The competition for resources between sugarcane and food crops is apparent with foreseen consequent increased food insecurity. Fifty percent of the arable land area good for food crop production is equally good for sugarcane.

A farm household that allocates all of its one hectare of land to sugarcane is expected to earn 359 $ at high input level, 338 $ at intermediate level and 261 $ at low input level " in [4] ". The 391 $ required to purchase maize meal is well above the net margins from one ha. This shows that proceeds from one hectare cannot sustain a household of 5. It is further revealed that maize produced from 0.63 ha can sustain a household nutritionally; however considering the annual household expenditure (760.8 $; "in [17] "), about three hectares of land under sugarcane are required at low input level to support a household " in [4]".

However, this study reveals that sugarcane sales accrued from ethanol under a scenario of a flourishing bio-fuel industry is associated with increased income that is likely to support households (Table 9). An ethanol gross sale per person per day is 1.6 dollars; an indication that the cultivation of sugarcane based biofuel is likely to contribute to alleviation of household poverty. A trickle-down effect on household income is expected from a foreseen expansion of bagasse-based electricity generation beyond the estate into the national electricity grid.

4. Further research

The expansion of cane production is largely driven by market forces oblivious to the detrimental impact the industry is likely to have on food, livelihood security and the status of biodiversity. In addition to lack of appropriate policies to support the small-scale cane farmer, the policies are largely sectoral with no linkages with other relevant policies. Information is required to support the sustainable development of the cane industry with minimal negative impact on food and livelihood security and the status of biodiversity.

Three long term assays were developed during December 4, 1999 to October 10, 2005 to complete this study as following:

The experiments were carried out in Piracicaba, state of Sao Paulo, Brazil (22o42′ S, 47o38′ W, and 560 m a. s.l). The soil, classified as an Arenic Hapludult, and a Typic Paleudult, was chemically characterized at different depths after cutting the green manure crop, before the sugarcane first planting. The soil was acidic and had low amounts of nutrients (Table 1), typical of many sugarcane growing areas.

Total monthly rainfall and local temperature were measured at the meteorological station near the experimental site (Figure 1).

Chemical composition of biomass feedstock affects the efficiency of biofuel production and energy output. The major parts of the chemical composition in the perennial biomass feedstocks are lignocellulose including cellulose, hemicellulose, and lignin; and mineral elements such as ash [3, 36-38]. Biomass may be converted into energy by direct combustion or by producing liquid fuels (mainly ethanol) using different technologies. For converting cellulosic biomass into ethanol, the conversion technologies generally fall into two major categories: biochemical and thermochemical [3, 37, 38]. Biochemical conversion refers to the fermentation of carbohydrates by breakdown of feedstocks. Thermochemical conversion includes the gasification and pyrolysis of biomass into synthetic gas or liquid oil for further fermentation or catalysis. Currently, the U. S. Environmental Protection Agency (USEPA) listed six conversion categories from different companies for ethanol from biomass [3]. Different conversion technologies may require different biomass quality attributes. For ethanol production from biochemical process (fermentation), ideal biomass composition would contain high concentrations of cellulose and hemicellulose but low concentration of lignin [37-38]. While for gasification-fermentation conversion technology, low lignin may not be necessary. For direct combustion and some thermochemical conversion processes, high ash content can reduce the effectiveness and chemical output [3, 37-38].

Among the perennial grasses for biofuel production, chemical composition of switchgrass has been investigated in many studies [19, 29-31, 35, 39]. There is little information in the lignocellulose contents in other species such as tall and intermediate wheatgrass when they are harvested at fall as biomass feedstocks because these species have been mainly used as forage. As with yield, biomass composition is affected by genetic and environmental factors as well as by management practices such as nitrogen (N) fertilization and harvest timing. In a study in the southern Iowa, both yield and quality traits were different among 20 switchgrass cultivars. The high yielding cultivars generally had low ash content [19]. In NGP, we reported the chemical composition of the above 10 perennial grasses and mixtures shown in Table 3 in 2007 harvest. The contents of neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), hemicellulose (HCE), cellulose (CE) and ash were determined. Biomass chemical composition was affected by environment and species/mixtures, and their interaction. Biomass under drier conditions had higher NDF, ADL and HCE contents but lower CE contents. Tall and intermediate wheatgrass had higher NDF, ADF and CE but lower ash contents than the other species and mixtures. Switchgrass and mixtures had higher HCE. Tall wheatgrass and Sunburst switchgrass had the lowest ADL content. Biomass with higher yield had higher cellulose content but lower ash content. Combining with higher yields, tall and intermediate wheatgrass and switchgrass had optimal chemical compositions for biomass feedstocks production (Table 5) [35]. In another study in NGP, Karki et al. (2011) showed that tall wheatgrass had similar composition to switchgrass and has potential for ethanol production [39].

It is appropriate to benchmark a proposed feedstock delivery system against existing commercial systems; for example, woodchips, grain, hay, sugarcane, and cotton. Ravula et al. [37] studied delivery to a cotton gin from over 2,100 field locations to better understand logistics system design for a biorefinery.

Switchgrass, corn stover, and other energy crops present two major challenges when benchmarked against other commercial examples. First, energy crops are spread over a greater number of sites when compared with woodchips harvested as byproducts from logging. Second, energy crops have low value when compared with hay, grain, sugarcane, or cotton. Applying benchmarked models to energy crop systems provides a starting point, but optimization techniques must be applied to "fine tune" the logistics system design.

Various kinds of strains exist in BAC column (see Fig. 15). The picture demonstrates, (L-R), 4 sections (1, 2, 3 and 4) from top to bottom of the down-flow BAC layers respectively. The shadows in the finger prints represent different strains, and the numbers of the shadow zones represents the numbers of strains, while every shade represents a kind of strain, so the different shades represent the number of species indicatively. Fig. 15. shows that the number of shadow zones is 16 that can be discovered obviously, which indicates that the number of strains exist in BAC column is 16 according to detection. Meanwhile, along the carbon layer from top to bottom of the picture (L-R), there are 7 shadow zone shades turning weak gradually, which indicates that with the increasing of the carbon layer depth, these strain kinds are reduced gradually. There are 5 shadow zone shades turning darker, which indicates that with the increasing of carbon layer depth, these strain kinds increase. Another 4 zones remained do not exist in every section, among which the quantity of one kind of strain is not changing, 2 kinds of strains only exist in the upper side of the activated carbon, while only 1 strain exists in the bottom of the activated carbon bed.

Figure 15. Analysis results of PCR-DGGE

Under aerobic conditions, the degradation of organic matter is an exothermic process during which oxygen acts as a terminal electron acceptor and the organic materials are transformed into more stable products, carbon dioxide and water are released, and heat is evolved. Under field conditions, aerobic degradation takes place slowly at the soil surface, without reaching high temperatures; but this natural breakdown process can be accelerated by heaping the material into windrows to avoid heat losses and thus allowing for temperature increases (composting) or by using specific species of earthworms as agents for turning,

fragmentation, and aeration (vermicomposting). Although both aerobic processes, composting and vermicomposting, have been widely used for processing different types of animal manure either separately or in combination with each other (see Table 1), most of the studies are not comparable mainly due to differences in the applied experimental designs, parent material, earthworm species, as well as the length of the experiments and the parameters used for analysis, among others. Despite these limitations, all these findings have largely contributed to better understand the changes that the material undergoes during these biological stabilisation processes, which is of great importance for their optimisation, and ultimately to obtain a high quality final product. In line with this, certain chemical characteristics of the animal manures can limit the efficiency of these processes, such as an excess of moisture, low porosity, a high N concentration in relation to the organic C content or high pH values [6]. Therefore, different aeration strategies, substrate conditioning-feedstock formulation, bulking agents and process control options have been considered in manure composting and vermicomposting so as to reduce the time and costs of both processes and enhance the quality of the end-products [6-7].

Before collecting phytoplankton samples from the lake, turbidity and temperature of the water were measured by Secchi disk. Phytoplankton samples have to be stored in 100-150 ml glass or polyethylene containers with 2% Lugol’s solution or 4% Buffere formalin solution [32].

2. Identification of phytoplankton

Water samples taken by phytoplankton scoop were identified according to world literature [19-29].

Biological conversion of lignocellulosic materials has been proposed as a renewable and sustainable route for the production of value-added products (Bayer et al., 2007, Doi et al., 2003). There is much interest in exploiting the properties of multienzyme complexes for practical purposes. The facultative bacterium, P. curdlanolyticus strain B-6 produces a unique extracellular multienzyme system under aerobic conditions that effectively degrade cellulose and hemicellulose by gaining access through the protective matrix surrounding the cellulose microfibrils of plant cell walls. Therefore, the multienzyme complex from strain B-6 is a promising enzyme which can potentially be used in many applications, such as enhancing extraction and production of value-added bioproducts by saccharification of cell wall components and application for construction of the modular enzymes creation (Fig. 10).

Biological treatment and saccharification using microorganisms and their enzymes selectively for degradation of lignocellulosic residues has the advantages of low energy consumption, minimal waste production, and environmental friendliness (Schwarz, 2001). The catalytic components of the multienzyme complex release soluble sugars, simple 5- and 6-carbon, from lignocellulose providing the primary carbon substrates, which can be subsequently converted into fuels by microorganisms. For enzyme saccharification, the close proximity between cellulolytic and xylanolytic enzymes is key to concerted degradation of the substrate, whereby the activities of the different enzymes facilitate the activities of their counterparts by promoting access to appropriated portions of the rigid insoluble substrates, since the release of sugar products was high. The synergistic action of the combination of enzymes by different modes of actions (xylanases and cellulases) and the presence of xylan — or cellulose-binding ability on lignocellulose enhanced soluble sugars released from the plant cell walls. In practicality, the multienzyme complex produced from P. curdlanolyticus B-6 allows access to lignocellulosic substrate and produces reducing sugar more than non-complexed enzymes from fungi (T. viride and Aspergillus niger) when the same cellulase activity (0.1 unit) was applied for degradation of corn hull and rice straw residues (unpublished data). In addition, the multienzyme complex of strain B-6 has been used to improve the extraction of plant food such as making low-cyanide-cassava starch by using multienzyme complex to enhance linamarin released by allowing more contact between linamarase and linamarin (Sornyotha et. al., 2009). Also, extraction of volatile compounds such as sea food-like flavor from seaweed, served for food supplement. Consequently, enzymatic treatment has advantages for the preparation of p-glucan and acidic a-glucan-protein complex from the fruiting body of mushroom, Pleurotus sajor-caju because the specificity of the multienzyme complex and gentle conditions allow for the recovery of high purity glucans in their native forms with minimal degradation (Satitmanwiwat et al., 2012a, b).

Typically, most plant cell wall degrading enzymes are composed of a series of separate modules (modular enzymes). These domains may fold and function in an independent manner and are normally separated by short linker. P. curdlanolyticus B-6, produces a number of glycosyl hydrolase (GHs) families and CBM families which have different substrates recognition affinity and increase amorphous regions of cellulose by H-bond elimination. Interestingly, modular architecture created by chimeric proteins creation with various tandem CBMs, GHs, and SLH-specific, should make it possible to construct effective lignocellulosic degrading enzymes, strongly binding, targeting enzyme to their substrates and bacterial cell surfaces for enhancing a variety of substrates hydrolysis. The strong carbohydrate-binding property of the cellulose-binding domain and xylan-binding domain, specific degradative activities exhibit important properties of the lignocellulosic material degrading enzymes that can be used in biotechnology.

2. Conclusion

A facultatively anaerobic bacterium P. curdlanolyticus strain B-6, isolated from an anaerobic digester fed with pineapple wastes, is unique in that it produces extracellular xylanolytic — cellulolytic multienzyme complex capable of efficient degradation of plant biomass materials under aerobic conditions. The production of strain B-6 multienzyme complex under aerobic conditions has several advantages: (i) a simple process, (ii) low price of medium, (iii) high growth rate, (iv) large quantities of extracellular enzymes yields, and (v) safe use with regard to health and environmental aspects. Thus, strain B-6 and its multienzyme complex is a promising tool for an industrial process employing direct hydrolysis for the bioconversion of cellulose as well as hemicellulose in biomass. This review shows that strain B-6 multienzyme complex is a novel enzymatic system known at the biochemical, genetic, and mechanism level. It also stresses that some points still need to be further investigated, mainly (i) the elucidation of scaffolding protein functions, (ii) the characterization of others key enzyme subunits, (iii) the assembly mechanism of the multienzyme complex, (iv) improvement of the efficiency in degradation of biomass of the multienzyme complex, and (v) improvement of the production of the multienzyme complex. The latter will certainly represent a challenge for future research.

As shown in Figure 1, about 94 wt% of residual microbial biomass was decomposed and hydrolyzed in the acid pretreatment (~44% biomass) and base treatment (~50% biomass). The remaining small amount (~6 wt %) of residual cell mass is most likely mineralized via

oxidation with hypochlorite, a strong oxidation agent. The acid hydrolysates are primarily the cytoplasm proteins released from the damaged cells (Figures 2 and 3). The released biological macromolecules were subjected to further hydrolysis in the thermal acidic solution. The acid hydrolysates solution had a clean brownish color and contained 30-45 g/ of soluble biomass, depending on the density of cell slurry and treatment conditions. The base hydrolysates solution with a dark color contained the hydrolysis products of hydrophobic cell components including lipids and membrane proteins. After centrifugation, the concentration of soluble biomass in the supernatant solution was 30 to 50 g/L. The sequential treatments disrupted and dissolved the structural components so that they could be removed from PHB granules. Equally important, the biomass and biological macromolecules were decomposed into small molecule hydrolysates such as amino acids and organic acids. These hydrolysates could become appropriate substrates that can be assimilated by microbial cells in microbial PHB production.

In addition to the two types of biomass hydrolysates described above, a mixed hydrolysates of residual biomass was generated when the acid pretreatment and base treatment were performed sequentially without solid/liquid separation. It eliminated one operation of solid/liquid separation, but the acid hydrolysates (primarily proteins) were subjected to additional hydrolysis in the base solution. Figure 6 shows the FTIR spectrum of an acid-base hydrolysates. As observed in the IR spectrum of the original cell mass in Figure 4, a major component of the hydrolysates was the amino acids or proteins with infrared radiation absorbance at 1500 to 1700 cm-1 [25]. Another major component in the acid/base hydrolysates had the IR absorbance at 1000 to 1200 cm-1, which was attributed to cell lipids and/or similar compounds. This was confirmed with the spectrum of cell mass extract in acetone. Acetone is a common solvent used to remove hydrophobic lipids, steroids and pigments from PHB-containing cell mass [17]. It does not dissolve and extract PHB and proteins. Based on the observations above, it was concluded that the major cellular components in the acid hydrolysates were derived from cytoplasm proteins and in the base hydrolysates from cell walls, lipids and membrane proteins. In the acid-base hydrolysates, the products were derived from both groups, i. e. amino acids or peptides derived from proteins, and lipids derived from cell walls and membranes. It should be pointed out that the composition of acid-base hydrolysates is not a simple mixture of acid — and base — hydrolysates because the acid hydrolysates were further hydrolyzed in base treatment.

Because of the hydrophobic properties of PHB granules, the residual hydrophobic impurities of cell mass might be attached to the granules and difficult to remove by washing with water. A surfactant such as SDS in the base treatment can remove most of the impurities to a high PHB purity (>99 % w/w). A large portion of the surfactant, however, may be left in the hydrolysates solution and may have an adverse effect on the reuse of the hydrolysates in PHB production.

We first consider a mortality rate in the model (1):

X = ц(Б)Х — mX

where parameter m > 0 becomes not negligible when ц(Б) takes small values. In addition, we consider an additional compartment Xd that represents the accumulation of dead cells:

Xd = 5mX,

where the parameter 5 Є (0,1) describes the part of non-viable cells that are not burst. We assume that the burst cells recycle part of the substrate that has been assimilated but not yet transformed. Then, the dynamics of the substrate concentration can be modified as follows:

S = —^y^-X + A(1 — S)mX,

where A > 0 is recycling conversion factor. It appears reasonable to assume that the factor A is smaller that the growth one:

In the following we assume that the growth function ц(-) and the yield coefficient Y of the classical Monod’s model are already known. Typically, they can be identified by measuring the initial growth slope on a series of experiments with viable biomass and different initial concentrations, mortality being considered to be negligible during the exponential growth. We aim at identifying the three parameters m, 5 and A, and on-line reconstructing the variables

X and Xd, based on on-line observations of the substrate concentration S and the total biomass B = X + Xd.

Without any loss of generality, we shall assume that the growth function ц(-) can be any function satisfying the following hypotheses.

Assumption A2. The function ц(-) is a smooth increasing function with у (0) = 0.

For sake of simplicity, we normalise several quantities, defining

s = S, x = X/Y, Xd = Xd/Y, a = (1 — S)m and k = XY.

Then, our model can be simply written as

S = — у (s) x + kax,

X = y(s)x — mx, (3)

X d = mx — ax,

s(0) = so > 0, xd(0) = 0 and x(0) = xo > 0 .

Our purpose is to reconstruct parameters m, a and k and state variable x(-) or xd(•), under the constraints m > a and k < 1, that are direct consequences of the definition of a and Assumption A1. Moreover, we shall assume that a priori bounds on the parameters are known

i. e.

(m, a, k) Є [m—, m+] x [a—,a+] x [k—, k+] . (4)