Keywords

2.1 Introduction

The tropical areas are characterized by hot and humid climate almost throughout the year (Teygeler et al. 2001) with relatively low seasonal fluctuations in temperature and rainfall (Bonebrake and Mastrandrea 2010) and highly prone to soil erosion. Tropical ecosystems are generally known for their highest biological diversity on earth (Gibson et al. 2011). Soil microbial diversity plays a crucial role in maintaining soil health and is considered as key biological, chemical, and physical indicators required for enhancing soil fertility. Microbial diversity is an excellent indicator of soil health (Nielsen and Winding 2002) and collectively helps nutrient cycling and other ecosystem services. However, the impact of microbial diversity on the stability of ecosystem functioning is still debatable (Harrison et al. 1968). Besides nutrient cycling, the soil microbial diversity may also suppress soil-borne diseases (Kennedy and Smith 1995; Van Elsas et al. 2000) and is considered as an indicator of soil health (Visser and Parkinson 1992). In this chapter we provide glimpses of microbial diversity and changes under agroecosystems, techniques, and tools being used to assess microbial communities and diversity and the key influencing factors affecting diversity. Microbial diversity is then correlated with crop and soil agricultural management practices for improving the quality of the soil and sustained crop productivity in agroecosystems.

2.2 Microbial Diversity in Relation to Tropical Agroecosystems

Tropical ecosystems are ranked topmost in terms of biological diversity and are often termed as the house of the highest biological diversity on earth (Gibson et al. 2011; Mandic-Mulec et al. 2015). Microbial biodiversity has been indicative of richness of species which include plants, animals, and microorganisms, whereas microbial diversity is the part that includes protozoa, fungi, fauna, and bacteria. Different types of microorganisms present in soil are assigned different roles by the nature like some are active in nutrient cycling, while others in the suppression of diseases and used as biocontrol agents. In an agroecosystem nutrient cycling is very diverse from unmanaged ecosystem because of agricultural practices such as crop rotation, nutrient management, and application of fertilizers and pesticides.

Tropical agroecosystem is a man-made ecosystem, i.e., distinct from natural ecosystem. Different management practices and biocontrol treatments affect soil microbial community. In case of unmanaged ecosystem, wide losses and gain of nutrients occur (Tivy 1987). Many studies have shown that agricultural and management practices such as agroforestry, organic farming (Bainard et al. 2012), etc. pose least soil perturbation. Reduced tillage (Capelle et al. 2012) and crop rotation (Altieri 1999) exert positive implications on the community structure, composition, abundance, and richness of specific group of organism (e.g. AMF, earthworms) and on soil microbial diversity.

Many authors have described the effect of agricultural practices on AMF functioning. Spore density was identified as the most crucial parameter for assessing tillage-induced changes on AMF. A clear demarcation between no-tillage and conventional tillage was observed due to differences in spore density (Castillo et al. 2006). No-tillage system involves no mechanical disruption of soil as a consequence of which AMF hyphal network remains intact. In case of no-tillage, the availability of AMF infectious propagules increases due to no disturbance of topsoil layers and new roots grow by making way through routes created by previously grown plants (Kabir 2005). The less detrimental effect of tillage has also been observed in case of enzyme activities (which imply better microbial activity), where significant increase in the activities of enzymes like amylase, cellulose, aryl sulfatase, and acid and alkaline phosphatase were observed in case of no-tillage particularly in the surface layer (0–5 cm) (Balota et al. 2004). Soil type was found to play important role in shaping microbial communities as compared to the effect exerted by plant roots; however the authors could not make any assumption about the role of rhizosphere effect in the same study. Higher concentration of PLFAs and soil microbial biomass carbon has been observed in case of no-tillage as compared to conventional tillage particularly for the topsoil layer (Feng et al. 2003).

A brief inference dealing with microbial community changes under different agrosystems has been given in Table 2.1.

Table 2.1 The influence of different systems/conditions on soil microbial communities in agroecosystem
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2.3 Tools Used for Assessing Microbial Community and Diversity in Soil

2.3.1 Structural Profiling Technique

Structural diversity can be defined as the number of species, genes, and communities in an ecosystem (Avidano et al. 2005). Species richness and species evenness describe the structural diversity of a community (Ovreas 2000). Analysis of fatty acid methyl ester/phospholipid-derived fatty acid (FAME/PLFA) profile in soil is being used to detect structural profiling of soil from a specific agroecosystem.

2.3.1.1 Fatty Acid Methyl Ester Analysis/Phospholipid-Derived Fatty Acid (FAME/PLFA) Profiling

FAME/PLFA profiling in soil is a culture independent technique. Analyzing PLFA in soil can help in distinguishing the microbial communities or groups of microorganisms based on fatty acid profiling (Ibekwe and Kennedy 1998). PLFA technique estimates both microbial community structure and biomass size (Kaur et al. 2005). Characterization of bacterial species based on fatty acid profiling (Purcaro et al. 2010) has become necessary to substantiate the results of 16S rRNA gene sequences and hence is being used as one of the biochemical methods to validate the identity of new taxa of unknown microbial cultures. PLFA can work as signatures for specific organisms e.g., Gram-negative or Gram-positive bacteria, methanotrophic bacteria, fungi, mycorrhiza, and actinomycetes (Zelles 1999). To interpret changes in the microbial community structure, simpler methods to determine the fatty acid composition of the microorganisms have been developed such as microbial identification system (MIDI). High-throughput method that uses much smaller solvent volumes than the traditional protocols has been developed recently. The method involved a 96-well solid phase extraction plates and may be useful for laboratories handling large numbers of samples (Buyer and Sasser 2012).

FAME profiles also are of great concern to microbial community structure which can be used to assess the impact of soil storage and community structure. However, that may change due to the temperature at which soils are stored (Petersen and Klug 1994). Butler et al. (2012) reported significant changes in the fungal: bacterial PLFA ratio in soils treated with triclosan. Buyer et al. (2010), while assessing the impact of management practices on PLFA biomarker fatty acids in tomato agroecosystem, observed that cover cropping increased the absolute amount of all microbial groups, but Gram-positive bacteria decreased in proportion under cover crops. The higher soil temperatures under certain treatments also increased the proportion of Gram-positive bacteria. They concluded that the imposed treatments were much more significant than soil temperature, moisture, pH, and texture in controlling microbial biomass and community structure.

Chaudhary et al. (2012) assessed PLFA microbial communities associated with the rhizosphere and bulk soils planted to switchgrass or Jatropha where they found that switchgrass soil contained higher abundance of Gram-positive (i14:0, i15:0, a15:0), Gram-negative (16:1ω5c, 16:1ω7c, 18:1ω5c), and saturated (14:0, 15:0) PLFAs compared to Jatropha soil. On the contrary, Jatropha had a higher abundance of fungal (18:2ω6,9c), 18:1ω9c, 20:1ω9c, and 18:0 PLFAs compared to switchgrass soil. PLFA and neutral lipid fatty acid (NLFA) provide a new and promising tool for the estimation of live AM fungal biomass in soil and roots (Olsson 1999; Olsson and Johansen 2000; Sharma and Buyer 2015) and for ectomycorrhizal fungi where 18:2ω 6,9 dominates among the fatty acids and can be used as an indicator for these fungi in soil in experimental systems (Olsson 1999) (Table 2.2). Soil FAME were found to be most responsible for differentiation among cropping systems including 12:0, 16:1 ω5c, 16:1 ω7c, 18:1 ω9c, and 18:2ω6c fatty acids.

Table 2.2 Fatty acid biomarker groups
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2.3.1.2 Molecular Genetic Profiling

The genetic diversity of microbial community can be assessed by measuring the heterogeneity of the DNA from the entire microbial community targeting 16S rDNA gene, the conserved sequence of eubacterial communities, by using (1) RFLP (restriction fragment length polymorphism), (2) T-RFLP (terminal-restriction fragment length polymorphism), (3) polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and PCR-temperature gradient gel electrophoresis (PCR-TGGE), (4) single-strand conformation polymorphism (SSCP), and (5) amplified ribosomal DNA restriction analysis (ARDRA) (Sharma et al. 2010).

2.3.2 Functional Profiling Techniques

The functional diversity analysis of microbial community is based on different techniques like catabolic profiling (substrate utilization), CLPP (community-level physiological profiling), and substrate-induced respiration. Nutrient cycling performed by different groups of bacteria represents one of the important components of microbial functional diversity. While the whole bacterial community represents the specific carbon utilization pattern, measurement of enzyme activity profile reveals the diversity (Nielsen and Winding 2002). Carbon substrate utilization by different microbial community is one of the approaches for examining the functional and metabolic diversity.

2.3.2.1 Catabolic Profiling Based on Substrate Utilization

A simple approach to measure functional diversity is to examine the number of different carbon (C)-substrates utilized by the microbial community. Zabaloy and Gómez (2005) carried out a diversity analysis of 76 rhizobial strains isolated from Argentine agricultural soils using C- utilization patterns. Similarly in India Sharma et al. (2010) showed a remarkable diversity among soybean rhizobia based on 15 carbon sources utilization pattern where isolates were matching with reference strains. Such analysis suggests the occurrence of metabolically distinct types of rhizobia, besides commonly known taxonomic identity (B. japonicum, B. elkanii, and S. fredii) (Sharma et al. 2010). Besides substrate utilization pattern based on C, the community-level physiological profiling by BIOLOG® plate method (Garland and Mills 1991) and in situ substrate-induced respiration (SIR) (Degens and Harris 1997; Campbell et al. 2003) are equally important tools to assess the communities.

2.3.2.2 Community-Level Physiological Profiling (CLPP)

CLPP method is based upon the Biological plate method using a range of 95 carbon substrates which applies for microbial community function and functional adaptation over space and time (Garland and Mills 1991). Difference between the communities can be compared and classified based on sole carbon source utilization pattern (CSUP) using BIOLOG® system in the late 1980s. CLPP involves inoculating mixed microbial community into BIOLOG®-GN (for Gram-negative bacteria), GP (Gram-positive bacteria), or 31 (ECO plates) with single carbon sources in addition to tetrazolium dye. The utilization of carbon source indicates respiration-dependent reduction of the tetrazolium dye and purple color formation that can be quantified and monitored over time. Being a culture-dependent technique, a wide variety of culture media have been used which maximizes the recovery of diverse microbial groups.

2.3.2.3 Substrate-Induced Respiration (SIR)

Anderson and Domsch (1973) introduced SIR method for measuring the total, fungal, and bacterial biomass in a short time (less than 6 h). SIR method involves activation of the microbial population in soil by the addition of a readily decomposable respiratory substrate (usually glucose), and the resulting initial maximal respiration is used for estimation of microbial biomass (Nakamoto and Wakahara 2004). West and Sparling (1986) proposed the method of SIR for biomass estimation using individual flask/bottles with soil sample added with various carbon substrates followed by measurement of CO2 produced in 4 h in each flask. It can be measured by using GC, infrared spectroscopy, or some other suitable assays. It measures the small fraction of the microbial community that can grow within the microtiter plate well. MicroRep™ (Campbell et al. 2003) was designed for a “whole soil” analysis for distinguishing the fungal and bacterial population.

2.4 Microbial Diversity Index

Species diversity at a community level is measured by mathematical diversity index. Diversity index provide more information about species richness and evenness. The number of species per sample is called richness, and evenness is a measure of relative abundance of different species in an area. Species diversity index relates the number of species and the relative importance of individual species. Species diversity is measured by Simpson index which is dominant index as it gives more weight to common or dominant species. The most widely used method of diversity is the Shannon–Weaver index (Shannon and Weaver 1963) which is sensitive to sample size, especially small size.

2.4.1 Simpson Diversity Index

Simpson index was first introduced by Edward H. Simpson in 1949 and was redescribed by Orris Herfindahl in 1950 where the degrees of concentration between the individuals are classified into index categories (Simpson 1949). Simpson diversity index refers to closely related indices such as Simpson index (D), Simpson index of diversity (1−D), and Simpson reciprocal index (1/D). We can calculate Simpson index for two individual random samples which belong to the same species. It is denoted by D, the value of which lies between 0 and 1. 0 represents infinite diversity and 1 represents no diversity; the bigger the value of D, the lower the diversity. D is often subtracted from 1.

$$ D=\sum {\left(n/N\right)}^2\kern0.5em \mathrm{or}\kern0.5em D=\sum n\left(n-1\right)/N\left(N-1\right) $$

  • n = The total number of organism of particular species

  • N = The total number of organism of all species

  • D = Simpson index

Inverse Simpson index and Gini–Simpson index (Hill 1973; Jost 2006) are called as Simpson index in the ecological literature.

$$ \mathrm{Inverse}\kern0.5em \mathrm{Simpson}\kern0.5em \mathrm{index}=1/D. $$

Gini–Simpson index measures the probability of two individuals belonging to the same species. But this aspect of compositional complexity does not seem to occupy the same place with biologically meaningful concept of diversity.

$$ \mathrm{Gini}\hbox{-} \mathrm{Simpson}\ \mathrm{index}=1-D. $$

2.4.2 Shannon Diversity Index

It is sensitive to both species richness and relative abundance and is very sensitive to sample size especially for small sample. The Shannon entropy quantifies the uncertainty (entropy or degree of surprise) associated with this prediction. It is most often calculated as follows:

$$ \mathrm{Shannon}\hbox{-} \mathrm{Weaver}\kern0.5em \mathrm{index}\kern0.5em \mathrm{of}\kern0.5em \mathrm{diversity}\ (H)=C/N\left(N\;\log\;N\backslash -\sum {n}_i\;\mathrm{v}\;\log\ {n}_i\right) $$

where

  • C = 2.3, N = number of individuals, n i  = number of individual ith species

  • Species richness (d) = d = S − 1/ log N

where

  • \( S=\mathrm{numbers}\kern0.5em \mathrm{of}\kern0.5em \mathrm{species} \)

  • \( N=\mathrm{numbers}\kern0.5em \mathrm{of}\kern0.5em \mathrm{individuals}, \)

  • \( \mathrm{Species}\kern0.5em \mathrm{evenness}\kern0.5em (e)=e=\overline{H}/\log \kern0.5em S \)

where

  • \( \overline{H}=\mathrm{Shannon}\hbox{-} \mathrm{weaver}\kern0.5em \mathrm{diversity}\kern0.5em \mathrm{index} \)

  • \( S=\mathrm{number}\kern0.5em \mathrm{of}\kern0.5em \mathrm{species} \)

2.5 Soil Health and Its Indicators

Soil health is defined as the continued capacity of soil to function as a vital living system by recognizing that it contains biological elements which are key to ecosystem function within land-use boundaries (Larson and Pierce 1994; Doran and Zeiss 2000; Karlen et al. 2001). Soil quality and soil health are interchangeable terms (Karlen et al. 2001), although it is important to define that soil quality defines soil function (Karlen et al. 2003), whereas soil health describes a finite nonrenewable and dynamic living resource (Doran and Zeiss 2000). Soil health concept directly includes the interaction between soil and plant and create healthy environment. Soil health is maintained by major soil health indicators that support the soil for crop productivity. A healthy soil is a stable soil that supports high microbial diversity with high levels of internal nutrient cycling (Elliott and Lynch 1994). These functions are able to sustain biological productivity of soil, maintain the quality of surrounding air and water environments, as well as promote plant, animal, and human health (Doran et al. 1996). Soil health is measured by a complex set of indicators i.e., biological, chemical, and physical. These indicators help to assess the soil condition and quality status capable of performing various functions required to support the plant growth under a particular set of conditions. In general soil health is dependent on the maintenance of four major functions: (C) carbon transformations, nutrient cycles, soil structure maintenance, and the regulation of pests and diseases.

2.5.1 Biological Indicators

Biological indicators of soil health that are commonly measured include soil organic matter, respiration, microbial biomass (total bacteria and fungi), and mineralizable nitrogen. Soil organic matter plays a key role in soil function determining soil quality, water holding capacity, and susceptibility of soil to degradation (Giller et al. 1997) (Table 2.1). In addition, since soil organic matter plays a key role in soil function and may serve as a source or sink to atmospheric CO2 (Lal 1997), an increase in the soil C content is indicated by a higher microbial biomass and elevated respiration (Sparling et al. 2003). Giller et al. (1997) measured soil health by respiration, enzyme activity, and microbial biomass (total bacteria and fungi) (Sparling et al. 2003).

2.5.2 Soil Physical Indicators

Soil physical indicators describe the soil texture, bulk density, aggregation, stability, and water holding capacity (Hillel 1982) (Table 2.3). Higher bulk density like compact soil layer restricts the root growth and movement of root toward the gravity and inhibits the movement of air and water through the soil (Enriqueta-Arias et al. 2005). Tillage decreases the aggregate stability and bulk density temporally and reduces the mycorrhizal biomass (Sharma et al. 2012). Aggregate stability is an indicator of organic matter content, biological activity, and nutrient cycling in soil. All these properties are influenced by the addition of organic matter into the soil. Soil aggregation is influenced by the mycorrhiza which produces glycoprotein (glomalin) that bind the soil particle together and are thus highly correlated with aggregate stability (Woignier et al. 2014).

Table 2.3 Commonly used indicator of soil health (Brevik 2009)
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2.5.3 Chemical Indicators

Chemical indicators specify the capacity of soil to supply mineral nutrients which depends on the soil chemical nutrient profile. For example soil pH, a measure of hydrogen-ion concentration in solution measures acidity or alkalinity of soil solution. Soil pH affects the activity of microorganisms involved in nutrient cycling and also solubility of nutrient that is used for plant productivity. Ion exchange capacity mostly affects soil cation exchange capacity (CEC) binding to negative charge organic matter, clay, and soil colloid. Amount if ions (dissolved salts) in solution are the indicator of electrical conductivity in soil–water mixture (Arias et al. 2005).

2.6 Strategies to Improve Soil Health by Managing Microbial Community and Diversity

2.6.1 Crop and Soil Management Practices

Key crop and soil management practices like crop rotation/crop sequences, intercropping, and tillage practices influence the microbial diversity and functioning in the soil ecosystem processes. Leguminous crops included in the crop rotation generally tend to increase soil fertility and crop yield. Disease suppression can be influenced by cropping and management practices, like monocropping of cauliflower suppressed R. solani infestation assessed in a long-term field trial (Huber and Watson 1970). In semiarid areas, continuous cropping reduces the level of soil organic matter and microbial biomass in soil (Campbell et al. 2000). Rotating crops with nonhost or less susceptible plants have caused a decline in the specific pathogenic populations due to their natural mortality and the antagonistic activities of other microorganisms (Kurle et al. 2001). Sharma et al. (2012) showed the importance of including maize in rotation with soybean under conventional reduced tillage helped in enhancing soybean yield, AM inoculum load, and organic carbon. Long-term sugarcane growing under intensive cropping system with intercrops of pulse crops and incorporation of their labile C substrates improved N mineralization. The buildup of the C pool and microbial biomass C in the case of cereals, mustard, and potato intercropping would promote long-term stability. In an irrigated maize (Zea mays)–wheat (Triticum aestivum) system nontraditional mulching (Sesbania, Jatropha, and Brassica) increased yields by >10% and net returns by >12% compared with no-mulch and improved water potential and soil biological properties (Jat et al. 2015).

Besides crop sequences, tillage is also important factor influencing the diversity. Adoption of conservation tillage increases the soil aggregation (Lal 2004) and mycorrhizal biomass and population in soil (Sharma et al. 2012; Douds et al. 1995). Tillage can disrupt the soil network formed by AMF filaments and colonized root systems left by previous AM crops, affecting the potential colonization of subsequent crops. The AMF hyphae can remain viable for long and wait for colonization with newly germinated plants and provide plants to acquire more nutrients and water. Sharma et al. (2012) evaluated the impact of tillage practices and crop sequences on AM fungal propagules and soil enzyme activities in a 10-year long-term field trial in Vertisols of soybean–wheat–maize (S–W–M) cropping system where S–M–W or S–W–M–W rotations under reduced-reduced tillage system showed higher soil dehydrogenase activity and fluorescein diacetate hydrolytic activity compared to other combinations. The inclusion of maize in the rotation irrespective of tillage systems showed comparatively higher mycorrhizal and higher phosphatase activities and organic carbon and maintained higher soybean yield.

2.6.2 Microbial Inoculations

Microbial inoculants are used to improve crop productivity in terms of managing nitrogen and phosphorus economy (Adhya et al. 2015). PGPR (plant growth-promoting rhizobacteria) have multifunctional activity known for survival of plants and their growth. Some bacteria and fungi are being used as microbial inoculants like Rhizobium, Azotobacter, and mycorrhizal fungi (Table 2.4). These are used for enhancing N and P nutrition in plants which are the most limiting crop nutrients. The occurrence of rhizobia (species of Rhizobium, Mesorhizobium, Bradyrhizobium,

Table 2.4 List of crops and strain for bio-inoculation
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Azorhizobium, Allorhizobium, and Sinorhizobium) plays a symbiotic role with the leguminous plant as inoculants for sustainable growth of pulses. For the improvement of root biomass in chickpea, co-inoculation of Rhizobia with Bacillus megaterium at flowering stage (Aparna et al. 2014; Wani et al. 2007) was found to be useful. Arbuscular mycorrhiza (AMF) is used as inoculum for P uptake and stabilization of soil particles eventually to improve upon soil structure. Using microbial inoculants to improve crop productivity especially in terms of N- and P-economy has been explored for quite some time (Adhya et al. 2015). A rhizobacterial strain Pseudomonas putida GAP-P45 improved plant biomass, relative water content, and leaf water potential in maize plants exposed to drought stress (Sandhya et al. 2010). The possible mechanism by which PGPR stimulate plant growth through nitrogen fixation, solubilization, and mineralization of various nutrients in plant parts produces various plant growth hormones and ACC deaminase, antagonism against plant pathogens, and ability to trigger tolerance to various abiotic stresses (Glick et al. 2007).

AMF hyphae stabilizes soil particles into aggregates, both by enmeshing them and releasing a glue-like substance called glomalin, which holds them together and stabilizes them. Among soil biological indicators, AM-derived glomalin is comparatively new and is considered as one of analytical based potential bioindicators contributing toward enhanced ecosystem productivity as a result of improved soil aeration, drainage, and microbial activity (Lovelock et al. 2004) and for assessing the sustainability of long-term cropping systems.

2.7 Future Perspectives and Conclusion

In the tropical agroecosystem, high temperature and humidity affect crop production. Microbial diversity is the key element required for maintaining soil health. The crop and soil management practices of an agroecosystem need to be customized to harbor favorable microbial community essentially needed to perform soil processes and sustain crop and soil productivity. Microbial diversity maintains soil health by suppressing the population of disease-causing organisms; improves nutrient cycling, etc. and can be enhanced by soil amendments and management practices such as conservation tillage, composting/organic amendments, manuring and fertilizers, crop rotation/crop sequences, etc. Identifying sustainable soil management practices through analyzing soil biological, chemical, and physical indicators of management practices being followed in an agroecosystem will help in optimizing a favorable system needed for sustaining the crop and soil productivity of a particular ecosystem.