1.3 Composition and biomass of earthworms

Four different types of organic waste sludge used in this study were as follows: municipal sewage sludge (MSS) collected from sewage treatment plants on Gwacheon (Gyeonggi Province, South Korea); industrial sewage sludge (ISS) collected from industrial complex on Ansan (Gyeonggi Province); alcohol fermentation processing sludge (AFPS) collected from Ansan industrial complex; and leather processing sludge (LPS) collected from sewage treatment plant on Cheongju (Chungbuk Province, South Korea). Pig manure compost (PMC) was purchased from Anjung Nong-hyup, Anjung (Gyeonggi Province). These materials were collected in early March 1994 and kept in deep freezers (-60°C) to be applied annually from 1994 to 2001.

Lysimeters which composed of 45 concrete plots (1.0 m length, 1.0 m width and 1.1 m depth) (Fig. 2) were made in the upland field of Suwon (Gyeonggi Province) in March 1993. Each plot was uniformly filled with the same sandy loam soil without earthworms up to the ground surface in mid-May 1993. Three levels (12.5, 25 and 50 tons of dry matter ha-1 year-1) of test materials were applied to each plot twice annually for 8 consecutive years (mid­March 1994 to mid-March 2001) and mixed into the soil of a depth of 15 cm. PMC served as a standard for comparison in lysimeter tests. A randomized complete block design with three replicates was used. Two radish, Raphanus sativus, cultivars (jinmialtari and backkyoung) were cultivated in every spring and autumn, respectively. Planting densities were 12 x 15 cm in spring and 25 x 30 cm in autumn with one plant. Other practices followed standard Raphanus culture methods without application of any mineral fertilizer and pesticide. The lysimeters were covered with a nylon net to prevent any access by birds or animals.

Earthworms were collected from each of the 45 lysimeter plots from an area of 1 m2 up to 0.3 m depth by hand sorting in mid-October 1997 and mid-October 2001 as described previously (Callaham & Hendrix, 1997). They were immediately transported to the laboratory in plastic containers and separated into juveniles and adults with a clitellum. The earthworm numbers, composition and biomass were investigated before they were fixed in a 10% formalin solution. Earthworm species identification followed Hong & James (2001), Kobayashi (1941) and Song & Paik (1969).

Pollution index (PI) was determined according to the method of Jung et al. (2005), PI = [^(heavy metal concentration in soil tolerable level-1) number of heavy metal-1]. Tolerable level of Cu, Zn, Cr, Cd, Pb and Ni were 125, 700, 10, 4, 300 and 100 mg kg-1 in Korean soil, respectively (Anon., 2007). PI values are employed to assess metal pollution in soil and indicate the average on ratios of metal concentration over tolerable level. A soil sample is judged as contaminated by heavy metal when PI value is greater than 1. Total toxic unit of PTEs was calculated by threshold level described under the Soil Environmental Conservation Act (Anon., 2007) in South Korea as follows: £ (Cu 50 + Zn 300 + Cr 4 + Cd 115 + Pb 100 + Ni 40). Bonferroni multiple-comparison method was used to test for significant differences among treatments in the fresh biomass of earthworms and pollution indices (SAS Institute, 2004). Correlations between accumulated pollutant contents and observed earthworm numbers and biomass were estimated from the Pearson correlation coefficients using SAS. pH values, heavy-metal contents and pollution indices of 8 consecutive yearly applications of three levels of four different organic waste materials and PMC in field lysimeters were reported previously (Na et al., 2011).

Effects on earthworm composition of 8 consecutive yearly applications of four organic waste materials and PMC were investigated using field lysimeters (Table 2). Earthworm composition in all treatments varied according to waste material examined, treatment level and application duration. Of 390 adults collected from 45 plots, earthworms were classified into 2 families (Megascolecidae and Moniligastridae), 2 genera (Amynthas and Drawida) and 5 species (Amynthas agrestis, Amynthas hupeiensis, Amynthas sangyeoli, Drawida koreana and Drawida japonica). The number of earthworm species in MSS-, ISS-, LPS-, AFPS — and PMC-treated soils was 2, 2, 2, 3 and 5, respectively. The dominant species were A. agrestis, A. hupeiensis, A. sangyeoli and D. japonica in the sludge treatments 4 years after treatment but was replaced with A. hupeiensis in all the plots 8 years after treatment. This finding indicates that A. hupeiensis was more tolerant to toxic heavy metals than other earthworm species. In ISS — and LPS-treated soils, the proportion of juveniles appeared was 67-100% 4 years after treatment, but no juveniles was observed 8 years after treatment.

At 4 years after treatment, effect of test waste material (F = 16.91; df = 4,44; P < 0.0001) and treatment level (F = 4.09; df = 2,44; P = 0.0268) on the number of earthworms was significant (Table 2). The material by level interaction was also significant (F = 2.63; df = 8,44; P = 0.0258). At 8 years after treatment, effect of test waste material (F = 17.33; df = 4,15; P < 0.001) and treatment level (F = 11.00; df = 3,29; P < 0.001) on the number of earthworms was significant. The material by level interaction was also significant (F = 20.53; df = 8,44; P < 0.001). The number of earthworms was significantly reduced in 25 and 50 ton MSS treatments, 25 and 50 ton AFPS treatments and 12.5 and 25 ton PMC treatments 4 years after treatments than 8 years of treatments. The total number of earthworms collected 4 and 8 years after treatment was as follows: MSS-treated soil, 66/29; ISS-treated soil, 4/2; LPS — treated soil, 15/1; AFPS-treated soil, 30/11; and PMC-treated soil, 127/439.

Earthworm biomass collected from 45 plots during the 8-year-investigation period is given in Fig. 3. The biomass in all treatments was dependent upon waste material examined, treatment level and application duration. At 4 years after treatment, effect of test waste material (F = 49.45; df = 4,44; P < 0.0001) and treatment level (F = 5.80; df = 2,44; P = 0.0074) on the earthworm biomass was significant. The material by level interaction was also significant (F = 3.88; df = 8,44; P = 0.0031). At 8 years after treatment, effect of test waste material (F = 165.13; df = 4,44; P < 0.0001) and treatment level (F = 14.39; df = 2,44; P < 0.0001) on the earthworm biomass was significant. The material by level interaction was also significant (F = 19.77; df = 8,44; P < 0.0001). Significant increase in biomass of soil treated with 50 ton PMC ha-1 year-1 was observed 8 years after treatment.

Table 2. Earthworm numbers and composition of 4 and 8 consecutive yearly applications (twice annually) of three levels of four different organic waste materials and pig manure compost using field lysimeters

To evaluate potential toxic effects of residual heavy metals, total toxic units of PTEs were determined (Fig. 4). The total toxic units in all treatments varied with waste material examined, treatment level and application duration. At 4 years after treatment, effect of test waste material (F = 34872.4; df = 4,44; P < 0.0001) and treatment level (F = 60.24; df = 2,44; P < 0.0001) on the the total toxic units of PTEs was significant. The material by level interaction was also significant (F = 2601.2; df = 8,44; P < 0.0001). At 8 years after treatment,
effect of test waste material (F = 52439.5; df = 4,44; P < 0.0001) and treatment level (F = 28451.0; df = 2,44; P < 0.0001) on the the total toxic unit of PTEs was significant. The material by level interaction was also significant (F = 13057.2; df = 8,44; P < 0.0001).

PI values of lysimeter soils sampled during the 8-year-investigation period are reported in Fig. 5. At 4 years after treatment, effect of test waste material (F = 34047.6; df = 4,44; P < 0.0001) and treatment level (F = 5957.3; df = 2,44; P < 0.0001) on the the total toxic unit of PTEs was significant. The material by level interaction was also significant (F = 2505.3; df = 8,44; P < 0.0001). At 8 years after treatment, effect of test waste material (F = 48793.6; df = 4,44; P < 0.0001) and treatment level (F = 26515.1; df = 2,44; P < 0.0001) on the the total toxic unit of PTEs was significant. The material by level interaction was also significant (F = 12190.9; df = 8,44; P < 0.0001). There was significant difference in PI values between the treatment duration. Particularly, PI value of ISS-treated soil was higher 8 years after treatment than 4 years after treatment, while PI value of LPS-treated soil was higher 4 years after treatment than 8 years after treatment.

Correlation between total toxic unit of PTEs and PI and earthworm individuals and biomass was determined (Table 3). At 4 years after treatment, earthworm individuals were correlated negatively with the total toxic unit of PTEs (r = -0.509) and PI (r = -0.508). At 8 years after treatment, earthworm individuals were correlated negatively with the total toxic unit of PTEs (r = -0.265), but were not correlated negatively with PI.

At 4 years after treatment, earthworm biomass was correlated negatively with the total toxic unit of PTEs (r = -0.673) and PI (r = -0.672) (Table 3). At 8 years after treatment, earthworm biomass was correlated negatively with the total toxic unit of PTEs (r = -0.308), but were not correlated negatively with PI.

The impact of heavy metals and sludge on lumbricid earthworms, particularly E. fetida and L. terrestris, has been well noted. Heavy metals cause mortality and reduce fertility, cocoon production and viability, growth, composition and biomass, and bioaccumulation and bioavailability of earthworms. The toxic values of heavy metals to earthworms vary according to an earthworm acute toxicity test. Based upon an artificial soil test, Spurgeon et al. (1994) determined no observed-effect concentrations (NOECs) for E. fetida exposed to heavy metals. The estimated NOEC values were 39.2 mg Cd kg-1, 32 mg Cu kg-1, 1,810 mg Pb kg-1 and 199 mg Zn kg-1. In soil contaminated by effluent containing Cr, the rate of 10 mg kg-1 was fatal to Peretima posthuma and other species (Abbasi & Soni, 1983). Copper caused higher mortality than Pb or Zn against E. fetida at the same rate and the LC50 and NOEC values for Cd could not be determined since no significant mortality was observed at the highest test rate (300 pg g-1) (Spurgeon et al., 1994).

Although heavy metals did not show direct lethal effects to earthworms, they can sensitively cause their reproduction and sperm count reduction and low hatching success of cocoons. Lumbricus terrestris worms exposed in artificial soil to sublethal concentrations of technical chlordane (6.25, 12.5 and 25 ppm) and cadmium nitrate (100, 200 and 300 ppm) exhibited significant reduction in spermatozoa from testes and seminal vesicles (Cikutovic et al.,

1993) . Eisenia fetida worms grew well in the lead-contaminated environment and produced cocoons at the same rate as the control worms, but the hatchability of these cocoons was much lower, indicating that lead toxicity affects reproductive performance by major spermatozoa damage (Reinecke & Reinecke, 1996). In addition, Zn, Mn and Cu produced slower growth, later maturation and fewer or no cocoons. Reinecke and Reinecke (1997) have shown the structural damage of spermatozoa, including breakage and loss of nuclear and flagellar membranes, thickening of membranes, malformed acrosomes and loss of nuclear material, and the results are associated with heavy metals, such as Pb and Mn. The toxicity order of metals on reproduction in earthworms is Cd, Cu, Zn and Pb. Similar results have been found in E. fetida exposed to a geometric series of concentrations of Cd, Cu, Pb and Zn in artificial soil and the effects of Cd and Cu on the reproductive rate were particularly acute (Spurgeon et al., 1994).

It has been well known that earthworms are able to inhabit soils contaminated with heavy metals (Becquer et al., 2005; Li et al., 2010; Maity et al., 2008) and can accumulate undesirably high concentration of heavy metals (Cu, Zn, Pb and Cd) that may give adverse effects on livestock (Hobbelen et al., 2006; Oste et al., 2001). Earthworms (L. rubellus and Dendrodrilus rubidus) sampled from one uncontaminated and 15 metal-contaminated sites showed significant positive correlations between earthworm and total (conc. nitric acid — extractable) soil Cd, Cu, Pb and Zn concentrations (Morgan & Morgan, 1988). The important factor in the accumulation of heavy metals in earthworms is bioavailability by uptake (Dai et al., 2004; Spurgeon & Hopkin, 1996) because there are significant correlations between the concentrations of heavy metal accumulated in earthworms and bioavailable metal concentrations of field soils (Hobbelen et al., 2006). Earthworm metal bioaccumulation and bioavailability have been well reviewed by Nahmani et al. (2007). There were positive relationships between earthworm tissue and soil metal concentrations and also earthworm tissue and soil solution metal concentrations with slightly more significant relationships between earthworm tissue and soil metal concentrations 42 days after treatment. Recently, Li et al. (2010) reported the positive logarithmic relationship between the bioaccumulation factors of E. fetida to heavy metals and the exchangeable metal concentration of pig manure. The differences in these accumulation and availability among earthworms may, in part, play a role in affecting their population density and genetic adaptation living in metal — contaminated soils.

However, Lee (1985) suggested that the differences in the relative toxicity of compounds may explain some of the conflicting data in the literature on the concentrations which have deleterious effects on earthworms. For instance, very high concentrations of lead that influence growth and reproduction of earthworms may be attributable more to the very low solubility of lead compounds that are found in soils and the ability of earthworms to sequester absorbed lead than to any lower toxicity of lead compared with other heavy metals. It has been suggested that E. fetida may regulate the concentration of zinc in their body tissue through allowing rapid elimination by binding zinc using metallothioneins in their chloragogenous tissue (Cotter-Howells et al., 2005; Morgan & Morris, 1982; Morgan & Winters, 1982; Prento, 1979). High tolerance of earthworms to cadmium poisoning may also result from detoxification by metallothionein proteins in the posterior alimentary canal (Morgan et al., 1989). In addition, heavy metals have high affinity for glutathione, metallothioneines and enzymes of intermediary metabolism and heme synthesis (Montgomery et al., 1980). The metals Zn, Pb, Bi and Cd which are not consistently prevailing toxicants were most accessible to earthworms and Cu, Zn and Cr were also accumulated in earthworm tissue and the contaminated soils imparied earthworm reproduction and reduced adult growth, while elevated superoxide dismutase activity suggested that earthworms experienced oxidative stress (Berthelot et al., 2008).

Lead, copper and zinc may inhibit d-aminolevulinic acid dehydratase (d-ALAD) which is a key enzyme in heme synthesis by lowering haemoglobin concentration in earthworm blood. Replacement of zinc, a protector of the active site of d-ALAD, by lead may result in its inhibition.

Soil pH has been comprehensively identified as the single most important soil factor controlling the availability of heavy metals in sludge-treated soils (Alloway & Jackson,

1991) . Soil pH is also one of the most important factors that limit the species, numbers and distribution of earthworms (Dunger, 1989; Edwards & Bohlen, 1996; Satchell & Stone, 1972) because it may affect the survival of adults and thus production and avoidance behaviour of juveniles (Aorim et al., 1999, 2005). van Gestel et al. (2011) reported that soil pH and organic matter content determine molybdenum toxicity to enchytraeid worm, Enchytraeus crypticus A higher pH resulted in a decreased sorption of the molybdate anion, and it caused increased bioavailability and toxicity.

A lot of studies concerning the effects of heavy metals on earthworms in terms of mortality, loss of weight, fertility, cocoon production, cocoon viability and growth were carried out during short-term experiments (14 or 21 days) in artificial soils contaminated with metal solution containing a single metallic element. Recently, Na et al. (2011) studied the effects of long-term (8 years) application of four organic waste materials on earthworm numbers and biomass. They reported that earthworm individuals were correlated positively with pH (r = 0.37) and negatively with heavy metals (r = -0.36 to -0.55) with the exception of Zn 4 years after treatment, while earthworm individuals were correlated positively with pH (r = 0.46) and negatively with Pb (r = -0.41) but positively with Zn (r = 0.59) 8 years after treatment. Earthworm biomass was correlated negatively with heavy metals (r = -0.43 to -0.72) with the exception of Zn 4 years after treatment, while earthworm biomass was correlated positively with pH (r = 0.57) and negatively with Pb (r = -0.50) and Ni (r = -0.30) but positively with Zn (r = 0.68) 8 years after treatment.

1.4 Effects of hexane extractable material on composition and biomass of earthworm

United States Environmental Protection Agency [USEPA] 9071B method (1998) was used to extract relatively non-volatile hydrocarbons from 45 lysimeter soils treated twice annually with three levels of four different organic waste materials and pig manure compost tested for 8 consecutive years, as stated in section 4.1. The extracts were generally designated hexane extractable material (HEM) because the solvent used was hexane. Soils were acidified with 0.3 ml of concentrated HCl and dried over magnesium sulfate monohydrate. After drying in a fume hood, HEM was extracted for 4 hr using a Soxhlet apparatus which was attached a 125 ml boiling flask containing 90 ml of hexane. Solvent was then concentrated under vacuum for less than 30 min at 35°C. The extracts were cooled in a desiccator for 30 min, and HEM concentrations were calculated by the formula, HEM (mg kg of dry weight-1) = (A x 1000)/BC, where A is gain in weight of flask (mg), B is weight of wet solid (g) and C is dry weight fraction (g of dry sample g of sample-1).

HEM amounts varied with treatment level and organic waste examined (Fig. 6). At 8 years after treatment, effect of test waste material (F = 49.45; df = 4,14; P < 0.001) and treatment level (F = 4.09; df = 2,30; P = 0.028) on the HEM was significant. The material by level interaction was also significant (F = 2.63; df = 8,44; P = 0.0258). Particularly, the amount of HEM in PMC-treated soil was the lowest of any of test materils at all treatment levels.

Correlation between HEM content (Fig. 6) and earthworm individuals and biomass (Table 2) was determined. At 8 years after treatment, earthworm individuals were negatively correlated with HEM (r = -0.313) and earthworm biomass (r = -0.335).

In general, organic compounds existed in sewage sludge have been potentially transferred to sludge-amended agricultural soils, and most organic compounds have been solved in hexane solvent. HEMs from sewage sludges contain a variety of contaminants, such as hydrocarbons, grease, plant or animal oils, wax, soap, polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) (Hua et al., 2008; Stevens et al., 2003). Drescher — Kaden et al. (1992) reported that 332 organic contaminants (e. g., pyrene, benzo(fl)pyrene, benzene and toluene) with potential to exert soil contamination were identified in German sewage sludges. Hembrock-Heger (1992) found that the concentrations of PAHs and PCBs appeared to be highest in soils treated with sewage sludge for 10 years. According to the United Kingdom Water Research Centre Report No. DoE 3625/1 on the occurrence, fate and behaviour of some of organic pollutants in sewage sludge (Sweetman et al., 1994), there was no evidence of any significant problems arising from organic contaminants in sludges applied to agricultural land.

Of some waste sludge and PMC applied into red pepper fields in South Korea from 2003 to 2004, the highest contents of HEM and PAHs were observed in cosmetic and pharmaceutical industy sludge, respectively, and the cosmetic industry sludge affected remarkedly growth of red pepper, which resulted in 25-60% of yield reduction (Lee, 2006). These results indicate that PMC may contain a lot of polar compounds with functional groups, such as COO-, O-, NR2H, COOH or OH, to be more easily metabolized by various soil-born organisms, including earthworm. Water drained from processing of ISS, LPS, MSS and AFPS may contain more non-soluble compounds than that of PMC. Considering the hexane fraction obtained from PMC containing plentiful P or N atom (Na, 2004), it may be biodegradable by long-term exposure to a variety of soil organisms owing to biological uses. In general, most hydrophobic compounds are accumulative and difficult to biodegrade them introducing into environments because most aliphatic hydrocarbons retain unfavorable large AG (minus value) with increase in chain length.

Balochistan can be divided into two zones regarding precipitation and grazing quality of the rangelands. The northern zone comprises the best ranges of the province located in the districts of Zhob, Loralai, Sibi, Nasirababd, Kohlu, Pishin, Quetta, Kalat, and the northern 18% of Khuzdar area. This zone, equivalent to only 38% of the total province area, carried 76.5% of the provincial livestock. The southern zone comprises the poorest ranges located in the rest of Khuzdar, Chagai, Khanar, Panjgur, Turbat, Gwadar and Lasbela district, which covers 62% of the province and carries only 23.5% of the livestock population (FAO, 1983). The high stocking rate and lack of grazing management in the Northern zone is rapidly depleting these ranges. Geomorphologically, the rangelands in Balochistan can be distributed into six types of landscapes, including mountains, uplands, piedmont, desert, flood plains and coastal plains. Muhammad (1989) divided rangelands of Balochistan into three main categories: Central Balochistan ranges, Western Balochistan Ranges, Eastern Balochistan Ranges. The biomass productivity varies from 30 to 380 kg/ha (Fig. 2.).

5% sedentary (FAO, 1983). Nomadic flocks move continuously in search of forage. They have no agricultural land and migrate from uplands to lowlands in winter and come back again in spring to uplands. In lowlands they purchase generally sorghum crop for animal grazing. The size of a nomadic flock may vary from 200 to 700 sheep and goats. Transhumant flock owners have agricultural land and dryland agricultural activities. In winter some of them also migrate along with the families to lowlands. Sedentary flock owner raise few animals (5-20) on orchards, crop stubbles and also stale feeding. However, these systems are under transformation due to many factors like increase in livestock and human population, water mining for agriculture and orchards, changes in traditional migratory routes due to Afghan war. In a recent study, two new nomads groups (Commercial nomads and Nomad Transhumant) have been identified in Balochistan (MINFAL, 2000).

2.3.1 Nutrition sources

Cellular organisms require specific internal conditions for optimal growth and function. The state of this internal milieu is strongly influenced by chemical, physical and biological factors in the growth environment. Understanding yeast requirements is important for successfull cultivation of yeast in the laboratory but also for optimalization of industrial fermentation process (Walker, 1998). Elemental composition of yeast cell gives a broad indication as to the nutritional reguirements of the yeast cell. Yeasts acquire essential elements from their growth environment from simple food sources which need to be available at the macronutrient level (approx. lO3 M) in the case of C, H, O, N, P, K, Mg and S or at the micronutrient level (approx. 10-6 M) in the case of trace elements. Yeasts are chemoorganotrophs as they use organic compounds as a source of carbon and energy. Yeasts can use a wide variety of substances as nutrient sources. Decreasing availability of one substrate can, in many instances, be compensated by the utilisation of another (Xiao, 2005).

When a single essential nutrient becomes limiting and eventually absent, the cellular proliferative machinery is efficiently shut down and a survival program is launched. In the absence of any one of the essential nutrients, yeast cells enter a specific, non-proliferative state known as stationary phase, with the ultimate aim of surviving the starvation period. In the presence of a poor carbon source, starvation for nitrogen induces sporulation and in the presence of a good carbon source stimulates pseudohyphal growth (Gasch & Werner — Washburne, 2002). Starvation is a complex, albeit common, stress for microorganisms. The nutrients for which a cell can be starved include carbon and nitrogen, with other elements such as phosphate, sulphur, and metals being less commonly evaluated.

The environment presents for yeasts a source of nutrients and forms space for their growth and metabolism. On the other hand, yeast cells are continuously exposed to a myriad of changes in environmental conditions (referred to as environmental stress). These conditions determine the metabolic activity, growth and survival of yeasts. Basic knowledge of the effect of environmental factors on yeast is important for understanding the ecology and biodiversity of yeasts as well as to control the environmental factors in order to enhance the exploitation of yeasts or to inhibit or stop their harmful and deleterious activity (Rosa & Peter, 2005).

In order to improve the yield of carotenoid pigments and subsequently decrease the cost of this biotechnological process, diverse studies have been performed by optimizing the culture conditions including nutritional and physical factors. Factors such as nature and concentration of carbon and nitrogen sources, minerals, vitamins, pH, aeration, temperature, light and stress have a major influence on cell growth and yield of carotenoids. Because carotenoid biosynthesis is governed by the levels and activities of enzymes employed to the total carbon flux through the carotenoid synthesizing system, the efficient formation of carotenoids can also be achieved by construction of hyperproducing strains with mutagenesis and genetic/metabolic engineering (Frengova & Beshkova, 2009).

The efficiency of the carbon source conversion into biomass and metabolites, and the optimization of the growth medium with respect to its availability and price has been subject of intensive studies. Numerous sources including pentoses and hexoses, various disaccharides, glycerol, ethanol, methanol, oils, n-alkanes, or wide variety of wastes derived from agricultural have been considered as potential carbon sources for biotechnological production of carotenoids. Carotenoid pigment accumulation in most yeasts starts in the late logarithmic phase and continues in the stationary phase (typically for secondary metabolites), and the presence of a suitable carbon source is important for carotenoid biosynthesis during the nongrowth phase. Yeasts can synthesize carotenoids when cultivated in synthetic medium, containing various simple carbon sources, such as glucose, xylose, cellobiose, sucrose, glycerol and sorbitol. Studies on carotenogenesis have led to a growing interest in using natural substrates and waste products from agriculture and food industry: grape juice, grape must, peat extract and peat hydrolysate, date juice, hydrolyzed mustard waste isolates, hemicellulosic hydrolysates (Parajo et al., 1998), hydrolyzed mung bean waste flour, sugar cane juice, sugar cane and sugar-beet molasses, corn syrup, corn hydrolysate, milk whey. In recent years, raw materials and by-products of agro-industrial origin have been proposed as low-cost alternative carbohydrate sources for microbial metabolite production, with the view of also minimizing environmental and energetic problems related to their disposal (Frengova & Beshkova, 2009).

The chemical composition and concentration of nitrogen source in medium might also be means of physiological control and regulation of pigment metabolism in microorganisms. Several inorganic and organic nitorgen sources as well as flour extracts and protein hydrolysates have been studied for improvement of carotenoid production. However, it seems that variation in carotene content in yeasts with regard to N-source used in a medium and the rate of pigment production is influenced by the products of catabolism of the nitrogen source rather than being the results of direct stimulation by the nitrogen compound itself (Certik et al., 2009, Somashekar & Joseph, 2000).

XAS specifically examines the local structure of elements in a sample. The structure of a material is deduced on theoretical basis, but usually the interpretation of XAS spectra is founded on databases of known structures. This technique is useful in the case of heterogeneous samples and a wide variety of solid materials can be examined directly and non-destructively. Also the structure of amorphous phases can be easily achieved, as the local structure does not depend on long-range crystalline order. The application of XAS varies from the trace element concentration up to that of major elements. So it is useful to speciate trace elements adsorbed on the surface of biomass. X-ray absorption spectroscopy consists in the absorption of high energy X-rays by an atom in a sample. This absorption takes place at the energy corresponding to the binding energy of the electron in the sample. The interaction of ejected electrons with the surrounding atoms produces the observed spectrum. (XAS) and extended X-ray absorption fine structure (EXAFS) were used to ascertain the ligands involved in metal binding and the coordination environment for Cr3+ bound to alfalfa shoot biomass by Tiemann et al., 1999, and by Gardea-Torresday et al., 2002.

Dry matter yield of potato tubers was significantly modified by the intercrop fertilization, straw fertilization and their interaction (table 5). The highest dry matter yield of potato tubers was collected from the object fertilized with a mixture of white clover with Italian ryegrass, white clover and phacelia used in the form of mulch. Dry matter yield of potato tubers fertilized with phacelia did not differ significantly from the yield recorded on the farmyard manure. Only after the application of Italian ryegrass dry matter yield of potato tubers was significantly lower than that recorded on the farmyard manure. However, in this case, dry matter yield was significantly higher than that obtained on control object, without intercrop fertilization. Straw fertilization also significantly differentiate dry matter yield of potato tubers. On objects with straw dry matter yield of potato tubers was greater than on the objects without straw. There has been an interaction, which shows that the highest dry

matter yield of potato tubers were obtained from the object fertilized with a mixture of white clover with Italian ryegrass in combinations without straw and with straw, white clover in combination with straw, and phacelia used in the form of mulch and also in combination with straw, and the smallest on control object, without intercrop fertilization.

2.1.3 Starch content in potato tubers

Statistical analysis showed a significant effect of examined factors and their interaction on starch content in potato tubers (table 6). Intercrops fertilization of potato, with the exception

of white clover caused a significant increase of starch content in potato tubers in comparison with farmyard manure fertilization. The starch content in potato tubers fertilized with white clover did not differ significantly from that observed in potato tubers fertilized with farmyard manure. However, on control object starch concentration in potato tubers was significantly lower than in tubers fertilized with farmyard manure. An interaction has been noted, which shows that the highest concentration of starch was noted in potato tubers fertilized with phacelia both plowed down in the autumn, and left till spring in the form of mulch in combination without straw and with straw and Italian ryegrass in combination with straw, and the lowest in potato tubers cultivated on control object.

This method is common in Finland, Norway and Sweden. It was introduced in Sweden by Tham (1988) with some modifications by Johansson and Lundh (1991). Currently, the same technique is used for birch and Norway spruce in Finland, Norway and Sweden. The principal aim is to create an initial mixed stand with an optimal density of birch.

The method involves two or three steps:

1. When the spruces are 1.5-2 m tall, the density of birch is reduced by cleaning to 800­1000 stems ha-1.

2. The "birch shelter" is cut when the birches are 30-35 years old with a diameter at breast height (dbh) of 15-20 cm.

3. An alternative is to cut all 30-35-year-old birches except 50-100 stems ha-1. The remaining stems should be evenly spread through the stand. These birches will produce high-quality timber during the following 20 years.

1.1.1 The “Kronoberg” method

This method was first introduced in southern Sweden (Anon., 1985). The aims are to avoid frost damage to Norway spruce plants and to control the number of sprouts that are able to establish after the removal of birch in each step.

The method involves three steps:

1. When the birches are 3-4 m tall the stand is cleaned. A total of 3000-4000 birch stems ha — 1 should be retained. The Norway spruce is not cleaned.

2. When the birches are 6-9 m tall the stand is cleaned again. A total of 1000-1500 birch stems ha-1 should be retained; the dbh of the birches should be about 5 cm.

3. When the birch stand is 20-25 years old the birches are felled. They will be 8-12 m tall with a dbh of 8 cm. The mean height of the Norway spruce will be 3-4 m. The spruce stand should be thinned to 2000-2500 stems ha-1.

Alternatively, instead of felling all the birches, 600-800 birches ha-1 could be left for 10-15 years. When the birches are finally cut, their mean dbh will be 15-20 cm.

The integrator of drought is the plant water status (Jones, 2007), as determined by plant water content or water potential. A direct measurement of these variables is difficult and currently not possible in a high-throughput phenotyping approach. Probably the most commonly used technique in this context is thermal infrared imaging, or infrared thermography (IRT) to measure the leaf or canopy temperature.

Plant canopy temperature is a widely measured variable because it provides insight into plant water status. Although thermal imaging does not directly measure stomatal conductance, in any given environment stomatal variation is the dominant cause of changes in canopy temperature (Jones and Mann 2004).

Thermal imaging is becoming a high-throughput tool for screening plants for differences in stomatal conductance (Merlot et al. 2002). Thermal infrared imaging for estimating conductance has potential value as it can be used at the whole plant or canopy level over time. Leaf temperature has been shown to vary when plants are subjected to water stress conditions. Recent advances in infrared thermography have increased the probability of recording drought tolerant responses more accurately.

In a greenhouse covered with transparent low density polyethylene, in Piracicaba — SP/Brazil, utilizing red, blue, black thermo-reflective screens (aluminized screen) all with 70% shading at 1.0 m above the cultivation bench, Holcman & Sentelhas (2006) evaluated the growth and development of the bromeliad (Aechmea fasciata) and concluded that the red screen resulted in the highest biometric values, however, the thermo-reflective screen was more favorable for the cultivation showing the best microclimate.

Seedlings of jasmine-oranges (Murraya exotica L) in full sun, under a white screen (30% shading) and black screen (50% shading), in Sao Cristovao-SE/Brazil, presented higher emergence in full sun and under the white screen; higher rate of emergence and number of leaves were observed in full sun, and greater dry matter of aerial part was found under both screens (Arrigoni-Blank et al., 2003). It is recommended to produce seedlings of jasmine — orange first in full sun and after emerge under a white screen with 30% shading (Tables 11 and 12).

Table 11. Mean values of the germination rate, plant height and number of leaves of jasmine orange (Murraya exotica) on different substrates and light conditions. Sao Cristovao-SE, 2000. Full sun Clarite® 30% Sombrite® 50% Substrate Dry weight of leaves Soil + sand 1:1 0.067 bcC * 0.142 bB 0.216 aA Soil + vermiculite + cattle manure 1:1:1 0.102 abC 0.232 aA 0.184 abB Soil + sand + cattle manure 1:1:1 0.055 cB 0.150 bA 0.166 bA Sand + cattle manure 1:1 0.131 aB 0.174 bA 0.182 abA Dry weight aerial part Soil + sand 1:1 0.091 bC 0.176 bB 0.271 aA Soil + vermiculite + cattle manure 1:1:1 0.124 abC 0.284 aA 0.223 abC Soil + sand + cattle manure 1:1:1 0.069 bB 0.184 bA 0.130 bA Sand + cattle manure 1:1 0.155 aB 0.218 bA 0.222 abA Dry weight of roots Soil + sand 1:1 0.061 aB 0.090 bB 0.142 aA Soil + vermiculite + cattle manure 1:1:1 0.087 aB 0.177 aA 0.106 aB Soil + sand + cattle manure 1:1:1 0.062 aB 0.117 bA 0.110 aA Sand + cattle manure 1:1 0.095 aB 0.129 bA 0.212 aAB * * Means followed by same uppercase letters in the rows, and same lowercase letters in the columns do not differ by the Tukey test at 5%; Adapted from Arrigoni-Blank et al. (2003)

Table 12. Mean values of dry weight of leaves, dry weight of the aerial part and dry weight of roots of jasmine orange (Murraya exotica) on different substrates and light conditions. Sao Cristovao-SE, 2000.

3. Conclusions

There are diverse crops produced and evaluated with regards to different growing environments, where yields and qualities are influence by the type, size and shape of the environment, covering material, climate, location, seasonality, interactions with containers and substrates and other factors.

Polyethylene film and shading screens used either individually or together, minimize direct radiation to the plant, depending on the format of the environment and the time of day, preventing this radiation from causing damage to plant tissues.

Matrix planting of vegetables, fruit, flowers and forest species, as well as acclimation and production of seedlings often requires initial shading with screens that present different degrees of shading, therefore care must be taken to select the mesh so that it does not cause irregular plant growth.

The protected environment maximizes the productive potential of plants and to obtain successful yields correct management of the environment is necessary along with the use of trained labor.

A diagnose of the occurrence of the SALLJ events for the 2002 was performed based on the modified Bonner’s first criterion for the strength and vertical shear of the wind field (Bonner, 1968; Saulo et al., 2000), using the 6-hourly analysis of the Global Data Assimilation System (GDAS) of the National Centers for Environmental Prediction (NCEP). This data set has one-degree horizontal resolution and is available every synoptic time (0000, 0600, 1200 and 1800 UTC), at 26 vertical pressure levels.

The information about fire spots over South America is obtained with remote sensors and after processing, it is freely available at http://www. cptec. inpe. br. The observations of aerosols in Buenos Aires that could give information of the intrusion of the regional smoke plumes consist on columnar aerosol content and derived quantities obtained from measurements at the CEILAP-BA (34.5° S, 58° W) (Buenos Aires) site of the AErosol RObotic NETwork (AERONET) from National Atmospheric and Science Administration (NASA) (http://aeronet. gfsc. nasa. gov).

The on-line atmospheric transport model CATT-BRAMS (Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System) was used to simulate the atmospheric transport of biomass burning smoke during the dry season of 2002. A detailed description of the CATT-BRAMS system can be seen at Freitas et al., 2009; Longo et al., 2010). The system considers the emission, transport and transformations of particulate matter (PM2.5) and gases (CO) and it is run operatively at Centro de Previsao de Tempo e Estudos Climaticos (CPTEC) with 40 km resolution over South America. It provides 72-hour predictions of the above mentioned aerosols and gases as well as the meteorological fields. Two SALLJ events were selected to perform a more in depth analysis of the transport patterns and the aerosol dispersion. The synoptic environment in which they took place was studied and the resulting spatial and temporal distributions of aerosols obtained with the CATT-BRAMS modelling system for each case were analysed.

3. Results

A. Almodares1, M. R. Hadi2 and Z. Akhavan Kharazian1

1Department of Biology, University of Isfahan, 2Department of Biology, Sciences and Research Branch of Fars,

Islamic Azad University, Iran

1. Introduction

Soil salinity is one of the main problems for plant growth in agriculture, especially in countries where crops should be irrigated (Ahloowalia et al., 2004). Soil salinity has been considered a limiting factor to crop production in arid and semi arid regions of the world (Munns, 2002). Saline soils are estimated about 5 — 10% of the world’s arable land (Szabolcs,

1994) , and the area affected by salinity is increasing steadily (Ghassemi et al., 1995). Salt — affected soils are distributed throughout the world and no continent is free from the problem (Brandy and Weil, 2002). Globally, a total land area of 831 million hectares is salt — affected (Kinfemichael & Melkamu, 2008; FAO, 2000). However, soil salt accumulation can change with time and place, as a function of soil management, water quality (Almodares & Sharif, 2005), irrigation method, and the weather conditions. Salt accumulation is mainly related to a dry climate, salt-rich parent materials of soil formation, insufficient drainage and saline groundwater or irrigation water (Almodares et al., 2008a). Salts in soils are chlorides and sulfates of sodium, calcium, magnesium, and potassium that among them sodium chloride has the highest negative effect on the plant growth and development. Salinity causes slow seed germination, sudden wilting, and reduce growth, marginal burn on leaves, leaf yellowing, leaf fall, restricted root development, and finally death of plants. The inhibitory effects of salinity on plant growth include: (1) ion toxicity (2) osmotic influence (3) nutritional imbalance leading to reduction in photosynthetic efficiency and other physiological disorders. Among agricultural crops, sorghum (Sorghum bicolor L. Moench) is naturally drought and salt-tolerant crop that can produce high biomass yields with low input. Also, it can thrive in places that do not support corn, sugarcane and other food crops. In addition, sweet sorghum has potential uses (six F) such as: food (grain), feed (grain and biomass), fuel (ethanol production), fiber (paper), fermentation (methane production) and fertilizer (utilization of organic byproducts), thus it is an important crop in semi-aired and aired regions of the world. Sorghum is grown on approximately 44 million hectares in 99 countries (ICRISAT, 2009). An estimation of the world-wide tonnage produced in 2007-2008 is shown in Table 1. The increasing cost of energy and deplete oil and gas reserves has created a need for alternative fuels from renewable sources. The consumption of biofule may reduce greenhouse gases. Also it can be replaced with lead tetraethyl or MTBE (Methyl tert-butyl ether) that are air and underground water pollutants,

respectively (Almodares & Hadi, 2009). Plants are the best choice for biofule global demands. Currently, ethanol production is based on sugar or starch of crops such as sorghum, corn, sugarcane, wheat and etc. In comparison with other crops, carbohydrate content of sweet sorghum stalk and its grain starch is similar to sugarcane and corn, respectively but its water and fertilizer requirements are much lower than both sugarcane and corn. Thus, in many tropical and temperate countries where sugarcane and corn cannot be grown, a growing interest is being focused on the potential of sweet sorghum to produce bioethanol feed stock (Almodares et al., 2006, 2008d). Sweet sorghum biomass has rich fermentable sugars such as sucrose, glucose, and fructose so it is an excellent raw material for fermentative production (Almodares et al., 2008d). The total soluble sugars can be increase in sweet sorghum with increasing salinity level and sucrose content could be an indicator for its salt tolerance. (2008b). Salt-stressed sorghum plants additionally accumulate organic solutes, like proline, glycinabetaine, sugars, etc. (Lacerda et al., 2001). These organic solutes may contribute to osmotic adjustment, protecting cell structure and function, and/or may serve as metabolic or energetic reserve (Hasegawa et al., 2000). Inorganic and organic solutes concentrations maintained during salt stress, therefore, they may be important during the salt stress recovery period (Pardossi et al., 1998). Since sweet sorghum is more salt tolerant than sugarcane and corn which currently are the main sources of bioethanol production. Therefore, it is suggested to plant sweet sorghum for biofule production in hot and dry countries to solve problems such as increasing the octane of gasoline and to reduce greenhouse gases.