Any comparison of the impacts of organic and conventional farming systems on biodiversity is likely to be problematic, largely as a result of the complexity of, and interactions between, the range of farming practices that comprise the two systems. The majority of studies seek to minimize apparently extraneous variation, unrelated to farming system with varying degrees of rigor and success. Some studies then go further, attempting to control for variation in crop-type, non-crop habitat or tillage method, either statistically or within a paired field/farm design. Others consider that such variation is part of the overall difference between regimes. The studies reviewed here comprise both extremes of this spectrum, potentially complicating any unbiased assessment. Above all, tool to measure the difference should be of high resolution so that clear-cut difference should be made.
Microbial diversity can be classified into genetic, functional and structural diversity. Soil genetic and structural diversity can be measured by various techniques. Genetic diversity of bacteria is most commonly studied by diversity of the 16S rDNA genes using Denaturing Gradient Gel Electrophoresis (PCR-DGGE) and Temperature Gradient Gel Electrophoresis (PCR-TGGE). Terminal Restriction Fragment Length Polymorphism (T-RFLP) (Liu et al. 1997) is an alternative method for examining diversity of 16S rDNA sequences of microbial communities. Structural diversity is measured by estimating PLFAs profile. The benefit of a high genetic diversity is currently under debate because it is not always correlated to functional diversity (2001, Griffiths et al. 2000).
The diversity of functions within a microbial population is important for the multiple functions of a soil. The functional diversity of microbial communities has been found to be very sensitive to environmental changes (Zak et al. 1994; Kandeler et al. 1996, 1999). However, the methods used mainly indicate the potential in vitro functionality. Functional diversity of microbial populations in soil may be determined by either expression of different enzymes (carbon utilization patterns, extracellular enzyme patterns) or diversity of nucleic acids (mRNA, rRNA) within cells, the latter also reflecting the specific enzymatic processes operating in the cells. Indicators of functional diversity are also indicators of microbial activity and thereby integrate diversity and function.
Carbon utilization patterns can be measured by the BIOLOG™ assay (Garland and Mills 1991). The result of the assay is a qualitative physiological profile of the potential functions within the microbial community. The BIOLOGTM assay has been shown to be more sensitive than microbial biomass and respiration measurements to impacts of soil management practices and of sewage sludge amendments to soil (Bending et al. 2000).
The enzymatic activity in soil is mainly of microbial origin, being derived from intracellular, cell-associated or free enzymes. Only enzymatic activity of ecto-enzymes and free enzymes is used for determination of the diversity of enzyme patterns in soil extracts. Enzymes are the direct mediators for biological catabolism of soil organic and mineral components. Thus, these catalysts provide a meaningful assessment of reaction rates for important soil processes. Enzyme activities can be measured as in situ substrate transformation rates or as potential rates if the focus is more qualitative. Enzyme activities are usually determined by a dye reaction followed by a spectrophotometric measurement.
Indicators of microbial activity in soil represent measurements at the ecosystem level (e.g., processes regulating decomposition of organic residues and nutrient cycling, especially nitrogen, sulfur and phosphorus). Measurements at the community level include bacterial DNA and protein synthesis. Frequency of bacterio-phages is a measurement at the population level.
Synthesis of DNA is a prerequisite for bacterial cell division and, as such, an indicator of bacterial growth. DNA is unique in the way that it only participates in cell division. DNA synthesis can be determined by incorporation of 3H- or 14Cthymidine into bacterial DNA as thymidine is a unique nucleoside, which only participates in DNA synthesis.
Bacterial protein synthesis is directly correlated to bacterial activity and can be determined by incorporation of 3H or 14C leucine, as this amino acid is incorporated into proteins only. The method for leucine incorporation (Baath 1994) is the same as for thymidine incorporation in case of DNA synthesis and the incorporation of both precursors can be carried out in a single assay if different radiolabels are used.
The RNA molecules, ribosomal RNA (rRNA) and messenger RNA (mRNA) play key roles in the protein synthesis. The amount of RNA in individual cells or in a community may, therefore, be taken as an indicator of protein synthesis and, thus, microbial activity. The number of active cells can be detected by fluorescent in situ hybridization (FISH) (Amann et al. 1995). By this method, individual cells carrying high concentrations of rRNA, situated on ribosomes, are quantified by fluorescence microscopy. The amount of rRNA in a community can also be detected by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR), where rRNA extracted from soil is detected by creating a DNA copy and separating by gel electrophoresis (Duineveld et al. 2001).
mRNA molecules are gene copies used for synthesis of specific proteins by the cell. The nucleotide sequences of mRNA molecules reflect the type of enzymes synthesized. Concentration of mRNA is correlated with the protein synthesis rate and as such with the activity of the microorganism. Therefore, the content and diversity of mRNA molecules will give very accurate pictures of the in situ function and activity of the microbial community. Detection and quantification of a specific mRNA molecule can be done by reverse transcription PCR (RT-PCR), which is a very sensitive method (Pfaffl and Hageleit 2001). A prerequisite of this technique is knowledge of the nucleic acid sequence of the mRNA for a specific gene. For certain genes, this information is available. However, the technique of quantifying mRNA is still in its developmental stage. Sensitivity of the method has though been improved by associating a magnetic capture system (Lleo et al. 2001).
PCR-based fingerprinting techniques give a higher resolution and provide information about changes in the whole community structure. These fingerprinting techniques, such as PLFA analysis, denaturant gradient gel electrophoresis (DGGE), amplified rDNA restriction analysis (ARDRA), T-RFLP and ribosomal intergenic spacer analysis (RISA), provide information on the species composition, and can be used to compare common species present in samples. However, there are some problems and biases in the PCR amplification step and, therefore, these methods cannot be used as definite indicators of species richness.
Perhaps the greatest challenge facing microbiology today is the problem of linking phylogeny and function. The methods based on 16S rRNA analysis provide extensive information about the taxa present in an environment, although they provide little insight into the functional role of each phylogenetic group. Metagenomic analysis provides some functional information through genomic sequence and expression of traits, but other methods are required to link specific functions with the group responsible for them. The concomitant quantitative and comparative analyses of expressed rRNA genes and genes for key enzymes in relation to environmental factors can be used to obtain information about the phylogeny and ecology of functional bacterial groups responsible for processes like denitrification, nitrification and methane oxidation.
All the tools have their own limitations especially in the context of functional diversity (Muyzer and Smalla 1998; Heuer et al. 2001; Nannipieri et al. 2003). Different techniques vary in their resolutions (Fig. 10.2).
Studies of sequence information from organisms in soil microhabitats and their gene expression under different management conditions will provide guidelines for designing new and improved culturing methods that resemble their natural niches.
New tools in bioinformatics and statistical analysis enable us to handle the huge amount of data obtained through multidimensional studies that combine growth independent molecular analyses with analyses of microbial growth, activity and physiology, and integrate measures of environmental parameters. Such polyphasic studies integrate different aspects of microbial diversity and provide a more complete picture of microbial diversity and a deeper understanding of the interactions in soil microbial ecosystems. Studies of microbial sequences, comparative genomics and microarray technology will improve our understanding of the structure/ function relationships and the effects of abiotic and biotic factors on soil microbial communities. It is conceivable that with these new tools it is possible to differentiate shifts in community structure.
Few studies have been done to compare the functional diversity of microbes under organic and conventional management practices. Functional diversity of microbes in organic soil was studied using molecular tool (Stark et al. 2007; Sekiguchi et al. 2007; Postma et al. 2008). Sekiguchi et al. (2007) investigated to find out difference in fungal community structure using DGGE and found no difference between organic and conventional soil. In case of bacterial communities greater diversity was reported by Marinari et al. (2006), Melero et al. (2006) and Widmer et al. (2006). In a lupin amendment study, Stark et al. (2007) analyzed microbial community structure of actinomycetes and eubacteria using PCR-DGGE of 16S rDNA and found differed significantly between the two soils with 4 and 8 Mg ha-1 after long-term application not by short-term incubation. Differences between the integrated, compost and mineral soils can be attributed to the weaker and less abundant bands in PCR-DGGE (Ros et al. 2006).
Effects of fertilization on the soil community fatty acid methyl esters (FAMEs) were apparent by the second year of the study. Compost-fertilized plots were distinguishable from mineral-fertilized soil (Carpenter-Boggs et al. 2000). Bacterial PLFA were unaffected by management practices, whereas fungal PLFA were greater in organic soil than conventional (Yeates et al. 1997). PLFA profile in organically managed soil differed from other treatment (Elfstrand et al. 2007). PLFA composition of the organic and conventional soils clearly differed in their mole percentages of numerous fatty acids (Lundquist et al. 1999). Typical Gramnegative bacteria PLFA biomarkers were significantly higher than conventional treatment (Peacock et al. 2001).
Was this article helpful?