The total biomass production of five commercial sugarcane varieties grown on the estate across all crop cycles (plant and three ratoons) was developed.

The data on cane yield and cane productivity of plant and ratoon crops between 1995-1996 and 2009-2010 were collected from annual reports and compared to experimental data. The total bio-mass (cane, trash and tops) production in plant and two ratoons were recorded at harvest age i. e. 18 and 17 months for plant and ratoon crops respectively. Data was collected from three locations of plot size 54 m2 (4 rows of 10m length). Nutrient status of crop residues on oven dry basis was adopted as suggested by " see [11] " for calculation of nutrient return to the soil and nutrients available to succeeding crop.

A replicated trial with four replications was established during 2009-2010 with different levels of chemical fertilizers and factory by-products (filter mud and boiler ash) to test their influence on cane and sugar yields.

Semi-commercial trials were established on the estate to study the influence of green manuring with sunn hemp against no green manured blocks (control), aggressive tillage against reduced tillage, and intercropping with legumes on cane yield and juice quality parameters.

Field studies were conducted during 2002-2004 and 2007-2009 to evaluate the influence of different levels of Nitrogen (N), Phosphorus (P), Potassium (K) and sulphur (S) on cane yield and juice quality of plant crop of sugarcane.

The cane yield data on green cane vs burnt cane harvesting systems, and aggressive vs reduced tillage operations were collected and analysed for biomass yield.

A comprehensive elemental analysis was performed for the HF system in both soil horizons and biomass compartments in the context of the framework for biomass investigations in northeastern Austria. HF was chosen because of lower soil fertility in comparison to the CS sites, which implies a higher sensitivity with regard to nutrient extraction.

The nutrient balance at a given site is an essential factor for stand productivity, species composition and biodiversity. Gains of nutrients that are in plant available forms and

therefore could be incorporated in new plant tissue are limited to originate from weathering of bedrock material, atmospheric deposition as well as fertilization. The major processes of decreasing nutrient availability to plants are removal (biomass extraction and leaching) or chemical transformation processes, resulting in recalcitrant and thus plant unavailable fractions. Soil microbes play an important role in these processes and there is even a competition for some elements, e. g. N between microbes and plant roots [52]. Interfering nutrient cycles, e. g. by changing forest management practices therefore influences a complex system. The consequences are not usually recognized immediately, depending on the nutritional status of the soil. If nutrient pools and cation exchange capacity (CEC) are low, the consequences are seen within just a few years, as is the case in tropical soils, for instance.

Figure 4. Nitrogen (N), Phosphorous (P) and Potassium (K) content in mg/g dry mass of different compartments for the dominating species, Quercus petraea in the HF system. Note that foliage is the compartment with the highest content of all macronutrients. Boxplots show the median (solid horizontal line), the bottom and the top of the box represent the 25th and the 75th percentile (interquartile range) and the whiskers show the highest and lowest values that are not outliers (< 1.5 times the interquartile range).

Based on an analysis of nutrient contents in various compartments (Figure 5), the aim was to compare plant available (exchangeable) pools of nutrient elements in soil with aboveground pools. This was done in order to determine potential nutritional bottlenecks when the management goal shifts towards biomass production as a source for energy, which implies

higher nutrient extraction rates as compared to conventional forestry or intermediate types (see Table 1). We focussed on the dominant tree species (Quercus petraea, Carpinus betulus and Corylus avellana) where we sampled whole trees on each plot to account for local differences. Only foliage and branches were sampled from less abundant species (e. g. Fagus sylvatica, Betula pendula and Prunus avium) where they occurred.

I Stembark>8 ■ Branches < 2

I Stemwood>8 ■ Regen. <1.3

■ Stem (B+W) <8 ■ Foilage Branches > 2

Figure 5. Relative amount of macronutrients in different compartments of Quercus petraea along the HF chronosequence (left) and a comparison of macronutrient contents in aboveground biomass and exchangeable soil pool (right). Category explanation (same order as legend): Bark of stems > 8 cm DBH, stemwood excluding bark of stems > 8 cm DBH, stems < 8 cm DBH (wood and bark), branches > 2 cm diameter (wood and bark), branches < 2 cm diameter (wood and bark), regeneration < 1.3 m height (total value), foliage (living). Soil exchangeable P pools were not actually measured, but estimated from data of the Austrian forest soil inventory [47].

Figure 5 illustrates the macronutrients nitrogen, phosphorous and potassium (NPK) contents of different compartments. Foliage clearly has the highest contents of macronutrients, followed by bark and thin branches. In the 91-year-old stand, foliage only accounts for 1.7% of the aboveground biomass, but represents 8.2% of the N pool, while 23.3% represent 37.5% in the youngest stand respectively. Wood (sapwood and heartwood) had the lowest contents. Similar patterns of nutrient distribution were previously reported for the same species [53]. The nutrient content of composite samples (wood and bark) depends on the respective proportions of wood and bark. However, it seems different for the case of P where higher contents in the composite as compared to separate wood and bark samples indicate higher contents of P in bark of thin branches (Figure 5). The bark sample consists of bark from branches and stem where the latter is therefore expected to have lower P contents. Approximately 40% of the macronutrients are stored in stems > 8 cm in diameter from an age of 50 years onwards (Figure 6) while representing approximately 60% of the stand aboveground biomass. Bark accounts for another 10% of the 40% stem pools. Consequently it was suggested to consider oak stem debarking to limit nutrient exports (especially Ca in the case of Quercus bark) from the stand [53]. A comparison with exchangeable soil pools revealed sufficient potential supplies from the soil matrix as the soil pools of macronutrients are well above the stand biomass pools. However, a simple comparison of pools does not necessarily represent the nutritional status of the vegetation since plant availability, stress and soil biogeochemical processes may cause uptake limitations of certain nutrients. For instance, the N:P ratio is well acknowledged as an indicator for either N or P limitation and values of < 14 indicate N deficiency where values of > 16 designate P limitation [54]. Obviously pools of soil exchangeable P are very high (Figure 6) which is also represented in our foliar N:P ratios. They are very stable at 14.2 for the 50, 74 and 91 year old stands and close to the threshold value (13.9) in the 32 year old stand. Interestingly the youngest stand (11 years) shows signs of N limitation with a ratio of 10.3. We suggest a combination of reasons for this observation. High rates of increment have a great N demand in the stand organization and subsequent aggradation phase. Herbaceous vegetation on this specific site might compete with woody species for topsoil N and the relatively high coarse material content contributes to low water holding capacities. In combination with low rates of precipitation, water availability might inhibit mobilisation and uptake of N at this site as suggested in a comprehensive review [34]. It is also shown that although the forest is surrounded by intensively managed agricultural land with associated atmospheric deposition of aerosols (dust), it has not led to P limitation as recently suggested [54]. In summary, despite evidence for N limits in the youngest stand, the macronutrient supply meets the demand under current forest management. However, it could be problematic if lower diameter compartments are extracted as biomass for energetic utilization. For instance, if the crown biomass is utilized at the final harvest and branches with a diameter > 2 cm are being extracted, it will account for a twofold extraction of N as compared to stem-only harvests. In a scenario of whole tree harvesting, close to 90% of N in biomass will be extracted, compared to approximately 40% in the stem-only scenario. Stem debarking could further reduce N extraction to below 30% of the total aboveground biomass pool. This example demonstrates the importance of assessing the dynamic nutrient pools in order to provide profound recommendations for sustainable forest management, as was concluded in a previous study [53]. Forest nutrition not only has implications on species diversity, sequestration of carbon, and provision for a magnitude of environmental services, but it has distinct implications on site productivity and thus earning capacity of a given management unit. Sustainable nutrient management is therefore an essential component of successful forest management, especially if management aims at harvests that are more intensive for bioenergy production.

Determining the schedule time required for harvest operations is an essential prerequisite to determine herbaceous biomass priority for securing inventory and effectively matching the transport of biomass from SSLs (satellite storage locations) to a biorefinery. Estimating the number of days expected to be available for baling is difficult because agricultural field work is heavily weather dependent and information concerning winter field operations is not well established. Harvest costs depend in part on the investment required in harvest machines (number of machines purchased), and this investment depends on the field capacity of the machines and the number of field workdays available during the harvest window. Therefore, an estimate of the number of harvest days is necessary to determine the total investment in harvest machines required to support a biorefinery.

The optimal perennial grass harvest strategy for a biorefinery remains to be determined. One potential strategy is to delay harvest until the plants standing in the field have transitioned from a non-dormant to a dormant state. Transitional events within the plant include translocation of nutrients from the above-ground plant parts to below-ground plant parts as initiation of degeneration (senescence) of above-ground tissues occur. Delaying harvest until the end of this transitional period, and harvesting over a short time period, can result in near maximum biomass yield, reduced moisture content, and reduced amount of nutrients removed with the biomass. This strategy would suggest an optimum harvest window for switchgrass from October to December. However, a harvest season this short would require substantial investment in harvest machines to complete harvest within a 3- month period. It also requires a large storage for inventory to supply the plant for year — around operation. By delaying harvest until nutrients have translocated, biomass tonnage will be decreased by this loss of mineral content, however since the relative amount of carbon in the material is increased, conversion efficiency and combustion quality may be improved [17]. Moreover, the translocated nutrients stored in the roots can be used for growth and development by the plant year after year, thus, reducing the need for and cost of supplementing the soil with nutrients through fertilization.

A second strategy is to extend harvest over as many months as possible. This would enable more economical use of harvest machines, reduce the quantity required in SSL storage, and potentially better match truck transport with SSL operations. With an extended harvest strategy, harvest would begin in July and extend through the following March. This extended harvest season would allow for spreading the fixed costs of the harvest machines over more Mg harvested per year thus reducing the $/Mg harvest cost. However, the earlier harvests before senescence (July through September) will remove more nutrients, and they will potentially conflict with other farm operations (i. e. grain/hay harvest, small grain seeding). To maintain productivity, additional fertilizer will be required, and this represents an additional cost to the farmer whose fields are harvested prior to October. The farmer whose fields are harvested January through March will have a lower yield because of leaf loss from the standing biomass exposed to winter conditions.

A delayed harvest raises an important issue relative to the payment to a farmgate contract holder. As previously explained, a certain loss occurs when the crop is left standing in the field for delayed harvest (December through March). Also, the delayed harvest increases the risk that a weather event will cause the crop to lodge so the harvest machine leaves material, and the "yield" is reduced. If these same fields were harvested during the optimum window (October through December) and stored longer in an SSL, there is a storage loss that increases with time in storage [18]. Considering the total reliance on stored biomass during the non-harvest months, which strategy is the better choice for the farmgate contract holder (i. e., generates the maximum quantity of biomass to sell to the biorefinery)? Is it better to delay harvest or build a larger SSL and invest in more harvesting machine capacity to complete the entire harvest within the 3-month optimum window? Epplin et al. [19] estimated differences in switchgrass harvest costs for both a 4-month and a 8-month harvest window. They accounted for differences in biomass yield across months, but they did not adjust for fertilizer requirements. They found that the estimated harvest costs varied from $25 per Mg for a 4-month harvest season to $11 per Mg for a 8-month harvest season. These results show that the length of the harvest season is a significant factor in determining the costs of harvesting biomass.

Epplin et al. [19] did not have refined estimates of the number of days that biomass could be harvested. They based their estimates of available harvest days on a study designed to determine the number of days that farmers in Southwestern Oklahoma of United States could conduct tillage operations. To determine more precise estimates of the number of harvest machines required to harvest, and thus obtain a more precise estimate of harvest costs, a more precise estimate of the number of harvest days is required.

Hwang et al. [20] determined the number of suitable field workdays in which switchgrass could be mowed and the number of days that mowed material can be baled. Empirical distributions of the days available for mowing and for baling switchgrass were determined for nine counties in Oklahoma. Distributions were determined for each month and for two potential harvest seasons (short, October-December and extended, July — February). Several conditions are necessary for safe baling of switchgrass. First, the soil must be sufficiently dry to support the weight of harvest machines. Second, prior to baling, the moisture content of the cut switchgrass must be at a level for safe storage. They provided evidence of the difference in harvest days across strategies.

Determining the time available for required harvest operations is a necessary, but not sufficient, prerequisite for determining the optimal harvest strategy. Additional information will also be required. As previously explained, if harvest begins in July prior to maturity and subsequent senescence in October, additional fertilizer will be required (the amounts are undefined) to offset the nutrients removed from the pre-senescence harvest. If harvest is delayed into December or later, the harvestable yield will be less (the amounts are undefined) than if it is harvested in October and November. Thus, the information regarding differences in harvest days across months must be combined with information regarding differences in fertilizer requirements, and biomass yields across harvest months, to determine an optimal harvest strategy.

2.1.2.1. Effect of ozonation on molecule weight distribution of organic matters

Pre-ozonation can change the properties and structures of organics in raw water. By using high performance liquid chromatography (HPLC), the variations of molecule weight before and after ozonation is studied, the result is shown in Fig. 5. As shown, the molecule weight mostly distributed within a range of 2000-6000, and 2000-3000 after the ozonation, the amount of organics with a relative molecule weight under 500 is increased dramatically. This indicates that macromolecules are in a high proportion in raw water, but after the ozonation, the proportion of macromolecular organic matter decreases while small molecule weight organics increases, which implies that part of the intermediate material from ozonation is namely the increased small molecular weight organic matter.

In this part, Gas Chromatography-Mass Spectrometer (GC-MS) is used to analyze the structure of organics in raw water before and after ozonation, results are shown in Fig. 6. As shown, the organics in raw water are mainly aromatic hydrocarbons, chain hydrocarbon and aliphatic organics; after ozonation, the amount of aliphatic organic matters increases significantly and the amount of esters tend to increase too, which indicate that part of the aromatic organics are oxidized into organics of oxygen-containing groups, such as fatty acids, carboxylic acids and esters.

Figure 6. GC-MS chromatogram of raw water before and after ozonation

For the MBBR pollutants removal efficiency and biomass concentration the crucial role plays the material of which carriers were made (table 1). The basic features are as follow: material type, specific surface area, shape and size of carriers and other features: porous surface, e. g. polyurethane or not porous material surface e. g. polyethylene [27,28].

1.1. Nursery and grow-out

Nursery phase is defined as an intermediate step between hatchery-reared early postlarvae and grow-out phase [48]. Such phase presents several benefits such as optimization of farm land, increase in survival and enhanced growth performance in grow-out ponds [49-51]. BFT has been applied successfully in nursery phase in different shrimp species such as L. vannamei [44, 48], P. monodon [51], F. paulensis [15, 46], F. brasiliensis [37, 52] and F. setiferus

[34] . The primary advantage observed is related to a better nutrition by continuous consumption of biofloc, which might positively influence grow-out performance a posteriori [53], but was not always the case [54]. In addition, optimization of farm facilities provided by the high stocking densities in BFT nursery phase seems to be an important advantage to achieve profitability in small farms, mainly in cold regions or when farmers are operating indoor facilities.

In [46] was observed that presence of bioflocs resulted in increases of 50% in weight and almost 80% in final biomass in F. paulensis early postlarval stage when compared to conventional clear-water system. This trend was observed even when postlarvae were not fed with a commercial feed (biofloc without commercial feed). In L. vannamei nursery in BFT conditions, references [48] and [55] reported survival rates ranging from 55.9% to 100% and 97% and 100%, respectively. In [51] was demonstrated that the addition of substrates in BFT systems increased growth and further enhanced production, while also contributing to more favorable water quality conditions. According to the same study, growth and survival was not affected by stocking density (2500 vs 5000 PL/m2), therefore greater production outputs were achieved at the higher density. Furthermore, in [37] was found that F. brasiliensis postlarvae grow similarly with or without pelletized feed in biofloc conditions during 30-d of nursery phase, which was 40% more than conventional clear-water continuous exchange system.

In grow-out, BFT has been also shown nutritional and zootechnical benefits. In [9] was estimated that more than 29% of the daily food intake of L. vannamei consisted of microbial flocs, decreasing FCR and reducing costs in feed. The reference [10] showed that juveniles of L. vannamei fed with 35% CP pelletized feed grew significantly better in biofloc conditions as compared to clear-water conditions. In [28] was showed that controlling the concentration of particles in super-intensive shrimp culture systems can significantly improve shrimp production and water quality. Also, the same authors demonstrated that environmentally friendly plant-based diet can produce results comparable to a fish-based feed in BFT conditions. In [56] was evaluated the stocking density in a 120d of L. vannamei BFT culture, reporting consistent survival of 92, 81 and 75% with 150, 300 and 450 shrimp/m2, respectively. Moreover, the study [57] performed in a heterotrophic-based condition detected no significant difference in FCR when feeding L. vannamei 30% and 45% CP diets and 39% and 43% CP diets, respectively. With these results in mind, floc biomass might provide a complete source of cellular nutrition as well as various bioactive compounds even at high density. It is not known exactly how microbial flocs enhance growth. Growth might be enhanced by continuous consumption of "native protein", protein source without previous treatment [18], which could possess a "growth factor" similar to the one investigated in squid [58]. Is well known that protein, peptides and aminoacids participate fully in synthesis of new membranes, somatic growth and immune function and biofloc can potentially provide such ingredients.

For fish and other species, BFT also has been demonstrated encouraged results. Intensive BFT Oreochromis niloticus tilapia culture could produce an equivalent of 155 ton/ha/crop [11]. Besides high yields, decrease of FCR and decreased of protein content in diets have also been observed. In [30] was estimated that feed utilization by tilapia is higher in BFT with a ration 20% less than conventional water exchange system. Studying the effect of BFT in juveniles tilapia, the reference [33] showed no difference in fish growth/production between 35% and 24% CP fed tanks under BFT, but both were higher than clear-water control without biofloc with 35% CP. Moreover, in [7] was investigated the effectiveness of BFT for maintaining good water quality in over-wintering ponds for tilapia. The authors concluded that BFT emerge as an alternative to overcome over-wintering problems, particularly mass mortality of fish due to low temperatures. In the study [14] was observed that biofloc consumed by fish (tilapia) may represent a very significant feed source, constituting about 50% of the regular feed ration of fish (assuming daily feeding of 2% body weight).

In M. rosenbergii larviculture was evaluated the effect of different carbon sources in a BFT culture conditions [39]. The authors found that using glucose or a combination of glycerol plus Bacillus as a carbon source in bioreactors led to higher biofloc protein content, higher n — 6 fatty acids, which resulted in improved survival rates. In a study with a Brazilian endemic tropical fish species tambaqui (Colossoma macropomum) was observed that BFT did not improve fish growth/production as compared to clear-water conditions [59], although some water quality problems in such study remained unsolved (i. e. turbidity and nitrite). The authors showed no differences in 44% CP fed tanks under BFT and clear-water conditions, as well as 28% CP BFT. Certainly further research is needed to clarify the effect of BFT in Colossoma macropomum. On the other hand, Piaractus brachypomus or pirapitinga seems to be a candidate species to BFT [60].

Lignocellulosic biomass is composed mainly of plant cell walls, with the structural carbohydrates, cellulose and hemicelluloses and heterogeneous phenolic polymer lignin as its primary components (Fig. 1). However, their proportions vary substantially, depending on the type, the species, and even the source of the biomass (Aspinall et al., 1980; Perez et al., 2002; Pauly et al., 2008).

Plant cell wall

Plant cell

Hemicelluloses (mainly xylan)

Lignin

Figure 1. Structure of lignocellulosic plant biomass. (This figure is adapted from Tomme et al., 1995).

Cellulose: Cellulose, the main constituent of the plant cell wall, is a polysaccharide composed of linear glucan chains linked together by |3-1,4-glycosidic bonds with cellobiose residues as the repeating unit at different degrees of polymerization, depending on resources. The cellulose chains are grouped together to form microfibrils, which are bundled together to form cellulose fibers. The cellulose microfibrils are mostly independent but the ultrastructure of cellulose is largely due to the presence of covalent bonds, hydrogen bonds
and Van der Waals forces. Hydrogen bonding within a cellulose microfibril determines ‘straightness’ of the chain but inter-chain hydrogen bonds might introduce order (crystalline) or disorder (amorphous) into the structure of the cellulose (Klemm et al., 2005). In the latter conformation, cellulose is more susceptible to enzymatic degradation (Perez et al., 2002). In nature, cellulose appears to be associated with other plant compounds and this association may affect its biodegradation.

Hemicelluloses: Hemicelluloses are the second most abundant polymers and differ from cellulose in that they are not chemically homogeneous. Hemicelluloses are branched, heterogenous polymers of pentoses (xylose, arabinose), hexoses (mannose, glucose, galactose) and acetylated sugars. They have lower molecular weight compared to cellulose and branches with short lateral chains that are easily hydrolysed (Saha, 2003; Scheller & Ulvskov, 2010). Hemicelluloses differ in composition. Hemicelluloses in agricultural biomass like straws and grasses are composed mainly of xylan, while softwood hemicelluloses contain mainly glucomannan. In many plants, xylans are heteropolysaccharides with backbone chains of 1,4-linked р-D-xylopyranose units. In addition to xylose, xylan may contain arabinose, glucuronic acid, or its 4-O-methyl ether, acetic acid, ferulic and p-coumaric acids. Hemicelluloses are bound via hydrogen bonds to the cellulose microfibrils in the plant cell wall, crosslinking them into a robust network. Hemicelluloses are also covalently attached to lignin, forming together with cellulose to form a highly complex structure.

Lignin: Lignin is the third most abundant polymer in nature. It is present in plant cell walls and confers a rigid, impermeable, resistance to microbial attack and oxidative stress. Lignin is a complex network formed by polymerization of phenyl propane units and constitutes the most abundant non-polysaccharide fraction in lignocelluloses (Perez et al., 2002; Sanchez, 2009). The three monomers in lignin are p-coumaryl alcohol, coniferyl alcohol and sinapyl alcohol; they are joined through alkyl-aryl, alkyl-alkyl and aryl-aryl ether bonds. Lignin embeds the cellulose thereby offering protection against microbial and enzymatic degradation. Furthermore, lignin is able to form covalent bonds to some hemicelluloses, e. g. benzyl ester bonds with the carboxyl group of 4-O-methyl-D — glucuronic acid in xylan. More stable ether bonds, also known as lignin carbohydrate complexes, can be formed between lignin and arabinose, or between galactose side groups in xylans and mannans.

The experiment 3 consisted to evaluate and characterize the biomass of leguminous residues, the natural arbuscular mycorrhizal (AM) fungus occurrence and the effect of leguminous on the nematodes (Pratylenchus spp.) in sugarcane crop. The experiment was carried out in Piracicaba, Sao Paulo State, Brazil. The soil was classified as Typic Paleudult and the sugarcane (Saccharum spp.) cultivar was IAC87-3396. The effects of previous cultivation of legumes were evaluated for five consecutive harvests. The treatments consisted of previous cultivation of legumes: peanut (Arachis hypogaea L.) cultivars IAC-Tatu and IAC-Caiapo, sunn hemp IAC 1 (Crotalaria juncea L.) and velvet-bean [Mucuna aterrima (Piper & Tracy) Holland], and a control treatment. We adopted the randomized block design with five replications.

The chronology of the events on the experimental field in experiment 3 is the same that experiment 1.

Weed competition is often a major cause of establishment failure in grasses [16, 17, 44]. Although the weed species varies from region to region and even between nearby locations, perennial forbs and warm-season grass species provide the most severe competition for warm season crops like switchgrass [17].

Application of herbicides generally provides very effective weed control. In switchgrass and other warm season grasses, atrazine has been used almost universally as both pre — and post­emergence herbicides for improving establishment [17]. However, atrazine is only labeled for roadside and CRP lands in some states, not for large area of switchgrass except for a special use in Iowa [17]. Alternatively, switchgrass was companion-planted with corn or sorghum-sudangrass using atrazine [59-60].

There are several other chemicals showing to be effective for controlling weed during switchgrass establishment. For pre-emergence application, Mitchell and Britton (2000) [61] used metolachlor for control of several warm season annual grasses. Chlorsulfuron and metsulfuron showed some efficacy in switchgrass [62]. For post-emergence application, imazapyr, sulfometuron, quincloric, 2, 4-D and dicamba have been reported or recommend for weeds control in switchgrass and other warm season grasses [17, 44]. Non-selective herbicides (e. g, glyphosate and paraquat) have been used to prepare seedbeds for no-till plantings for establishing grasses. In addition, Buhler et al. (1998) listed a few more herbicides that showed potential to provide selective weed control to improve establishment of perennial warm season grasses [63].

Herbicides are generally effective and largely available in the market. However, many herbicides are not currently registered for perennial crops for biomass production [16, 63, 71]. As a result, weed control during the establishment year can not be solely relied on chemical applications. Other control methods must be adopted to achieve the best weed control. Buhler et al. (1998) reviewed weed management in biofuel crops and provided several non-chemical control options. These options include timing seeding date, tillage and cropping practices, using companion crops and clipping. Ultimately, the best weed management strategy will be the integration of various options [63].

The overall goal of non-chemical options for weed control is to create an environment that favors to crop growth and development but disfavors weeds. A typical example is manipulation of seeding date to minimize the weed competition, by changing the relative emergence of crop and weed. In general, if crops emerged earlier than weeds, they would have advantage to acquire resources. Therefore, seeding crops before the weeds emergence is an effective way to avoid weeds pressure.

Several other management practices have been successfully used to increase crop competitive ability. In western US, Canada and Australia, increasing seeding rate has been

an effective measure to suppress wild oat in barley and spring wheat [64-66]. Using large seeds also provided competitive benefit in sparing wheat against wild oat [67. 68]. Choosing cultivars with more competitive ability also provided benefit to weed control during establishment stage.

3.3.1. Traditional model

The producer grows, harvests, stores, and delivers raw material (biomass) in accordance with a contract with the processing plant. Deliveries are made to insure the plant has a supply for continuous operation during the processing season. For many agricultural industries (i. e. cotton, grain, sugar cane, fruits and vegetables), the processing season approximately coincides with harvest season, and it is only part of the year.

The advantage of the traditional model, from a processor’s point of view, is that all quality issues reside with the producer. There is no question who is responsible if a quality standards are not met. Business people planning the operation of a bioenergy plant tend to prefer this model, though they typically balk at paying a feedstock price that adequately compensates the producer for their additional risk.

3.3.2. Cotton model

A cotton producer grows, harvests, and stores the raw material (seed cotton) in modules at an edge-of-field location with suitable access for highway hauling trucks (Figure 7). The gin (processing plant) operates a fleet of trucks to transport the modules as required for operations during the ginning season. Farmers are paid for the seed cotton that crosses the scale at the gin. The gin operates a warehouse and stores bales of ginned cotton for periodic delivery to provide a year-round supply for their textile mill customers.

The advantage of the cotton model is that the specialized equipment used for hauling the modules is owned, scheduled, and managed by the gin. Thus, the producer does not own a piece of equipment they will use only a few times a year. The gin deploys the module haulers to their customers, thus the haulers accumulate more hours per years over what a producer would use, and the hauling cost ($/Mg) is minimized [40]. Typically, gins haul modules in the order they are "called in" by the producers. Module storage time in the field, and any subsequent losses (at the gin), are not dealt with in the producer-gin contract. The producer is paid the contract price for the mass of cotton fiber (and co-products) that the gin produces from a particular module.