The following sorption experiments were performed using the above-mentioned marine biomass. Experimental conditions (i. e., pH, contact time and biosorbent dose rate) in this work were optimized and determined based on our preliminary experiments [e. g., 21] and other literatures [24, 31]. The pH of each solution was adjusted by using 0.1 mol-dm-3 NH3aq / 0.1 mol-dm-3 HNO3.

The seaweed biomass

Samples of 0.4 g of the biomass were contacted with 200 cm3 of solution containing known initial each lanthanide (La, Eu or Yb) concentration ranging from 0.1 to 4 mmol • dm-3. Afterwards, the suspensions were shaken for 24 h in a water bath at ambient temperature (~25 °C) at pH 4.

The shell biomass

Each sample of 0.2 g was contacted with 100 cm3 of multi-element standard solution (prepared by XSTC-1) including known initial lanthanide concentration (10 to 500 pg-dm-3) in a 200 ml conical flask. Afterwards, the suspensions were shaken for 30 min in a water bath at room temperature at pH 5.

Following with each sorption experiment, the suspension containing biomass and lanthanides standard solution was filtered through a 0.10 pm membrane filter (Advantec Mixed Cellulose Ester, 47mm) to remove lanthanides that have been adsorbed into the biomass, and the concentration of these metals in the filtrate was determined with ICP-MS or ICP-AES.

The metal uptake by the marine biomass was calculated using the following mass balance equation [33]:

q = (С{ — Cf )V / W [mg x g-1] (1)

where q = metal uptake (mmol-g-1); Ci = initial metal concentration (mmol-dm-3); Cf = equilibrium metal concentration (mmol-dm-3); V = volume of the solution (dm3); and W = dry mass of seaweed (g).

The removal efficiency (RE, %) of the biosorbent on the metal in the solution was determined by the following equation [24]:

RE = (Ci — Cf) x 100 / Ct (2)

1.1. Vegetative period — from emergence till the end of tillering (BBCH 10-29)

Vegetative period lasts from emergence to the end of tillering (BBCH 10-29), it is time limited by the transition to the double ridge stage. It includes the growth of the first leaves, formation of buds in leaf axils, and tillering (Figure 2). A number of code systems have been proposed for describing tillering pattern, of which the Rawson’s system [24] has become the most universally accepted. Tillers are designated by letter T and the index which is determined by the order of leaves in whose axils the tillers emerge (Figure 3). Tiller buds in axils of the fifth and sixth leaves only sporadically grow more than 5-6 mm, and in the axils of other leaves they remain small, clearly visible only after magnification. No buds can usually be observed in the axils of the eighth leaf and other leaves of the main stem [25].

Tillering plant of wheat

5 ‘leaf

4′ leaf

3 leaf

2 leaf

[coleoptile tiller

adventitious roots

wheat gram

primary roots

Figure 2. Illustration of tillering wheat plant [27]

After emergence of seeds, number of plants per area unit is determined, i. e. space limitation of their growth, and pool of tillers for further selection is thus formed. By sampling it is possible to identify individual plants and their tillers in the stand. Weight distribution of plants is left-sided skewed. Plant variability indicates stand establishment quality (Graph 1). The values of the CV range between 30 and 60 % in common stands [30].

Weight variability of tillers is the highest (CV = 50 to 80 %) in this period. It is caused by their gradual formation. The number of tillers per square meter in winter wheat and spring barley usually ranges from 1600 to 2500, in some cases exceeds 3000. Distribution of their weight is also strongly left-sided skewed, under favorable conditions is continuous and unimodal (Graph 1), which indicates good conditions for stem formation and stand structure development. Bimodal distribution indicates the effect of unfavorable conditions. Growth reduction occurs, tillers are smaller and apical dominance is strongly expressed. The segregating distribution therefore corresponds with the distribution of main shoots (Graph 1). As individual plants and tillers can be identified, the intra-plant relationships can directly be assessed, e. g. by regression analysis of tillers weight dependence on their sequence in the plants, however, it is very laborious.

Shoot growth is practically measurable since the first leaf emergence above ground (in the main shoot), and since bud emergence in leaf axils (in tillers). Tillers can also be formed, under certain circumstances, during the generative development (BBCH 30-59). Tillering at that time is undesirable with regard to efficient use of biomass for grain production as most of the late tillers are not fertile and those which are fertile increase grain variability.

Determination of plant and tiller density is in this stage important for assessment of the production potential of the stand and modification of cultivation measures, especially timing and dosing of N fertilizers, growth regulators and pesticides.

To be feasible the use of DRIS to assess the nutritional status of plants, the first step is establish the DRIS norms or standard. The DRIS norms consist on average and standard deviation of dual ratio between nutrients (N/P, P/N, N/K, K/N, etc.) obtained from a crop reference population (Table 1), but, it is necessary that the crop reference shows high yield (Beaufils, 1973). This method has been followed along the years (Jones, 1981; Alvarez V. & Leite, 1999; Silva et al., 2009; Maccray et al., 2010; Serra et al., 2010a, b; Serra et al., 2012).

The data bank to compose the DRIS norms is formed by the crop yield and chemical analysis of leaf tissue, and this information can be obtained from commercial crop or experimental units. The size of the data bank is not a factor that is directly related to the quality of the DRIS norms (Walworth et al., 1988; Sumner, 1977).

Walworth et al. (1988) observed that, when they used 10 data to establish the DRIS norms, the results obtained were more accurate then the use of a large number of data. What is more important to improve efficiency on DRIS norms is the quality of the data, because it is not accepting the use of sick plants to compose the data bank to establish the DRIS norms.

To make part of the DRIS norms, the rations between nutrients can be selected by the direct form (N/P) or reverse (P/N), but, there is more than one way to change the ratio that is going to compose the DRIS norms. Bataglia et al. (1990) used the entire dual ratio without selecting the direct or reverse form, and other researchers used the transformation by natural log (Beverly, 1987; Urano et al., 2006, 2007; Serra et al., 2010a, b; Serra et al., 2012).

With many ways to select the ratio to compose the DRIS norms there is a necessity to establish the most efficiency way for each crop that results in a better efficiency of the system. Silva et al. (2009) tested the dual ratio selection using the "F" value (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987) and "r" value (Nick, 1998) in cotton crop, on his turn, Silva et al. (2009) did not test the criterion of choice the ratio by log transformation or the use of all nutrient ratio as it were made by Alvarez V. & Leite (1999) and Serra et al. (2010a, b).

Results obtained by Serra et al. (2012) showed that the use of "F" value or log transformation in nutrient ratio to define the norms produced different DRIS index, furthermore, when the DRIS index is interpret by Beaufils ranges the difference observed among index was reduced, showed less difference between the two groups of norms.

Following the premises of DRIS proposed by Beaufils (1973), it is feasible to change the dual ratio (A/B or B/A) that is more important to compose the DRIS norms. This way it is expected that the dual ratio from crop with high-yielding (reference population), composed with healthy plants, shows less variation than the population of plants with low-yielding (non-reference population), thus, the relation between variance ratio method, the F value, was defined as the variance ratio of low-yielding (non-reference) and high-yielding population (reference), and the order of the ratio with the highest value was chosen among the variance ratios (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987).

The utilization of the relationship between variance ratio method ("F" value) from low- yielding and high-yielding is the most used method to define the DRIS norms. The method "F" value is defined on the data bank divided into two groups (non-reference and reference), and the choice of ratio directly (A/B) or inverse (B/A) defined by relationship between variances from the two populations, in which the ratio chosen will result arises from the following analysis (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987):

Then: the dual ratio that will make part of the DRIS norms will be A/B, on the another it will be B/A. S2 is the variance of the dual ratio of the reference population and non-reference.

Besides the selection of forward or reverse ratio to compose the DRIS norms, the same principle can be selected with regard to the significance of F value, which can be 1%, 5% or 10% (Wadt, 1999), and feasible to use all dual ratio, which was selected by the largest ratio of variances, without the rigour of significance (Beaufils, 1973; Jones, 1981; Walworth & Sumner, 1987; Serra, 2011).

One can observe on literature that there are not any consensus about which methodology is more efficient to use. Jones (1981) did not select for significance, but he selected by the biggest reason of variances, as well as Raghupathi et al. (2005); Guindani et al. (2009); Sema et al. (2010); Serra et al. (2012). However, Wadt (2005) used the "F" value for the selection of dual ratio with a significance of 10%, excluding from the norms the dual ratio that was with significance above this value.

When selecting the dual ratio by significance of the "F" value, the sum of DRIS indexes does not give a zero value, in this case some nutrients can remain with a larger number of dual ratio than those with fewer ratios. However, Wadt et al. (1999) concludes that the rigour of the selection by the significance of "F" value generates greater efficiency for the diagnosis, in studies made with coffee crop (Coffea canephora Pierre).

In common with other resource assessments, the potential for energy crops is, in theory, large. It is also highly dependent on which crops are deemed to be most likely to be grown, what type of land is converted to their cultivation, and the areas of land used.

The estimations were based on two reasonably scenarios [5]:

a. 10% of land currently used for grazing/pasture plus 5% of fallow land are used to grow perennial grasses, and

b. 10% of land currently used for grazing/pasture plus 25% of fallow land are used to grow perennial grasses.

Total available land is 95.791 ha and 147.118 ha under scenarios A and B, respectively. 72% of the available land is found in the Federation of Bosnia and Herzegovina. Then the potential of energy crop i in the region j was calculated according to the following equation [5]:

Enercropi, j=Aencrj CYi BYi Hi Aencrj available land in region j [ha],

CYi country specific yield of crop i [t/ha],

BYi biofuel yield of crop i [t biofuel/ t crop],

Hi biofuel energy content of crop i [GJ/t].

Table 2 presents the energy crops considered in the two scenarios for Bosnia and Herzegovina, main energy markets and the energy potentials under the two scenarios.

The calculations are made for the whole land available in each case, e. g. if all the available land in Scenario A was used for biodiesel production with oilseeds the total potential would amount to 2,12 PJ, while if it was used for second generation bioethanol from Short Rotation Coppice (SRC) it would reach 6,21 PJ. These figures summarize potentials based on conversion efficiencies. In all cases the potential in the Federation of Bosnia and Herzegovina makes 72% of the total potential.

The respective Technical Potentials are estimated to be 15,33PJ and 23,54 PJ resource. The half of the resource would support local small scale energy crop fired baled fired boilers or energy crop pellet boilers supplying residential properties with heat. This would equate to 1.703 GWh of useful heat production per year.

Traditionally the oil palm (Elaeis guineensis) was grown in semi-wild groves in tropical Africa. It was first introduced to Malaysia for planting in the Botanical Gardens in Singapore in 1870 [7]. Germination takes around 3 months, after which the seedlings are planted in small plastic bags where they are left in a so-called pre-nursery for several months. They are transplanted into bigger plastic bags and grow in a nursery for several more months to a size of about 1 meter, before they are transplanted into a field at an age of around 1 year.

The new improved crosses begin to flower after less than one year of transplantation and produce their first bunches of fruit after less than 2 years. At this age, their leaves have a size of over 2 meters in height and diameter. During its young age, the trunk grows at a rate of about 35 to 75 cm per year and produces alternate rows of leaves, depending on its gene [8]. The base of the old leaves surround the stem and begin falling off at the age of 12 to 15 years [9]. By this time, growth and production have slowed down.

The number of leaves in an oil palm plant increase from 30 to 40 in a year at the age of 5 to 6 years. After that, the generation of leaves decreases to about 20 to 25 per year [9]. The average economic life-span of the oil palm is 25 years to 30 years [10]. A marked increase in the cultivation of oil palm began in 1960 [11], for which by the year 1990 onwards there was a peak in replanting. This provided a good opportunity to harness the by-products of the oil palm. During the re-plantation, the heights of the oil palm tree are in the range of 7 m to 13 m, with a width of between 45 cm to 65 cm, measuring 1.5 m from the surface of the soil. There are about 41 leaves in each frond of the mature oil palm tree. It is estimated that in the year 2000, the process of re-plantation would generate about 8.36 million tonnes dried biomass, consisting of 7.02 million tonnes of trunk and 1.34 million tonnes of leaves [5]. Due to the high moisture of about 70% fresh weight, the newly chopped tree trunk cannot be burnt in the plantation. To leave the old trunk for natural decomposition not only obstructs the re-plantation process but harbours insects that would harm the new trees as well. The tree trunk usually takes between five to six years to decompose [12].

Most crude palm oil mills harness the energy from the fibre and shell in their own low pressure boilers and normally, the EFB’s are burnt causing air pollution or returned to the plantation. A 60 tonnes of fresh fruit bunches (FFB) per hour mill based within a 10,000 hectare plantation, can generate enough energy to be self sustaining and supply surplus electricity to the grid if it utilises all of its wastes. In order to provide a better understanding of the palm oil industry in Malaysia, the following sections give an overview of the oil palm industry in Malaysia including oil palm plantation and the mass balance of the oil palm industry as it is self-sufficient in energy.

• Absence of an integrated policy and regulatory framework within Bosnia and Herzegovina that would otherwise encourage the use of biomass residues for energy generation;

• Suitable policies and regulations are yet to be enacted to provide a level playing field for renewable sources, including the biomass energy;

• Policies and governmental linkages between biomass energy use and income generation activities are weak and/or non-existent;

7.1. Information barriers

• There is limited availability and access to existing renewable energy resource information. Data frequently does not exist, and a central information point is lacking — information is scattered between sectors; e. g. public sector, private sector (including consultancy firms), development assistance, R&D centres and academia;

• There is a limited knowledge of the biomass energy potential due to lack of detailed market surveys;

• Where information on economics, market development, marketing, and technical issues exist, it is distributed between organizations that do not co-operate;

Langmuir adsorption isotherm model was applied based on Tsui et al. [24] in this study, and the model assumes monolayer sorption onto a surface and is given as below.

q = (qe x Cf) /(Л_1 + Cf) (3)

where qe = maximum metal uptake (mmol-g-1) (i. e., the maximum attainable binding capacity); and A = affinity constant (1 mmol-1) (i. e., the affinity of the metal ion toward the biomass).

The Freundlich equation is widely used in the field of environmental engineering, and was applied based on based Dahiya et al. [10-11]. Freundlich isotherm can also be used to explain adsorption phenomenon as given below.

l°gwqe = log10KF +(1/ n)log10Cf (4)

where KF and n are constants incorporating all factors affecting the adsorption capacity and an indication of the favorability of metal ion adsorption onto biosorbent, respectively. It is shown that 1/n values between 0.1 and 1.0 correspond to beneficial adsorption. That is, qe versus Cf in log scale can be plotted to determine values of 1/n and Kf.

Generative period is delimited by the stages of ‘double ridge’ (BBCH 30) and anthesis (BBCH 60) [3]. This period involves stages of stem elongation and heading, i. e. stages of an intensive growth and differentiation of tillers, their dying-off and formation of stems from the most robust ones; this is the result of selection resulting from competition among plants and tillers [8]. This results in a stabilization of numbers of productive stems per unit area at the end of canopy establishment. Depending on the variety and growing conditions, the number of productive stems per m2 ranges from 500 to 800 and from 700 to 1000 in stands of winter wheat and spring barley, respectively [30].

Individual plants can be credibly identified within the stand only till the beginning of heading [29]. This limits the evaluation of their variability and intraplant competition.

The distribution of the weight of tillers has two peaks and this is the reason why the variability evaluated by means of the variation coefficient does not give an exact picture of differentiation processes [29]. As shown in Graph 2, the distribution of non-productive (V) and productive tillers (G) overlaps in the zone of local minimum. Critical weight of winter wheat tillers for transition to generative stage in analyses performed by [29] was about 2 g. This illustration of stem differentiation shows stochastic character of the development of stand structure. This means that tillers belonging to weight categories corresponding with the local minimum may either be transformed into productive stems or die off; their fate is dependent on the course of weather and on the efficiency of applied growing measures. It is quite logical that with running time and intensifying differentiation the numbers of these tillers (and thus the possibility of a modification of stand productive density) decrease.

An identification of productive and non-productive tillers is important for the estimation of the stand production potential and for the yield prognosis. This means that main objective should be the formation of a maximum possible number of productive tillers, i. e. of the maximum possible share of the so-called productive biomass in the total above-ground biomass. The development of the stand structure should be optimized in such a way that the produced biomass would maximally participate in the formation of grain yield.

Several changes in the methodology of DRIS indexes calculation were proposed in order to increase the accuracy in the nutritional diagnosis for several crops. The calculation of the functions or standard deviation units can be defined by the methodology originally developed by Beaufils (1973), Jones (1981) or Elwali & Gascho (1984), there are some conflicting results in the literature regarding the effectiveness of each method of calculation. According to Mourao Filho (2004), there is still no clear definition of what would be the best recommendation to calculate the functions or standard deviation units for the DRIS.

According to Serra (2011), the use of the methodology proposed by Jones (1981) when compared with Beaufils (1973) and Elwali & Gascho (1987) showed better efficacy on DRIS index for cotton crop (Gossypium hirsutum r latifolium). The measure of the efficacy used by Serra et al. (2011) was the relation between yield and nutritional balance index (NBI).

Beaufils (1973):

For A/B < a/b;

a/b] 100- A/b ‘ CV%

f(A/B)=0, for A/B = a/b For A/B > a/b;

Jones (1981):

Elwali & Gascho (1984): For A/B < a/b-1s

a/b~ 100 •K

ш’ cv%

f(A/B)=0, to the range between a/b-1s to a/b+1s For A/B > a/b+1s

After defining the functions DRIS, the DRIS index is calculated and for each nutrient a DRIS index is determined, which may have positive or negative values, that represent the arithmetic average of functions in which the nutrient is involved, when the result is negative
(below zero), this means deficiency and when the positive value indicates excess, as proposed by Beaufils (1973):

е/Й)-е/(5

DRIS Index A =—— ———— —

n

n=number of DRIS functions of each dual ratio defined by criteria of chosen of the norms, in that the A nutrient is involved.

The sum of DRIS index in module of the nutrients in a sample diagnosed, generates the nutritional balance index (NBI), in an increasing scale, the higher NBI the greater nutritional imbalance in the plant and consequently low productivity, and the correlation between NBI and yield is considered one measure of the effectiveness of the system DRIS (Beaufils, 1973; Nachtigall & Dechen, 2007b; Guindani et al., 2009).

Mourao Filho (2004) concludes that researches on DRIS are still incipient, therefore, many accurately factors must still be better studied, factors such as the criteria for choosing the reference populations, the combination of methods to be used, so there is a need to more refined studies on these aspects.

Municipal solid waste (MSW) refers to waste collected by or on behalf of municipalities; this mainly originates from households but waste from commerce and trade, offices, institutions and small businesses is also included.

According to the EU legislation (Directive 2001/77/EC) energy produced from the biodegradable fraction of MSW is considered as renewable and therefore organic waste, waste paper and cardboard and textiles are a source of biomass. Due to lack of data regarding the share of the biodegradable part to the total quantities of MSW in BiH, the biodegradable fraction of 50% found in neighboring Serbia was employed. Furthermore, a lower heating value of 7,2 GJ/t for the biodegradable part was assumed [5].

Landfill gas. Municipal Solid Waste (MSW) production expected to reach 0,5 t/person/year (the EU 15 average). It is disposed and methane is captured and used to generate power. This assumes that, due to the location of the landfills, there are no local uses for heat. The theoretical biogas potential estimated in this study is 4,28 PJ.

In 2008, 1.367.097 t MSW was generated in Bosnia and Herzegovina, 86% of which (1.181.887 t) was collected [1,2]. This is equivalent to 308 kg of collected waste per capita per year. Other sources report a higher value of waste generation at around 500 kg/ per capita/ per year [4]. Nevertheless, it was decided to accept the number reported by the Agency for Statistics of Bosnia and Herzegovina, since it is in good agreement with waste generation rates found in other Western Balkan countries.

Table 3 shows estimated total MSW and household waste (HHW) amounts, in accordance with the methodology recommended in the SWMS, and population statistic [1,2,9].

Taking the above into account the theoretical potential of biomass from MSW can be estimated according to the following equation [5]:

Emsw = PpCoHo (F.5)

P population,

p per capita waste generation [t/yr],

Co biodegradable waste fraction in MSW [%],

Ho biodegradable waste lower heating value [GJ/t].

The estimated theoretical potential amounts to 4,28 PJ or 1,9% of the country’s total primary energy supply in 2008.

Currently, the main option for disposal of municipal waste is still landfilling, while most of the landfills are not sanitary. Furthermore, it is estimated that there are more than 2.000 open dumps, many located near to small municipalities in rural areas.

Implementation of SWMS commenced with WB/IDA credit for Project "Solid Waste Management Project" (Ex. Environmental Infrastructure Protection Project) in 2002. An analysis of the current situation in this sector has shown that the objectives concerning the construction of regional sanitary landfills defined in the SWSM are unrealistic. The plan is to have 16 regional landfills by December 2009, but until now, only 2 landfills have been constructed. Two regional sanitary landfills are anticipated in FBiH for 2010: "Smiljevac"- Sarajevo and "Moscanica" — Zenica, where 10% and 8% of the total MSW collected in the FBiH would be disposed respectively. For RS, one regional sanitary landfill for MSW disposal "Ramici"- Banja Luka, is anticipated, where 16,7% of the total MSW collected in RS would be disposed. At the sanitary landfill in Sarajevo, the collected landfill gas is used for electricity generation, while at the Zenica landfill a flare system for the combustion of landfill gas has been constructed. The combustion of landfill gas by flare is also envisaged at the future sanitary landfill in Banja Luka.

In addition to landfills, according to the initial national communication of BiH under the UN framework convention on climate change (UNFCC), incineration of 20% of MSW with energy recovery is anticipated by 2030 [4]. It is further foreseen that recycling rates will be 10% of the total household waste (HHW) in 2020 and 20% for 2030. Moreover, 50% of the recycled HHW is foreseen to be biodegradable waste.[5].