Как выбрать гостиницу для кошек
14 декабря, 2021
Jan Kren, Tamara Dryslova, Lubomir Neudert and Vojtech Lukas
Additional information is available at the end of the chapter http://dx. doi. org/10.5772/54528
Cereals are economically the most important group of field crops. Detailed knowledge of stand structure and its development including interrelationships among individual plants in the stand (inter — and intra-plant competition) is a significant precondition for effective cropping treatments during the growing season, including agrochemicals application.
To attain higher effectiveness of crop management practices, extensive research on cereal stand structure was conducted in the 1980s and 1990s [1-6]. The stand state and structure reflect variability in soil conditions as well as cropping treatments. Results of individual methods used for modification of cropping treatments depend on a level of stand organization which is observed — a stand (plant population), plant, plant part (leaf, tiller) [7].
Stems and spikes are the most often assessed units of stand structure as for final and resulting expression of all factors affecting stand development. However, they are also reproductive units and basic units of an important cereal adaptation system — tillering [8]. Therefore, their appropriate assessment allows obtaining information of great biological and economic importance.
The chapter gives a review of evolution of approaches used for the assessment of cereal stand structure. Weaknesses and strengths of these approaches are discussed.
The percentage removal of lanthanides under the presence of common ions (Ca2+, Mg2+, Na+ and K+) at different concentrations 50, 100 and 200 mg-dm-3 is shown in Fig. 10. From this figure, the remarkable decrease of sorption capacity of lanthanides was not observed. Even when the concentrations of common ions are 200 mg-dm-3, the percentage removal of light REE (LREE) such as La or Ce decreased slightly (2-3%), whereas the removal decreased about 5% for heavy REE (HREE) such as Yb or Lu. This implies that the shell biomass can be an efficient adsorbent for lanthanides in aqueous environment such as seawater, although it requires further investigations to apply the shell biomass to use as an adsorbent for lanthanides more practically.
Figure 10. Effect of common ions (Ca2+, Mg2+, Na+ and K+) on the removal efficiency of lanthanides using ground original sample. |
3.1.1. Forest sector and its characteristics
Forests represent one of the major natural resources in Bosnia and Herzegovina, due to their natural and diverse structure as well as their extensive natural regeneration. The main species found in BiH forests are mostly fir, spruce, Scotch and European pine, beech, different varieties of oak, and a less significant number of noble broadleaves along with fruit trees.
The professional development and management of the forestry sector has been dedicated to traditional systems and has recently (especially after a turbulent post-war period where forests have been neglected and misused) faced higher demands in terms of contributing more to the protection and enhancement of all forest functions, ranging from economical viability to social responsibility and environmental and ecological sustainability. Total forest area in Bosnia and Herzegovina amounts to 2,61 million ha, 1,59 ha in FBiH and 1,03 ha in RS, In BD, where there are approximately 11,000 ha of forests, of that 8,500 ha being privately owned and merely 2,500 ha within the public management system [4].
2,186.300 ha or 81% of forests and forest land is under state ownership, while private ownership consists of 523.500 ha or 19%. Most of these properties are very small in size (up to 2ha) and vastly scattered throughout the country, with outstanding issues in ownership due to population migration.
According to Constitutional provisions, the ownership of forests lies in authority of entities (FBiH, RS) and BD, where ministries of forestry are responsible for administrative management of these areas through the public forest management enterprises. Public forest land amounts to 73% in RS and 83% in FBiH of the total forest land, while the rest is private. Standing volume of forest biomass amounts to 350m m3 in Bosnia and Herzegovina, however the real figure is higher since no data were available for private forests in FBiH. Furthermore, forests net annual increment is estimated to approximately 10m m3 or 3% of the total woodstock. Although annual growth seems high, annual wood increment is constrained by inadequate local forest management practices [3].
In conformity with data shown above, almost 400,000 ha (186,141 ha for FBiH and 207,719 ha for RS) have been assumed as being bare lands with a productive function and in those terms could be potentially included in reforestation programs.
The customary management system of natural regeneration that has been practiced in BiH throughout the centuries has contributed to realizing significant forest diversity in this sense.
Nevertheless, some preceding studies (mostly based on the satellite surveys within the EU CORINE program) have shown that actual forest cover size might be lower by 10-15% than previously projected.
Due to activities such as illegal logging, ore mining, construction, forest fires and others, forested areas have been shrinking rapidly; furthermore, a significant part of the forest cover has been declared as area with land-mines (numbers indicate some 10%) and has evident damages due to war activities. In addition there are extensive unresolved property disputes and illegal land acquisition which await resolution due to complex legal mechanisms and administration.
In the recent years, significant progress has been made in the area of forest certification, where three of the forest management public enterprises have undergone scrutiny of international auditing against the Forest Stewardship Council (FSC) certification, while several others are presently preparing to undergo the same procedure and promote sustainable forest management within their practices. Currently around 50% of state managed forests in BiH have been certified according to FSC Standards.
As mentioned before, forestry legal and institutional framework has been structured through two entities. In FBiH there are cantonal forest management companies, whereas in RS, the forestry management operations are led by a single public enterprise. This decentralization of forest management authority, legal framework (two separate laws on forests) and administration has led to further difficulties in establishing appropriate mechanisms for controlling forest operations, especially illegal logging and land acquisition in bordering areas [4].
The process of tiller differentiation was evaluated by means of histograms illustrating the frequency distribution of their weights — To respect given size limits of this chapter, only data about the differentiation of tillers evaluated in experimental variants in Kromeriz in 2007 are presented: Graph 4 contains data about winter wheat and Graph 5 informs about spring barley. As shown in these histograms, the process of tiller differentiation was influenced both by the stand density and by an N dose. A higher density of plants and a lack of nitrogen accelerated the process of differentiation and made it also more intensive. The separation of tillers into subgroups of productive and non-productive ones could be identified on the basis of a local minimum. In variants without application of nitrogen (variant A in the experiment with winter wheat and variants C and E in the experiment with spring barley), symptoms of tillers separation were manifested in both crops as early as at the beginning of the stage of stem elongation (BBCH 31). On the other hand, however, the fertilised variants showed in this period a marked shift of values to the left, i. e. the proportion of lighter tillers was increased due to a more intensive tillering, which was supported by nitrogen. The differentiation of tillers appeared in histograms under conditions of a lack of resources (i. e. nitrogen). The lack of resources occurred in stands with a high density of plants (Graph 5, variant E in the experiment with spring barley). Application of nitrogen prolonged processes of differentiation till the stage of heading. From the viewpoint of yield formation, a gradual differentiation of tillers is beneficial because potentially productive tillers can be preserved for a longer time interval. On the other hand, however, too dense stands can suffer from a lack of resources (e. g. during dry periods).
To estimate the production potential and predict the yield, it is an advantage to know the numbers of productive tillers and their critical weight for the transition from the vegetative to the generative stage of growth and development. In our analyses this was done in two different ways:
— basing on the position of local minima in histograms (Graphs 4 and 5),
— by deduction of the strongest tillers (evaluated on the basis of the number of ears at BBCH 91) from their total number.
The observed critical weights of winter wheat and spring barley individual tillers were approximately 2 g and 1.5 g, respectively.
The separation of tillers into two groups, i. e. vegetative and generative (potentially productive) ones enabled us to determine the share of productive and non-productive biomass in the total above-ground biomass of the stand. This value can be an important indicator of the effectiveness of farming inputs into the crop cultivation. Thereafter an analysis of metapopulations of potentially productive tillers was performed.
Further analyses were focused on the evaluation of relationships between populations of productive and non-productive tillers. Correlations between total above-ground biomass and biomass of potentially productive tillers were positive and statistically highly significant while correlations between the total above-ground biomass and the share of the biomass of potentially productive tillers were very variable. Similar values and a similar character of correlations were also between the content of nitrogen in the total above-ground biomass and biomass of potentially productive tillers and also their share in the total aboveground biomass on the other hand (Table 5). Analysis of variability (Table 6) revealed low
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values (i. e. less than a half) of the CV for the proportion of biomass of productive tillers in the total above-ground biomass (ranging from 7.06 to 15.79 % and from 7.37 to 17.46 % for winter wheat and spring barley, respectively) while values of CV for other traits under study ranged from 29.81 to 54.43 %. Analogical correlations calculated on the basis of all and of productive tillers showed a similar character but they were lower (Table 7). Values of CV for correlated traits (Table 8) were mostly higher, including the proportion of productive tillers in their total number (22.85 to 26.11 % and 12.67 to 32.12 % for winter wheat and spring barley, respectively). Basing on this observation, it can be concluded that there is a close relationship between the total above-ground biomass and that of potentially productive tillers already at the stage of stem elongation. Because of a low variability the proportion of potentially productive tillers in the total above-ground biomass can be used for the estimation of a productive potential of the crop. This finding can be useful for more effective methods of canopy control based on spectral characteristics and on indirect estimation of above-ground biomass using the NDVI (Normalized Difference Vegetation Index).
Develop |
Correlation coefficient between |
|||||||||
Crop |
-mental stage BBCH |
n-2 |
Total and productive biomass |
Total biomass and share of productive biomass |
N-content in bio-mass and productive biomass |
N-content in biomass and share of productive biomass |
||||
Winter |
31 |
8 |
0.9111** |
0.0850 |
0.8657** |
0.1841 |
||||
wheat |
37 |
6 |
0.9659** |
-0.3515 |
0.9409** |
-0.1133 |
||||
65 |
8 |
0.9720** |
0.2182 |
0.9130** |
-0.2676 |
|||||
Spring |
31 |
14 |
0.9501** |
-0.7810** |
0.8847** |
-0.7603** |
||||
barley |
37 |
6 |
0.9995** |
0.9657** |
0.9664** |
0.8949** |
||||
55/65 |
18 |
0.9835** |
0.1695 |
0.8743** |
0.3365 |
|||||
Table 5. Relationships in stands of winter wheat and spring barley (*statistical statistical significance) |
significance, ** |
high |
||||||||
Develop mental stage |
Trait |
|||||||||
Crop |
n-2 |
Total biomass |
N-content in biomass |
Productive biomass |
Share of productive tillers |
|||||
BBCH |
Mean (g. m-2) |
CV (%) |
Mean (g. m-2) |
CV (%) |
Mean (g. m-2) |
CV (%) |
Mean (g. m-2) |
CV (%) |
||
Winter |
31 |
10 |
1,586 |
33.12 |
8.78 |
39.86 |
1031 |
38.74 |
0.65 |
15.79 |
wheat |
37 |
8 |
3032 |
30.76 |
12.65 |
46.64 |
2667 |
29.81 |
0.89 |
7.06 |
65 |
10 |
3775 |
35.00 |
16.14 |
45.13 |
3533 |
34.31 |
0.94 |
7.57 |
|
Spring |
31 |
16 |
1376 |
39.58 |
6.55 |
36.24 |
1060 |
30.99 |
0.80 |
12.80 |
barley |
37 |
8 |
2430 |
54.43 |
8.03 |
49.95 |
2573 |
50.36 |
0.84 |
17.46 |
55/65 |
20 |
2777 |
38.19 |
9.83 |
34.46 |
2485 |
40.44 |
0.89 |
7.37 |
Table 6. Mean values and coefficients of variation for traits in stands of winter wheat and spring barley |
Correlation coefficient between |
||||||
Crop |
Develop- |
n-2 |
Total number |
Total number |
N-content in |
N-content |
mental |
of tillers and |
of tillers and |
biomass and |
in biomass |
||
stage |
number of |
share of |
number of |
and share of |
||
BBCH |
productive |
productive |
productive |
productive |
||
tillers |
tillers |
tillers |
tillers |
|||
Winter |
31 |
8 |
0.5262 |
-0.5621 |
0.7987** |
0.0123 |
wheat |
37 |
6 |
0.8140** |
-0.7203** |
0.7729* |
-0.6106 |
65 |
8 |
0.8260** |
-0.6578* |
0.8114** |
0.5930* |
|
Spring |
31 |
14 |
0.7107** |
— 0.7505** |
0.5803* |
-0.7458** |
barley |
37 |
6 |
0.3323 |
0.0008 |
0.9156** |
0.8332* |
55/65 |
18 |
0.9007** |
-0.0612 |
0.7669** |
0.1316 |
Table 7. Relationships in stands of winter wheat and spring barley (^statistical significance, ** high statistical significance) |
Trait
Crop Devel°p — n-2 Total number of N-content in Number of Share of mental tillers biomass productive tillers productive stage tillers
Table 8. Average values and coefficients of variation for traits in stands of winter wheat and spring barley |
The stand structure is a result of a simultaneous growth of individual plants within the framework of a population and changes in dependence on dynamics of growth processes, which are limited both spatially and temporally, in the course of the growing season. The temporal limitation results from developmental changes taking place during the life cycle of plants and while the spatial limitation is given by the size of their nutritive area and by available resources. These limitations are mutually interlinked and can stand for each other; this results from the law of "final constant yield" [43]. In practice this means that a shortening of the growing season, and especially of the period of generative growth and development, which is characterized by intensive growth, can be compensated by higher sowing rates and vice versa. An adequate supply of water and nutrients reduces the decrease in numbers of tillers both in dense and thin stands. Under conditions of abundance of resources the highest yields can be obtained in stands with a high productive density, which
can be obtained on the basis of a lower number of plants. This can be explained by the fact that the competition is postponed till the end of the period of stem elongation. This observation corresponds with results of our earlier experiments, in which it was found out that the highest grain yield could be reached on the basis of maximum amount of aboveground biomass and density of productive stems at the minimum density of plants [49].
Corroborated were also conclusions drawn by Muravjev [8] that under favorable conditions the uniformity of stems and ears can be increased by competition induced in the period of stem elongation. Such a competition results in the selection of the strongest and the most vigorous tillers. However, the developmental biology of cereals permits only a relative synchronization of growth and development. Allometry and temporal sequence of the establishment and formation of identical organs (modules) support and work in favor of their increasing variability [3]).
An uneven growth of plants and tillers as well as their competition are the major factors which influence the stand structure. It is very difficult to evaluate directly the intensity of mutual interactions and competition of plants growing together only on the basis of the depletion of available resources [52]. For that reason the ecologists use the size distribution of plants as a suitable indicator of these relationships and of changes taking place in the structure of plant populations. The use of this indicator enabled to characterize the differentiation of tillers and to define relationships between potentially productive tillers on the one hand and non-productive ones on the other.
An effective utilization of growing factors and farming inputs is dependent on the fulfillment of two controversial requirements:
— formation of a maximum possible amount of above-ground biomass,
— establishment of a maximum possible proportion of productive tillers (stems) in the total above-ground biomass.
Basing on our results, it can be concluded that there is a close relationship between the total above-ground biomass and N-content in the total above-ground biomass on the one hand and the biomass of potentially productive tillers already in the period of stem elongation on the other. Because of a low degree of variability it is possible to use the share of potentially productive tillers in the total above-ground biomass for the estimation of overall productive capacity of a stand. This creates preconditions for a more efficient use of indirect methods of canopy assessment on the basis of its spectral characteristics, e. g. when using of the NDVI for optimization of the canopy management in dependence on the intensity of inputs. The obtained results also indicated that the relationships calculated at the level of biomass were more exact than those obtained by counting of the number of tillers. This indicates that values of the NDVI (and possibly also of other spectral canopy characteristics) will enable to obtain more exact estimation of the amount of above-ground biomass (both total and productive) than of its structure (numbers of plants and tillers).
The results also indicate that there is a possibility of the occurrence of various, dynamically changing situations in cereal crops. The population concept applied in studies concerning modular units (tillers) enabled to create a unifying base for these relatively chaotic phenomena. They can therefore be used when studying and testing new methods of efficient and areal screening of the condition of cereal stands by means of spectral characteristic and technologies of remote and terrestrial sensing [23,53,54].
The determination of the Beaufils ranges consists in optimal ranges of nutrients for the assessment of leaf nutrients (Table 3 and 4). This method consists of determining the ranges by means of statistical models of the relationship between leaf concentrations and DRIS index, and, Beaufils (1973) found that from the optimal values of DRIS index were determined intervals of standard deviation of DRIS index for each range of nutritional assessment. Following this criterion, the range that would include the nutrients that would be deficiency was below — 4/3 standard deviation (s); deficiency-prone between — 4/3 to 2/3 s; sufficient between — 2/3 to 2/3 s and a excess-prone 2/3 to 4/3 s; excessive greater than 4/3 s (Table 3).
Thus, it creates the Beaufils ranges, which can be used to interpretate the nutrient concentration in chemical analysis of leaves (Table 3 and 4). As such use, recommended for the specific regions where they were certain, because if extrapolated to other region, it is expected that the results do not follow a favorable response.
Nutrient |
Norm |
Deficiency |
Tendency to deficiency |
Sufficient |
Tendency to excess |
Excess |
g kg’1 |
||||||
N |
NL |
<42.5 |
42.5 — 43.9 |
43.9 — 46.7 |
46.7 — 48.1 |
>48.1 |
F value |
<42.6 |
42.6 — 43.9 |
43.9 — 46.7 |
46.7 — 48.1 |
>48.1 |
|
P |
NL |
<2.5 |
2.5 — 2.8 |
2.8 ‘ 3.3 |
3.3 — 3.5 |
>3.5 |
F value |
<2.5 |
2.5 — 2.8 |
2.8 — 3.3 |
3.3 — 3.5 |
>3.5 |
|
K |
NL |
<17.2 |
17.2 — 19.0 |
19.0 — 22.6 |
22.6 — 24.4 |
>24.4 |
F value |
<17.2 |
17.2 — 19.0 |
19.0 — 22.6 |
22.6 — 24.4 |
>24.4 |
|
Ca |
NL |
<25.2 |
25.2 — 27.5 |
27.5 — 32.1 |
32.1 — 34.4 |
>34.4 |
F value |
<25.3 |
25.3 — 27.5 |
27.5 — 32.0 |
32.0 — 34.3 |
>34.3 |
|
Mg |
NL |
<3.6 |
3.6 — 4.0 |
4.0 — 4.8 |
4.8 — 5.3 |
>5.3 |
F value |
<3.6 |
3.6 — 4.0 |
4.0 — 4.8 |
4.8 — 5.2 |
>5.2 |
|
S |
NL |
<4.8 |
4.8 — 8.7 |
8.7 — 16.5 |
16.5 — 20.4 |
>20.4 |
F value |
<5.2 |
5.2 — 8.9 |
8.9 — 16.3 |
16.3 — 20.0 |
>20.0 |
|
mg kg’1 |
||||||
B |
NL |
<40.8 |
40.8 — 53.6 |
53.6 — 79.4 |
79.4 — 92.2 |
>92.2 |
F value |
<41.2 |
41.2 — 53.8 |
53.8 — 79.2 |
79.2 — 91.8 |
>91.8 |
|
Zn |
NL |
<21.7 |
21.7 — 24.9 |
24.9 — 31.2 |
31.2 — 34.4 |
>34.4 |
F value |
<21.8 |
21.8 — 24.9 |
24.9 — 31.2 |
31.2 — 34.3 |
>34.3 |
|
Cu |
NL |
<3.9 |
3.9 — 9.8 |
9.8 — 22.0 |
22.0 — 27.9 |
>27.9 |
F value |
<3.7 |
3.7 — 9.8 |
9.8 — 22.0 |
22.0 — 28.1 |
>28.1 |
|
Mn |
NL |
<14.9 |
14.9 — 33.4 |
33.4 — 70.8 |
70.8 — 89.3 |
>89.3 |
F value |
<14.9 |
14.9 — 33.4 |
33.4 — 70.8 |
70.8 — 89.4 |
>89.4 |
|
Fe |
NL |
<52.3 |
52.3 — 80.2 |
80.2 — 136.5 |
136.5 — 164.4 |
>164.4 |
F value |
<52.6 |
52.6 — 80.4 |
80.4 — 136.3 |
136.3 — 164.1 |
>164.1 |
Serra et al. (2012) |
Nutrient |
Norm |
Deficiency |
Deficiency — p rone |
Sufficient |
Excess- prone |
Excess |
% of plots |
||||||
N |
NL |
19.44 |
15.74 |
38.89 |
9.26 |
16.67 |
F value |
19.44 |
15.74 |
39.81 |
8.33 |
16.67 |
|
P |
NL |
7.41 |
19.44 |
45.37 |
12.04 |
15.74 |
F value |
7.41 |
19.44 |
45.37 |
12.04 |
15.74 |
|
K |
NL |
16.67 |
14.81 |
44.44 |
13.89 |
10.19 |
F value |
16.67 |
14.81 |
44.44 |
13.89 |
10.19 |
|
Ca |
NL |
19.44 |
17.59 |
46.30 |
7.41 |
9.26 |
F value |
19.44 |
17.59 |
43.52 |
10.19 |
9.26 |
|
Mg |
NL |
12.04 |
9.26 |
31.48 |
18.52 |
28.70 |
F value |
12.04 |
9.26 |
31 .48 |
14.81 |
32.41 |
|
S |
NL |
2.78 |
54.63 |
21.30 |
11.11 |
10.19 |
50.93 |
20.37 |
2.78 |
18.52 |
|||
F value |
7.41 |
|||||
% of plots |
||||||
B |
NL |
13.89 |
33.33 |
32.41 |
9.26 |
11.11 |
F value |
14.81 |
32.41 |
32.41 |
9.26 |
11.11 |
|
Zn |
NL |
25.00 |
25.93 |
25.93 |
10.19 |
12.96 |
F value |
25.93 |
25.00 |
25.93 |
8.33 |
14.81 |
|
Cu |
NL |
0.00 |
36.11 |
42.59 |
11.11 |
10.19 |
F value |
0.00 |
37.96 |
40.74 |
11.11 |
10.19 |
|
Mn |
NL |
0.00 |
22.22 |
55.56 |
11.11 |
11.11 |
F value |
0.00 |
22.22 |
55.56 |
11.11 |
11.11 |
|
Fe |
NL |
0.93 |
37.04 |
50.00 |
2.78 |
9.26 |
F value |
0.93 |
37.04 |
50.00 |
2.78 |
9.26 |
Serra et al. (2012) Table 4. Percentage of plots diagnosed by Beaufils ranges as deficient, deficiency-prone, sufficient, excess-prone or excess leaf nutrient contents of cotton, based on the criteria of natural log transformation (NL) and F value (Serra et al., 2012). |
The DRIS developed by Beaufils (1973) had among its objectives, to correct the problem of correlation with the sampling time of the plant nutrients, and using dual ratio that promote the relationship among. Hence, improving efficacy of plant nutritional diagnosis allows the determination of the evaluation of the nutritional balance.
With the advent of Diagnose and Recommendation Integrated System (DRIS) by Beaufils (1973), researchers were setting to this system of nutritional diagnosis in order to increase their efficiency. However, evolution has brought a number of possibilities for calculation of DRIS’ norms and functions, that are needed to be tested to determine the best combination of methodology.
The use of DRIS is still being widely disseminated in the world, DRIS brings results consistently good in assessing the nutritional status of plants, showing the nutritional balance, a fact which is not observed with traditional systems (sufficiency range and critical level).
Currently biomass consumption comprises individual, traditional small stoves, ovens, boilers etc., with low efficiencies. The significant use of fuelwood indicates that there could be opportunities for the development of the market for modern biomass heating appliances. Over 54% of the total energy consumption in Bosnia and Herzegovina is in the household sector and 70% of this is fuel wood. Improved stoves and alternative fuels, while outside the scope of this study, are highly relevant in this context [6]).
The study estimated that if 20% of the 18,45 PJ of available fuel wood could be exploited for this purpose this would result in generating 820 GWh of heat annually [5].
6.1.5. Straw fired units
Agricultural residues in Bosnia and Herzegovina consisting primarily of straw account for some 6,63 PJ [5]. Straw may be directly used either in decentralized small, mainly farm based units producing heat for various purposes or in centralized CHP units. Large scale central straw fired units usually require strong economies of scale (capacities in EU are around 100 MW) and are coupled with an alternative fuel, usually conventional one. Considering the significant geographical spread of straw supply and the fact that logistics play a critical role to the economics of such plants it is unrealistic to expect new straw alone fired units to be built. In this respect straw could be merely used for heating purposes either in straw bale fired units or as straw pellet in pellet stoves and boilers. If one third of the technical available straw could be directed to this use it could produce nearly 491 GWh of useful heat [5]. One potential model for utilization of some agricultural residues is in the formation of rural agricultural processing companies. The company supplies seeds, fertilizers, equipment, and training to small rural farmers, and collects harvests (including residues) for centralized, high-tech processing. Sale for process heat and electricity generation converts residues into a valuable marketable product for local and international markets (and ash potentially used for fertilizers). This business model (excluding the energy components) is currently successfully used in some developing countries [6].
Bio-oil is a renewable, which is produced from biomass through a process known as fast pyrolysis. Fast pyrolysis represents a potential route to upgrade the biomass to value added fuels and renewable chemicals. There is an urgent need to develop a sustainable energy supply as the impact of burning fossil fuels on our climate is becoming ever more obvious and the availability of fossil fuels is decreasing. Bio-oil contributes to the reduction of greenhouse gas emissions and it offers several advantages, as it is easy to use, to store, and to transport. Bio-oil that can be extracted from dried biomass including dried oil palm wastes is currently under investigation as a substitute for petroleum [65]. Bio-oil contains fragments of cellulose, hemicelluloses, lignin, and extractives and they are typically brown liquids with a pungent odor. For woody feedstocks, temperatures around 500°C together with short vapour residence times are used to obtain bio-oil yields of around 70%, and char and gas yields of around 15% each [66]. Bio-oil is a high density oxygenated liquid, which can be burned in diesel engines, turbines or boilers, though further work is still required to demonstrate long term reliability [67]. It is also used for the production of speciality chemicals, currently mainly flavourings. Renewable resins and slow release fertilisers are other potential applications, which have been the subject of research [68]. At this stage, fast pyrolysis is a novel and relatively untested technology. There are several pilot plants in North America and Europe, but no consistent track record yet outside of the manufacture of flavourings.
To date, fast pyrolysis of biomass has received very limited attention by researchers in Malaysia. Normally, fibre and shells are burnt in the palm oil processing plants to generate fuel to produce power for running the mill (self-sufficient) [13,69]. So far, research involving fast pyrolysis has been carried out by Universiti Teknologi Malaysia and Universiti Malaya on oil palm shell, rubber waste and rice husk waste, scrapped tyres and tubes [70-74]. One of the authors of this book and the research group from MPOB investigated on the fast pyrolysis of empty fruit bunches (EFB) [75,76].
The utilisation of bio-oil derived from pyrolysis process of oil palm wastes to substitute for synthetic phenol and formaldehyde in phenol formaldehyde resins is possible. Phenol can be used to manufacture moulding products for automotive parts, household appliances, and electrical components; in bonding and adhesive resins for laminating, plywood, protective coating, insulation materials, abrasive coating; in foundry industries for sand moulds and cores. However, producing resins from bio-oil has received very limited attention by researchers in Malaysia and still in research stage. A group of researchers from Universiti Teknologi Malaysia had studied the extraction of phenol from oil palm shell bio-oil [77]. They found that the quantity of phenol in the extracted oil was 24.2 wt% of total extracted oil.
In 2005, with the co-operation between Malaysian based Genting Sanyen Bhd and BTG Biomass Technology Group BV from The Netherlands, the first commercial bio-oil plant has already started production in Malaysia on a scale of 2 t/hr [78,79]. The main achievements of this project are more than 1,000 tonnes of bio-oil have been produced, the bio-oil is co-fired, replacing conventional diesel in a waste disposal system located 300 km from site, maximum capacity of the plant so far is about 1.7 t/hr on a daily continuous basis, the bio-oil quality can be controlled by the operating conditions, the drying of EFB to 5 wt.% moisture is possible using the excess heat from the pyrolysis process, the energy recovered from the process can be used effectively for drying the wet EFB, and potentially to generate the electricity required. Indeed, this is a breakthrough step in Malaysia for the utilisation of oil palm wastes as a source of bio-oil [80].
The growth in biology is usually described by observing temporal development of average measurement values. Similarly, for analyses of yield formation a number of formed and reduced yield elements per unit area (tillers, reproductive organs and grains) is observed and their average values are determined.
The assessment of cereal stands and yield formation is usually based on the classical concept as reported by Heuser [9] and later on by a number of other authors who divided grain yield into spike number per unit area, grain number per spike and grain weight (1000-grain weight) — Figure 1.
At present, this concept based on the plant number and numbers of formed and reduced tillers per stand unit area prevails in both applied research and practice. It uses advantages of plant as well as stand description on the basis of changes in the tiller number when no destructive analyses and higher labour intensity are needed. Recently, however, the concept has been often criticized because it does not provide enough precise quantification of differences in stands. Hunt [10] drew an attention to changes in the number and size of plant parts (modules) during plant growth and development. It is evident that the procedures based on the growth analysis are rather labour intensive; their simplification for practical use results in lower accuracy and does not allow to record spatial heterogeneity of the stand. Therefore, some authors expressed a need of available innovated criteria for stand assessment [11].
Figure 1. Schematic illustration of dynamics of yield elements formation and reduction in tillering cereals through the growing season [12] |
X-ray diffraction (XRD) patterns of four kinds of sieved samples after adsorption of metals are shown in Fig. 11. Similar to the XRD patterns before adsorption of metals, aragonite and calcite were found as the main crystal structure in (a): Ground original sample and (b): Heat — treatment (480°C, 6h) sample, respectively. However, the decrease of peak and increase of noise were also observed in both patterns, particularly in the ground original material as shown in Fig. 11(a). Bottcher [37] pointed out that the natural powdered aragonite was transformed to mixed rhombohedral carbonates by the reaction with (Ca, Mg)-chloride solutions. Therefore, there is the possibility that the transformation of aragonite occurred by the reaction with lanthanides in our experiment.
Moreover, according to XRD analysis, the main crystal structure of (c): "Heat-treatment (950°C) sample" was transformed from calcium oxide (CaO) to the mixture of calcium hydroxide (Ca(OH)2) and calcite (CaCO3) after exposing metals; and that of (d): "Heat — treatment (950°C) and water added sample" was transformed from calcium hydroxide(Ca(OH)2) to calcite (CaCO3) after adsorption of metals. These changes may be due to the reaction with water or carbon dioxide in atmosphere.
SEM pictures of four kinds of shell biomass after adsorption of metals are shown in Fig. 12. By comparing SEM pictures in Fig. 8 with that in Fig. 12, it is found that the morphology of sample (a) and (b) has hardly changed even after exposing metals. From this observation, these sieved samples should be predicted to withstand the repeated use; and hence it can be a good adsorbent.
In contrast to sample (a), clear crystal structure (sizes are mostly 0.25-2.0pm) was observed in sample (b) even after adsorption of metal. In case of Cd conducted by Kohler et al. [38], the difference of procedure for reaction with metals between aragonite and calcite was suggested. According to their work, the precipitation of several distinct types of crystals was observed after exposing metals in the case of aragonite. Then, it is anticipated that similar phenomenon were occurred by adsorption of lanthanides in case of our samples. On the
0 20 40 60 80 100
2 в
(d)
Figure 11. X-ray diffraction (XRD) patterns of Buccinum tenussimum shell biomass after adsorption of metals. (a) ground original sample, (b) heat-treatment (480°C) sample, (c) heat-treatment (950°C) sample, (d) heat-treatment (950°C) and water added sample
(d)
other hand, the surface of sample (c) and (d) after exposing metals have changed largely compared to that before adsorption of metals (Fig. 8). This is in good accord with the results of XRD patterns. Particularly, remarkable transformation was observed in the morphology of sample (d). The reaction of sample (d) with metal is supposed to proceed rapidly.
Bosnia and Herzegovina has abundant forests, with 46 % of the country area covered by forests. The production, harvesting and processing of timber is one of the country’s oldest economic activities, and currently has major strategic importance for the country’s economic development.
High forest predominates and deciduous species are the most dominant with beech (Fagus spp.) accounting for almost 40% of all species cover in the country. Oaks (Quercus spp.) contribute another 20%. Spruce and fir, located in the higher elevations and generally on the steepest terrain comprise an additional twenty percent of the forest cover in BiH. Annual allowable cut is calculated to 7,44 million m3 according to an ongoing UNDP Project, while actual harvest was 5,60 million m3 in 2008 [3]. From the 4,33 million m3 of roundwood that were produced in 2008, 1,69 million m3 were used as fuel wood (~40%), while 2,64 million m3 were directed towards the wood industry (~60%). Furthermore, around 1,18 million m3 of forest residues were produced at the logging sites.
The tradition of use biomass as energy source in Bosnia and Herzegovina has existed for a long time, but that use is characterized with a very low rate of utilization, mainly in rural and sub-urban areas as primary source for heating and cooking purposes in households and buildings. According to the recent findings from the total 77.19 PJ of final energy consumption in households, biomass makes 45.84 PJ. However, since energy demand and prices of fossil fuels rise rapidly other forest based biomass resources apart from fuelwood are also being considered for energy exploitation. These include forest residues and bark as well as residues/by-products arising from the processing of industrial wood.
Forest residues in BiH that can be utilized for energy production include tops, branches and stumps that are left at the logging sites. According to forest expert’s estimation, forest residues that are available for energy purposes amount to 20% and 10% of the harvested volume of industrial roundwood and fuelwood respectively. However, no more than half these residues can be harvested due to difficulties in their collection [5].
Wood industries produce residues, such as chips and particles, sawdust, slabs, edgings and shavings. These residues can either be used in particleboards or pulp production or used for energy purposes in industrial boilers and for densified wood fuels production (pellets and briquettes). Bark is also included in industrial residues, since industrial wood is mainly debarked at the sawmills. However, in order to estimate the produced residues one needs to know the products output.
Wood industry production figures were not available on a regional level and therefore information on a national level from the Industrial Bulletin for FBiH and RS was used [1,2]. In 2008, almost 1 million m3 sawmill products, 40.733 m3 plywood and veneer sheets products and 2.428 m3 particleboards were produced on a country level. Furniture and secondary wood industry products, such as doors, windows and parquets, were not included in this study’s calculations, since they are given in different units (pieces, m2, etc.).
Feedstock was calculated by employing FAO conversion factors for each wood processing industry. Sawmill residues (excluding bark) were assumed to comprise 40% of sawmill feedstock, while plywood and veneer sheets industry residues were assumed to comprise 45% of feedstock. Bark was separately calculated as 7% of sawmill feedstock [5]. These factors depend on a number of assumptions with regards type and modernization level of each process, the production capacity of each industry, the tree species processed, etc. The factors were found to be in good agreement with literature values for the Western Balkans region. Furthermore, the availability of wood industry residues is restricted by various technical factors and was assumed equal on average to 80% for all types of wood industry residues with the exception of bark for which availability was assumed to be 60%.
Black liquor is a byproduct of the chemical wood pulp production process. According to the Industrial Bulletin for FBiH and RS Statistics, 32.809 t of unbleached coniferous chemical wood pulp (90% dry substance) were produced in 2008 in FBiH, which in terms of energy is equivalent to 74.476 m3 of fuelwood Moreover, 98.041 t of paper and paperboard were produced on a country level in 2008 [1,2]. Paper production is not a significant source of woody biomass in BiH, since the solid waste produced is very heterogeneous and contains non paper components, such as sand, metal, and glass, which cannot be used as a fuel [5].
Forest timber (fuel wood and forest residue) and wood waste from wood processing industry represent the major source of biomass for energy production in Bosnia and Herzegovina. Biomass residues from agricultural production have a significant energy potential in parts of northern and north-eastern Bosnia. Forests are one of the most important natural resources of Bosnia and Herzegovina. Bosnia and Herzegovina is one of the richest countries in Europe by the criteria of the forest coverage and diversity considering the total size of the State territory. The largest areas are covered by forests of broadleaf or deciduous trees, while about 10% of the country is covered by barren soils (i. e. one fifth of the forest soils). The total growing wood stocks in the forests of Bosnia and Herzegovina amount to 317,565,740 m3 or 203.6 m3/ha (62% broadleaf trees and 38% conifers). The annual volume increment of forests in Bosnia and Herzegovina is 9,500,600 million m3 or 6.1 m3/ha, the annual allowable level of wood cutting is 7,451,450 million m3 or 4.75 m3/ha [3].
The energy potentials of the natural wood residue resources in Bosnia and Herzegovina are presented in Table 1.
The production, harvesting and processing of timber is one of the oldest economic activities in the Country, and has a strategic importance for the country’s economic development. Some statistical estimations shows that the wood export value within the total Bosnia and Herzegovina export value is probably in order of 15%. It is further estimated that 15% of the total population receives its livelihood through the activities in forestry and forest industry.
quantities |
quantities |
minimum inferior calorific value |
energy potential |
|||
m3/a |
t/a |
GJ/t |
TJ/a |
|||
wood residue |
broadleaf |
295.529 |
212.781 |
10.28 |
2187 |
|
conifers |
202.866 |
91.290 |
10.28 |
938 |
||
sawmill wood waste |
sawdust |
broadleaf |
283.300 |
203.976 |
10,28 |
2097 |
conifers |
145.227 |
65.352 |
10.28 |
672 |
||
wood chops |
broadleaf |
212.475 |
152.982 |
10,28 |
1573 |
|
conifers |
145,227 |
65.352 |
10,28 |
672 |
||
Total |
1.284.624 |
791.733 |
8139 |
Table 1. Quantities, types, structure and energy-related potential of wood residue in Bosnia and Herzegovina (based on an average volume of cutting in the period of 2007 — 2010.) [5]. |
Fuel wood is considered to have high value for local, small scale energy use, i. e. stoves, open fires and ovens. While this is clearly neither efficient nor perhaps environmentally optimal use of resource, it is nevertheless an essential, low cost resource for large numbers of rural people. From the 18,45 PJ estimated by this study, it is assumed that 20% will be available for new, efficient small scale wood fired boilers, stoves, etc. This would account for 820 GWh heat production annually.
Saw mill waste production is generally high due to a low process efficiency of sawmills: the net end product (lumber) represents an estimated 40 — 45% of the log (a well managed mill in Europe runs at up to 50% efficiency). The waste produced consists of wet sawdust, slabs, and the trimmings from cutting to length and width. Based on this ratio, waste from the primary and secondary wood processing industries would amount to approximately 1.14 x 106 m3.[6]