looks at alternative shorter routes to reduce the transport costs. A case study was carried out to demonstrate the tool and show the savings that could be realised. Compaction Soil compaction caused by infield vehicles is a complex process which is likely to impact cane production; however, because of complex soil mechanics and the many factors that influence it, the damage is difficult to quantify. Marx et al. (2006) used a model named SOCOMO (van den Akker, 2004), as the basis to develop an easy to use system of investigating the impact of wheel traffic under various conditions. Bezuidenhout et al. (2006) then used the DSS in an attempt to link long term simulations of soil moisture content, derived using the ACRU agrohydrological model (Schulze, 1995), to simulations of soil compaction obtained using the SOCOMO model. Soil moisture content and compaction were simulated for a reference soil and a reference agricultural vehicle. The spatially reported results display a discrete traffic season, during which chances of causing severe compaction damage are significantly reduced. This reference traffic season could be used to strategically determine the best times for vehicles to enter sugarcane fields. Supply chain Stutterheim et al. (2006) developed the model CAPCONN, an integrated supply chain modeling tool, to provide a suitable approach for supply chain management and planning. The aim of the work was to demonstrate an integrated sugar supply chain model framework from field to the back-end of the mill. The system enables one to vary the performance, capacity or costs of any element in the supply chain and determine its impact on the cost of the end product, raw sugar. According to Stutterheim et al. (2006) the CAPCONN model provides a suitable diagnostic framework to analyse and investigate sugarcane supply chains. Bottlenecks are highlighted and the model facilitates capacity manipulation for efficiency improvements under different harvesting methods. CAPCONN was successfully used to investigate the economic impact in 216

12 terms of the increase in production cost of raw sugar in a mill area of a change from manual to mechanical harvesting. Bio-energy With the continuing increase in energy costs and carbon emissions there is also an increasing interest in the production of bio-energy from sugarcane. However, according to Botha and van den Berg (2009), many questions remain regarding the extent to which agronomic practices need to be adapted, and the related economic impacts and tradeoffs are quantified. These authors addressed these issues by developing a model chain consisting of a cane growth model, an economics of trashing model (Wynne and van Antwerpen, 2004), and the farm and sector-level models of the Bureau for Food and Agricultural Policy (BFAP, 2008). They modelled realistic farms for three South African production regions (irrigated north, midlands and coastal dryland). The model emulated current practices which were varied to suit different mill processing strategies, including sugar production, electricity cogeneration from bagasse with or without trash and bio-ethanol production (Botha and van den Berg, 2009). Yields were simulated using historical weather data for the period 1998 to Four plausible macro-economic scenarios for 2008 to 2017 were compared and the results showed which scenarios were profitable under which conditions. This type of model could be used to consider various alternatives in bioenergy production, and could also be used as an interactive tool to contribute to farm and mill level discussion and decision making regarding bio-energy production. Miscellaneous There are some items which can be included in a miscellaneous category. Carbon footprint With increasing concern regarding the impact of emissions on the environment, researchers have developed a host of carbon calculators which are available and enable one to estimate the emissions from their operations. A reliable method is discussed by Rein (2010) and can be used to determine the carbon footprint of sugar production. GIS applications GIS and its query facilities make useful tools to study the impact of various factors on the sugarcane production system. For example, Schulze et al. (2008) produced the Agrohydrological Atlas for South Africa, which contains detailed primary and derived data relating to agricultural systems. This information is all in a GIS and can be queried for details such as appropriate areas in which to grow sugarcane and the impact of climate change on sugarcane production areas. Conclusions The sugar industry is a complex system operating in a highly competitive environment and with client pressures to reduce its carbon footprint. This means that to remain sustainable, managers have to make informed decisions; one has to be proactive and explore every opportunity to improve production, reduce costs and be environmentally friendly. One can no longer rely on tradition or experience alone; quantified options, what if? questions and different scenarios have to be explored. DSSs provide a valuable tool to achieve this. There are many available to 217

13 optimise individual elements of the supply chain and others to examine larger segments of the chain. Many have proved their worth in case studies and every effort should be made utilise these management tools. The decisions can be long term and strategic, medium term tactical decisions to support the strategy, or day to day operational decisions. In the case of FREDD in vehicle scheduling, they could be minute by minute decisions as disturbances to the daily plan occur. There are a huge number and a wide range of systems available, mostly from research institutions, although more and more are being marketed and supported commercially. Although some are generic and can be applied internationally, others such as cultivar selection would have to be locally applied. Adoption has proved to be a serious limiting factor and a number of reasons have been given for this. However, despite this, they are generally valuable tools and managers should be proactive and utilise them to improve their enterprises. The ability to carry out a sensitivity analysis cannot be underestimated; determination of the factors which have the largest impact on the system enables one to focus on the correct issues and get an improved return on investment. Managers can also be informed and make the right decision at the right time, while keeping future scenarios in mind, minimise their inputs and risk and maximise their outputs. Although there has been an emphasis on ensuring that individual elements of the supply chain are not optimised at the expense of the whole system, most of the DSSs mentioned do focus on sugarcane production without considering the impact on the sugar recovery system. This is because researchers tend to concentrate on sugarcane production or on milling, and seldom on both. One needs to take this into account and ensure that the factory receives a steady supply of fresh, high quality sugarcane. REFERENCES Agtrix (2011). FREDD, vehicle scheduling system. Online: [accessed 15 January 2011]. Armitage RM, Lecler NL, Jumman A and Dowe K (2008). Implementation of the Irriecon v2 decision support tool to assess net returns to irrigation systems. Proc S Afr Sug Technol Ass 81: BFAP (2008). Bureau for Food and Agricultural Policy. Online: [accessed 15 January 2011]. Bernardes MS, Suguitani C, Laclau PR, Martiné JF and Chopart L (2007). Evaluation of Mosicas sugarcane growth model in Brasil. Proc Int Soc Sug Cane Technol 26: Bezuidenhout CN and Bodhanya S (2010). Identifying opportunities in South African supply chain system. Research Report for the South African Sugarcane Research Institute, Mount Edgecombe, South Africa. Bezuidenhout CN and Meyer E (2005). A decision support tool for upgrading farm roads, locating loading zones and establishing cane extraction profiles. Proc S Afr Sug Technol Ass 79: Bezuidenhout CN, Leibbrandt NB and Raja A (2002). The development of a computerised decision support tool for weed control in the South African sugar industry. Proc World Congress of Computers in Agriculture and Natural Resources. pp Bezuidenhout CN, Lusso CD, Lyne PWL and Meyer E (2004). Minimising transport costs in two-phase sugarcane extraction systems through optimal upgrading of roads and transloading zones. Sug Cane Int 22(4):

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