Despite significant progress, there remain critical gaps in our knowledge regarding the intricate interactions and functions of SMCs within agroecosystems. Addressing these gaps is paramount for several reasons: Firstly, sustainable agriculture, which is necessary due to the growing global population and increasing demand for food, meaning there is an urgent need to develop sustainable agricultural practices. Soil microbes are integral to soil health and fertility, influencing crop yield and quality (Wei et al., 2024). A deeper understanding of microbial dynamics can lead to the development of strategies that enhance soil health and promote sustainable agricultural practices. The second reason is environmental impact: agroecosystems are significant contributors to environmental changes, including greenhouse gas emissions, soil degradation, and loss of biodiversity. The goal of microbiome rescue is to initiate a resilience trajectory for community functions, which can be achieved through demographic, functional, adaptive, or evolutionary recovery of disturbance-sensitive populations. This process is supported by various ecological mechanisms such as dispersal, reactivation from dormancy, functional redundancy, plasticity, and diversification. These mechanisms can interact to enhance recovery, with controlling microbial reactivation from dormancy being a particularly promising but underexplored target for rescue efforts.

Plant and animal diversity, as well as fungal and bacterial communities, play vital roles in decomposition and humus formation both above and below the ground (Wood et al., 2015). Early studies on SMCs relied on culture-based techniques, which were limited in scope and often biased towards easily culturable organisms. These methods provided a basic understanding but lacked comprehensive insights into the vast diversity of soil microbes. Advances in cutting edge tools and statistical and computational biology have simplified SMC assessments (Herrera-Paredes and Lebeis, 2016). For instance, DNA-based techniques are used to assess SMC variation across geographical regions worldwide. Advancement of GIS and spatial analysis tools have been integrated with microbial data to map the distribution of soil microbes across different regions and agroecosystems (Sanad et al., 2024). Nevertheless, to determine microbiota function and interactions within SMCs, it is necessary to increase the spatial resolution of community analysis of individual aggregates and investigate microbial diversity in each spatial entity. Szoboszlay and Tebbe (2021) demonstrated that DNA extraction from each soil aggregate provides new insights into how SMCs are assembled. Furthermore, biochemical and molecular techniques provide information about the spatial stratification of microbial populations between the inner and outer layers of soil aggregates (Helgason et al., 2010; Mummey et al., 2006; Turner et al., 2017). Understanding soil microbial aggregates is crucial for comprehending the structural organization of SMCs within the soil matrix. Microbial aggregates are clusters of microorganisms bound together by extracellular substances such as polysaccharides and proteins (Rillig et al., 2019). These aggregates play essential roles in soil fertility, nutrient cycling, and overall soil health. By connecting these, we can infer that assessing SMCs provides insights into the composition and functioning of microbial aggregates, elucidating their roles in soil ecosystem processes. This integrated approach enhances our understanding of soil microbial dynamics and their impact on soil health and ecosystem functioning.

Many case studies have shown that crop-associated beneficial microbes (bacteria, fungi, and actinomycetes) could be associated with disease-suppressive soil against pathogens (Kurm et al., 2023; Xiong and Lu, 2022). This phenomenon could suppress soil-borne plant diseases, often without the need for synthetic chemical interventions. This suppression occurs due to the activities of beneficial microorganisms present in the soil, which antagonize or outcompete pathogenic organisms, thereby reducing their populations or preventing their proliferation. This process is involved in the complex interplay of various factors, including competition for nutrients, the induction of plant defenses, the alteration of soil physicochemical properties, and the production of antimicrobial compounds. For instance, some bacteria and fungi produce antibiotics or secondary metabolites that inhibit the growth of pathogens. Others may compete with pathogens for space and nutrients, limiting their establishment and spread. Additionally, certain beneficial microbes can form symbiotic relationships with plants, enhancing their resistance to diseases by stimulating plant immune responses or directly antagonizing pathogens. Studies have demonstrated the significance of disease-suppressive soil in sustainable agriculture by reducing the reliance on chemical pesticides and fertilizers while promoting plant health and productivity (Mendes et al., 2011). Harnessing the potential of crop-associated beneficial microbes and understanding their interactions within the soil microbiome are critical for developing strategies to enhance disease-suppressive soil and improve agricultural sustainability.

Conversely, a broad range of edaphic variables, such as soil salinity, soil pH, organic carbon (C), types of soil, redox potential, and nutrient availability [(N), (P), and (S)], influence the diversity of SMCs (Ashworth et al., 2017; Jackson et al., 2012; Chaparro et al., 2012; Xu et al., 2013; Pasternak et al., 2013). Essentially, N cycling is involved in the nitrogen fixation, nitrification, and denitrification processes. C cycling is engaged in the decomposition of organic matter and contribution to the soil organic carbonic pool. Phosphorus solubilization is used to make phosphorus available to plants. SMCs play a pivotal role in mitigating these impacts through processes such as carbon sequestration and nutrient cycling (Wu et al., 2024). Recent research has discussed the taxonomical and functional dynamics of SMCs and their involvement in the processes of N fixation, organic matter decomposition, degradation of organic pollutants, and plant‒soil interactions (Cascio et al., 2021; Farzadfar et al., 2021). However, temperature, moisture, and seasonal variations also impact microbial activity and distribution. For example, climate change poses a significant threat to agricultural productivity and sustainability (Yuan et al., 2024). Soil microbes are key players in maintaining soil structure and function under changing climatic conditions. Understanding how microbial communities respond to climate change is essential for developing resilient agroecosystems (Shade, 2023). Additional factors such as land management, which involves crop rotation, tillage, irrigation, and fertilization practices, alter the soil environment and microbial habitats.

These reports further support the use of advanced methodological strategies (microbiome engineering) to improve the quality and quantity of food for the future population. Therefore, it is necessary to understand the fundamental mechanism of soil‒plant‒microbe interactions, and worldwide mapping of SMC diversity and functional importance, such as biogeochemical transport, organic management, and disease suppression, is needed in different agroecosystems. On the other hand, methodological strategies can be used to identify links between soil nutrients and bacterial and fungal allocation as well as spatial changes in biological processes (Li Y. et al., 2023; Gupta and Germida, 2015). Various approaches have been employed to investigate the composition of SMCs and their response to changes in different environments (Ligi et al., 2014; Vanwonterghem et al., 2014). This information allows for the identification of relationships between microbial aggregate properties and organic matter distribution. Metagenomic analysis can provide descriptions of both taxonomic and functional SMC assemblages in natural environments and their potential roles in ecosystems (Maron et al., 2011). For instance, a meta-analysis showed a 15.1% increase in microbial taxa (microbial phyla/classes) and a 3.4% increase in microbial diversity in crop rotation cultivation compared to single crop cultivation (Venter et al., 2016). The complexity of SMCs and their interactions with plants, soil, and the environment necessitate a multidisciplinary approach. There is a need for long-term and large-scale studies to capture the temporal and spatial dynamics of SMCs. Recent studies can provide insights into how SMCs respond to agricultural practices, environmental changes, and management interventions over time (Kabir et al., 2024). The variability in methodologies and data interpretation across studies poses a challenge for comparing and synthesizing research findings. Developing standardized protocols for sampling, data analysis, and reporting can enhance the reproducibility and comparability of soil microbiology studies. Although significant progress has been made in identifying microbial taxa, there is still a limited understanding of the functional roles of many soil microbes. Over the last 10 years, environmental genomic techniques have provided a much better understanding of microbial composition, yet detailed data about microbial populations are still lacking in areas such as community-environment interactions and species-species interactions.

Some reports examine the spatial variability of microbial communities across different climatic conditions, land uses, and agricultural practices. A study by Du et al., 2023 found that microbial community compositions at various taxonomic levels (e.g., species, genus, family) exhibit relatively consistent distribution patterns and mechanisms. These outcomes suggest that geographic or spatial factors play a crucial role in shaping microbial distributions. Consequently, this review was constructed from a comprehensive electronic database search for specific terms such as “soil microbial diversity,” “disease suppressive soil,” “soil nutrient promoter,” “plant growth nutrients,” “molecular ecological characterization,” and “agricultural management.” In this review, we collected 395 observations from 182 studies to examine the geographical distribution of soil microbes unevenly across climate zones and continents. All references were organized in a Microsoft Excel TM spreadsheet (see Supplementary Material). The articles were classified based on publication year, country of origin (experimental zone), and affiliation of the corresponding author. Consequently, this review explores the mapping, distribution, function, and cutting-edge strategies used by SMCs in agroecosystems, especially in the years 2000, 2008, and 2012. The data were obtained from the following electronic databases: ^{1 - 10} . Figure 1A shows the number of SMC studies per country, and (Figure 1B) shows the density of SMC studies per country (M km²) (see the Supplementary Material). Supplementary Figure S1 shows the global studies on SMCs promoting sustainable ecosystems between 2011 and 2020. Supplementary Figure S2 displays the top specific journals published in the investigation of soil microbial community diversity, function, and methodology strategies obtained from the abovementioned editorial databases.

Recent advancements in spatial mapping technologies and the integration of GIS have significantly enhanced our ability to study and understand microbial community distribution in soil ecosystems (Guseva et al., 2022). Recently, Li et al. (2024a) designed the Microgeo to enable visualization of SMC traits on geographical maps, especially on large or unevenly distributed spatial scales Meanwhile, He et al. (2023) offered important insights into how land use changes impact SMC composition and diversity, offering a theoretical basis for understanding microbial dynamics in river basins. Spearman analysis and redundancy analysis (RDA) were used to examine correlations between soil properties and microbial community composition; Partial least squares path model (PLS-PM) was used to express causal relationships between soil properties and microbial diversity. Kalaimurugan et al., 2019; Arumugam et al., 2022 employed GIS to analyze the spatial distribution of SMCs across different land use types, revealing how land management practices influence microbial diversity and abundance. Finding and integrating microbial community data with GIS, researchers can map microbial populations and their associations with environmental variables. Additionally, GIS combined with machine learning and statistical modeling allows for predictive mapping of microbial communities based on environmental data. These models can forecast microbial distribution patterns under various scenarios, such as climate change or land management practices (Diaz-Gonzalez et al., 2022). Furthermore, Beatty et al., 2021 used remote sensing technologies to provide data on soil properties from a distance, allowing for the large-scale monitoring of soil health and microbial communities.

In the soil microbiome, bacteria and fungi are the most abundant organisms, and their presence is essential for the regulation of plant growth. Interestingly, SMCs act as sources of essential nutrients, enzymes, and other biochemical compounds essential for the growth and development of plants, animals, and microorganisms. Moreover, SMCs are involved in the decomposition of organic matter, releasing nutrients that plants can take up, thus contributing to soil fertility. Additionally, certain microbial species produce bioactive compounds with beneficial properties, such as plant growth-promoting hormones or antibiotics, that can suppress plant pathogens. These microbial-derived substances can be harnessed for agricultural practices to enhance crop productivity and health. On the other hand, SMCs also serve as reservoirs of biodiversity and genetic diversity, which play crucial roles in ecosystem resilience and adaptation to environmental changes. Moreover, soil microbes play roles in biogeochemical cycles, such as carbon, nitrogen, and phosphorus cycling, which are fundamental processes for sustaining life on Earth. The genetic diversity within SMCs represents a vast reservoir of potential traits and functions that can be exploited for various applications, including bioremediation, rhizosphere biology, and biotechnology. Recently, Banerjee and van der Heijden (2022) demonstrated that SMCs function as sources and reservoirs for healthy soils, plants, and animals and humans. In this report, the authors determined that SMCs directly and indirectly support fundamental soil processes and functions through organic matter decomposition, N fixation, C transformation and cycling, and soil structure maintenance.

A meta-analysis of SMCs revealed that the biogeography of microorganisms is driven by variables that differ from those of macroorganisms (Dequiedt et al., 2011). In fact, SMCs are structurally dependent on local factors rather than large-scale factors such as geographic isolation (Ranjard et al., 2013). However, Fernandez et al. (2016) suggested that soil physicochemical test values, along with bacterial family and phylum abundances, provided a better explanation of the variation in soil functional profiles than did physicochemical test values alone. This result shows that derived SBC profiles might be useful tools for site-specific predictions of the effects of fertility management and crop-related soil functions. SBCs display distinct levels of rhizosphere compartments that decrease from the bulk soil toward the roots (Donn et al., 2015; Fan et al., 2017). Similarly, in rice and grapevines, the root and plant-tissue-associated communities represent a subset of the bulk soil community (Edwards et al., 2015; Zarraonaindia et al., 2015). However, smaller microbial network structures in rhizospheres than in bulk soil were observed in short-term plantations (Mendes et al., 2014). Davinic et al. (2012) examined the connection between SBC quality and the quantity of organic C in different fractions. According to the results of the present study, the less dominant bacterial taxa exhibited stronger interactions with specific chemical functional groups than did the more dominant bacterial taxa. Trivedi et al. (2016) revealed that gene abundance, SBC taxonomic composition, and soil organic matter quantity are linked to C-degrading enzymes.

The abundances of several bacterial phyla and fungal taxa increased in soils fertilized with manure. Additionally, many studies have reported that nitrogen availability largely affects SBCs and SFCs (Carrara et al., 2018). Ruamps et al. (2011, 2013) suggested that fungal use of C substrates in smaller pores was greater than that of bacteria because of fungi’s ability to spread via hyphal growth. Consequently, pore size, microbial community structure, geometry, and connectivity can significantly affect habitat suitability for microbial growth. Further studies have shown that PLFA biomarkers for AMF are relevant to composted green waste as well as persistent organic management (Lori et al., 2017; Moeskops et al., 2012). Another viewpoint suggests that temperate forest trees associate with fungal communities (AMF or ECM) to increase their access to nutrients and water availability, thus altering soil C and nutrient cycling (van der Heijden et al., 2015). In tropical forests, SFCs exert a strong influence on the ratio of inorganic to organic soil N (Waring et al., 2013), and the effects of ECM on N cycling enable the monodominance of tree species in tropical montane forests (Corrales et al., 2017).

In agro-systems, SMCs provide a wide variety of functions (such as biogeochemical nutrient cycling and amendments) and benefits (pathogen resistance) to crop growth and health (Arif et al., 2020; Singh et al., 2020). Recent advances in multiomics methods have supported studies of SMC diversity, composition, and function in soil (Xiong and Lu, 2022; Matsumoto et al., 2021; Xu et al., 2021a). Multiomics studies suggest that sorghum carries multiple genes related to iron-chelating agents in the rhizosphere root system under drought stress (Xu et al., 2021b).

The Shannon, Simpson, dominance, and evenness indices are used to describe the alpha diversity of microbial communities (Dubey et al., 2016; Haegeman et al., 2013). The Simpson (D) index can determine whether two individuals randomly selected from a sample belong to the same species (Pathan et al., 2015). The Shannon (H) index characterizes species diversity. It provides indicators for both the abundance and evenness of microbial species (Faoro et al., 2010); evenness is used to measure the relative abundances of various species (Thébault et al., 2014). These observational studies investigated changes in soil abiotic properties along with SMC structure and composition changes from lower to higher altitudes at three vegetation sites (Nothofagus betuloides, N. pumilio, and alpine). The data show that increases in the soil C:N ratio at alpine sites were associated with shifts from bacterial dominance at lower altitudes to fungal dominance at higher altitudes. This can be attributed to several interrelated factors, such as temperature, substrate availability, soil pH, nutrient availability, and plant‒microbe interactions. First, moisture conditions often favor fungal dominance over bacterial dominance, as fungi are generally more efficient at decomposing complex organic matter under colder temperatures and lower moisture conditions. Additionally, the types of organic matter available for decomposition can vary with altitude due to differences in vegetation composition and productivity. At lower altitudes, where plant litter tends to be fresher and more labile, bacterial populations thrive due to their ability to rapidly metabolize simple organic compounds. In contrast, at higher altitudes, where plant litter may be more recalcitrant and dominated by tougher materials such as lignin and cellulose, fungi are better equipped to break down these complex compounds through their extracellular enzymes. Moreover, soil pH tends to decrease with increasing altitude due to factors such as decreased microbial activity and increased leaching of fundamental cation exchange capacity. Fungi generally thrive in acidic environments, whereas many bacterial species prefer neutral to slightly alkaline conditions. This shift in soil pH can favor fungal dominance at relatively high altitudes. Consequently, the functional properties of vegetation communities may influence SMCs. The relationship between biodiversity and ecosystem function is relevant to agricultural ecosystems (de Menezes et al., 2015; Constancias et al., 2015). This interaction influences key processes such as nutrient cycling, pest regulation, and crop productivity. Studies have shown that diverse plant communities can increase soil organic matter content and nutrient availability, leading to higher crop yields (Eisenhauer et al., 2017). Several studies have explored the associations of Proteobacteria, Actinobacteria, and Bacteroides bacteria with copiotrophic behaviors and Verrucomicrobia, Firmicutes, and Acidobacteria bacteria with oligotrophic behaviors (Goldfarb et al., 2011). Microbial coherence is directly related to soil bacterial taxa. For example, soil bacterial taxa are influenced by a wide range of environmental factors, including pH, moisture, temperature, nutrient availability, and organic carbon content. These environmental drivers shape the composition and structure of SMCs, leading to patterns of coherence or divergence among bacterial taxa across different soil types, land uses, or management practices. Moulin et al. (2023) assessed the impact of different agroecological practices on the composition and diversity of bacterial and fungal communities in the soil. Their results showed enhanced beneficial soil bacteria, particularly Actinobacteria. These effects positively impact soil health by enhancing microbial diversity and enriching soil with nutrients.

The abundances of “copiotrophic” Betaproteobacteria and Bacteroides are linked to increased rates of soil C mineralization, while “oligotrophic” Acidobacteria are correlated with decreased rates of soil C mineralization (Finn et al., 2017). Actinobacteria play important roles in organic matter turnover, and the breakdown of recalcitrant molecules (cellulose and polycyclic aromatic hydrocarbons) has been characterized by two-bacterium models (Thermobifida fusca and Cellulomonas fimi) (de Menezes et al., 2012). Consequently, microaggregates of Rubrobacteriales represented a small portion of the total organic C, whereas other Actinobacteria (excluding Rubrobacteriales) and Alphaproteobacteria were abundant in macroaggregates, making up the majority of the soil organic C.

SMCs play a significant role in global element cycling, organic matter turnover, xenobiotics breakdown, and soil aggregate formation. Unlike plant, macro, and meso fauna diversity, soil microbial diversity has only recently been studied (Mamabolo et al., 2024). Traditional microbiological cultivation techniques failed to characterize most soil microorganisms, but cultivation-independent methods have expanded our understanding. These methods include direct DNA extraction from soil for molecular marker gene analysis and molecular techniques to study microbial functions in situ. The phospholipid approach, analyzing membrane components for taxonomic differentiation, complements genotypic methods and is well-established in soil ecology. In this aspect, functional metagenomics begins with the separation of DNA from ecosystem SMCs. There are two steps to understanding how these genes function in soil nutrient elevation. First, amplification is necessary, and 16S rDNA gene meta-transcriptomic and metaproteomic data are obtained from the soil microbiome. We identified specific genes of interest that are involved in nutrient cycling, nutrient uptake, or other relevant processes related to soil nutrient elevation. These genes could include those encoding enzymes involved in nitrogen fixation, phosphorus solubilization, and carbon mineralization. Bioinformatics tools are essential for acquiring all necessary information from the procedures mentioned above (Ortiz-Estrada et al., 2019). Second, molecular shotgun sequencing or denominated whole-metagenome shotgun sequencing of SMC genomic DNA is needed. However, with the increased use of high-throughput sequencing methods, the accessibility of genome data plays a significant role in functional metagenomics studies (Lam et al., 2015; Garibay-Valdez et al., 2020). Figure 2 establishes the overview of meta-genomics approaches on soil microbial diversity. This sequencing technique involves randomly fragmenting the amplified DNA into small pieces, sequencing these fragments using high-throughput sequencing platforms, and then assembling the sequenced fragments into longer contigs or scaffolds. Table 3 lists key bioinformatics tools that provide information for the functional metagenomics of a microbiome.

Shotgun metagenomics analyses became possible due to the development of several bioinformatics tools (Illumina protocol) that can be used to generate databases [e.g., CAZYmes (Cantarel et al., 2009), MG-RAST (Meyer et al., 2008), MEGAN 5 (Mitra et al., 2011), and RDP (Cole et al., 2003)]. Table 4 shows the top journal publications employing soil metagenomics approaches based on the total number of citations. Functional metagenomics tools require sequence alignment with a large database of annotated sequences to identify homologs (Mendoza et al., 2015). Unfortunately, many bioinformatics tools for functional profiling are slow; Lind Green’s group suggested a need for improved speed and accuracy of metagenomics tools (Lindgreen et al., 2015). Homology-based pre-NGS tools such as BLAST match suitable sequence alignments in a large database, while other homology search tools such as RAPSearch2 (Zhao et al., 2012) and DIAMOND (Buchfink et al., 2015) have also been established to reduce run time. On the other hand, exact match or k-mer-based tools use oligonucleotides to identify the best match in a metagenome. For instance, Edwards’s group found all words of length k-mer-related proteins using real-time RTMg (Edwards et al., 2012). This approach can assign taxonomic labels to metagenomic sequences (Ounit et al., 2015). Recently, SUPER-FOCUS tools were used to identify subsystems in metagenomic samples that show taxonomical conservation and functional adaptation (Silva et al., 2016). Compared to standard metagenomics pipelines (e.g., RTMg and MGRAST), SUPER-FOCUS speeds up the three-way sequence functional annotation process via SEED database clustering, querying sequences in FOCUS and performing comparisons using RAPSearch2. Additionally, Kraken and Anvio are feasible options for relatively fast analysis of bacterial community functions. With high-performance analytical hardware (128 Gb RAM Linux server), SUPERFOCUS may sacrifice accuracy and detail for speed. The relevant data obtained from these methods open new doors for understanding soil microbiomes. Although amplicon-based profiling does not provide a direct measure of microbial community function, information about SMC composition can help predict crucial soil functions such as nutrient cycling (Graham et al., 2016; Weedon et al., 2017; Callahan et al., 2019). These studies indicate that soil function is primarily dependent on the connection between microbial composition and the improvement of biogeochemical cycling. Global changes associated with alterations in the phylogenetic composition of the bacterial community support this conclusion.

Protocols for sequencing and QIIME bioinformatics pipelines for the analysis of 16S rDNA data are becoming increasingly well established and, with sufficient sequencing depth, can yield detailed phylogenetic profiles of both abundant and rare taxa (Caporaso JG et al., 2010; Boon et al., 2014). QIIME was used to estimate distances between communities (both taxonomic and phylogenetic metrics). Perhaps the gold standard for microbial diversity units is the commonly accepted “operational taxonomical unit” (OTU) for the analysis of AVS (amplicon sequence variant). (Schmidt et al., 2014). For instance, the ecotype model of bacterial speciation describes basic diversity units since ecologically coherent group diversity is limited by cohesive genetic forces. Dedicated algorithms exist to demarcate ecotypes from environmental sequencing data (Koeppel et al., 2008).

PLFA measurements were performed to infer the functional capacity of microbial communities, while enzyme assays, respiratory measurements, or substrate utilization assays were used to measure the functional activity of SMCs (Liang et al., 2019). Multivariate analysis contributes to microbial ecology by assessing variance, aiding in understanding relationships in constraint variables and describing discriminant functions (Paliy and Shankar, 2016). According to Guan et al. (2018), fatty acid profiling followed by multivariate statistical analysis has been used to study SMC structure under different agronomic regimes because different microbial taxa contain fatty acids that vary in their chain lengths, numbers of unsaturated bonds, and positions of double bonds (Bowles et al., 2014; Liu et al., 2012).

This review describes the multidisciplinary approach that allows for a comprehensive understanding of SMC and their interactions within agroecosystems. Recent decades has seen advancements in mapping techniques, distribution models, functional analyses, and high-throughput methodologies. For example, high-throughput methodologies are crucial for generating large-scale data, enabling detailed insights into microbial community dynamics. Similarly, emphasizing the function of SMC in agroecosystems can offer practical implications for agriculture. Certainly, some studies may not encompass a broad range of agroecosystems. Figure 3 demonstrates the recent trends, limitations, and adaptation to methodology related to SMC composition. However, SMCs can vary significantly over time due to seasonal changes, agricultural practices, and environmental conditions, which may not be fully captured in single-timepoint studies. Though high-throughput sequencing methods have revolutionized soil microbiome studies, there are still challenges such as PCR biases, DNA extraction methods, and bioinformatics pipelines that could impact data accuracy and comparability. While there is a growing body of literature on microbial community composition, understanding the functional roles of different microbial taxa in agroecosystems remains a challenge. It is necessary to address the challenges of handling and integrating large-scale data from high-throughput methods, including bioinformatics and statistical issues. Though issues such as difficulties in scaling studies from laboratory to field settings and the need for more refined spatial and temporal resolution, bridging the gap between fundamental research and practical applications is essential for translating scientific findings into actionable strategies for farmers and land managers. Collaborations between researchers, agricultural stakeholders, and policymakers can facilitate the implementation of microbial-based solutions in agroecosystems.

The high diversity of SMCs in agroecosystems makes maintaining soil quality challenging because of the numerous important soil processes. These assessments emphasize the need to develop and improve methodological strategies and guidelines to protect SMCs based on important environmental drivers, such as plant nutrient use, land conversion, nitrogen enrichment, climate change, and soil microbial diversity monitoring programs. Methods are available, but researchers and future stakeholders should mobilize to develop standards for soil collection and SMC analysis that can close these gaps and provide reliable information about the status of SMCs. Furthermore, the effects of land use changes, climate change, and further ecosystem degradation on communities responsible for crop production and a wide range of other ecosystem services must be determined. With continued sequencing and identification of taxa, the challenges of studying SMCs should be met. Modern data acquisition methods allow for better analysis of soil microbial function and provide insight into agricultural practices that will yield increased food production in a sustainable manner.