Soils are invaluable resources linked to agriculture, human health, and food security. At present, soil health is under threat due to rapid climate change, erosion, contamination with toxic heavy metals or pesticides, nutrient depletion, salinization, and compaction. Soils harbor a plethora of microbes that are strong indicators of their health. Technological advancements have enabled scientists to design different techniques to assess soil health based on microbial abundance or functions.
The Benefits and Challenges of Using Soil Microbes as Indicators of Soil Health
Soil microbes are a rich source of information about soil health. These microbes are not only associated with important soil processes but indicate biotic and abiotic soil stresses. Alterations in soil microbial communities are linked with changes in soil pH, phosphorous availability, and moisture levels.
Identifying particular microbial taxa and functional genes can help determine the changes in soil characteristics and processes (e.g., nitrification, denitrification, methane production, and cellulose degradation) that indicate soil health. The advancements in DNA-based microbial analyses have particularly helped to assess soil health based on the soil microbiome.
It must be noted that it is extremely difficult to analyze and interpret the complexity of the microbial composition and its functional attributes. Not all microbial taxa or genes will be commonly available in all types of soil. Even if particular taxa increase or decrease based on phosphorus availability at a single site, it does not mean that the same taxa will respond similarly in different soils. This presents the challenge of validating microbial taxa that consistently indicate a particular aspect of soil health, irrespective of soil type. To overcome this limitation, a comprehensive, cross-site soil analysis helps identify robust microbial bioindicators.
Appropriate identification of microbial bioindicators greatly simplifies the process of soil health assessment. For instance, detecting the presence/absence of a particular microbial community in the soil indicates soil contamination with heavy metals, which is essential for managing soil pollution. Identifying heavy metal contamination based on microbial analysis is a cost-effective and quick method.
Techniques to Identify Soil Microbiome to Determine Soil Health
Common techniques to determine microbes and their functioning are Denaturing Gel Gradient Electrophoresis (DGGE), Phospholipid Fatty Acid Analysis (PLFA), Amplified Ribosomal DNA Restriction Analysis (ARISA), and Terminal Restriction Fragment Length Polymorphism (TRFL). Molecular techniques enable the determination of soil microbiome composition at a coarser level, which helps assess how individual taxa respond to particular conditions. Application of molecular omics and sequencing technologies have helped uncover the soil-microbe interaction at a higher resolution. Some of the common methods used to determine soil microbiome to assess soil health are discussed below:
Soil microbes release carbon dioxide as a byproduct of metabolism. These flushes of carbon dioxide release are quantified using standard methods. Typically, an increased microbial respiration indicates better soil health. A higher soil respiration measure implies the building of organic matter, which acts as a food source for their activity. Increased respiration is associated with reduced tillage, organic amendment, and residue retention.
Phospholipid Fatty Acids (PLFAs):
Cellular membranes are composed of PLFAs, i.e., phospholipid heads and fatty acid tails. PLFA indicates the measure of living microbial community in soil because these cellular membrane components quickly degrade after cell death. Individual PLFAs are detected based on the number of carbon atoms in the fatty acid tails. The bond saturation and conformation also help identify a specific PLFA.
Although it was originally hypothesized that different soil microbial communities, such as Gram positive or negative bacteria, actinomycetes, and arbuscular mycorrhizal fungi, can be distinguished by their PLFA fingerprint, this observation has been contradicted by the latest research.
Recent studies have shown that PLFA fingerprints of individual microbes may change based on environmental conditions. Therefore, PLFA data cannot be considered to be an accurate measure of the presence of a particular microbial community in soil.
Enzyme activity determines the soil’s capacity to degrade organic molecules. In this regard, the potential activity of four enzymes, i.e., N-aceytl beta-glucosiminidase, Beta-glucosidase, arylsulfatase, and phosphomonoesterase, are measured. These enzymes use similar pathways to hydrolyze specific bonds and degrade organic compounds.
Phosphomonoesterase initiates the release of phosphates, making it available for crops, while arylsulfatase catalyzes the release of sulfate. N-acetyl beta-glucosiminidase catalyzes chitin degradation and beta-glucosidase hydrolyzes beta-glucosidic bonds in cellulose and releases glucose molecules.
DNA provides insights about the structure of microbial communities and their function. However, this is a multi-step process that requires specific DNA sequences of bacterial, fungal, and archaeal origin to identify specific microbial community members.
To detect bacterial or archaeal communities in the soil, 16S rRNA amplicon sequencing is performed, while for fungal communities ITS or 18S rRNA sequencing technique is used. These sequences are matched against the existing database to identify the presence of an individual microbe. Alteration in microbial communities could result from management practices, a row-cropping system, or stress.
Genome-Assisted Molecular Tools
The five common genome-assisted molecular tools are genomics, metaproteomics, metatranscriptomics, metabolomics, and metagenomics. Among these, metagenomics is frequently used to detect a range of microorganisms. Metagenomics provides all genetic information about the soil.
Soil DNA is aligned with gene libraries to detect specific genes that aid a specific microbial function. The advent of next-generation sequencing methodologies has proved to be a highly reliable and accurate system of detecting microbial cultures and their associated activities in soil.
Machine Learning Techniques
The soil microbiome contains a plethora of information regarding the chemical, physical, and biological status of the soil, which could be leveraged by machine learning (ML) to predict soil health. In the past, microbiome-based MLs have shown promise in predicting crop productivity. However, its efficacy in predicting soil health needs further validation.
Taken together, the assessment of microbial composition, structure, and size is a prominent indicator of soil health. In the future, more microbiome-based ML models must be developed for effective soil health prediction.
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