INTRODUCTION

The microbial decomposition of lignocellulosic agricultural wastes is recently recognized as a major renewable resource, with potential for conversion to a variety of products such as soil biofertilizers, industrial enzymes, animal feeds and biofuels (Howard et al., 2003). However, cellulose crystallinity and lignin - which offers cellulose physical protection against cellulolytic enzymes-limit the decomposition of polysaccharides present in lignocellulose residues for biotechnological application (Lynd et al., 2002). Rice straw is one example of the lignocellulosic wastes that is often disposed by biomass burning; a practice which is not restricted to developing countries alone, but is considered a global phenomenon (Levine, 1996). Rice straw composed mainly of cellulose and hemicellulose encrusted by lignin, in addition to a small amount of protein which makes it high in C:N ratio and persistent to microbial decomposition compared to straw from other protein-rich grains such as wheat and barely (Parr et al., 1992).

Many actinomycetes can degrade cellulose and solubilize the lignin structure as their primary metabolic activity at high nitrogen levels compared to white rot fungi (Eriksson et al., 1990). Previous studies shown that actinomycete strains, particularly Streptomyces, Micromonospora and Nocardioides are capable of attacking the lignocellulosic components of rice straw, causing significant release of carbon and to degrade synthetic organic dyes that are structurally related to lignin (El-Shatoury et al., 2005a, b; Abdulla and El-Shatoury, 2006). The addition of sludge, as a rich source of nitrogen and labile carbon, enhanced the decomposition of straw by the above mentioned actinomycete strains and was suggested to accelerate its incorporation into soil.

Traditional methods of optimizing straw decomposition have involved changing one variable, while fixing the others at a certain level. This single dimensional approach is time consuming and fails to locate optimal conditions because it doesn't consider the effect of possible interactions between variables. Factorial design and response surface analysis, first described by Box and Wilson (1951), is an experimental strategy for seeking the optimum conditions for a multivariable system. This technique has been widely used in applied sciences such as optimizing enzyme production and assessment of drug interactions (Kalil et al., 2000); however, it has not been well exploited to optimize decomposition of agriculture wastes by microorganisms. Although few previous studies have focused on response surface optimization of lignocellulosic enzymes in fermentation media (Cacchio et al., 2001; Trupkin et al., 2003), this is the first report-to the best of our knowledge-regarding optimization of agriculture wastes incorporation into soil.

The goal of this study is to study the effect of various ratios of sludge, rice straw and M. chalcea inoculum on accelerating the straw decomposition in soil and to investigate the extracellular enzyme profile of M. chalcea that may contribute to accelerating the decomposition process. Response surface optimization technique was applied to determine the optimal conditions for accelerating straw incorporation into soil.

MATERIALS AND METHODS

Microorganism and Inoculum Preparation
Micromonospora chalcea strain isolated by Abdulla and El-Shatoury (2006) was used in the experiment. The inoculum was produced from spore suspension preserved at -20°C, inoculated into yeast extract dextrose broth and incubated at 28°C, 100 rpm for 5 days. Mycelia were harvested by centrifugation, homogenized, washed twice in phosphate buffer and used for straw incorporation and enzyme profiling experiments.

Straw Incorporation Factorial Design
A Box-Wilson Central Composite Design (CCD) with five levels for each of three factors (straw, sludge and inoculum size) was studied which required 20 experimental runs, including 6 central points (Table 1). Central points provide additional degree of freedom for error estimation, which increase power when testing the significance of effects (Carvalho et al., 1997). All runs were performed in duplicates using 17x12x7 cm plastic pots containing 100 g sandy soil.

Table 1:	Values for the 20 runs in the CCD

Table 2:	Activity of extracellular enzymes from Micromonpspora chalcea measured on solid media

Table 3:	Activity of extracellular enzymes from Micromonpspora chalcea measured using API-ZYM® system
a Zero corresponds to a negative reaction, 5 to a reaction of maximum color intensity. For the purpose of this study, values were reported as low activity (1), moderate activity (2-3) and high activity (4-5)

Thermally treated municipal sludge (to eliminate pathogens), coarsely chopped rice straw and M. chalcea harvested mycelia were mixed thoroughly with soil in the pots according to the corresponding levels for each run and incubated under laboratory conditions. Moisture content was adjusted gravimetrically at 40-50% by water spraying and mixing. Organic carbon was detected by wet oxidation using modified Walkely-Black method (Alf and Nannipieri, 1995). Samples were analyzed at intervals and statistical analysis was performed using multiple regressions and ANOVA using Minitab v12 statistical computing package.

Enzyme Profiling
Extracellular enzyme profiling was determined on solid media and using the API-ZYM^® system (bioMérieux). Extracellular enzymes detection on solid media (Table 2) was performed according to the methods of (Wollum II, 1982; Williams et al., 1983). Enzyme screening plates were inoculated with 5 ΜL of M. chalcea mycelia suspension and scored for enzymatic activities after incubation at 28°C for 7-14 days.

The API-ZYM^® system (bioMérieux) is a semi-quantitative method allowing examination of 19 hydrolysates (Table 2) in a strip with a series of microcupules containing dehydrated chromogenic substrates of 19 different enzymes against a control (substrate free) microcupule. A 65 μL of M. chalcea mycelia suspension were dispensed into each of the 20 microcupules of the API-ZYM^®, incubated and color reactions were performed according to the producer instructions. Readings were scored using the API-ZYM^® color chart ranging from 0 (negative) to 5 (maximum) (Table 3).