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. |
- Alef, K. and Nannipieri, P. 1995. Methods in Applied Soil Microbiology and Biochemistry. Academic Press, London.
- Aliasgharzad, N. 2010. Methods in Soil Biology. Translated to Farsi, 2nd Edition, Tabriz University Press.(In Persian)
- Aliasgharzad, N., Mårtensson, L.M. and Olsson, P.A. 2010. Acidification of a sandy grassland favours bacteria and disfavours fungal saprotrophs as estimated by fatty acid profiling. Soil Biology and Biochemistry 42,1058–1064.
- Alkorta, I. Aizpurua, A. Riga, P. Albizu, I. Amezaga, I. and Garbisu, C. 2003. Soil enzyme activities as biological indicators of soil health. Review Environmental Health 18,65–73. doi: 10.1515/REVEH.2003.18.1.65
- Alvyar, Z. Shahbazi, F. Oustan, S. Dengiz, O. and Minasny, B. 2022. Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline. Science of the Total Environment, 808,
- Amann, R.I. Binder, B.J. Olson, R.J. Chisholm, S.W. Devereux, R. and Stahl,D.A. 1990. Combination of 16S rRNAtargeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Applied and Environmental Microbiology 56, pp.1919–1925.
- Amann, R.I. Ludwig, W. and Schleifer, K.H.1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. FEMS Microbiology Reviews 59, pp.43–169.
- Ananyeva, N.D. Susyan, E.A. and Gavrilenko, E.G. 2011. Determination of the soil microbial biomass carbon using the method of substrate-induced respiration. Eurasian Soil Science, 44, pp.1215–1221. https://doi.org/10.1134/S1064229311030021.
- Anderson, T.H. 2003. Microbial eco-physiological indicators to asses soil quality. Agriculture, Ecosystems and Environment 98, pp.285-293.
- assessment of side-effects of agrochemicals on soil micro-organisms. Residue Reviews 86, 65–105.
- Bååth, E. 1990. Thymidine incorporation into soil bacteria. Soil Biology and Biochemistry 22,803–810
- bacterial and fungal biomass in soil. Biology and Fertility of Soils 22,
- Barin, M. Aliasgharzad, N. Olsson, P.A. Rasouli-Sadaghiani, M.H. and Moghddam, M. 2013. Abundance of arbuscular mycorrhizal fungi in relation to soil salinity around Lake Urmia in northern Iran analyzed by use of lipid biomarkers and microscopy. Pedobiologia, 56,225-232.
- Bastida, F. Zsolnay, A. Hernandez, T. and Garcia, C. 2008. Past, present and future of soil quality indices: a biological perspective. Geoderma 147, pp.159–171.
- Bloem, J. Bolhuis, P.R. Veninga, M.R. and Wieringa, J. 1995. Microscopic methods
- Bölter, M. Bloem, J. Meiners, K. and Möller,R. 2002. Enumeration and biovolume
- Bölter, M. Möller, R. and Dzomla, W. 1993. Determination of bacterial biovolume with epifluorescence microscopy: comparison of size distributions from image analysis and size classifications. Micron 24,31–40
- Breiman, L. 2001. Random forests. Machine Learning, 45, pp.5–32.
- Bressan, M. Trinsoutrot Gattin, I. Desaire S. Castel, L. Gangneux, C. and Laval, K. 2015. A rapid flow cytometry method to assess bacterial abundance in agricultural soil. Applied Soil Ecology 88,60–68. https://doi.org/10.1016/j.apsoil.2014.12.007
- Breure, A.M. and Rutgers, M. 2000. The application of Biolog plates to characterize microbial communities. In: Benedetti, A. Tittarelli, F. de Bertoldi, S. and Pinzari, F. (eds) Proceedings of the COST Action 831, Joint Working Group Meeting Biotechnology of Soil, Monitoring, Conservation and Bioremediation. 10–11, December 1998, Rome, Italy, (EUR 19548),pp.179–185.
- Brookes, P.C. 1995. The use of microbial parameters in monitoring soil pollution
- Bünemann, E.K. Bongiorno, G. Baic, Z. Creamer, R.E. et al. 2018. Soil quality – A critical review. Soil Biology Biochemistry 120, pp.105–125
- by heavy metals. Biology and Fertility of Soil 19,269–279.
- Das, S. Deb, S. Sahoo, S.S. and Sahoo, U.K. 2023. Soil microbial biomass carbon stock and its relation with climatic and other environmental factors in forest ecosystems: A review, Acta Ecologica Sinica, 43 (6), pp.933-945,https://doi.org/10.1016/j.chnaes.2022.12.007
- determination of microbial cells – a methodological review and recommendations for applications in ecological research. Biology and Fertility of Soils 36,249–259.
- Domsch, K.H., Jagnow, G. and Anderson, T.H. (1983) An ecological concept for the
- Doran JW, Zeiss MR, 2000. Soil health and sustainability: managing the biotic component of soil quality. Applied Soil Ecology 15:3–11.
- 2024. Standard operating procedure for soil microbial biomass (carbon): chloroform fumigation-extraction method. Rome.
- for counting bacteria and fungi in soil. In: Alef, K. and Nannipieri, P. (eds) Methods
- Frostegård, Å. and Bååth, E. 1996. The use of phospholipid fatty acid analysis to estimate
- Garland, J.L. Cook, K.L. Loader, C.A. and Hungate, B.A. 1997. The influence of microbial community structure and function on community-level physiological profiles. In: Insam, H. and Rangger, A. (eds) Microbial Communities: Functional versus Structural Approaches. Springer, New York, pp.171–183
- Griffiths, B.S. Römbke, J. Schmelz, R.M. Scheffczyk, A. Faber, J.H. Bloem, J. Peres, G. Cluzeau, D. Chabbi, A. Suhadolc, M. et al. 2016. Selecting cost effective and policy-relevant biological indicators for European monitoring of soil biodiversity and ecosystem function. Ecological Indicators 69, pp.213-223.
- Hartmann, A. Pukall, R. Rothballer, M. Gantner, S. Metz, S. Schloter, M. and Mogge, B. 2004. Microbial community analysis in the rhizosphere by in situ and ex situ application of molecular probing, biomarker and cultivation techniques. In: Varma, A. Abbott, L. Werner, D. and Hampp, R. (eds) Plant Surface 288 Plant–Microbe Interactions and Soil Quality Microbiology. pp. 449–469 Springer-Verlag, Berlin.
- Heidarianpour, M.B. Aliasgharzad, N. Olsson, P.A. 2020. Positive effects of co-inoculation with Rhizophagus irregularis and Serendipita indica on tomato growth under saline conditions, and their individual colonization estimated by signature lipids. Mycorrhiza 30,455–466. https://doi.org/10.1007/s00572-020-00962-y
- in Applied Soil Microbiology and Biochemistry. Academic Press, London, pp.162–173.
- Kempen, B. Brus, D.J. Stoorvogel, J.J. Heuvelink, G.B.M. and de Vries, F. 2012. Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps. Soil Science Society of American Journal, 76, pp.2097–2115.
- Klauth, P. Wilhelm, R. Klumpp, E. Poschen, L. and Groeneweg, J. 2004. Enumeration of soil bacteria with the green fluorescent nucleic acid dye Sytox green in the presence of soil particles. Journal of Microbiological Methods 59,189–198. https://doi.org/10.1016/j.mimet.2004.07.004
- Lee, J. Kim, H.S. Jo, H.Y. and Kwon, M.J. 2021. Revisiting soil bacterial counting methods: Optimal soil storage and pretreatment methods and comparison of culture-dependent and -independent methods. PLoS One 16(2):e0246142. doi:10.1371/journal.pone.0246142.
- Lee, J.S. Lee, Y. Lee, E.J. Pros, K. Kim, K. and Han, G.H. 2021. A simple method to determine soil microbial biomass carbon by facilitating gaseous diffusion of chloroform into pores. Korean Journal of Soil Science and Fertilizer 54(4), 660-666. doi:10.7745/KJSSF.2021.54.4.660
- Lewe, N. Hermans, S. Lear, G. Kelly, L.T. Thomson-Laing, G. Weisbrod, B. Wood, S.A. Keyzers, R.A. and Deslippe, J.R. 2021. Phospholipid fatty acid (PLFA) analysis as a tool to estimate absolute abundances from compositional 16S rRNA bacterial metabarcoding data. Journal of Microbiological Methods,188,106271, https://doi.org/10.1016/j.mimet.2021.106271
- Liu, C. Tian, J. Cheng, K. Xu, X. Wang, Y. et al. 2023. Topsoil microbial biomass carbon pool and the microbial quotient under distinct land-use types across China: A data synthesis. Soil Science and Environment 2(5). https://doi.org/10.48130/SSE-2023-0005
- McBratney, A.B. Mendoça Santos, M.L. and Minasny, B. 2003. On digital soil mapping. Geoderma, 117, pp.3–52.
- Minasny, B. and McBratney, A.B. 2016. Digital soil mapping: A brief history and some lessons. Geoderma, 264, pp.301–311.
- Mori, T. Wang, S. Wang, C. Mo, J. and Zhang, W. 2021. Is microbial biomass measurement by the chloroform fumigation extraction method biased by experimental addition of N and P?. iForest 14, pp.408-412. doi: 10.3832/ifor3374-014
- Muyzer, G. and Smalla, K. 1998. Application of denaturing gradient gel electrophoresis
(DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie van Leuwenhook 73, pp.127–141.
- Nakamoto, T. and Wakahara S. 2004. Development of substrate induced respiration (SIR) method combined with selective inhibition for estimating fungal and bacterial biomass in humic Andosols. Plant Production Science 7(1), 70-76.
- Olsson, P.A. Rahm, J. and Aliasgharzad, N. 2010. Carbon dynamics in mycorrhizal symbioses is linked to carbon costs and phosphorus benefits. FEMS Microbiology Ecology 72, pp.125–131.
- Paz-Ferreiro, J. and Fu, S. 2016. Biological Indices for Soil Quality Evaluation: Perspectives and Limitations. Land Degradation and Development 27, pp.14–25.
- Paz-Ferreiro, J. Fu, S. Méndez, A. and Gascó, G. 2015. Biochar modifies the thermodynamic
parameters of soil enzyme activity in a tropical soil. Journal of Soils and Sediments15(3), 578-583.
- Quideau, S.A. McIntosh, A.C. Norris, C.E. Lloret, E. Swallow, M.J. and Hannam, K. 2016. Extraction and analysis of microbial phospholipid fatty acids in soils. Journal of Visualized Experiments, 26 (114),54360. doi: 10.3791/54360.
- Rahul, R. Sharma, P. Singh, A. Singh, J. and Kumar, M. 2022. Soil enzymes and their role in soil health improvement. In: Nayak, S.K. Baliyarsingh, B. Mannazzu, I. Singh, A. Mishra, B.B. (eds) Advances in Agricultural and Industrial Microbiology. Springer, Singapore. https://doi.org/10.1007/978-981-16-8918-5_3
- Rezaei, H. Jafarzadeh, A. Aliasgharzad, N. and Alipoor, L. 2015. Soil quality investigation based on biological and micro-morphological traits under different land uses. Carpathian Journal of Earth and Environmental Sciences, 10 (1), pp.241 – 254.
- Risch, A.C. Zimmermann, S. Schütz, M. et al. 2023. Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography 32 (6), pp.904–918. https://doi.org/10.1111/geb.13664
- Ritz, K. Black, H.I. Campbell, C.D. Harris, J.A. and Wood, C. 2009. Selecting biological indicators for monitoring soils: a framework for balancing scientific and technical opinion to assist policy development. Ecological Indicators 9, pp.1212-1221.
- Saviozzi, A. Cardelli, R. Di Puccio, R. 2011. Impact of salinity on soil biological activities: A laboratory experiment. Communications in Soil Science and Plant Analysis, 42(3),358–367. https://doi.org/10.1080/00103624.2011542226
- Schloter, M. Nannipieri, P. Sørensen, S.J. and van Elsas, J.D. 2018. Microbial indicators for soil quality. Biology and Fertility of Soils 18,1-10.
- Siami, A. Aliasgharzad, N. Maleki, L.A. Najafi, N. Shahbazi, F. and Biswas, A. 2022. Recalcitrant C source mapping utilizing solely terrain-related attributes and data mining techniques. Agronomy 12,
https://doi.org/10.3390/agronomy12071653
- Sparling, G.P. 1995. The substrate-induced respiration method. In: Alef, K. and Nannipieri, P. (eds) Methods in Applied Soil Microbiology and Biochemistry. Academic Press, New York, pp. 397–404.
- Sreenivas, K. Dadhwal, V.K. Kumar, S. Harsha, G.S. Mitran, T. Sujatha, G. Janaki R. Suresh, G. Fyzee, M.A. and Ravisankar, T. 2016. Digital mapping of soil organic and inorganic carbon status in India. Geoderma, 269, pp.160–173.
- Taha, M. Kadali, K.K. AL-Hothaly, K. et al. An effective microplate method (Biolog MT2) for screening native lignocellulosic-straw-degrading bacteria. Annals of Microbiology 65, pp.2053–2064. https://doi.org/10.1007/s13213-015-1044-y
- Tavasolee, A. Aliasgharzad, N. Salehi, G.R. Mardi, M. Asgharzadeh, A. and Akbarivala, S. 2011. Effects of co-inoculation with arbuscular mycorrhizal fungi and rhizobia on fungal occupancy in chickpea root and nodule determined by real-time PCR. Current Microbiology 63(2), 107-114. doi: 10.1007/s00284-011-9951-z.
- Vestergaard, G. Schulz, S. Schöler, A. and Schloter, M. 2017. Making big data smart—how to use metagenomics to understand soil quality. Biology and Fertility of Soils 53, pp.479-484.
- White, D.C. and Ringelberg, D.B. 1998. Signature lipid biomarker analysis. In: Burlage, R.S. Atlas, R. Stahl, D. Geesey, G. and Sayler, G. (eds) Techniques in Microbial Ecology. Oxford University Press, New York, pp. 255–272.
|
