نوع مقاله : مقاله مروری
نویسنده
استاد بیولوژی و بیوتکنولوژی خاک، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسنده [English]
Background and Objectives: Soil is a foundational, non-renewable component of the terrestrial environment, essential for global food production. The escalating pressures of industrialization, population growth, and climate change have placed soil resources under severe threat from pollution, salinity, and inappropriate land management. Consequently, the accurate assessment and monitoring of soil health and quality (SHQ) have become critical priorities for researchers and legislators worldwide. While physical and chemical parameters are established components of soil assessment, biological and biochemical indices are gaining prominence due to their unique sensitivity. Unlike more stable properties, the soil microbiome responds rapidly to environmental changes and management interventions, acting as sensitive, early-warning indicators of soil degradation or restoration. However, a significant challenge persists in their application: a vast and diverse array of potential biological parameters exists, and their measurement is often complex, costly, and time-consuming. It is impractical to measure all available indices in a single study. Therefore, a judicious selection based on clear objectives is necessary to optimize resources. This review article aims to provide a comprehensive analysis and synthesis of the principal microbiological and biochemical indices used for assessing soil health. The objectives are to: (1) categorize and critically evaluate the most common methods for measuring microbial population, biomass, activity, and diversity; (2) analyze the role of soil enzymes and key ecophysiological quotients; and (3) introduce modern, cost-effective solutions, including estimation models and the use of satellite data, to overcome the high cost and labor demands of traditional biological measurements.
Review Methods: This study is a comprehensive narrative review based on a synthesis of scientific literature. To gather the requisite information, systematic searches were conducted in major scientific databases, primarily Scopus, Web of Science, and Google Scholar. The search strategy employed a combination of keywords relevant to the article's scope, including "soil health indicators", "soil quality assessment", "biological indicators", "soil microbial biomass", "soil enzymes", "microbial diversity", "phospholipid fatty acids" (PLFA), "metabolic quotient", "rhizosphere interactions", and "digital soil mapping". The criteria for selecting articles for inclusion prioritized foundational papers establishing benchmark methodologies, contemporary research articles demonstrating the application of these indices under various stress conditions, and recent publications detailing methodological and computational advancements. The synthesized information was then structured to provide a critical evaluation of each category of biological index.
Results: This review synthesizes the vast field of soil biological indicators into key categories, evaluating their methodologies and applications. Methods for quantifying microbial abundance range from the accurate but laborious chloroform fumigation (FE/FI) and its faster proxy, Substrate-Induced Respiration (SIR), to culture-independent techniques that also reveal community structure. These modern methods, which supersede limited plate counts, include metagenomics (16S/18S sequencing) for deep taxonomic diversity and Phospholipid Fatty Acid (PLFA) analysis, a powerful tool that simultaneously quantifies viable biomass, community structure, and physiological stress. Soil function is assessed via overall metabolic activity (e.g., Basal Respiration, Dehydrogenase Activity) and a suite of specific soil enzymes (e.g., urease, phosphatase) that govern nutrient cycling. These data are further interpreted using key ecophysiological quotients (e.g., qmic, qCO2) to diagnose carbon dynamics and environmental stress, alongside monitoring crucial plant-microbe interactions (e.g., mycorrhiza, rhizobia) as integrated health indicators. Finally, to overcome the high cost and labor of these analyses, the review highlights emerging solutions, most notably Digital Soil Mapping (DSM), which uses machine learning (e.g., Random Forest) integrated with satellite and terrain data to cost-effectively create predictive, large-scale spatial maps of these complex biological properties.
Conclusion: Biological indices are sensitive, responsive, and indispensable tools for the modern assessment of soil health and quality. This review confirms that no single parameter can comprehensively capture the complexity of soil biological function. A holistic evaluation requires either a carefully selected suite of complementary indices (e.g., combining biomass, activity, and diversity metrics) or the use of multi-parameter composite indices (e.g., SHI, SQI). The selection must be hypothesis-driven, aligning the chosen indicators with the specific ecological question or management goal. Significant challenges remain, primarily the high cost, technical expertise, and time required for many of the most informative analyses (e.g., metagenomics, PLFA). Furthermore, the lack of method standardization across different soil types, climates, and ecosystems hinders the establishment of universal benchmarks for soil health. The future of soil health monitoring lies in overcoming these challenges through integration and technology. We recommend: (1) the establishment of long-term soil microbiome monitoring projects to move beyond static snapshots and understand the temporal dynamics of soil biology. (2) A concerted effort toward international standardization of key methods and the creation of global databases for soil biological data, which are essential for developing robust predictive models. (3) Most promisingly, the continued development and application of Digital Soil Mapping (DSM). By synergistically integrating field-level biological data with remote sensing (satellite) data and machine learning, DSM provides the only viable path toward creating the cost-effective, large-scale, and spatially-explicit soil health maps that land managers and policymakers urgently need for sustainable soil stewardship.
کلیدواژهها [English]
https://doi.org/10.3390/agronomy12071653
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