- Bundaberg Sugar
- Tutorials/Sugar cane farming
- Manual farm designs
- Semi automatic farm designs
- Fully automatic designs
- Production and Management of Sugarcane Biomass — Process Optimization
- Planting Sugarcane: Whole Stalks Versus Billets
Sugar cane grows for 12 to 16 months before being harvested between June and December each year. When harvested, the cane stands two to four metres high. Queensland’s sugar cane is harvested by self-propelled harvesting machines. Some growers contract machine owners to harvest their crop, while others own their machines or share ownership with other growers.
There are two methods used to harvest cane. In some cane-growing areas it is possible to harvest the cane green. The left over cuttings form a mulch which keeps in moisture, stops the growth of weeds and helps prevent soil erosion. In other areas, the sugar cane is burnt to remove leaves, weeds and other matter which can make harvesting and milling operations difficult.
In both processes the harvester moves along the rows of sugar cane removing the leafy tops of the cane stalks, cutting the stalks off at ground level and chopping the cane into small lengths called ‘billets’. These are loaded into a haul-vehicle travelling alongside the harvester. The cane is then taken to a tramway siding or road haulage delivery point for transport to the mill.
After harvesting, the stubble left behind grows new shoots, producing a “ratoon” crop. Two or three ratoon crops can be grown before the land is rested (or planted with an alternative crop such as legumes), ploughed and replanted for the cycle to start again.
Tutorials/Sugar cane farming
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Sugar cane is a valuable plant for crafting rockets and trading paper. Sugar cane can also be used with a composter to get bonemeal, however, melon farms are probably more suited for this. The large amount of sugar cane obtainable from some of these farms can make it much easier to get rockets or emeralds.
Sugar cane can only be planted on grass, dirt, podzol, and sand blocks. The block must be directly adjacent to water and not merely above or diagonal as with crops. If a plant’s water source is removed, it will break when it is next updated. In Bedrock Edition, the sugar cane is updated with its water, so it breaks immediately.
Every 16 random ticks, sugar cane grows 1 block in height, similar to how cactus, kelp, and bamboo grow. On average, sugar cane will grow 1 block every 18 minutes. Sugar cane’s growth rate is unaffected by the absence of light.
Sugar cane can naturally grow up to 3 blocks in height. This limit can be bypassed by placing additional plants on top of an existing one but it will still not grow naturally any further.
Sugar cane, like saplings, wheat, and cacti, will only grow if the chunk they are on is loaded into memory, so you should not venture too far from the field if you want it to grow. In Bedrock Edition the growth range is based on simulation distance.
Manual farm designs
The first step in building a sugar cane farm is choosing a design. When starting out, simply placing sugar cane on a river bank should be sufficient. However, this quickly becomes impractical when implemented on a large scale. Sugar cane farms must balance between compactness, ease of harvest, and difficulty to build.
A double rowed design, while not the most efficient of designs as it has only 2 canes per water, is relatively easy to build and harvest. It is also a good choice for some of the semi-automatic designs below. With this design, it is recommended to use flowing water rather than water sources. Not only is it easier to build it flowing, but when harvesting, any items that fall into the water will flow into a central location.
A more efficient grid pattern design can also be used. This design has 4 sugar canes per water source, so it is highly compact. The downsides are that is is more difficult to both build and harvest. The difficulty in harvesting can be removed by placing lily pads or something similar on top of all water blocks. This makes the ground smooth and easy for the player to walk on without falling. Light blocks can be used under or above the water to prevent mob spawning.
When harvesting, walk slowly and sweep side to side breaking all but the bottom block of each sugar cane. Then, pick up any missed items and continue.
Semi automatic farm designs
In Bedrock Edition, when sugar cane’s water source is removed, it immediately breaks. Using this principle, it is easy to create semi-automatic farms that harvest the sugar cane. These designs should still work in Java Edition, however, it will take a bit more time for the sugar cane to break. Some other designs here are classified as semi-automatic due to their lack of ability to pick up the sugar cane. These can often be easily converted into automatic designs as seen in the next section.
Water canal design
Build the double rowed design as shown in the manual farms list. Then, place dispensers containing water buckets to control the water flow. Removing the water streams with the dispensers should cause the sugar cane to break so the player can pick them up and replant.
Top view of an extended piston harvester
Side view of a piston harvester
This design uses pistons to harvest the sugar cane. If the sugar cane is only two blocks tall, it can all be pushed into a water stream. However, if it grows any taller, the top blocks may fall down to the sand where the player can pick it up. This design is often used as the basis for fully automatic farms, however, it must be modified to push the top blocks as well or some of the sugar cane may be lost.
Bone meal design
See also: Tutorials/Bone meal farming
Side view of a simple bone meal farm
In Bedrock Edition, bone meal can be used to instantly grow sugar cane to maximum height. This mechanic can be used to create automatic sugar cane farms.
In the shown design, the dispenser can be filled with bone meal to constantly grow the sugar cane. Since bone meal is not used up on fully grown sugar cane, none is wasted. The player can then stand and constantly break the middle block of sugar cane to quickly farm large quantities.
It is not difficult to connect this with a piston to make it more automatic. However, since pistons cannot push and retract as quickly as the observer clock, it may be desirable to use a different redstone clock. Additionally, a single hopper may not be able to keep up with the large amount of sugar cane, so multiple hoppers or a slower clock should be used. Ideally, a 4 gametick delay clock should be used instead of an observer clock.
Fully automatic designs
Fully automatic designs automatically harvest and collect sugar cane, usually relying on some sort of redstone clock or growth detection. These designs are often expensive to build and more lag prone than other designs. However, a large amount of sugar cane they produce can pay off.
There are four main types of automatic sugar cane farms: Stationary, flying, sim-tick, and zero-tick. Stationary designs, while simpler for platforms without quasi-connectivity, are generally more resource and space intensive as compared to flying designs. Flying designs usually require slime blocks however which may be difficult to obtain for some players. Sim-tick designs move the player in and out of render distance, forcing a growth update. Zero-tick designs remove and replace a requirement for the plant within the same game tick, also forcing a growth update. These designs usually use pistons or sand manipulation. Zero-tick sugar cane farms are faster in Bedrock Edition, with some getting at or above 2,000 sugarcane per hour per plant.
Automatic observer farm
By using a daylight sensor or other clock circuit, the semi-automatic piston design is shown above can be made fully automatic. To make it more lossless, it is recommended to add another layer of pistons above the original one. In Bedrock Edition, these designs can be an alternative to flying machines that are difficult to create and use for this purpose.
Other variations are also possible, such as this diagonal design which uses a hopper clock rather than a daylight sensor.
Rather than use a clock, some designs use observers to harvest the sugar cane as soon as it grows. Designs such as these inefficiently use space compared to the clock method. Since all the pistons activate anytime sugar cane grows, they are usually less lag efficient too. When constructing, the sugar cane goes on the dirt and rails run where the minecart is shown.
It is possible to speed up the process of sugar cane by removing the water source and replacing it in the same game tick, this process is called zero-ticking.
The use of flying machines and hopper minecarts can be combined to create some of the most efficient farms. Flying machine designs generally use only a few pistons and don’t create lag except when harvesting. This is usually the preferred type of design when creating a large farm. The main disadvantage to farms such as these is that they can break if unloaded while running. Due to this, it can be risky to have these run without supervision.
This video has some useful information regarding flying sugar cane farming. It contains a practically lossless flying machine design similar to the one above. The lossless design works by covering the water with leaves and using the flying machine to ensure items pushed to places they can be picked up.
Production and Management of Sugarcane Biomass — Process Optimization
3.2. Approaches within sugarcane processes
The quality and quantity of biomass to be produced and the activities involving growing, harvesting, transportation, processing, and commercialization of the sugarcane are factors that may be optimized with the help of optimization techniques. Several studies dedicated to optimization models to resolve the above-mentioned Problems 1, 2, 3, and 4 have been published in recent years.
Problem 1: Optimized partitioning of the land into plots.
Consider an available area for planting sugarcane in a field. k is the number of possible plots that can be allocated to sugarcane in this area. Cherry et al. defined the plot generation problem as follows. The planting area must be partitioned into rectangular plots with dimensions (lj, wj), where lj is the length and wj is the width of the plot j (j=1,2,…,k) in order to increase yield, reduce traffic, and minimize the maneuvers of the sugarcane harvesting machines while respecting all the constraints imposed by mill.
According to Cherry et al. , the planning begins with soil preparation and the partitioning of the planting area into sugarcane plots. The main feature of plots is that they must be rectangular to prevent excessive maneuvers by the harvesting machines. The cited authors propose a methodology using an NLP model for planning the division of the plantation area into plots in order to perform mechanized harvesting. As the plots are rectangular, the authors used a two-dimensional cutting theory based method to solve the problem. Computational experiments were performed regarding real cases, and the proposed methodology shows a reduction of over 40% in the number of maneuvers of the sugarcane harvesting machines, thus implying many economic and environmental advantages.
Problem 2: Selection of sugarcane varieties to be planted.
This problem consists in deciding which of the n varieties of sugarcane, adaptable to local climate and soil, should be planted in each of the k plots, with size Lj, and distance from the cane’s processing center given by Dj (j=1,2,…,k), in such a way that it optimizes one or more objectives, whether it be to minimize costs and/or to maximize production, maximize profit, or others. The solution should meet the company’s recommendations to maintain cane quality and the demand for sugar and alcohol. Examples of these constraints include the limitation of the average sucrose and cane fiber content and the utilization of the entire area set aside for the sugarcane plantation.
Sartori et al. proposed two LP models for this problem. The first model involved the selection of varieties of sugarcane to be planted meeting the mill requirements to minimize the quantity of residue produced. The second model discussed the use of residue to produce energy. This is related to the selection of varieties and quantities to be planted in order to meet the requirements of the mill, to reduce the quantity of residue and to maximize the energy production. The models developed permit the optimization of the energy available in sugarcane residue and its quantity, with the purpose of selecting the best adapted varieties for the production of energy from the biomass or for the production of compost. With these models, it is also possible to determine the area to be planted per variety, the amount of pol to be produced, the amount of residue, and the amount of energy to be extracted.
Florentino and Sartori linked the two problems proposed by Sartori et al. with a BLP model to support variety selection and planting quantity of sugarcane in order to reduce crop residue, maximize energy generated by this residue, and satisfy the demand of the mill. They solved the conflict between these objectives by using nonzero-sum game theory. One player was associated with residue and another with energy. From the Nash equilibrium points supplied by the game, it was possible to choose a solution that satisfies the mill’s interests, thus reducing the sugarcane crop residue and increasing the energy generated by this residue.
Sartori and Florentino presented a BLP approach to a problem that is similar to the ones discussed by Sartori et al. . The approach does not involve determination of the planting area per sugarcane variety but instead focuses on the decision about the variety to plant in each plot. The model of is more realistic since in practice there is only one variety per plot.
Piewthongngam et al. proposed an optimization model for planning cultivation of sugarcane by selecting the time and the varieties that each producer in the Northeast of Thailand should plant, avoiding the generation of excess supply during the peak of harvest. The planning takes place over a long time period and determines the cultivation period, the varieties to be planted, and the time windows of harvest for each farm so that the total sugar production is optimized. The proposed LP model allows decision makers to visualize the sugarcane production in each farm individually on different dates and with different varieties. The results presented by the authors using mathematical programming showed a potential increase of the 23% in sugar production when compared with the traditional planning method.
Florentino et al. proposed a multiobjective ILP model to choose sugarcane varieties so as to minimize costs in the use of crop residue and simultaneously to maximize the energy balance. The model assists the selection of planting varieties by supplying the lowest costs for transferring residual biomass from the harvest in the field to the production center and the optimized residual energy balance. It thus provides the mean energy and fiber content of the varieties of sugarcane selected for planting, taking into account the mill’s requirements. The above-mentioned authors encountered difficulty in solving this model using exact methods for large-sized instances. The two works mentioned below followed up by trying to remedy these difficulties. Thus, Homem et al. used a hybrid procedure involving the primal–dual interior point and the branch-and-bound method to solve the problem. The methodology presented a good computational performance and produced reliable practical solutions, but only for small size problems. Florentino and Pato studied the computational complexity of the problem and showed that it is NP-hard. They proposed a solution methodology using a bi-objective genetic algorithm. A computational experiment undertaken with a set including real and semirandomly generated instances was reported, thus showing the practicality of the technique.
Problem 3: Planning the planting and harvesting of sugarcane.
The problem consists in determining the period of the year in which the cane should be planted and harvested in each plot over four consecutive years, so as to maximize the total cane production over the planning horizon. Constraints should be respected, such as imposing the guarantee that the cane be planted in all plots in the first year, a single variety be planted per plot, the guarantee of meeting the mill’s pol and fiber demands every planning year and the guarantee that the factory’s cane milling capacity be satisfied in all the harvest periods.
Milan et al. presented an ILP model to minimize the cost of transportation of sugarcane from field to mill by integrating road and rail transport systems. The model presents constraints related to the continuous supply of sugarcane in the mill, time that the harvesters can work, type and capacities of vehicles for transportation, and the storage capacity of sugarcane and the availability of routes. According to the authors, the results showed that the model is useful for minimizing the cost of transportation and also for scheduling the transportation of sugarcane, even with the large number of variables and constraints that are present in the model.
The transportation logistics during the sugarcane harvest process is a difficult problem for mill managers to solve. Higgins formulated and implemented an MILP model to assist in resolving operational problems and costs of transportation of sugarcane in Australia. The model improved the scheduling of vehicles and thereby reduced the number of vehicles needed as well as the queues and downtime of vehicles at the mill. Such transport scheduling facilitates the service of traffic agents at the mill during production. The Tabu Search and Variable Neighborhood Search metaheuristics were used to determine solutions to the model. These methodologies were able to find solutions with an average reduction of approximately 90% in vehicles’ queue time, as compared with schedules produced manually by traffic agents of the mill. The solution showed also a potential savings of AU$240,000 per year compared to schedules produced manually by the traffic agent of the mill.
Mele et al. formulated a multiobjective MILP model intended to optimize both the economic and the environmental performance of the production chain of cane sugar. The model is used as a quantitative tool to support decision making in the area of supply chain project planning for the combined production of sugar and ethanol, with sustainable strategic alternatives. An analysis of the model was made using a case study based on a real scenario. The authors conclude that this mathematical tool can help authorities in the analysis of strategic agroindustries and energy policies.
Silva et al. proposed an integer GP model for the aggregate production planning of a Brazilian sugar and ethanol company. This model was based on conventional selection and processing techniques for the design of lots, representing the production system of sugar, ethanol, molasses, and derivatives. The work deals decisions on the agricultural and harvesting stages, sugarcane loading and transportation and energy cogeneration, selecting the production process. This approach allows decision makers to set multiple aspiration levels for their problems. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted and discussed.
Ramos presented a BNLP model to help solve Problem 3. The authors use strategies to calculate the model’s parameters so that if the choice of harvesting dates lies outside the PUI, then the objective function suffers a penalty. This strategy also rewards harvesting dates close to the point of the cane’s maximum maturing curve. Thus, the mathematical optimization model delivers an optimum plan with an estimated production figure 17.8% above production obtained by conventional means in the area in which it was applied.
Problem 4: Utilization of residual biomass to generate energy.
This problem consists in optimizing the processes involved in the exploitation of the sugarcane harvest’s residual biomass for the purpose of energy generation.
Sartori et al. developed a model to minimize the cost of the residual biomass transfer process, to evaluate the economics of using this material and to address sucrose production and planting area constraints, considering distances from the plots to the processing center. To solve this problem, multiobjective BLP techniques were used. The model enables one to determine an estimate of the total sucrose yield at a minimum cost and to demonstrate the economic viability of the use of the harvest’s residual biomass to generate energy. Spadotto proposed the application of optimization theory to improve a system designed to use the straw resulting from the mechanized harvest of sugarcane to generate energy. The goal is to maximize the volume of straw to be loaded onto the truck in the form of straw bales, thus minimizing transport costs to the processing center.
Sartori et al. proposed the optimization of the sugarcane residual biomass energy balance by considering the difference between generated and consumed energy in the process of transferring this biomass from the field to the processing center. The corresponding model is a BLP model taking into account enterprise demand restrictions and cane planting area constraints. The authors concluded that using the residual biomass produced in sugarcane harvests is viable, thereby generating more energy and reducing biomass in the field. Therefore, the methodology can be applied to optimize the energy balance.
Capacity of the mechanical harvesting process of sugar cane billets
Capacidade do processo de corte de rebolos de cana-de-açúcar colhidos mecanicamente
Paulo Rodrigues PeloiaI; Marcos MilanII,; Thiago Libório RomanelliII
IUSP/ESALQ Programa de Pós-Graduação em Máquinas Agrícolas
IIUSP/ESALQ Depto. Engenharia de Biossistemas, C.P. 9 13418-900 Piracicaba, SP Brasil
The mechanized harvest of sugar cane (Saccharum officinarum L.) in Brazil is an irreversible trend and it comes with a great concern about the quality of the cane delivered to the industry. A key component to quality is the billet length which affects the processing of raw material, cane deterioration, invisible losses and load density of transport vehicles. Thus, due to the importance of the billet standard in quality and cost of raw material, this study aimed to evaluate if the mechanized harvesting of sugar cane can supply the quality requirements for the crushing process, regarding the billet length. A plot with burnt sugar cane (3.2 ha) and another one with green sugar cane (8.0 ha) were selected to be harvested by two (2) self-propelled sugar cane harvesters. For each harvested 0.4 ha a sample from each infield wagon was collected. The sample was composed by ten billets. The variability in burnt sugar cane was higher than in green sugar cane, and both harvesters did not present the capacity of keeping the billets with similar lengths when operating either in burnt or green conditions.
Key words: mechanization, sugar cane harvester, statistical process control
A colheita mecanizada de cana-de-açúcar (Saccharum officinarum L.) no Brasil é uma tendência irreversível e junto a ela vem à preocupação com a qualidade da matéria-prima que chega à indústria. O tamanho de rebolos tem influência nessa qualidade por afetar o processo de deterioração da cana, perdas invisíveis e a densidade de carga no transbordo e transporte. Considerando-se a importância do padrão do rebolo na qualidade e custo da matéria-prima, avaliou-se se a colheita mecanizada de cana-de-açúcar pode atender às exigências de qualidade da moagem no que se refere ao indicador tamanho de rebolo. Para tanto, uma área de cana queimada (3,2 ha) e outra de cana crua (8,0 ha) foram selecionadas. As áreas foram colhidas por duas colhedoras automotrizes. Para cada colhedora e área, uma amostra a cada 0,4 ha colhidos foi coletada, junto ao conjunto de transbordo e cada amostra era composta por dez rebolos. Os resultados foram analisados por meio de gráficos de controle, e a capacidade do processo de corte foi determinada. Houve maior variabilidade na condição de cana queimada em relação à cana crua, e que ambas as colhedoras não têm a capacidade de manter os rebolos em tamanhos semelhantes, quando operando nessas duas condições diferentes.
Palavras-chave: mecanização, colheita mecanizada, controle estatístico do processo
For continuous improvement of a process the analysis of usual variations under the statistical process control (SPC) (with no effect of special variation) is necessary in some cases in order to evaluate if it complies with customer specifications. In the SPC theory this methodology is called a process capacity. The first developments of this theory dates back to the early 20th century, with Shewhart (1925). The application of SPC is a usual practice at the industry. In agriculture, its application has increased (Fernandes et al., 2000; Milan and Fernandes, 2002) but the determination of process capacity is rare. Salvi et al. (2007) is an example of the application of this technique.
In Brazilian agriculture, sugar cane (Saccharum officinarum L.) has received special attention due to its potential as a renewable energy source (Santos et al., 2006). In its production the mechanized harvest process is an irreversible trend but the quality of the raw material delivered to the industry through this process has been an issue of concern and the size of the billets is an item to be considered. Billet length affects the load density of wagons and trucks, the level of sugar cane deterioration, the invisible losses, the transport and the industrial process. The shorter the billet the higher the load density, however it increases the possibility of sugar cane deterioration (Ripoli, 1996; Ripoli and Ripoli, 2002). The size of the billet also affects the rate of invisible losses. The smaller the billet, the greater the number of cuts of the stalk, increasing the invisible loss (Neves et al., 2003).
Considering the importance of the billet standard in the quality of process and in the cost of raw material, this study aimed to analyze if the mechanized harvesting of sugar cane can supply the quality requirements for the crushing process, regarding the billet length in both burnt and green harvested canes.
Material and Methods
The analysis of the process of billet cutting was made by two tools: control charts and studies of capability. Control charts only assess the behavior of the process related to variability, indicating whether it follows a consistent pattern (predictable) over time. The statistical theory developed by Shewhart (1925) for calculating the limits of the control charts is based on the idea that the process in study, which is under statistical control, has a statistics W that is calculated from the sample values with a mean µ (W) and a standard deviation σ (W). It will have a probability close to 100% of being in the range of µ (W) ± 3σ (W). Thus, when an observed value is outside the range established by the limits of control, it indicates the presence of a special cause or an assignable cause. In practice, as µ (W) and σ (W) are unknown, it is necessary to estimate them by sample values obtained from the process. Therefore, small samples (subgroups) are collected periodically, and then estimates of µ (W) and σ (W) are made.
Capability studies define if the process generates acceptable products in usual operational conditions, regarding either the specifications or the customer needs. To check if a process is capable, indexes of capacity that compare customer specifications to the natural variation of the process, given by 6s, are used. As the value of s is unknown, in practice it is estimated through sampling.
Data collection was made in a commercial stand of a sugar mill and distillery, in the south-east region of the Sao Paulo state, Brazil, during September, 2006. The basic procedure used in the study was to analyze the process of harvest in the regular routine of the company, as a prerequisite for the use of control charts and capability studies. One field of burnt sugarcane (3.2 ha, variety RB72454, 2nd cut, erect stalks) and another one of green (8.0 ha, variety SP80-3280, 4th cut, erect stalks) were selected to be harvested, both presenting the same yield (100 t ha-1). The harvest was carried out using two machines here called CA and CB, both manufactured in 2006. The harvester CA and CB had worked 508.8 and 904.1 hours, respectively, before this study. The specifications of the machines during the harvest followed the standards adopted by the sugar mill. The harvesting work speed in the two proposed conditions was kept within the specification limits determined by the company. The operator adjusted the speed within the limits under the working conditions in order to achieve the requirements of raw material quality and productivity of the process. Both harvesters were adjusted to chop stalks in smallest possible billets aiming to supply the specification suggested by the technical staff: billets from 140 to 180 mm. At each field random samples were collected for every harvested 0.4 ha. Each sample was composed by ten billets, called sample size or subgroup (n = 10). Thus, for each harvester 80 billets were collected and measured for the burnt sugar cane and 200 for the green cane. Samples were collected at the top of the load of the infield transport wagon.
The selected control chart type was (average by range), and data were analyzed by t and F tests. The process capacity for the stalk chopping was determined by an efficacy index (Cp) according to Montgomery (2004), through the mean value of the sample range as an estimate of the process variance. Cp was estimated through the confidence interval following the methodology suggested by Lewis (1991) and represented by p. The upper (USL) and the lower specification limits (LSL), 140 and 180 mm, respectively, were used in the determination of the efficacy index.
The determination of the process capacity demands statistical stability and normal distribution of the individual data. The stability can be proven by the chart of the behavior process and was obtained artificially by the removal of points with special causes. This procedure is not adequate when searching for process improvement in actual work conditions, but is justifiable when used to study the process capacity (Vieira, 1999). Individual data normality was verified by the Anderson-Darling method (α = 0.10). When the data distribution found was not of the normal type, another standardized non-normal distribution was found and the values of USL and LSL were replaced, from ±3σ (applied for the normal distributions) by percentiles 0.135 and 99.865 (Ramos, 2000).
Results and Discussion
Operating in the green sugar cane field, the harvester CA presented only sample 18 as “non predictable”, in both average and range charts (Figure 1A), indicating that there was external interference on this sample. In this case a sampled billet had a length of 550 mm, which meant that it passed through the internal system of the harvester without being chopped. Also for the green sugar cane, sample 9 of harvester CB was “non predictable” in both charts, due to a billet with 560 mm for the same reason already mentioned. In the average chart, samples 1 to 4 were too close to the UCL. This indicates that something affected the cutting process in the area, such as sugarcane crop characteristics (yield, lodging, variety) or the way the harvester was operated. From sample 9 to 20 one can observe a gradual tendency of length reduction in the billets, which shall be investigated and corrected.
The harvester CA presented more homogeneous billets in the green sugar cane field while harvester CB was more susceptible to external variations. Harvester CB presented a tendency of length reduction and billets close to quality limits in 4 consecutive samples. This is undesirable for the client (mill) that would be willing to receive raw material (billets) with higher homogeneity. When operating in the burnt sugar cane field the harvester CA (Figure 2A) presented all values within the limits and the process was considered predictable. Under the same conditions, the harvester CB (Figure 2B) presented sample 8 out of limit. It occurred due to a billet with 70 mm, but it was not short enough to bring the average also out of the quality limits.
The average billet length in the green sugar cane field did not differ between harvesters, but the standard deviation differed, being higher for harvester CB. For the burnt sugar cane field, harvester CB presented longer billets, but had similar standard deviation than CA (Table 1). Comparing the green and burnt situations, both harvesters presented longer billets and higher variances for the burnt cane. Since both machines were adjusted to chop the shortest length as possible, it is not possible to standardize the billet length when the crop condition changes from burnt to green conditions.
Harvester CB chops more heterogeneously the billets than CA for green sugar cane (Table 2). Considering the operational and work conditions for green sugarcane the mill should receive billets varying up to 98 mm from harvester CA, average 160.3 mm, and 177 mm from CB, average 158.3 mm. For the burnt sugarcane the values are up to 197 mm (CA), average 181.4 mm, and 226 mm (CB), average 202.2 mm.
The billet length for harvester CB presented higher variability for green sugarcane and more susceptibility to external interference than CA. Both harvesters were not able to keep length uniformity, when comparing burnt and green sugarcanes. The billet length for harvester CA for green sugarcane (Figure 3A) presented normal distribution with 10.73 and 11.31% of the data out of LSL and USL, respectively. The same harvester for the burnt sugarcane (Figure 3C) presented the largest extreme-value distribution type, with 3.48% of the billets shorter than 140 mm and 44.68% longer than 180 mm. The best distribution adjustment for harvester CB in green sugarcane (Figure 3B) was the 3-parameter Weibull distribution, with 32.75% and 22.92% of the population below the lower limit and above the upper limit, respectively. Finally, harvester CB for burnt sugarcane (Figure 3D) presented normal distribution with 77.47% of the billets out of the limits (3.57% below and 73.90% above).
The efficacy index (Table 3) measures the capacity of the process to fulfill the specified limits. A high value of this index indicates a high capacity to fulfill the specifications. The efficacy index (Cp) is a measure of the potential of the process but not a real measure, once it does not consider the current average of the process. A minimum value of the confidence intervals (p) equals to one (1) represents 99.78% within the specified limits, if the process average could be centralized (Levine et al., 2002). The p interval for green sugarcane is better than the one for burnt sugarcane and the harvester CA presented superior performance than the CB, but all intervals were lower than 1. It is necessary to analyze the harvest process in order to check the possible causes when the standards are not achieved or to reevaluate if the specified limits of the company for the billet length (140 to 180 mm) are really mandatory to supply the quality demanded for crushing.
Lewis, S.S. 1991. Process capability estimates for small samples. Quality Engineering 3: 381-394.
Montgomery, D.C. 2004. Introduction to statistical quality control. 4ed. John Wiley, New York, NY, USA.
Shewhart, W.A. 1925. The applications of statistics as an aid in maintaining quality of manufactured products. Journal of the American Statistical Association 20: 546-548.
Received February 27, 2009
Accepted May 04, 2010
Corresponding author <[email protected]>
As a visitor to Tropical North Queensland, at any time of the year, you will notice the many sugar cane fields. Our warm tropical temperatures are ideal for growing sugar.
Approximately 95% of Australia’s sugar cane is grown in Queensland and approximately 80 to 85% of Queensland’s raw sugar is exported.
With over 20 different varieties of cane growing in the area, the Mossman Sugar Mill production area spans over 8500 hectares all the way from the Daintree Rainforest up to Atherton Tablelands.
The sugar cane flowers early May through June and July to November is typically cane harvesting season for our local sugar industry.
Originally the cane was burnt before harvesting but nowadays in the tropical north it is generally cut ‘green’. The remaining roots then produce new shoots and several crops may be grown from the same stock before ploughing and replanting is necessary.
If visiting during these months you will see many cane carriages standing and waiting to be filled. Giant harvesters bustle along the fields cutting the cane by removing the leafy tops of the cane stalks, cutting the stalks off at ground level and chopping the cane into small lengths which are immediately loaded into wire bins drawn by a tractor alongside the harvester. Each full load is then tipped into huge cane carriages for transport by small railway locomotives to the Mossman Sugar Mill for processing into raw sugar.
The narrow gauge tracks line the roads and occasionally you may have to stop at one of the rail crossings and wait as they busily criss-cross the roads. In some areas huge cane trucks will be busy ferrying the cut cane to the mill, so be extra careful on our roads.
This is a great photo opportunity so give yourself plenty of time to pull over to take that extra holiday snapshot.
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Suar cane trains at Mossman Queensland
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- Table sugar is produced from sugar cane in Australia and New Zealand
- The cane grows from tropical north Queensland down to northern New South Wales
- ‘Raw sugar’ from a mill is typically not edible. It must go through a level of refining to make it safe to consume.
At the farm
At the mill
At the refinery
NEXT: The functional role of sugar in food
Sugar cane field burning is carried out before harvesting the cane to make the process easier and require less manual labor. It takes place during the harvest season, lasting from May to November (dry season) in the South and East, (Cannavam Rípoli, 2000) with the peak of the burning season being in August. ((1)Lara, 2005) In the burning process, the field is set fire to and the leaves are burned off of the stalks. About 80% of the “trash,” including straw, the tops, and green and dry leaves, are burned off. These components constitute about 25% of the entire sugar cane stalk. The burning kills microorganisms and burns the trash, both of which keep the soil rich when left in the fields. In place of burning the cane, the leaves could be removed and burned to create steam for electricity generation or be converted into fuel themselves. (Cannavam Rípoli, 2000)
The Amazon is blanketed with clouds for the majority of the year due to the large amount of water vapor released into the air by the thick canopy. The sugar cane industry is strongly rooted in the Amazon, where the soil and climate are well suited for sugar cane growth. Southern and Southeastern Brazil are heavily involved in the production of sugar cane in response to the increase in demand for bio-fuels such as Ethanol.
During the burning season, smoke covers huge areas of the Amazon, warming the cloud layer and reducing the updrafts which form clouds. Smoke has a lower albedo than the clouds, allowing more solar energy to enter the Earth’s atmosphere, affecting the climate. Concentrations of smoke over the Amazon were measured from 2000 to 2007, with the results showing an increase of 60% in concentration from 2000-2005, a drop in 2006, and then a large spike in 2007. In 2007, much of the land which was previously used for soy farms and cattle pastures were converted to sugar cane fields. Many of the farmers are phasing out burning over the next ten years, leaving the smoke emissions from mechanical harvest an issue. (Lindsey, 2008)
The aerosols emitted from sugar cane field burning act as cloud condensation nuclei (CCN), enabling the formation but decreasing the size of cloud droplets. Associated decrease in formation of larger water droplets and precipitation allows for increase in water and pollutant transport to the upper troposphere. Although smaller droplet size increases the reflectivity of the clouds, the increase in aerosol optical depth (AOD) resulting from smoke in the atmosphere counteracts the temperature decrease. As AOD increases from 0.1 to 1.0, cloud temperature increases by about 3K (Yu, 2007) (clean background atmosphere is considered to be AOD <0.2, and very hazy conditions are indicated by an AOD = 1 ). (Remer, 2009) Atmospheric residence time for the aerosols lasts from days to several weeks and they can be widespread from one hundred to thousands of kilometers. ((1)Lara, 2005)
Other resulting air pollutants from sugar cane burning include acidic fine particles, such as secondary nitrates and sulfates and carbon compounds (Allen, 2004) Carbon monoxide and methane react with atmospheric hydroxyl radicals, which decreases oxidation efficiency, nitric oxide and hydrocarbons produce high ozone concentrations during the dry season. (Crutzen, 1990) One study carried out showed that burning season produced significantly higher concentrations of HCOO-, CH3COO-, C2O2-4, SO2-4, NO-3, , K+, NH+4, Mg2+and Ca+, with the only tested species unaffected being Cl- and Na+. The aerosols are a mechanism for transport of species which affect soil nutrients and cause surface acidification, such as sulfates, nitrates, ammonium and organic acids. (Allen, 2004) Results of some research shows that sugar cane fires contribute to about 60% of fine particle mode mass, 64% of the black carbon mass, and 25% of the course particle mode mass present in urban areas adjacent to cane fields.. ((1)Lara, 2005)
Comparative to forest fires, which result in 200 to 300 tons of burned organic material per hectare, sugar cane fires produce only about a tenth of the carbon emissions. About 20 tons of sugar cane trash is burned per hectare, which releases about 0.48 Tg of carbon into the atmosphere annually,. Sugar cane burning occurs over a comparatively short period of time, after which the crop does not smolder, which is the main cause of carbon emissions in forest fires. ((2)Lara, 2005)
While removal of leaves may also be carried out by field workers, field burning costs much less money and makes the cane easier to harvest. The practice burns scorpions, snakes, and bees which would otherwise be a danger to the laborers harvesting “green,” or unburned, cane. Workers also collect on average only about a fifth of green cane as they can burned cane per day. Modern day mechanical cane harvesters are also inefficient, yet the difference between harvesting burned and green cane is, at maximum, 20 percent. (Cannavam Rípoli, 2000)
Allen, A., Cardoso, A., and Da Rocha, G. (2004). Influence of sugar cane burning on aerosol soluble ion composition in Southeastern Brazil. Atmospheric Environment ISSN 1352-2310. vol. 38, no30, pp. 5025-5038 (1 p.1/4). http://cat.inist.fr/?aModele=afficheN&cpsidt=16068007
Cannavam Rípoli, T., Molina, W., and Cunali Rípoli, M. (December, 2000). Energy Potential of Sugarcane Biomass in Brazil. Scientia Agricola. http://www.scielo.br/scielo.php?pid=S0103-90162000000400013&script=sci_arttext
Crutzen, P. and Andreae, M. (December 21, 1990). Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles. Science, Vol. 250. no. 4988, pp. 1669 – 1678
DOI: 10.1126/science.250.4988.1669. http://www.sciencemag.org/cgi/content/abstract/250/4988/1669
(1)Lara, L., Artaxo, P., Martinelli, L., Camargo, P., Victoria, R., and Ferraz, E. (April 11, 2005). Properties of aerosols from sugar-cane burning emissions in Southeastern Brazil. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VH3-4GFNGD0-2&_user=2139813&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1093310659&_rerunOrigin=google&_acct=C000054276&_version=1&_urlVersion=0&_userid=2139813&md5=7b14fd271fd0892e4f1f2b4045a616db
Remer, L. (October, 2009). Global Maps: Fire/Aerosol Depths. NASA Earth Observatory. http://earthobservatory.nasa.gov/GlobalMaps/view.php?d1=MODAL2_M_AER_OD
Yu, H., Fu, R., Dickinso, R., Zhang, Y., Chen, M., and Wang, H. (March 26, 2007). Interannual variability of smoke and warm cloud relationships in the Amazon as inferred from MODIS retrievals. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V6V-4NT2511-1&_user=2139813&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1093225197&_rerunOrigin=google&_acct=C000054276&_version=1&_urlVersion=0&_userid=2139813&md5=a806407078a79c2c036b14a2cebe75ee
Planting Sugarcane: Whole Stalks Versus Billets
Jeffrey W. Hoy
Mechanization of the harvest was a major turning point in the history of sugarcane production in Louisiana. Harvesters were developed that would cut whole stalks of sugarcane and drop them across rows on the ground to be picked up and placed in transport wagons. This system was used for many years.
Within the last five years, however, another major change has occurred in the sugarcane harvesting system used in Louisiana. Sugarcane is now harvested with what are known as “chopper” or “combine” harvesters that cut cane stalks into sections or “billets” as they pass through the machine. The billets are carried up an elevator and then dropped into a wagon traveling alongside.
This type of harvesting system offers several advantages. Sugarcane can be cut without burning, and it never touches the ground. Burning and contact with the ground both can accelerate deterioration and sugar losses in cane stalks. In addition, and of even greater significance to farmers, this type of harvester can more effectively pick up sugarcane that has “lodged” or fallen down. This then makes it possible to grow high tonnage sugarcane varieties that typically lodge by the end of the season. Because the old, whole-stalk harvesters could not handle high tonnage, lodged cane, varieties of this type could not be developed by the sugarcane breeding program. As mentioned in other articles in this issue, the ability to grow new, heavy tonnage varieties is increasing the yields obtained by Louisiana sugarcane farmers and keeping them in business.
Climate Affects Planting Method
Combine harvesters have been used in most other places around the world for many years, but they were evaluated several times in the past in Louisiana and deemed a failure. The problem was the climate. Sugarcane is grown at the northern limit of its cultivation range in Louisiana, so the growing season is short. The crop must be harvested before a killing freeze occurs. As a result, the harvest must take place during a short period of time and must proceed even in wet weather.
Because early versions of combine harvesters bogged down in the muddy fields, the Louisiana industry kept using whole stalk harvesters. In addition, whole stalks were needed for planting to help control a disease called stalk rot (Figure 1) that rots planted sugarcane and can severely reduce spring shoot populations and yield. Stalks of sugarcane are planted in the late summer, and bud germination and shoot elongation begin in the fall. The occurrence of frosts and freezes during winter then kills the above-ground growth, and the planted stalks and young shoots must sit in the cold, wet ground for several months. Severe stalk rot can result in a stand failure and necessitate replanting.
The failure to establish a plantcane (first year) crop is the worst loss a farmer can sustain. When the previous crop is plowed out, the land does not produce a crop during that season. Then, the stalks used for planting represent a loss, since that cane would have been harvested and sent to the mill for sugar extraction. With whole stalks, it is less likely that the rot will progress through the entire stalk than with the shorter billets. In addition, a higher planting rate (more stalks planted) is used than in other regions of the world to be able to sustain losses to stalk rot and still be able to establish an adequate spring shoot population. This expensive planting system was adopted to ensure that adequate plant cane stands are established each year.
Billets are planted in other places around the world, but planting billets also has been evaluated in the past in Louisiana and deemed to be too risky because of the greater potential for stand failures. Yet, with the increasing use of combine harvesters, there has been intense, renewed interest within the industry in finding methods that would allow successful billet planting. It is expensive to maintain two harvesting systems just to be able to cut whole stalks for planting. In addition, there are advantages associated with billet planting. Labor requirements are reduced with mechanical planting, and the current mechanical planters plant billets more effectively than whole stalks. Planting billets goes rapidly, and the amount of time and labor required for planting is reduced.
Switching to Billets
Considering the failures of the past, is there any reason to think that billet planting would be more successful this time? One difference is in the varieties being grown. Many of the new sugarcane varieties have come from what is known as basic breeding, in which agronomically desirable sugarcane varieties are crossed to wild relatives with plants consisting of large numbers of grassy, small-diameter shoots. The basic breeding program has been conducted by the USDA Sugarcane Research Unit in Houma, La., for more than 30 years. The material generated by this program is used in the Louisiana Sugarcane Breeding Program, which is a cooperative effort among the LSU AgCenter, USDA’s Agricultural Research Service and the American Sugar Cane League. The higher shoot populations and increased vigor of some of the new varieties could affect billet planting performance. In addition, new fungicides and other types of biological and chemical treatments are available that might reduce stalk rot severity.
A research project evaluating factors affecting billet planting has been conducted cooperatively by the LSU AgCenter, the American Sugar Cane League and sugarcane farmers for the last six years. The early field experiments demonstrated that varieties vary in tolerance of billet planting, and this offers some hope for breeding and selecting for varieties that can be successfully planted as billets. Unfortunately, no chemical or biological treatment has been identified that will control stalk rot and improve billet planting performance. Instead, a series of cultural practices that indirectly reduce disease severity and promote vigorous plant growth has been identified that will maximize the chances of success in billet planting.
Billets are more sensitive than whole stalks to stress conditions. This is because stalk rot severity is increased by stressful conditions such as drought or water-logging. As a result, good planting practices are essential for billets. These include proper soil preparation and depth of cover, establishment of good drainage and careful weed control. Adding fertilizer at planting has been beneficial in most experiments and continues to be evaluated.
Other important factors affecting billet planting performance are related to the type and amount of billets used for planting. Normal billets cut during harvest for the mill are about 10 inches long. The harvester must be modified to cut billets 20-24 inches for planting. In addition, it is important to minimize physical damage to the billets, because the pathogens that cause stalk rot gain entry to the internal stalk tissues through wounds. Planting a longer billet with minimal damage will reduce the severity of stalk rot. Research is identifying harvester modifications that will reduce damage and produce the highest quality billet possible.
Finally, the rate of planting affects billet planting (Figure 2). Cutting and planting more seedcane is expensive, but higher rates of planting are needed with billet planting. Billet plantings must be able to sustain stalk rot damage and still produce an adequate stand.
The research results have shown that whole-stalk planting will produce the maximum yield over multiple crops and years with the least risk. Thus, it will continue to be a recommended practice. However, severe stand problems have not been encountered with billet planting when using the practices described above. In addition, the new high-yielding variety, LCP 85-384, has shown some tolerance of billet planting. As a result, billet planting will now be recommended as an alternative to whole-stalk planting. Billet planting may offer some advantages over whole-stalk planting that would be attractive to some farmers, but perhaps the greatest factor leading to a recommendation for billet planting is that severe lodging before the planting season is common with high tonnage varieties, such as LCP 85-384. Whole-stalk harvesters cause extensive damage to badly lodged cane, so the best option in this case will be to cut and plant billets.
Farmers would prefer to plant billets, but many keep a whole-stalk harvester in working condition to cut seedcane if the stalks are standing. Caution is still in order. However, if methods for successful billet planting are proven, the Louisiana sugarcane industry will rapidly switch to billet planting. Planting billets under Louisiana growing conditions is a challenge. The LSU AgCenter conducts research to meet that challenge.
Jeffrey W. Hoy, Professor, Department of Plant Pathology & Crop Physiology, LSU AgCenter, Baton Rouge, La.
(This article was published in the fall 2001 edition of Louisiana Agriculture.)