Root-associated fungal communities along productivity gradients in a perennial alfalfa

Root-associated fungal communities along productivity gradients in a perennial alfalfa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Root-associated fungal communities along productivity gradients in a perennial alfalfa Adnan Šišić, Leonard V. Theisgen, Deise A. Knob, Andreas Gattinger, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9161471/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Perennial forage legume leys such as alfalfa are a cornerstone of organic production and integrated crop-livestock systems. Spatial yield variability is common in agricultural fields but the belowground factors contributing to this variation are poorly understood. This study examined how soil chemical properties and soil- and root-associated fungal communities correspond to persistent productivity gradients in an organically managed alfalfa ( Medicago sativa L.) ley. Methods Alfalfa plants and rhizosphere soil were sampled from high- and low-productivity field zones over two consecutive years to assess plant biomass, root health and soil chemical properties. Fungal communities were profiled using ITS1 amplicon sequencing. Diversity, community composition, temporal stability and the distribution of core, indicator and putative pathogenic taxa were evaluated. Results Plant biomass in high-productivity zones was ~ 55% higher although plants in both zones appeared healthy. Soil P and K levels were similar between zones, whereas Mg differed strongly but remained within sufficiency ranges. Soil pH differed by one unit and mineral N showed no consistent spatial pattern. Fungal communities were primarily structured by compartment. Plant productivity explained only ~ 9% of β-diversity. About one-third of root-associated taxa persisted across years and productivity zones, forming a dominant and stable community enriched in putative pathogens. Core and indicator analyses identified only few zone-associated taxa, including a Glomeraceae indicator detected in high-productivity roots. Conclusion Soil nutrients and broad fungal community patterns did not explain the persistent productivity gradient. Differences were confined to a small number of root-associated taxa, suggesting that additional edaphic and biological factors beyond fungi likely contribute to the observed biomass differences. Alfalfa (Medicago sativa) productivity gradients plant–microbe interactions core microbiome putative pathogens Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Legume-based leys are the foundation of organic crop rotations and strongly affect the performance of subsequent crops (Watson et al. 2002). In a global meta-study, they were shown to enhance succeeding main crop yield by 20% on average (Zhao et al. 2022). Therefore, the successful establishment and productivity of legume-based leys are critical, as impaired performance can have cascading negative effects across the entire crop rotation. Among the legumes used in leys, alfalfa ( Medicago sativa L.) is important forage crop valued for its high biomass production, nitrogen-fixing capacity and nutritional value for livestock. Owing to its high palatability and high concentrations of crude protein, digestible fiber, minerals and vitamins, alfalfa is a high-quality fodder for ruminants and other livestock (Undersander 2021). In livestock farming systems, alfalfa cropping contributes to on-farm protein supply, supporting the closure of nutrient cycles, and reducing dependence on external inputs such as imported soy. Alfalfa leys are usually grown perennially in monoculture or in mixtures with other forage crops such as clovers and grasses. Through biological nitrogen fixation and a deep rooting system, alfalfa enhances soil fertility by increasing soil organic matter and sequestering carbon and nitrogen (Wang et al. 2023; Fernández-Ortega and Cantero-Martínez 2025). The combined benefits for soil health and animal nutrition make alfalfa an excellent choice in integrated crop-livestock systems and climate-smart agricultural systems (Desta 2025). Despite its high yield potential, alfalfa yields are often unstable with a substantial yield gap, with maximum attainable yields two- to threefold higher than median on-farm yields. Although yields can be increased through improved agronomic management, environmental constraints, including soil conditions, often limit attainable yields (Putnam 2021). On the field scale, alfalfa yield may exhibit pronounced spatial variability (Weigelt et al. 2025) that has been attributed to heterogeneity in soil properties (Groß et al. 2023), soil moisture (Vergopolan et al. 2021) as well as to differences in (soil) microbial communities (O’Brien et al. 2016). Soil microbes are key drivers of plant productivity, given their central role in nutrient cycling, nutrient acquisition and plant health (Van Der Heijden et al. 2008). Soil fungi, mycorrhizae and fungal endophytes contribute to nutrient solubilization, soil aggregation, water availability and plant growth promotion, while also enhancing resistance against biotic and abiotic stresses (Akter et al. 2025). At the same time, pathogens can severely impede plant productivity. Like most legumes, alfalfa is susceptible to a range of fungal pathogens causing root rot diseases, including Fusarium spp., Olpidium spp., Ascochyta medicaginicola, Rhizoctonia solani, Alternaria alternata and several Phoma and Paraphoma species (Finckh et al. 2015). Proliferation of root rot pathogens is linked to yield depressions in systems with repeated legume cultivation – also referred to as legume fatigue (Fuchs et al. 2014). Under continuous cropping of alfalfa, its yield decline has been accompanied by negative successions of rhizosphere metabolites and microbial community composition (Wang et al. 2022; Ma et al. 2024). Together, these findings suggest that variation in soil and root-associated fungal communities can play an important role in constraining or supporting alfalfa productivity, potentially contributing not only to long-term yield decline but also to spatial variation in yield within fields. Understanding how fungal communities vary across productivity gradients can therefore provide valuable insights into belowground factors influencing plant performance. Differences in productivity may be associated with shifts in beneficial or pathogenic fungi as well as with variation in soil nutrient levels, but these relationships are not well understood in perennial alfalfa. To examine how fungal communities and key soil properties correspond to field-scale productivity patterns, we compared soil and root fungal communities from high- and low-productivity zones within an alfalfa stand over two consecutive years. Specifically, we addressed the following questions: (i) do measured soil chemical properties relate to spatial variation in alfalfa biomass and fungal community structure in high- and low-productivity zones? (ii) to what extent do fungal communities differ between soil and roots given contrasting niches and plant-mediated selection? (iii) to what extent does the root-associated fungal community differ between high- and low-productivity zones, and how stable are these communities across years? and, (iv) how do the distributions of putative pathogenic and beneficial fungal taxa relate to the observed productivity gradient in alfalfa? 2. Materials and Methods 2.1. Field site description and sampling Alfalfa ( Medicago sativa L.) plants and soil samples were collected from an organically managed field (“Kreuz”) at the Gladbacherhof Research Farm (Justus Liebig University Giessen) located in Villmar, Hesse, Germany. The field site and UAV-based productivity mapping approach correspond to the same study area described by (Weigelt et al. 2025). The field is situated in the Taunus region (50.39706°N, 8.250238°E) at 176–188 m above sea level. Long-term (1990–2020) mean annual precipitation is 653 mm and mean annual temperature is 9.5°C (on-farm weather station records). The soil at the site is classified as an Orthic Luvisol. The stand was established on 09 May 2021 as a forage mixture consisting of Medicago sativa (15.5 kg ha⁻¹), Lolium perenne (4 kg ha⁻¹), Festuca pratensis (4 kg ha⁻¹), Phleum pratense (4 kg ha⁻¹) and Trifolium repens (0.5 kg ha⁻¹). The field was harvested four times per year. Productivity zones were mapped once approximately four weeks prior to the first sampling using multispectral images captured by a MicaSense RedEdge-M camera mounted on a DJI Matrice 300 RTK drone (Weigelt et al. 2025). Normalized Difference Vegetation Index (NDVI) maps derived from the multispectral data were used to evaluate spatial variability in alfalfa canopy vigor and biomass production. UAV data acquisition and processing followed Weigelt et al. (2025), and NDVI maps were used here solely to stratify sampling locations into likely high- and low-productivity zones. Based on NDVI patterns, 14 areas (seven high- and seven low-productivity zones) were selected and georeferenced using a high-precision GPS receiver. Sampling was conducted in these areas in two consecutive years one (17 May 2022) or two (23 May 2023) days prior to mowing. At each sampling point, 5–8 alfalfa plants and the surrounding soil within an approx. 50 cm radius of the georeferenced GPS location were carefully collected using a spade. The soil mass adhering to and surrounding the roots was collected directly from the spade for subsequent analyses, while the root systems were placed into a separate labeled bag. All samples were kept cool in insulated containers with frozen ice packs. 2.2. Assessments of plant biomass, root health and soil properties To verify that NDVI patterns reflected actual biomass differences between high- and low-productivity zones, fresh plant above-ground biomass was recorded shortly after collection. Dry biomass was determined after oven-drying the plant material at 105°C to a constant weight (approximately 48 h). Roots were kept cool and, in the laboratory, thoroughly washed under running tap water to remove soil debris. Clean roots were then visually examined within 24h from sampling for symptoms of root rot rated on a 1–9 scale, where 1 indicated a healthy plant and 9 indicated a dying plant, following the method described previously (ref). After visual inspection, roots were frozen at − 18°C until DNA extractions and further processing. Soil samples were air-dried, sieved (2 mm) and analyzed for key physicochemical properties by a commercial laboratory. Soil pH, mineral nitrogen (Nmin), magnesium (Mg), potassium (K), phosphorus (P) and carbon-to-nitrogen ratio (C/N) were determined in compliance with the standard DIN EN ISO/IEC 17025:2018-03. Phosphorus and potassium were reported as P₂O₅ and K₂O. For consistency with scientific literature, values were converted to elemental P and K using standard conversion factors (P = P₂O₅ × 0.436; K = K₂O × 0.830) 2.3. DNA extraction and sequencing of fungal communities Soil DNA was extracted using the DNeasy PowerSoil Kit (Qiagen, Germany) according to the manufacturer’s instructions. A subsample of six roots per sampling point (7 high and 7 low productivity zones) was used for total genomic DNA extraction. Root samples were first surface-sterilized with 3% NaOCl for 10 s, rinsed in 70% ethanol and thoroughly washed under sterile distilled water (Baresel et al. 2025). Roots were then cut to small pieces, freeze-dried for 48 h and ground to a fine powder using a ball mill. Genomic DNA was extracted from 100 mg of lyophilized tissue following the protocol of (Sreelakshmi et al. 2010). DNA quantity and quality were assessed using a NanoDrop spectrophotometer and the extracts were stored in TE buffer at − 18°C until analysis. DNA extracts were initially submitted to an external sequencing provider; however, all DNA extracts from the 2022 sampling campaign were lost during processing and could not be recovered. Sequencing was therefore repeated with LGC Genomics (Berlin, Germany). As a result, sequencing data were obtained for root samples from both years, but for soil samples only from 2023. The ITS1 region was amplified with primers ITS1-F (Gardes and Bruns 1993) and ITS2 (White et al. 1990), each extended at the 5′ end with unique error-tolerant barcodes for multiplexed sequencing. Amplicons were generated following the standard Illumina MiSeq ITS metabarcoding workflow provided by LGC Genomics (Berlin, Germany). PCR cycling conditions were 96°C for 1 min, followed by 33 cycles of 96°C for 15 s, 55°C for 30 s and 70°C for 90 s. Amplicons were verified by gel electrophoresis, pooled, purified to remove primer dimers and short fragments, and converted into Illumina libraries. Libraries were sequenced on an Illumina MiSeq platform using v3 chemistry. 2.4. Sequencing data processing and bioinformatic analysis Sequencing data were processed by LGC Genomics (Berlin, Germany) using a standardized bioinformatics pipeline. Raw read quality was assessed using FastQC v0.11.9 (Andrews 2010). Demultiplexing was performed using Illumina bcl2fastq v2.20, followed by sorting of reads according to amplicon inline barcodes. Adapter sequences were removed and reads shorter than 100 bp were discarded. Primer sequences were detected allowing up to three mismatches per primer, and reads lacking complete primer pairs were removed. Forward and reverse reads were merged using BBMerge v34.48 (Bushnell 2014). OTUs were generated using Mothur v1.35.1 (Schloss et al. 2009) with chimera removal using the UCHIME algorithm, followed by clustering with CD-HIT-EST v4.6.1 at 97% sequence identity (Li and Godzik 2006). Taxonomic classification was performed against the UNITE v6 reference database (Abarenkov et al. 2024). Singleton OTUs were excluded from downstream analyses. 2.5. Data analysis All data analyses were performed in R version 4.4.1. (R Core Team 2013). The effects of productivity zone (high vs. low), year (2022, 2023) and their interaction on plant biomass and soil properties were evaluated using linear models (Rencher and Schaalje 2008). Significant main factor effects were evaluated with ANOVA followed by Tukey’s comparison of group means (P < 0.05) using the package emmeans (Lenth 2016). When assumptions of homoscedasticity were violated, generalized least squares (GLS) models were fitted using the gls function from the nlme package (Pinheiro et al. 2025), applying a variance structure to account for unequal variances between years and productivity zones (weights = varIdent(form = ~ 1 | year * productivity)). All examined roots appeared completely healthy in both sampling years and were therefore not subjected to any statistical analyses. The fungal community sequencing data were analyzed using the phyloseq package (McMurdie and Holmes 2013). Prior to analyses, unidentified and non-fungal OTUs at the kingdom level were excluded from data set. In addition, to minimize tag-switching effects and account for potential PCR errors, OTUs with fewer than five reads per sample were set to zero and OTUs with fewer than ten total reads across all samples were also removed from the dataset (Lindahl et al. 2013; Oliver et al. 2015). Alpha-diversity metrics were calculated from the rarefied dataset normalized to the lowest number of reads. Alpha diversity was estimated using the Shannon diversity, Simpson richness and Chao1 indices in r package RAM (Chen et al. 2015). Differences in alpha-diversity among compartments (soil vs. root), productivity zones and years were evaluated using the non-parametric Kruskal–Wallis test using the the ‘agricolae’ package (Mendiburu 2014). When significant differences were observed (P < 0.05), pairwise comparisons of mean ranks were performed using the Kruskal multiple comparison procedure. To account for multiple testing, false discovery rates were controlled using the Benjamini–Hochberg correction (Benjamini and Hochberg 1995). Beta-diversity analyses were based on Bray–Curtis dissimilarities to assess differences in fungal community composition among experimental factors. Prior to analysis, count data were normalized using variance-stabilizing transformation in the DESeq2 package (McMurdie and Holmes 2014). Ordination was performed via Constrained Analysis of Principal Coordinates (CAP) using the ‘ordinate’ function in phyloseq (McMurdie & Holmes, 2013). The homogeneity of multivariate dispersions (PERMDISP) was tested using the ‘betadisper’ function (Anderson et al. 2006; Oksanen et al. 2009) to check the variance among groups. Community-level differences were evaluated by PERMANOVA using the ‘adonis2’ function in vegan (Oksanen et al. 2009) with 9999 permutations. To assess the overlap and persistence of fungal taxa among years and roots from different productivity zones, a Venn diagram was constructed to visualize shared and unique OTUs across root-associated fungal communities (Chen and Boutros 2011). This was complemented by a turnover analysis, in which OTUs were classified as either “shared” (present in both years within a productivity zone) or “dynamic” (present in only one of the two years), and the relative abundance of each group was quantified to evaluate whether temporal variation involved dominant or low-abundance taxa. The ten most abundant taxa were identified based on their relative abundance, calculated separately for soil and root compartments to highlight dominant community members within each habitat. Core root taxa, defined as those with a minimum relative abundance of 0.1% occurring in at least 75% of samples, were identified using the ‘core_members’ function in the microbiome package (Lahti and Shetty 2017). In addition, indicator taxa associated with roots and productivity zones were determined using the indicspecies package (De Cáceres et al. 2010). Functional guilds were assigned using the FUNGuild in Python (Nguyen et al. 2016). OTUs assigned to any guild category containing the term “pathogen” were grouped and presented collectively as potential pathogens in data analyses. 3. Results 3.1. Plant biomass, root health, soil and weather properties Alfalfa plants collected from field areas classified as high-productivity zones based on drone imagery analysis had across years approx. 55% higher biomass (p < 0.001) compared to those from low-productivity zones (Table 1 ). These differences were more pronounced in the first sampling year (73% higher) than in the second (49% higher), as the fresh biomass in the low productivity zones had increased by a factor of 2.8 compared to 2.4 in the high productivity zones. Roots from all plants appeared healthy in both years, with no visible symptoms of root rot or any other disease observed. In 2022, the soil C:N ratio was lower in the high- than in low-productivity zones, whereas the opposite was observed in 2023. Across both years, high-productivity zones had consistently lower soil pH and higher concentrations of P, K and Mg than low-productivity zones (Table 1 ). While differences in P and K were considerably smaller in 2023,, the differences in Mg (on average 81% higher in high-productivity zones) remained high across years. Soil mineral nitrogen was 21% higher (p = 0.09) in the high compared to the low productivity zone in 2022 but 18% lower in 2023 (p = 0.0495) (Table 1 ). Consequently, soil mineral nitrogen did not correlate with alfalfa biomass (p = 0.83). Weather conditions differed substantially between years (Supplementary Table 1). During stand establishment and the period up to the first sampling (sowing on 09 May 2021 to 17 May 2022), total precipitation in 2021 (652.2 mm) was close to the long-term mean (653.6 mm, 1990–2020), and mean annual temperature in 2021 (9.77°C) was also close to the long-term mean (9.50°C). However, rainfall distribution differed across months, with particularly low precipitation in October and November 2021 (− 53% and − 42% compared to the long-term mean). In early 2022, temperatures were mostly above the long-term mean, especially in January (+ 1.8°C), February (+ 3.3°C) and May (+ 2.3°C). Precipitation during this period was variable, with a very dry March and May 2022 (65% and 40% lower precipitation than the long-term mean), while February and April were 44% and 54% wetter than average, respectively. Between the first sampling (17 May 2022) and the second sampling (23 May 2023), annual precipitation in 2022 (610.3 mm) was only moderately lower than the long-term mean (− 6.6%), but summer rainfall was far below average. July and August 2022 were particularly dry (− 64% and − 89%). In contrast, September 2022 was unusually wet (+ 140%), and precipitation in March, April and May 2023 preceding the second sampling were also above average wet (+ 103%, + 84% and + 40%, respectively). Mean annual temperature was substantially higher than the long-term mean (2022: 11.43°C; 2023: 11.56°C vs 9.50°C). Table 1 Alfalfa biomass and soil chemical properties across field productivity zones. Location / year Fresh biomass [g/plant] Dry biomass [g/plant] C-N ratio pH Nmin [kg/ha] P [mg/100g] K [mg/100g] Mg [mg/100g] High-productivity zones (mean) 19.42 ** 5.07*** 9.67 ns 6.20 *** 31.43 ns 1.81 ** 9.25 * 18.41 *** 2022 11.5 ab 2.46 a 10.06 b 6.17 a 33.14 ab 1.81 b 10.79 c 18.19 b 2023 27.34 c 7.68 c 9.29 a 6.23 a 29.71 a 1.81 ab 7.71 ab 18.63 b Low-productivity zones (mean) 12.51 3.26 10.54 7.29 31.79 1.53 8.18 10.14 2022 6.64 a 1.45 a 12.37 c 7.16 b 27.29 a 1.37 a 9.13 b 10.76 a 2023 18.38 b 5.06 b 8.71 a 7.43 b 36.29 b 1.68 ab 7.23 a 9.51 a Asterisks indicate statistically significant differences between mean values of high- and low-productivity zones according to one-way ANOVA (p < 0.05 *, *< 0.01 **, *< 0.001 ***). Different lowercase letters indicate statistically significant differences among years and productivity zones according to Tukey’s HSD test ( p < 0.05). 3.2. Fungal community structure A total of approx. 1.5 million high-quality sequence reads were obtained after quality filtering, representing 1256 fungal operational taxonomic units (OTUs). Read counts per sample ranged from 8698 to 89288. Rarefaction curves (Supplementary Fig. 1) reached clear plateaus for all samples, indicating that the dataset captured the majority of fungal taxa associated with alfalfa roots. Fungal community structure varied between soils in 2023 (data from 2022 missing) and roots and to a lower extent between productivity zones and years. Alpha diversity metrics were similar in soils from high- and low-productivity zones and consistently higher than in roots (Fig. 1 ). Within roots, fungal diversity tended to be higher in 2022 than in 2023, and in roots from low-productivity zones compared to high-productivity zones. Within-year root associated fungal communities in low-productivity zones showed higher species richness (Chao1 index), while Shannon and Pielou evenness were similar. The constrained ordination (CAP) analysis also indicated distinct differences in fungal community composition (β-diversity) between soil and root samples, as well as between productivity zones and years (Fig. 2 ). The first canonical axis (CAP1, 28.1% of the variance explained) primarily separated soil from root samples, indicating distinct fungal communities associated with each sample type. The second axis (CAP2, 9.6%) separated communities from high- and low-productivity zones. Root samples from different years (2022 and 2023) clustered within the same productivity group, suggesting that temporal variation was less pronounced than differences driven by productivity or compartment (soil vs. root). Overall, sample type (p < 0.001, R² = 0.277) accounted for the largest share of the fungal community variation, followed by much weaker productivity zone (p < 0.001, R² = 0.093) and year effects (p < 0.001, R² = 0.080). Complementary β-dispersion test indicated significant heterogeneity of variance across groups (p < 0.001). Further analysis revealed that this heterogeneity resulted from high variation in structural differences between soil and root fungal communities. When analyzed separately, β-dispersion was non-significant within both soil (p = 0.927) and root samples (p = 0.271), suggesting that the observed differences are due to genuine differences in community composition rather than variance artifacts (Supplementary Table 2). 3.3. Community overlaps and temporal persistence of the root fungal microbiome To examine the overlap of fungal taxa among roots from different productivity zones and sampling years, a Venn diagram was constructed (Fig. 3 A). A total of 61 OTUs were shared among all four root groups (high/low-productivity × 2022/2023), representing a set of persistent taxa consistently detected across years and productivity levels. These taxa accounted for approximately 17% of all OTUs detected in high-productivity roots and 12% in low-productivity roots. Across years within each zone, roots shared 112 OTUs in high-productivity zones (~ 32% of taxa in that zone) and 184 OTUs in low-productivity zones (~ 37%). Although these persistent OTUs represented only about one third of the total richness, they dominated the community by abundance, contributing approximately 85–90% of all reads in both zones Fig. 3 B). The remaining fraction consisted of transient, year-specific OTUs ranging from 48 in high-productivity in 2023 to 124 OTUs in low-productivity roots in 2022, which together accounted for only about 10–15% of total abundance. Additional overlap between productivity zones within the same year comprised 148 shared OTUs in 2022 and 98 in 2023 (Fig. 3 A). 3.4. Taxonomic classification Structural differences observed between soil and root associated fungal communities as well as between productivity zones were clearly visible in the taxonomic profiles. Overall, Ascomycota dominated, particularly in roots, followed by Basidiomycota which occurred at similar rates across samples (Fig. 4 ). Mortierellomycota and unclassified fungi were more abundant in soils, whereas Olpidiomycota were detected at higher levels in alfalfa roots collected in 2022 from both high- and low-productivity zones. At the OTU level (Fig. 5 ) taxa such as Penicillium sp. (OTU 2364), Mortierella sp. (OTU 3501) and Solicoccozyma (OTU 2743) dominated in soils, particularly in productive zones. In contrast, low-productivity zones contained higher abundance of unclassified taxon (OTU 4792) completely absent from high-productivity soils, as well as Cladorrhinum foecundissimum (OTU 3090). These dominant soil taxa were either absent or occurred at very low abundances in root samples. Root samples were characterized by higher relative abundances of Fusarium solani (OTU 3557), Paraphoma radicina (OTU 2521) and an unclassified taxon (OTU 2162) especially in productive zones, while Ascochyta medicaginicola (OTU 4144), Plectosphaerella sp. (OTU 4069), Ilyonectria macrodidyma (OTU 4294) and Olpidium sp. (OTU 4517) were more frequent in roots collected from low-productivity zones. Temporal differences were less pronounced but still evident. Root-associated communities showed moderate shifts between 2022 and 2023, particularly in the relative abundances of Ascochyta medicaginicola (OTU 4144) which decreased in 2023 in both root types, whereas opposite was observed for Paraphoma radicina (OTU 2521) and unclassified taxa (OTU 2993 and OTU 3555). Overall, the most abundant fungal communities reflected strong compartmental structuring (soil vs. root), with moderate productivity-related and year-to-year variation within the root-associated assemblages. The same dominant taxa were present in both sampling years, indicating that temporal shifts were primarily quantitative rather than due to the appearance or disappearance of specific taxa. The core fungal microbiome of alfalfa, representing taxa present in at least 75% of the root samples (i.e., 11 of 14), consisted of 17 key taxa (Table 2 ). Of these, 10 were found in roots collected from both high- and low-productivity zones. Three taxa were part of the core root microbiome in high-productivity zones, namely Paraphoma radicina (OTU 2521), Fusarium redolens (OTU 2871) and an unclassified taxon from the family Nectriaceae (OTU 3555). Four core taxa were associated with roots from low-productivity zones including Olpidium sp. (OTU 4517), Penicillium sp. (OTU 2364), an unclassified Lasiosphaeriaceae taxon (OTU 2740), and Beauveria pseudobassiana (OTU 2960). Complementary indicator species analysis (Table 2 ) identified nine additional root associated taxa linked to productivity. Among them, three taxa were associated with roots collected from high-productivity zones, including Cadophora sp. (OTU 700) and members of the orders Glomerales (OTU 5055) and Helotiales (OTU 3194). Six taxa were associated with low-productivity, comprising two members of Penicillium (OTU 2364) and Serendipita (OTU 1441), an unclassified Lasiosphaeriaceae taxon (OTU 2740), a Sordariomycetes member (OTU 3575) and members from the phylum Ascomycota (OTU 4318, OTU 3375). Table 2 Root-associated core ( minimum relative abundance of 0.1% occurring in at least 75% of the roots) and indicator taxa across productivity zones and years. The table shows taxonomic classification to the highest rank and relative abundance in roots collected from high- and low-productivity zones. Measure Roots from OTU Phylum Order or Family Genus Species Relative abundance (%) High productive Low productive Core OTU High-productivity zone OTU_2521 Ascomycota Dothideomycetes Paraphoma Paraphoma radicina 12.68 1.24 OTU_2871 Ascomycota Nectriaceae Fusarium Fusarium redolens 1.59 0.43 OTU_3555 Ascomycota Nectriaceae - - 5.05 3.18 Low-productivity zone OTU_2364 Ascomycota Aspergillaceae Penicillium Penicillium sp. 0.04 0.62 OTU_2740 Ascomycota Lasiosphaeriaceae 0.00 0.88 OTU_2960 Ascomycota Cordycipitaceae Beauveria Beauveria pseudobassiana 0.14 0.39 OTU_4517 Olpidiomycota Olpidiaceae Olpidium Olpidium sp. 2.8 5.31 Both groups OTU_1314 Ascomycota Nectriaceae Fusarium Fusarium oxysporum 2.57 1.59 OTU_1579 Ascomycota Herpotrichiellaceae Exophiala Exophiala sp. 2.25 2.63 OTU_2458 Ascomycota Phaeosphaeriaceae Paraphoma Paraphoma sp. 1.89 3.83 OTU_2993 Ascomycota Pleosporales 3.32 3.05 OTU_3521 Ascomycota Nectriaceae - - 0.83 1.95 OTU_3557 Ascomycota Nectriaceae Fusarium Fusarium solani 8.47 4.74 OTU_4047 Ascomycota - - - 1.20 1.54 OTU_4069 Ascomycota Plectosphaerellaceae Plectosphaerella Plectosphaerella sp. 1.87 6.23 OTU_4144 Ascomycota Didymellaceae Ascochyta Ascochyta medicaginicola 7.67 10.5 OTU_4294 Ascomycota Nectriaceae Ilyonectria Ilyonectria macrodidyma 3.03 6.74 Indicator species High-productivity zone OTU_3194 Ascomycota Helotiales - - 0.57 0 OTU_700 Ascomycota Helotiales Cadophora Cadophora sp. 0.55 0 OTU_5055 Glomeromycota Glomeraceae - - 0.15 0 Low-productivity zone OTU_3575 Ascomycota Sordariomycetes - - 0.02 0.74 OTU_2364 Ascomycota Aspergillaceae Penicillium Penicillium sp. 0.04 0.62 OTU_2740 Ascomycota Lasiosphaeriaceae - - 0 0.883 OTU_3375 Ascomycota - - - 0 0.319 OTU_4318 Ascomycota - - - 0 0.431 OTU_1441 Basidiomycota Serendipitaceae Serendipita Serendipita sp. 0 0.141 3.5. Putative plant pathogens in soils and roots The relative abundance of putative pathogenic fungi varied between soil and root samples. In soil, putative pathogens accounted for approx. 25% of the fungal community, with only minor differences between low-productive and productive zones. In contrast, root samples contained substantially higher proportions of putative pathogenic taxa, representing around 65% of the community, with no apparent effect of productivity or sampling year (Fig. 6 ). 4. Discussion This study investigated whether persistent alfalfa productivity differences within an organically managed field could be explained by key soil chemical properties and/or root- and soil-associated fungal communities. Across both sampling years, biomass remained consistently higher in zones classified as high productivity, confirming that UAV-based NDVI mapping captured spatial patterns in plant performance correctly. Plants collected in the high-productivity zones produced about 55% more biomass than those in the low-productivity zones, yet plants in both groups appeared healthy and showed no signs of nutrient deficiency or disease. At the same time, strong year-to-year differences in yield in both productivity zones likely reflected stand establishment effects and warmer-than-average conditions in early 2022 combined with particularly low rainfall in March and May. Interestingly, the relative difference in biomass between zones decreased from 73% in 2022 to 49% in 2023. Together, these results indicate that strong year-to-year variation in weather influenced overall biomass production, while persistent within-field factors maintained the spatial yield gradient. While soil phosphorus, potassium and especially magnesium levels were consistently higher in the high-productive zones for phosphorus and potassium the differences were lower in 2023 than in 2022. Overall, the absolute values in the low-productivity zones remained within ranges typically considered adequate for alfalfa production (Meyer et al. 2007; Franzen and Berti 2023; Baker et al. 2024). Phosphorus (14–18 ppm) was within the recommended range (10–20 ppm), and potassium (72–108 ppm) was slightly below the optimal range for multi-cut systems (> 125–150 ppm) but similarly low in both zones and therefore unlikely to explain the large biomass differences. Even magnesium (95–186 ppm), which showed the largest contrast, was still within the lower end of the optimum range in low-productivity zones, as deficient soils are considered below 50–100 ppm (Undersander et al. 2011). The switch of mineral N and C:N ratios with Nmin 21% higher and 18% lower in high- compared to low-production zones in 2022 and 2023, respectively overall is surprising as no external inputs were made and could be a reflection of more intensive biological N-fixation under lower N-conditions (Binacchi et al. 2023). The difference of soil pH by about one unit (6.20 in productive vs. 7.29 in low-productivity zones, respectively) was also unlikely to cause differences as both values were within the recommended range for forage legumes (6.0–7.5), and neither approached levels reported to suppress nodulation or N fixation (Rice et al. 1977). With respect to fungal communities, if nutrients were the major driver of the microbiome structure, stronger and more consistent changes in α- or β-diversity would be expected, particularly in soils where nutrient effects are most clearly manifested (Berg and Smalla 2009; Xu et al. 2012; Wang et al. 2017; Bay et al. 2021). Instead, the community wide differences were minor: soil α-diversity was similar in both zones and productivity explained a small portion of β-diversity (R² ~0.09). Overall, while soil fertility varied across zones, the magnitude of these direct and indirect effects mediated via shifts in soil fungal communities was likely too small to account for the ~ 55% biomass contrast and therefore unlikely to be the primary driver of the observed productivity differences. The strongest structuring factor of the fungal communities was the compartment (soil vs. root), which explained the largest portion of variation (R² ~0.28) and far more than either productivity zone or sampling year. This agrees with previous studies which showed that host selection is the dominant force shaping microbial community assembly (Berg and Smalla 2009; Li et al. 2022). The soil communities in 2023 showed consistently higher richness and evenness whereas root communities represented a selected subset of soil fungi in both years, a well-described pattern in legumes and other crops (Bainard et al. 2017; Bay et al. 2021; Li et al. 2022). Several phyla that were abundant in soil, including Mortierellomycota , Zoopagomycota and Rozellomycota , were strongly reduced or nearly absent in roots. In contrast, Ascomycota which constituted about half of the soil fungal communities, accounted for 70–95% of the root communities across years and productivity zones. At the OTU level, soil samples were dominated by predominantly saprotrophic and free-living taxa such as Penicillium spp. (OTU2364), Mortierella sp. (OTU3501) (Tamayo-Vélez and Osorio 2018; Ozimek and Hanaka 2021), Solicoccozyma (OTU2743) (Glushakova et al. 2021; Yang et al.) and Cladorrhinum foecundissimum (OTU3090) (Suzuki 2009; Barrera et al. 2019), fungi commonly reported to be abundant in agricultural soils. These were either absent or present only at very low rates in roots, reflecting their primary role as decomposers rather than root colonizers. In contrast, roots contained a distinct set of fungi that were either much more abundant or almost exclusive to the roots. Taxa such as Fusarium solani (OTU3557), Plectosphaerella sp. (OTU4069) and Ascochyta medicaginicola (OTU4144) occurred in both soil and roots but consistently at higher rates in roots, whereas several other taxa, including Paraphoma sp. (OTU2458), Paraphoma radicina (OTU2521), Ilyonectria macrodidyma (OTU4294) and Olpidium sp. (OTU4517) were predominantly or exclusively root-associated. These fungi are well adapted to living plant tissues and the nutrient-rich environment and are generally poor competitors in soil (Hornby 1998; Farr and Rossman 2025; Singh et al. 2025). Productivity-related differences in the overall root associated fungal community structure were detectable but minimal. Only in 2022, α-diversity in roots was significantly higher in low-productivity zones, while overall richness, Shannon diversity and evenness were similar between productivity zones and years. The absence of a consistent pattern suggests that productivity had only a weak influence on root fungal diversity and that host-driven selection was the primary factor shaping root fungal community structure. The β-diversity in this study further supports this, as productivity explained only ~ 9% of community variation, a minor effect compared with the influence of host selection (28%). While host selection is widely recognized as a key determinant of plant-associated microbiomes (Berg and Smalla 2009; Li et al. 2022), multi-year analyses in perennial forage crops remain scarce, as most agricultural microbiome studies addressed management effects and/or seasonal variability within a single year (e.g. Shi et al. 2015; Hilton et al. 2018; Banerjee et al. 2019; Bay et al. 2021). In our study, root-associated fungal communities contained both stable and variable components across years. Approximately one third of all OTUs detected within each productivity zone occurred in both 2022 and 2023, and 61 OTUs were shared across all four sample groups, representing a persistent portion of root-associated mycobiome. The α- and β-diversity patterns including the turnover analysis all confirmed that these persistent taxa represented the predominant portion of the community, whereas most year-to-year variation involved low-abundance taxa. Measures of α-diversity, including Shannon and evenness remained similar across years and zones showing that the overall balance between common and rare taxa remained stable. Likewise, year explained only a small proportion of β-diversity variation (R² ~0.08), consistent with quantitative adjustments among rare taxa rather than shifts in dominant groups. This was further reflected in the turnover analysis, where persistent OTUs accounted for 85–90% of total abundance, while year-specific taxa contributed only 10–15%. These results suggest that alfalfa hosts a resilient and predictable fungal community dominated by taxa that remain stable across contrasting years. Given that productivity-related differences were subtle relative to the strong compartment effect, much of the observed structure reflects host selection acting on the local soil fungal pool. Multi-year temporal sampling of plant root microbiomes has been conducted far more extensively in annual crops (Mavrodi et al. 2018; Stopnisek and Shade 2021; Chen et al. 2022; Lupwayi et al. 2022; Quiza et al. 2023; Yang et al. 2025), whereas long-term studies in agricultural perennial crops remain limited and includes mostly fruit trees, shrubs and orchids (Zeng et al. 2021; Becker et al. 2022; Macaya-Sanz et al. 2023; Argiroff et al. 2024). These studies also reported a stable dominant microbiome with temporal turnover largely confined to low abundance taxa, suggesting that a persistent root mycobiome with a dynamic rare fraction may be characteristic of many perennial crops. Such fungi are likely to contribute to nutrient acquisition, stress tolerance and hormonal modulation, however, most existing studies remain descriptive and the specific functional consequences for the host require further investigation. Root communities contained a surprisingly high proportion of putative pathogenic fungi (~ 65%), yet all sampled plants looked healthy with no symptoms of disease. It should be noted that FUNGuild assigns “putative pathogens” based on broad guild annotations, and many of these taxa may not be pathogenic to legumes, exhibit context-dependent lifestyles and commonly occur as endophytes and/or saprotrophs in roots. Therefore, while functional roles based on OTU data must be interpreted with caution, the repeated detection of these taxa across years and productivity zones points to a background reservoir of stress-responsive fungi that commonly inhabit healthy alfalfa plants. Many of the dominant (top 10) taxa, however, including Fusarium solani, Plectosphaerella sp., Ascochyta medicaginicola, Paraphoma spp., Ilyonectria macrodidyma and Olpidium sp., are well known pathogens of legumes and other crops (Farr and Rossman 2025). Their abundances varied across years and productivity zones without a consistent increase in roots from low-productivity zones, indicating that these fungi alone are unlikely to explain the observed productivity gradient. Instead, their persistent presence in roots suggests that these fungi exist likely as tolerated endophytes rather than active disease agents. This is consistent with our earlier findings in small seeded and grain legumes which reported similar pathogen spectra in symptomatic and asymptomatic plants (Šišić et al. 2018b, a, 2022, 2025; Baresel et al. 2025). Such asymptomatic colonizations result from a balanced antagonism between host defense and fungal pathogenicity factors (Schulz and Boyle 2005; Liao et al. 2025) which results in disease often only after abiotic stresses occur. While pathogens can persist in root tissue without triggering visible disease, sustaining these interactions however, may require metabolic costs for the host. To what extent these interactions influenced plant growth in our study, and whether such ‘hidden costs’ were greater in low-productivity zones and contributed to the biomass differences remains unknown and needs further study. An important issue is that we sampled plant groups in productivity zones. It is well possible that sampling on a per plant basis, taking the exact biomass produced by a given plant and the microbiome per plant could yield more specific results. In contrast to pathogens, differences in a small number of but functionally important taxa might have had a stronger influence on plant performance. Shifts in key fungal groups can affect nutrient uptake, stress responses and overall plant health without major changes in community-wide diversity patterns (Gu et al. 2022; Sena et al. 2024). Core and indicator taxa are particularly useful in this context (Zhou et al. 2024) because they identify fungi that occur consistently within a group and are therefore more likely to exert a stronger effect on the host than other members. However, in our study, core taxa patterns did not align with a simple presence–absence explanation of the productivity gradient. Several taxa with known pathogenic potential were part of the core community in both zones, and even zone-specific core taxa included taxa commonly reported as pathogens. For example, Paraphoma radicina (OTU 2521) and members of Nectriaceae (OTU 3555) and Fusarium redolens (OTU 2871) were core taxa in productive roots, indicating that productivity differences cannot be explained by pathogen presence–absence alone (Šišić et al. 2018b, 2022, 2025). In contrast, roots from non-productive zones contained a distinct set of core taxa including Olpidium sp. (OTU 4517), which has been frequently associated with reduced plant performance (Farr and Rossman 2025), as well as Penicillium sp. (OTU 2364), an unclassified Lasiosphaeriaceae taxon (OTU 2740) and Beauveria pseudobassiana (OTU 2960). While these taxa are not classical root pathogens, their consistent occurrence may reflect differences in root physiological status and overall soil conditions in low-productivity areas. Indicator taxa associated with roots from high-productivity zones, namely Cadophora sp. (OTU 700), a Helotiales member (OTU 3194) and a Glomeraceae taxon (OTU 5055), points to a different (functional) pattern than that observed in low-productivity roots. These fungi have been reported to support nutrient uptake and improve plant tolerance to soil or water stress (Lee et al. 2013; Knapp et al. 2018; Yakti et al. 2019; Abdalla et al. 2023; Das and Sarkar 2024; Li et al. 2025; de Oliveira and Pereira 2025). Their presence may have been relevant during the dry establishment year in 2022, when water and nutrient acquisition were likely more limiting. While these associations do not demonstrate causation, they indicate that roots in high-productivity zones hosted fungal partners with a greater potential to support plant health than those found in low-productivity zones. Taken together, our results show that the strong and spatially stable productivity differences in this field cannot be fully explained by variation in measured soil chemical properties or by broad shifts in fungal diversity or community structure. Soil nutrient levels in low-productivity zones were generally within ranges considered adequate for alfalfa, and productivity explained only a small proportion of fungal β-diversity compared with the strong compartment effect and host-mediated selection. Most dominant root-associated fungi persisted across years and productivity zones, forming a stable root mycobiome in which taxa classified as putative pathogens were common. The absence of visible disease symptoms suggests that alfalfa can maintain largely asymptomatic associations with these fungi under field conditions, likely reflecting a balance between fungal pathogenic potential and host control. Although core taxa did not provide a simple explanation for the productivity gradient, differences in indicator taxa and in a small subset of consistently associated fungi suggest that specific root-associated taxa may still contribute to plant performance, particularly through effects on nutrient acquisition and stress tolerance. However, given the weak zone effect on overall community structure, fungal communities are unlikely to be the primary driver of the ~ 55% biomass contrast. Instead, the productivity gradient likely reflects a combination of additional edaphic and other biological factors not captured by the present study. To further clarify the mechanisms underlying persistent within field yield differences, future work should include measurements of soil physical constraints, root system architecture, rhizobial symbiosis efficiency and broader multi-trophic interactions, including bacteria, protists and nematodes. Statements and Declarations Acknowledgments: We thank Jayan Wijesingha, Matthias Wengert and Alborz Saidi for conducting the drone flights and providing the remote-sensing data. Funding This study was funded by the LOEWE priority program ‘GreenDairy – Integrated Livestock-Plant-Agroecosystems’ of Hesse’s Ministry of Higher Education, Research, and the Arts, grant number LOEWE/2/14/519/03/07.001-(0007)/80. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Conceptualization: Adnan Šišić, Maria R. Finckh, Andreas Gattinger and Jelena Baćanović-Šišić; Methodology: Adnan Šišić and Deise A. Knob; Investigation and data collection: Adnan Šišić and Jelena Baćanović-Šišić; Formal data analysis and visualization: Adnan Šišić and Leonard V. Theisgen; Writing – original draft: Adnan Šišić, Leonard V. Theisgen and Jelena Baćanović-Šišić; Writing – review and editing: All authors; Funding acquisition: Adnan Šišić, Maria R. Finckh, Andreas Gattinger. All authors read and approved the final manuscript. Data Availability All relevant data are included within the article and its supplementary materials. The datasets generated during and/or analysed during the current study are also available from the corresponding author on reasonable request. References Abarenkov K, Nilsson RH, Larsson KH, et al (2024) The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Res 52:D791–D797. https://doi.org/10.1093/nar/gkad1039 Abdalla M, Bitterlich M, Jansa J, et al (2023) The role of arbuscular mycorrhizal symbiosis in improving plant water status under drought. J Exp Bot 74:4808–4824. https://doi.org/10.1093/jxb/erad249 Akter S, Mahmud U, Shoumik BAA, Khan MdZ (2025) Although invisible, fungi are recognized as the engines of a microbial powerhouse that drives soil ecosystem services. Arch Microbiol 207:79. https://doi.org/10.1007/s00203-025-04285-4 Anderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecology Letters 9:683–693. https://doi.org/10.1111/j.1461-0248.2006.00926.x Andrews S (2010) FastQC: a quality control tool for high throughput sequence data Argiroff WA, Carrell AA, Klingeman DM, et al (2024) Seasonality and longer-term development generate temporal dynamics in the Populus microbiome. mSystems 9:e00886-23. https://doi.org/10.1128/msystems.00886-23 Bainard LD, Navarro-Borrell A, Hamel C, et al (2017) Increasing the frequency of pulses in crop rotations reduces soil fungal diversity and increases the proportion of fungal pathotrophs in a semiarid agroecosystem. Agriculture, Ecosystems & Environment 240:206–214. https://doi.org/10.1016/j.agee.2017.02.020 Baker M, Yost M, Anderson C, et al (2024) Alfalfa nutrient management guide. Utah State University Extension Banerjee S, Walder F, Büchi L, et al (2019) Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J 13:1722–1736. https://doi.org/10.1038/s41396-019-0383-2 Baresel JP, Šišić A, Pittam AR, et al (2025) Organic minimum tillage systems based on intensification of legume cover cropping: agronomic and phytopathological aspects. Agron Sustain Dev 45:35. https://doi.org/10.1007/s13593-025-01029-y Barrera VA, Martin ME, Aulicino M, et al (2019) Carbon-substrate utilization profiles by Cladorrhinum (Ascomycota). Revista Argentina de Microbiología 51:302–306. https://doi.org/10.1016/j.ram.2018.09.005 Bay G, Lee C, Chen C, et al (2021) Agricultural management affects the active rhizosphere bacterial community composition and nitrification. mSystems 6:10.1128/msystems.00651-21. https://doi.org/10.1128/msystems.00651-21 Becker MF, Hellmann M, Knief C (2022) Spatio-temporal variation in the root-associated microbiota of orchard-grown apple trees. Environ Microbiome 17:31. https://doi.org/10.1186/s40793-022-00427-z Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57:289–300. https://doi.org/0035-9246/95/57289. Berg G, Smalla K (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1–13. https://doi.org/10.1111/j.1574-6941.2009.00654.x Binacchi F, Niether W, Brock C, et al (2023) Demystifying the agronomic and environmental N performance of grain legumes across contrasting soil textures of central Germany. Agriculture, Ecosystems & Environment 356:108645. https://doi.org/10.1016/j.agee.2023.108645 Bushnell B (2014) BBMap: A Fast, Accurate, Splice-Aware Aligner Chen H, Boutros PC (2011) VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12:35. https://doi.org/10.1186/1471-2105-12-35 Chen J, Sharifi R, Khan MS, et al (2022) Wheat microbiome: structure, dynamics, and role in improving performance under stress environments. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.821546 Chen W, Simpson J, Levesque A (2015) RAM: R for amplicon-sequencing-based microbial-ecology. R package version 1 2 1 7 Das S, Sarkar S (2024) Arbuscular mycorrhizal fungal contribution towards plant resilience to drought conditions. Front Fungal Biol 5:1355999. https://doi.org/10.3389/ffunb.2024.1355999 De Cáceres M, Jansen F, Endicott S, Dell N (2010) indicspecies: relationship between species and groups of sites. 1.8.0 de Oliveira JA, Pereira OL (2025) Opening the black box of an underexplored and megadiverse group: history, taxonomy and functionality of dark septate endophytes (DSE). Fungal Biology Reviews 54:100457. https://doi.org/10.1016/j.fbr.2025.100457 Desta AG (2025) The role of alfalfa (Medicago sativa L.) in soil health and greenhouse gas mitigation in integrated crop-livestock systems: a review. Archives of Agronomy and Soil Science 72:1–16. https://doi.org/10.1080/03650340.2025.2610090 Farr DF, Rossman AY (2025) Fungal Databases, Systematic Mycology and Microbiology Laboratory, ARS, USDA. http://nt.ars-grin.gov/fungaldatabases/. Fernández-Ortega J, Cantero-Martínez C (2025) Influence of soil management and previous crop nitrogen fertilization on alfalfa productivity in Mediterranean agroecosystems. Field Crops Research 329:109963. https://doi.org/10.1016/j.fcr.2025.109963 Finckh MR, Yli-Mattila T, Nykänen A, et al (2015) Organic temperate legume disease management. In: Finckh MR, van Bruggen AHC, Tamm L (eds) Plant diseases and their management in organic agriculture. APS press, Saint Paul, Minnesota, USA, pp 293–311 Franzen DW, Berti M (2023) Alfalfa soil fertility requirements in North Dakota soils. North Dakota State University Fuchs JG, Thuerig B, Brandhuber R, et al (2014) Evaluation of the causes of legume yield depression syndrome using an improved diagnostic tool. Applied Soil Ecology 79:26–36. https://doi.org/10.1016/j.apsoil.2014.02.013. Gardes M, Bruns TD (1993) ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol Ecol 2:113–118. https://doi.org/10.1111/j.1365-294x.1993.tb00005.x Glushakova AM, Lysak LV, Kachalkin AV, et al (2021) Transformation of microbial Complexes in Components of Soil Constructions of Different Origin (Soil, Peat, Sand) during Freezing-thawing Processes. Microbiology 90:176–186. https://doi.org/10.1134/S002626172102003X Groß J, Gentsch N, Boy J, et al (2023) Influence of small-scale spatial variability of soil properties on yield formation of winter wheat. Plant Soil 493:79–97. https://doi.org/10.1007/s11104-023-06212-2 Gu Y, Banerjee S, Dini-Andreote F, et al (2022) Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations. ISME J 16:2448–2456. https://doi.org/10.1038/s41396-022-01290-z Hilton S, Bennett AJ, Chandler D, et al (2018) Preceding crop and seasonal effects influence fungal, bacterial and nematode diversity in wheat and oilseed rape rhizosphere and soil. Applied Soil Ecology 34–46. https://doi.org/10.1016/j.apsoil.2018.02.007 Hornby D (1998) Diseases caused by soilborne pathogens. In: Jones DG (ed) The Epidemiology of Plant Diseases. Springer Netherlands, Dordrecht, pp 308–322 Knapp DG, Németh JB, Barry K, et al (2018) Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci Rep 8:6321. https://doi.org/10.1038/s41598-018-24686-4 Lahti L, Shetty S (2017) Lee E-H, Eo J-K, Ka K-H, Eom A-H (2013) Diversity of Arbuscular Mycorrhizal Fungi and Their Roles in Ecosystems. Mycobiology 41:121–125. https://doi.org/10.5941/MYCO.2013.41.3.121 Lenth RV (2016) Least-Squares Means: The R package lsmeans. J Stat Soft 69:1–33. https://doi.org/10.18637/jss.v069.i01 Li F, Jin Z, Wang Z, et al (2022) Host plant selection imprints structure and assembly of fungal community along the soil-root continuum. mSystems 7:e00361-22. https://doi.org/10.1128/msystems.00361-22 Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. https://doi.org/10.1093/bioinformatics/btl158 Li Y, Shen Y, Zheng R, et al (2025) Arbuscular mycorrhizal fungus reshapes the rhizosphere microbiome of alfalfa in response to above-ground attack by aphids and a fungal plant pathogen. Functional Ecology 39:2149–2169. https://doi.org/10.1111/1365-2435.70078 Liao C, Doilom M, Jeewon R, et al (2025) Challenges and update on fungal endophytes: classification, definition, diversity, ecology, evolution and functions. Fungal Diversity 131:301–367. https://doi.org/10.1007/s13225-025-00550-5 Lindahl BD, Nilsson RH, Tedersoo L, et al (2013) Fungal community analysis by high-throughput sequencing of amplified markers – a user’s guide. New Phytol 199:288–299. https://doi.org/10.1111/nph.12243 Lupwayi NZ, Blackshaw RE, Geddes CM, et al (2022) Multi-year and multi-site effects of recurrent glyphosate applications on the wheat rhizosphere microbiome. Environmental Research 215:114363. https://doi.org/10.1016/j.envres.2022.114363 Ma Y, Shen Y, Zhou X, et al (2024) Biological decline of alfalfa is accompanied by negative succession of rhizosphere soil microbial communities. Plants 13:. https://doi.org/10.3390/plants13182589 Macaya-Sanz D, Witzell J, Collada C, et al (2023) Core endophytic mycobiome in Ulmus minor and its relation to Dutch elm disease resistance. Front Plant Sci 14:. https://doi.org/10.3389/fpls.2023.1125942 Mavrodi DV, Mavrodi OV, Elbourne LDH, et al (2018) Long-term irrigation affects the dynamics and activity of the wheat rhizosphere microbiome. Front Plant Sci 9:. https://doi.org/10.3389/fpls.2018.00345 McMurdie PJ, Holmes S (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8:e61217. https://doi.org/10.1371/journal.pone.0061217 McMurdie PJ, Holmes S (2014) Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput Biol 10:e1003531. https://doi.org/10.1371/journal.pcbi.1003531 Mendiburu DF (2014) Agricolae: statistical procedures for agricultural research. R package version 1 2 4 1:1–6 Meyer RD, Marcum DB, Orloff SB, Schmierer JL (2007) Alfalfa fertilization strategies. In: Summers CG, Putnam DH (eds). University of California Agriculture and Natural Resources Publication, Oakland, USA Nguyen NH, Song Z, Bates ST, et al (2016) FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248. https://doi.org/10.1016/j.funeco.2015.06.006 O’Brien SL, Gibbons SM, Owens SM, et al (2016) Spatial scale drives patterns in soil bacterial diversity. Environmental Microbiology 18:2039–2051. https://doi.org/10.1111/1462-2920.13231 Oksanen J, Kindt R, Legendre P, et al (2009) The vegan Package Oliver AK, Callaham MA, Jumpponen A (2015) Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. Forest Ecology and Management 345:1–9. https://doi.org/10.1016/j.foreco.2015.02.020 Ozimek E, Hanaka A (2021) Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11:7. https://doi.org/10.3390/agriculture11010007 Pinheiro P, Douglas B, Saikat D, et al (2025) nlme: Linear and Nonlinear Mixed Effects Models Putnam DH (2021) Factors Influencing Yield and Quality in Alfalfa. In: Yu LX, Kole C (eds) The Alfalfa Genome. Springer International Publishing, Cham, pp 13–27 Quiza L, Tremblay J, Pagé AP, et al (2023) The effect of wheat genotype on the microbiome is more evident in roots and varies through time. ISME Commun 3:32. https://doi.org/10.1038/s43705-023-00238-4 R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria Rencher AC, Schaalje GB (2008) Linear models in statistics, 2nd ed. Wiley-Interscience, Hoboken, N.J Rice WA, Penney DC, Nyborg M (1977) Effects of soil acidity on rhizobia numbers, nodulation and nitrogen fixation by alfalfa and red clover. Can J Soil Sci 57:197–203. https://doi.org/10.4141/cjss77-024 Schloss PD, Westcott SL, Ryabin T, et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541. https://doi.org/10.1128/AEM.01541-09 Schulz B, Boyle C (2005) The endophytic continuum. Mycological Research 109:661–686. https://doi.org/10.1017/S095375620500273X. Sena L, Mica E, Valè G, et al (2024) Exploring the potential of endophyte-plant interactions for improving crop sustainable yields in a changing climate. Front Plant Sci 15:. https://doi.org/10.3389/fpls.2024.1349401 Shi S, Nuccio E, Herman DJ, et al (2015) Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6:10.1128/mbio.00746-15. https://doi.org/10.1128/mbio.00746-15 Singh BK, Jiang G, Wei Z, et al (2025) Plant pathogens, microbiomes, and soil health. Trends in Microbiology 33:887–902. https://doi.org/10.1016/j.tim.2025.03.013 Šišić A, Baćanović-Šišić J, Al-Hatmi AMS, et al (2018a) The ‘forma specialis’ issue in Fusarium : A case study in Fusarium solani f. sp. pisi . Scientific Reports 8:1252. https://doi.org/10.1038/s41598-018-19779-z. Šišić A, Baćanović-Šišić J, Karlovsky P, et al (2018b) Roots of symptom-free leguminous cover crop and living mulch species harbor diverse Fusarium communities that show highly variable aggressiveness on pea ( Pisum sativum ). PLOS ONE 13:e0191969. https://doi.org/10.1371/journal.pone.0191969. Šišić A, Baćanović-Šišić J, Schmidt H, Finckh MR (2022) Farming system effects on root rot pathogen complex and yield of faba bean ( Vicia faba ) in Germany. Frontiers in Plant Science 13:. https://doi.org/https://doi.org/10.3389/fpls.2022.1009906 Šišić A, Baćanović-Šišić J, Schmidt H, Finckh MR (2025) Effect of management system and pedoclimatic factors on Fusarium and Didymella communities associated with pea ( Pisum sativum ) roots in Germany. Sci Rep 15:2653. https://doi.org/10.1038/s41598-025-86018-7 Sreelakshmi Y, Gupta S, Bodanapu R, et al (2010) NEATTILL: A simplified procedure for nucleic acid extraction from arrayed tissue for tilling and other high-throughput reverse genetic applications. Plant Methods 6:3. https://doi.org/10.1186/1746-4811-6-3. Stopnisek N, Shade A (2021) Persistent microbiome members in the common bean rhizosphere: an integrated analysis of space, time, and plant genotype. ISME J 15:2708–2722. https://doi.org/10.1038/s41396-021-00955-5 Suzuki A (2009) Propagation strategy of ammonia fungi. Mycoscience 50:39–51. https://doi.org/10.1007/S10267-008-0453-1 Tamayo-Vélez Á, Osorio NW (2018) Soil fertility improvement by litter decomposition and inoculation with the fungus Mortierella sp. in avocado plantations of Colombia. Communications in Soil Science and Plant Analysis 49:139–147. https://doi.org/10.1080/00103624.2017.1417420 Undersander D (2021) Economic importance, practical limitations to production, management, and breeding targets of alfalfa. In: Yu LX, Kole C (eds) The Alfalfa Genome. Springer International Publishing, Cham, pp 1–11 Undersander D, Cosgrove D, Cullen E, et al (2011) Alfalfa management guide. American Society of Agronomy, Inc, Madison, WI Van Der Heijden MGA, Bardgett RD, Van Straalen NM (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters 11:296–310. https://doi.org/10.1111/j.1461-0248.2007.01139.x Vergopolan N, Xiong S, Estes L, et al (2021) Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrology and Earth System Sciences 25:1827–1847. https://doi.org/10.5194/hess-25-1827-2021 Wang L, Ali G, Wang Z (2023) Deep soil water depletion and soil organic carbon and total nitrogen accumulation in a long-term alfalfa pasture. Land Degradation & Development 34:2164–2176. https://doi.org/10.1002/ldr.4597 Wang R, Liu J, Jiang W, et al (2022) Metabolomics and microbiomics reveal impacts of rhizosphere metabolites on alfalfa continuous cropping. Front Microbiol 13:. https://doi.org/10.3389/fmicb.2022.833968 Wang Z, Li T, Wen X, et al (2017) Fungal communities in rhizosphere soil under conservation tillage shift in response to plant growth. Front Microbiol 8:. https://doi.org/10.3389/fmicb.2017.01301 Watson CA, Atkinson D, Gosling P, et al (2002) Managing soil fertility in organic farming systems. Soil Use and Management 18:239–247. https://doi.org/10.1111/j.1475-2743.2002.tb00265.x Weigelt L, Wengert M, Wachendorf M, Wijesingha J (2025) Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures. Precision Agric 27:8. https://doi.org/10.1007/s11119-025-10301-w White TJ, Bruns T, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for pylogenetics. In: Innins MA, Gelfand DH, Sninsky JJ (eds) PCR protocols: a guide to methods and applications. Academic Press, San Diego, pp 315–322 Xu L, Ravnskov S, Larsen J, Nicolaisen M (2012) Linking fungal communities in roots, rhizosphere, and soil to the health status of Pisum sativum. FEMS Microbiol Ecol 82:736–745. https://doi.org/10.1111/j.1574-6941.2012.01445.x Yakti W, Kovács GM, Franken P (2019) Differential interaction of the dark septate endophyte Cadophora sp. and fungal pathogens in vitro and in planta. FEMS Microbiol Ecol 95:fiz164. https://doi.org/10.1093/femsec/fiz164 Yang L, Zhao H, Wu K, et al Soil Microbial community composition and diversity response to varying farmland types. Land Degradation & Development 0: https://doi.org/10.1002/ldr.70131 Yang M, Schlatter DC, LeTourneau MK, et al (2025) Eight Years in the Soil: temporal dynamics of wheat-associated bacterial communities under dryland and irrigated conditions. Phytobiomes Journal 9:173–188. https://doi.org/10.1094/PBIOMES-02-24-0028-R Zeng X, Diao H, Ni Z, et al (2021) Temporal variation in community composition of root associated endophytic fungi and carbon and nitrogen stable isotope abundance in two Bletilla species (Orchidaceae). Plants 10:18. https://doi.org/10.3390/plants10010018 Zhao J, Chen J, Beillouin D, et al (2022) Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers. Nat Commun 13:4926. https://doi.org/10.1038/s41467-022-32464-0 Zhou Y, Liu D, Li F, et al (2024) Superiority of native soil core microbiomes in supporting plant growth. Nat Commun 15:6599. https://doi.org/10.1038/s41467-024-50685-3 Supplementary Files FigS1.tif SupplementaryTablesAlfalfaMSSisicetal.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9161471","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617082399,"identity":"84dbea26-372c-4f9e-92d0-4cade255f159","order_by":0,"name":"Adnan Šišić","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adnan","middleName":"","lastName":"Šišić","suffix":""},{"id":617082400,"identity":"12cc5f2e-487f-4495-af80-35bb3b525abf","order_by":1,"name":"Leonard V. Theisgen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Leonard","middleName":"V.","lastName":"Theisgen","suffix":""},{"id":617082401,"identity":"d37b0452-ede9-43b7-aa39-9a081c07388a","order_by":2,"name":"Deise A. Knob","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Deise","middleName":"A.","lastName":"Knob","suffix":""},{"id":617082402,"identity":"6abffa5d-482c-44a0-9b62-453c9d0b5654","order_by":3,"name":"Andreas Gattinger","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Gattinger","suffix":""},{"id":617082403,"identity":"c5b80ae8-522a-47ee-91a7-6c67958565e7","order_by":4,"name":"Maria R. Finckh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"R.","lastName":"Finckh","suffix":""},{"id":617082404,"identity":"1cd9c30f-b06c-4343-8f95-1ab37eabe1d7","order_by":5,"name":"Jelena Baćanović-Šišić","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3PMWvCQBTA8RcKuryS9SQFv8KVQoKY1q9yErjphk4ho5MuQlad/ApOnR/cGnQVXCKFTg5CF0Fo+ypScDhIN4f733DL/Xj3AHy+G0xeboT2CEDx4QKCVhOC9EegEQEQ59cNSNK2u8/Xwj6E831c16C73dISQZ46SW+qn6JZZVFsTSIVmMflRiuClXZ/jAxE92ONMjKxGH4VwVKgpGBs3WS9vzudSaeKeZ1isCjXBybfbrIxLZ6SohT4S8xwxHOZkHuX2UfcxypFUemcic54F0lqlTlJEmbvWyzEIJzYt84RsudFaXf1IX9xEkfqv8Dn8/l8V/0AKgBRCgluvcUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0000-3323-3845","institution":"University of Kassel: Universitat Kassel","correspondingAuthor":true,"prefix":"","firstName":"Jelena","middleName":"","lastName":"Baćanović-Šišić","suffix":""}],"badges":[],"createdAt":"2026-03-18 16:11:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9161471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9161471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106498237,"identity":"77df18ff-4f99-4cdb-9102-eb4f69959de7","added_by":"auto","created_at":"2026-04-09 08:42:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":613476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity of fungal communities in soil and root samples collected in high and low biomass producing zones in 2022 and 2023.\u003c/strong\u003eDifferent lowercase letters denote statistically significant differences among groups (Kruskal–Wallis test followed by FDR-adjusted post-hoc comparisons, p \u0026lt; 0.05). Black triangles indicate mean values.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/865356db7de31b11fd12ccfa.png"},{"id":106498235,"identity":"4eb06510-9442-43aa-9b0f-69dd1f152271","added_by":"auto","created_at":"2026-04-09 08:42:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1283368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBeta diversity based on canonical analysis of principal coordinates (CAP) of fungal communities in soil and root samples across productivity zones and years collected from high and low productivity field zones.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/f41a0646b2062e54fe3099c0.png"},{"id":106498242,"identity":"641f3223-3031-45ad-b616-9c13359e801c","added_by":"auto","created_at":"2026-04-09 08:42:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1263033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eVenn diagram showing the overlap and unique fungal OTUs among root samples from high- and low-productivity zones across 2022 and 2023 (A). Mean relative abundance of year-specific and persistent OTUs in roots collected from high- and low-productivity zones across years (B).\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/8c297200b58292b7ae09415b.png"},{"id":106498241,"identity":"3c77ee07-2f01-4d0a-8270-e9e6f1b0a162","added_by":"auto","created_at":"2026-04-09 08:42:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1220843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMost abundant fungal phyla in soil and root samples across productivity zones and years.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/dedc14e308bd9615e0e1158a.png"},{"id":106498234,"identity":"c01631ad-8b72-4a06-abef-db8752054545","added_by":"auto","created_at":"2026-04-09 08:42:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2763181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance patterns of the ten most abundant fungal taxa in soil and root compartments across high- and low-productivity zones in 2022 and 2023.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/0512d8315ceee4f8dc4f9f6d.png"},{"id":106498184,"identity":"e3d5c936-ef46-4130-83ff-fe72f6b9643f","added_by":"auto","created_at":"2026-04-09 08:42:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1188128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of potential pathogenic and non-pathogenic fungal species in soil and roots across productivity zones and years.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/febcb45735ba7c9fb1edeb0e.png"},{"id":108823687,"identity":"800c0712-28b4-4051-ad7f-ed6effdb17ec","added_by":"auto","created_at":"2026-05-08 16:55:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8829444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/6ab001c3-e997-49ec-a7ef-0f15de2c98e6.pdf"},{"id":106498244,"identity":"1f7b0b78-a863-4763-b804-e6e7e9a5c2c3","added_by":"auto","created_at":"2026-04-09 08:42:59","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":188760,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/9bb2dd87e2585898bfcea438.tif"},{"id":106498233,"identity":"912417ed-de6d-47a7-9b5e-bfc7bcda72e1","added_by":"auto","created_at":"2026-04-09 08:42:53","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":17183,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesAlfalfaMSSisicetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9161471/v1/cbfc4ae1323ef75bab7cafff.xlsx"}],"financialInterests":"","formattedTitle":"Root-associated fungal communities along productivity gradients in a perennial alfalfa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLegume-based leys are the foundation of organic crop rotations and strongly affect the performance of subsequent crops (Watson et al. 2002). In a global meta-study, they were shown to enhance succeeding main crop yield by 20% on average (Zhao et al. 2022). Therefore, the successful establishment and productivity of legume-based leys are critical, as impaired performance can have cascading negative effects across the entire crop rotation.\u003c/p\u003e \u003cp\u003eAmong the legumes used in leys, alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e L.) is important forage crop valued for its high biomass production, nitrogen-fixing capacity and nutritional value for livestock. Owing to its high palatability and high concentrations of crude protein, digestible fiber, minerals and vitamins, alfalfa is a high-quality fodder for ruminants and other livestock (Undersander 2021). In livestock farming systems, alfalfa cropping contributes to on-farm protein supply, supporting the closure of nutrient cycles, and reducing dependence on external inputs such as imported soy. Alfalfa leys are usually grown perennially in monoculture or in mixtures with other forage crops such as clovers and grasses. Through biological nitrogen fixation and a deep rooting system, alfalfa enhances soil fertility by increasing soil organic matter and sequestering carbon and nitrogen (Wang et al. 2023; Fern\u0026aacute;ndez-Ortega and Cantero-Mart\u0026iacute;nez 2025). The combined benefits for soil health and animal nutrition make alfalfa an excellent choice in integrated crop-livestock systems and climate-smart agricultural systems (Desta 2025).\u003c/p\u003e \u003cp\u003eDespite its high yield potential, alfalfa yields are often unstable with a substantial yield gap, with maximum attainable yields two- to threefold higher than median on-farm yields. Although yields can be increased through improved agronomic management, environmental constraints, including soil conditions, often limit attainable yields (Putnam 2021). On the field scale, alfalfa yield may exhibit pronounced spatial variability (Weigelt et al. 2025) that has been attributed to heterogeneity in soil properties (Gro\u0026szlig; et al. 2023), soil moisture (Vergopolan et al. 2021) as well as to differences in (soil) microbial communities (O\u0026rsquo;Brien et al. 2016).\u003c/p\u003e \u003cp\u003eSoil microbes are key drivers of plant productivity, given their central role in nutrient cycling, nutrient acquisition and plant health (Van Der Heijden et al. 2008). Soil fungi, mycorrhizae and fungal endophytes contribute to nutrient solubilization, soil aggregation, water availability and plant growth promotion, while also enhancing resistance against biotic and abiotic stresses (Akter et al. 2025). At the same time, pathogens can severely impede plant productivity.\u003c/p\u003e \u003cp\u003eLike most legumes, alfalfa is susceptible to a range of fungal pathogens causing root rot diseases, including \u003cem\u003eFusarium\u003c/em\u003e spp., \u003cem\u003eOlpidium\u003c/em\u003e spp., \u003cem\u003eAscochyta medicaginicola, Rhizoctonia solani, Alternaria alternata\u003c/em\u003e and several \u003cem\u003ePhoma\u003c/em\u003e and \u003cem\u003eParaphoma\u003c/em\u003e species (Finckh et al. 2015). Proliferation of root rot pathogens is linked to yield depressions in systems with repeated legume cultivation \u0026ndash; also referred to as legume fatigue (Fuchs et al. 2014). Under continuous cropping of alfalfa, its yield decline has been accompanied by negative successions of rhizosphere metabolites and microbial community composition (Wang et al. 2022; Ma et al. 2024).\u003c/p\u003e \u003cp\u003eTogether, these findings suggest that variation in soil and root-associated fungal communities can play an important role in constraining or supporting alfalfa productivity, potentially contributing not only to long-term yield decline but also to spatial variation in yield within fields. Understanding how fungal communities vary across productivity gradients can therefore provide valuable insights into belowground factors influencing plant performance. Differences in productivity may be associated with shifts in beneficial or pathogenic fungi as well as with variation in soil nutrient levels, but these relationships are not well understood in perennial alfalfa.\u003c/p\u003e \u003cp\u003eTo examine how fungal communities and key soil properties correspond to field-scale productivity patterns, we compared soil and root fungal communities from high- and low-productivity zones within an alfalfa stand over two consecutive years. Specifically, we addressed the following questions: (i) do measured soil chemical properties relate to spatial variation in alfalfa biomass and fungal community structure in high- and low-productivity zones? (ii) to what extent do fungal communities differ between soil and roots given contrasting niches and plant-mediated selection? (iii) to what extent does the root-associated fungal community differ between high- and low-productivity zones, and how stable are these communities across years? and, (iv) how do the distributions of putative pathogenic and beneficial fungal taxa relate to the observed productivity gradient in alfalfa?\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Field site description and sampling\u003c/h2\u003e \u003cp\u003eAlfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e L.) plants and soil samples were collected from an organically managed field (\u0026ldquo;Kreuz\u0026rdquo;) at the Gladbacherhof Research Farm (Justus Liebig University Giessen) located in Villmar, Hesse, Germany. The field site and UAV-based productivity mapping approach correspond to the same study area described by (Weigelt et al. 2025). The field is situated in the Taunus region (50.39706\u0026deg;N, 8.250238\u0026deg;E) at 176\u0026ndash;188 m above sea level. Long-term (1990\u0026ndash;2020) mean annual precipitation is 653 mm and mean annual temperature is 9.5\u0026deg;C (on-farm weather station records). The soil at the site is classified as an Orthic Luvisol. The stand was established on 09 May 2021 as a forage mixture consisting of \u003cem\u003eMedicago sativa\u003c/em\u003e (15.5 kg ha⁻\u0026sup1;), \u003cem\u003eLolium perenne\u003c/em\u003e (4 kg ha⁻\u0026sup1;), \u003cem\u003eFestuca pratensis\u003c/em\u003e (4 kg ha⁻\u0026sup1;), \u003cem\u003ePhleum pratense\u003c/em\u003e (4 kg ha⁻\u0026sup1;) and \u003cem\u003eTrifolium repens\u003c/em\u003e (0.5 kg ha⁻\u0026sup1;). The field was harvested four times per year.\u003c/p\u003e \u003cp\u003eProductivity zones were mapped once approximately four weeks prior to the first sampling using multispectral images captured by a MicaSense RedEdge-M camera mounted on a DJI Matrice 300 RTK drone (Weigelt et al. 2025). Normalized Difference Vegetation Index (NDVI) maps derived from the multispectral data were used to evaluate spatial variability in alfalfa canopy vigor and biomass production. UAV data acquisition and processing followed Weigelt et al. (2025), and NDVI maps were used here solely to stratify sampling locations into likely high- and low-productivity zones. Based on NDVI patterns, 14 areas (seven high- and seven low-productivity zones) were selected and georeferenced using a high-precision GPS receiver. Sampling was conducted in these areas in two consecutive years one (17 May 2022) or two (23 May 2023) days prior to mowing. At each sampling point, 5\u0026ndash;8 alfalfa plants and the surrounding soil within an approx. 50 cm radius of the georeferenced GPS location were carefully collected using a spade. The soil mass adhering to and surrounding the roots was collected directly from the spade for subsequent analyses, while the root systems were placed into a separate labeled bag. All samples were kept cool in insulated containers with frozen ice packs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Assessments of plant biomass, root health and soil properties\u003c/h2\u003e \u003cp\u003eTo verify that NDVI patterns reflected actual biomass differences between high- and low-productivity zones, fresh plant above-ground biomass was recorded shortly after collection. Dry biomass was determined after oven-drying the plant material at 105\u0026deg;C to a constant weight (approximately 48 h). Roots were kept cool and, in the laboratory, thoroughly washed under running tap water to remove soil debris. Clean roots were then visually examined within 24h from sampling for symptoms of root rot rated on a 1\u0026ndash;9 scale, where 1 indicated a healthy plant and 9 indicated a dying plant, following the method described previously (ref). After visual inspection, roots were frozen at \u0026minus;\u0026thinsp;18\u0026deg;C until DNA extractions and further processing.\u003c/p\u003e \u003cp\u003eSoil samples were air-dried, sieved (2 mm) and analyzed for key physicochemical properties by a commercial laboratory. Soil pH, mineral nitrogen (Nmin), magnesium (Mg), potassium (K), phosphorus (P) and carbon-to-nitrogen ratio (C/N) were determined in compliance with the standard DIN EN ISO/IEC 17025:2018-03. Phosphorus and potassium were reported as P₂O₅ and K₂O. For consistency with scientific literature, values were converted to elemental P and K using standard conversion factors (P\u0026thinsp;=\u0026thinsp;P₂O₅ \u0026times; 0.436; K\u0026thinsp;=\u0026thinsp;K₂O \u0026times; 0.830)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. DNA extraction and sequencing of fungal communities\u003c/h2\u003e \u003cp\u003eSoil DNA was extracted using the DNeasy PowerSoil Kit (Qiagen, Germany) according to the manufacturer\u0026rsquo;s instructions. A subsample of six roots per sampling point (7 high and 7 low productivity zones) was used for total genomic DNA extraction. Root samples were first surface-sterilized with 3% NaOCl for 10 s, rinsed in 70% ethanol and thoroughly washed under sterile distilled water (Baresel et al. 2025). Roots were then cut to small pieces, freeze-dried for 48 h and ground to a fine powder using a ball mill. Genomic DNA was extracted from 100 mg of lyophilized tissue following the protocol of (Sreelakshmi et al. 2010). DNA quantity and quality were assessed using a NanoDrop spectrophotometer and the extracts were stored in TE buffer at \u0026minus;\u0026thinsp;18\u0026deg;C until analysis.\u003c/p\u003e \u003cp\u003eDNA extracts were initially submitted to an external sequencing provider; however, all DNA extracts from the 2022 sampling campaign were lost during processing and could not be recovered. Sequencing was therefore repeated with LGC Genomics (Berlin, Germany). As a result, sequencing data were obtained for root samples from both years, but for soil samples only from 2023. The ITS1 region was amplified with primers ITS1-F (Gardes and Bruns 1993) and ITS2 (White et al. 1990), each extended at the 5\u0026prime; end with unique error-tolerant barcodes for multiplexed sequencing. Amplicons were generated following the standard Illumina MiSeq ITS metabarcoding workflow provided by LGC Genomics (Berlin, Germany). PCR cycling conditions were 96\u0026deg;C for 1 min, followed by 33 cycles of 96\u0026deg;C for 15 s, 55\u0026deg;C for 30 s and 70\u0026deg;C for 90 s. Amplicons were verified by gel electrophoresis, pooled, purified to remove primer dimers and short fragments, and converted into Illumina libraries. Libraries were sequenced on an Illumina MiSeq platform using v3 chemistry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sequencing data processing and bioinformatic analysis\u003c/h2\u003e \u003cp\u003eSequencing data were processed by LGC Genomics (Berlin, Germany) using a standardized bioinformatics pipeline. Raw read quality was assessed using FastQC v0.11.9 (Andrews 2010). Demultiplexing was performed using Illumina bcl2fastq v2.20, followed by sorting of reads according to amplicon inline barcodes. Adapter sequences were removed and reads shorter than 100 bp were discarded. Primer sequences were detected allowing up to three mismatches per primer, and reads lacking complete primer pairs were removed. Forward and reverse reads were merged using BBMerge v34.48 (Bushnell 2014). OTUs were generated using Mothur v1.35.1 (Schloss et al. 2009) with chimera removal using the UCHIME algorithm, followed by clustering with CD-HIT-EST v4.6.1 at 97% sequence identity (Li and Godzik 2006). Taxonomic classification was performed against the UNITE v6 reference database (Abarenkov et al. 2024). Singleton OTUs were excluded from downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data analysis\u003c/h2\u003e \u003cp\u003eAll data analyses were performed in R version 4.4.1. (R Core Team 2013). The effects of productivity zone (high vs. low), year (2022, 2023) and their interaction on plant biomass and soil properties were evaluated using linear models (Rencher and Schaalje 2008). Significant main factor effects were evaluated with ANOVA followed by Tukey\u0026rsquo;s comparison of group means (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using the package emmeans (Lenth 2016). When assumptions of homoscedasticity were violated, generalized least squares (GLS) models were fitted using the gls function from the nlme package (Pinheiro et al. 2025), applying a variance structure to account for unequal variances between years and productivity zones (weights\u0026thinsp;=\u0026thinsp;varIdent(form\u0026thinsp;=\u0026thinsp;~\u0026thinsp;1 | year * productivity)). All examined roots appeared completely healthy in both sampling years and were therefore not subjected to any statistical analyses.\u003c/p\u003e \u003cp\u003eThe fungal community sequencing data were analyzed using the phyloseq package (McMurdie and Holmes 2013). Prior to analyses, unidentified and non-fungal OTUs at the kingdom level were excluded from data set. In addition, to minimize tag-switching effects and account for potential PCR errors, OTUs with fewer than five reads per sample were set to zero and OTUs with fewer than ten total reads across all samples were also removed from the dataset (Lindahl et al. 2013; Oliver et al. 2015).\u003c/p\u003e \u003cp\u003eAlpha-diversity metrics were calculated from the rarefied dataset normalized to the lowest number of reads. Alpha diversity was estimated using the Shannon diversity, Simpson richness and Chao1 indices in r package RAM (Chen et al. 2015). Differences in alpha-diversity among compartments (soil vs. root), productivity zones and years were evaluated using the non-parametric Kruskal\u0026ndash;Wallis test using the the \u0026lsquo;agricolae\u0026rsquo; package (Mendiburu 2014). When significant differences were observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), pairwise comparisons of mean ranks were performed using the Kruskal multiple comparison procedure. To account for multiple testing, false discovery rates were controlled using the Benjamini\u0026ndash;Hochberg correction (Benjamini and Hochberg 1995).\u003c/p\u003e \u003cp\u003eBeta-diversity analyses were based on Bray\u0026ndash;Curtis dissimilarities to assess differences in fungal community composition among experimental factors. Prior to analysis, count data were normalized using variance-stabilizing transformation in the DESeq2 package (McMurdie and Holmes 2014). Ordination was performed via Constrained Analysis of Principal Coordinates (CAP) using the \u0026lsquo;ordinate\u0026rsquo; function in phyloseq (McMurdie \u0026amp; Holmes, 2013). The homogeneity of multivariate dispersions (PERMDISP) was tested using the \u0026lsquo;betadisper\u0026rsquo; function (Anderson et al. 2006; Oksanen et al. 2009) to check the variance among groups. Community-level differences were evaluated by PERMANOVA using the \u0026lsquo;adonis2\u0026rsquo; function in vegan (Oksanen et al. 2009) with 9999 permutations.\u003c/p\u003e \u003cp\u003eTo assess the overlap and persistence of fungal taxa among years and roots from different productivity zones, a Venn diagram was constructed to visualize shared and unique OTUs across root-associated fungal communities (Chen and Boutros 2011). This was complemented by a turnover analysis, in which OTUs were classified as either \u0026ldquo;shared\u0026rdquo; (present in both years within a productivity zone) or \u0026ldquo;dynamic\u0026rdquo; (present in only one of the two years), and the relative abundance of each group was quantified to evaluate whether temporal variation involved dominant or low-abundance taxa.\u003c/p\u003e \u003cp\u003eThe ten most abundant taxa were identified based on their relative abundance, calculated separately for soil and root compartments to highlight dominant community members within each habitat. Core root taxa, defined as those with a minimum relative abundance of 0.1% occurring in at least 75% of samples, were identified using the \u0026lsquo;core_members\u0026rsquo; function in the microbiome package (Lahti and Shetty 2017). In addition, indicator taxa associated with roots and productivity zones were determined using the indicspecies package (De C\u0026aacute;ceres et al. 2010). Functional guilds were assigned using the FUNGuild in Python (Nguyen et al. 2016). OTUs assigned to any guild category containing the term \u0026ldquo;pathogen\u0026rdquo; were grouped and presented collectively as potential pathogens in data analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Plant biomass, root health, soil and weather properties\u003c/h2\u003e \u003cp\u003eAlfalfa plants collected from field areas classified as high-productivity zones based on drone imagery analysis had across years approx. 55% higher biomass (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those from low-productivity zones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These differences were more pronounced in the first sampling year (73% higher) than in the second (49% higher), as the fresh biomass in the low productivity zones had increased by a factor of 2.8 compared to 2.4 in the high productivity zones. Roots from all plants appeared healthy in both years, with no visible symptoms of root rot or any other disease observed.\u003c/p\u003e \u003cp\u003eIn 2022, the soil C:N ratio was lower in the high- than in low-productivity zones, whereas the opposite was observed in 2023. Across both years, high-productivity zones had consistently lower soil pH and higher concentrations of P, K and Mg than low-productivity zones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While differences in P and K were considerably smaller in 2023,, the differences in Mg (on average 81% higher in high-productivity zones) remained high across years. Soil mineral nitrogen was 21% higher (p\u0026thinsp;=\u0026thinsp;0.09) in the high compared to the low productivity zone in 2022 but 18% lower in 2023 (p\u0026thinsp;=\u0026thinsp;0.0495) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, soil mineral nitrogen did not correlate with alfalfa biomass (p\u0026thinsp;=\u0026thinsp;0.83).\u003c/p\u003e \u003cp\u003eWeather conditions differed substantially between years (Supplementary Table\u0026nbsp;1). During stand establishment and the period up to the first sampling (sowing on 09 May 2021 to 17 May 2022), total precipitation in 2021 (652.2 mm) was close to the long-term mean (653.6 mm, 1990\u0026ndash;2020), and mean annual temperature in 2021 (9.77\u0026deg;C) was also close to the long-term mean (9.50\u0026deg;C). However, rainfall distribution differed across months, with particularly low precipitation in October and November 2021 (\u0026minus;\u0026thinsp;53% and \u0026minus;\u0026thinsp;42% compared to the long-term mean). In early 2022, temperatures were mostly above the long-term mean, especially in January (+\u0026thinsp;1.8\u0026deg;C), February (+\u0026thinsp;3.3\u0026deg;C) and May (+\u0026thinsp;2.3\u0026deg;C). Precipitation during this period was variable, with a very dry March and May 2022 (65% and 40% lower precipitation than the long-term mean), while February and April were 44% and 54% wetter than average, respectively.\u003c/p\u003e \u003cp\u003eBetween the first sampling (17 May 2022) and the second sampling (23 May 2023), annual precipitation in 2022 (610.3 mm) was only moderately lower than the long-term mean (\u0026minus;\u0026thinsp;6.6%), but summer rainfall was far below average. July and August 2022 were particularly dry (\u0026minus;\u0026thinsp;64% and \u0026minus;\u0026thinsp;89%). In contrast, September 2022 was unusually wet (+\u0026thinsp;140%), and precipitation in March, April and May 2023 preceding the second sampling were also above average wet (+\u0026thinsp;103%, +\u0026thinsp;84% and +\u0026thinsp;40%, respectively). Mean annual temperature was substantially higher than the long-term mean (2022: 11.43\u0026deg;C; 2023: 11.56\u0026deg;C vs 9.50\u0026deg;C).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlfalfa biomass and soil chemical properties across field productivity zones.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation / year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFresh biomass [g/plant]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry biomass [g/plant]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC-N ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNmin [kg/ha]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP [mg/100g]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK [mg/100g]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMg [mg/100g]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-productivity zones (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e19.42 **\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.07***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.67 ns\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.20 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e31.43 ns\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.81 **\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e9.25 *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.41 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.06 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.14 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.79 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.19 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.34 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.68 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.29 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.23 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.71 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.71 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.63 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-productivity zones (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e31.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e10.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.64 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.37 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.16 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.29 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.37 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.13 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.76 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.38 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.06 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.71 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.43 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.29 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.23 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.51 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAsterisks indicate statistically significant differences between mean values of high- and low-productivity zones according to one-way ANOVA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 *, *\u0026lt; 0.01 **, *\u0026lt; 0.001 ***). Different lowercase letters indicate statistically significant differences among years and productivity zones according to Tukey\u0026rsquo;s HSD test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Fungal community structure\u003c/h2\u003e \u003cp\u003eA total of approx. 1.5\u0026nbsp;million high-quality sequence reads were obtained after quality filtering, representing 1256 fungal operational taxonomic units (OTUs). Read counts per sample ranged from 8698 to 89288. Rarefaction curves (Supplementary Fig.\u0026nbsp;1) reached clear plateaus for all samples, indicating that the dataset captured the majority of fungal taxa associated with alfalfa roots.\u003c/p\u003e \u003cp\u003eFungal community structure varied between soils in 2023 (data from 2022 missing) and roots and to a lower extent between productivity zones and years. Alpha diversity metrics were similar in soils from high- and low-productivity zones and consistently higher than in roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within roots, fungal diversity tended to be higher in 2022 than in 2023, and in roots from low-productivity zones compared to high-productivity zones. Within-year root associated fungal communities in low-productivity zones showed higher species richness (Chao1 index), while Shannon and Pielou evenness were similar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe constrained ordination (CAP) analysis also indicated distinct differences in fungal community composition (β-diversity) between soil and root samples, as well as between productivity zones and years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first canonical axis (CAP1, 28.1% of the variance explained) primarily separated soil from root samples, indicating distinct fungal communities associated with each sample type. The second axis (CAP2, 9.6%) separated communities from high- and low-productivity zones. Root samples from different years (2022 and 2023) clustered within the same productivity group, suggesting that temporal variation was less pronounced than differences driven by productivity or compartment (soil vs. root). Overall, sample type (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026sup2; = 0.277) accounted for the largest share of the fungal community variation, followed by much weaker productivity zone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026sup2; = 0.093) and year effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026sup2; = 0.080). Complementary β-dispersion test indicated significant heterogeneity of variance across groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further analysis revealed that this heterogeneity resulted from high variation in structural differences between soil and root fungal communities. When analyzed separately, β-dispersion was non-significant within both soil (p\u0026thinsp;=\u0026thinsp;0.927) and root samples (p\u0026thinsp;=\u0026thinsp;0.271), suggesting that the observed differences are due to genuine differences in community composition rather than variance artifacts (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Community overlaps and temporal persistence of the root fungal microbiome\u003c/h2\u003e \u003cp\u003eTo examine the overlap of fungal taxa among roots from different productivity zones and sampling years, a Venn diagram was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A total of 61 OTUs were shared among all four root groups (high/low-productivity \u0026times; 2022/2023), representing a set of persistent taxa consistently detected across years and productivity levels. These taxa accounted for approximately 17% of all OTUs detected in high-productivity roots and 12% in low-productivity roots. Across years within each zone, roots shared 112 OTUs in high-productivity zones (~\u0026thinsp;32% of taxa in that zone) and 184 OTUs in low-productivity zones (~\u0026thinsp;37%). Although these persistent OTUs represented only about one third of the total richness, they dominated the community by abundance, contributing approximately 85\u0026ndash;90% of all reads in both zones Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The remaining fraction consisted of transient, year-specific OTUs ranging from 48 in high-productivity in 2023 to 124 OTUs in low-productivity roots in 2022, which together accounted for only about 10\u0026ndash;15% of total abundance. Additional overlap between productivity zones within the same year comprised 148 shared OTUs in 2022 and 98 in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Taxonomic classification\u003c/h2\u003e \u003cp\u003eStructural differences observed between soil and root associated fungal communities as well as between productivity zones were clearly visible in the taxonomic profiles. Overall, \u003cem\u003eAscomycota\u003c/em\u003e dominated, particularly in roots, followed by \u003cem\u003eBasidiomycota\u003c/em\u003e which occurred at similar rates across samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eMortierellomycota\u003c/em\u003e and unclassified fungi were more abundant in soils, whereas \u003cem\u003eOlpidiomycota\u003c/em\u003e were detected at higher levels in alfalfa roots collected in 2022 from both high- and low-productivity zones.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the OTU level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) taxa such as \u003cem\u003ePenicillium\u003c/em\u003e sp. (OTU 2364), \u003cem\u003eMortierella\u003c/em\u003e sp. (OTU 3501) and \u003cem\u003eSolicoccozyma\u003c/em\u003e (OTU 2743) dominated in soils, particularly in productive zones. In contrast, low-productivity zones contained higher abundance of unclassified taxon (OTU 4792) completely absent from high-productivity soils, as well as \u003cem\u003eCladorrhinum foecundissimum\u003c/em\u003e (OTU 3090). These dominant soil taxa were either absent or occurred at very low abundances in root samples. Root samples were characterized by higher relative abundances of \u003cem\u003eFusarium\u003c/em\u003e solani (OTU 3557), \u003cem\u003eParaphoma radicina\u003c/em\u003e (OTU 2521) and an unclassified taxon (OTU 2162) especially in productive zones, while \u003cem\u003eAscochyta medicaginicola\u003c/em\u003e (OTU 4144), \u003cem\u003ePlectosphaerella\u003c/em\u003e sp. (OTU 4069), \u003cem\u003eIlyonectria macrodidyma\u003c/em\u003e (OTU 4294) and \u003cem\u003eOlpidium\u003c/em\u003e sp. (OTU 4517) were more frequent in roots collected from low-productivity zones. Temporal differences were less pronounced but still evident. Root-associated communities showed moderate shifts between 2022 and 2023, particularly in the relative abundances of \u003cem\u003eAscochyta medicaginicola\u003c/em\u003e (OTU 4144) which decreased in 2023 in both root types, whereas opposite was observed for \u003cem\u003eParaphoma radicina\u003c/em\u003e (OTU 2521) and unclassified taxa (OTU 2993 and OTU 3555). Overall, the most abundant fungal communities reflected strong compartmental structuring (soil vs. root), with moderate productivity-related and year-to-year variation within the root-associated assemblages. The same dominant taxa were present in both sampling years, indicating that temporal shifts were primarily quantitative rather than due to the appearance or disappearance of specific taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe core fungal microbiome of alfalfa, representing taxa present in at least 75% of the root samples (i.e., 11 of 14), consisted of 17 key taxa (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, 10 were found in roots collected from both high- and low-productivity zones. Three taxa were part of the core root microbiome in high-productivity zones, namely \u003cem\u003eParaphoma radicina\u003c/em\u003e (OTU 2521), \u003cem\u003eFusarium redolens\u003c/em\u003e (OTU 2871) and an unclassified taxon from the family \u003cem\u003eNectriaceae\u003c/em\u003e (OTU 3555). Four core taxa were associated with roots from low-productivity zones including \u003cem\u003eOlpidium\u003c/em\u003e sp. (OTU 4517), \u003cem\u003ePenicillium\u003c/em\u003e sp. (OTU 2364), an unclassified \u003cem\u003eLasiosphaeriaceae\u003c/em\u003e taxon (OTU 2740), and \u003cem\u003eBeauveria pseudobassiana\u003c/em\u003e (OTU 2960). Complementary indicator species analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified nine additional root associated taxa linked to productivity. Among them, three taxa were associated with roots collected from high-productivity zones, including \u003cem\u003eCadophora\u003c/em\u003e sp. (OTU 700) and members of the orders \u003cem\u003eGlomerales\u003c/em\u003e (OTU 5055) and \u003cem\u003eHelotiales\u003c/em\u003e (OTU 3194). Six taxa were associated with low-productivity, comprising two members of \u003cem\u003ePenicillium\u003c/em\u003e (OTU 2364) and \u003cem\u003eSerendipita\u003c/em\u003e (OTU 1441), an unclassified \u003cem\u003eLasiosphaeriaceae\u003c/em\u003e taxon (OTU 2740), a \u003cem\u003eSordariomycetes\u003c/em\u003e member (OTU 3575) and members from the phylum \u003cem\u003eAscomycota\u003c/em\u003e (OTU 4318, OTU 3375).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRoot-associated core \u003cb\u003e(\u003c/b\u003eminimum relative abundance of 0.1% occurring in at least 75% of the roots) and indicator taxa across productivity zones and years. The table shows taxonomic classification to the highest rank and relative abundance in roots collected from high- and low-productivity zones.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoots from\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrder or Family\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRelative abundance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh productive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLow productive\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCore OTU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh-productivity zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDothideomycetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eParaphoma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eParaphoma radicina\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e12.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFusarium redolens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNectriaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow-productivity zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePenicillium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePenicillium\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLasiosphaeriaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBeauveria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eBeauveria pseudobassiana\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOlpidiomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOlpidiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOlpidium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eOlpidium sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBoth groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_1314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFusarium oxysporum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_1579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHerpotrichiellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExophiala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eExophiala sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePhaeosphaeriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eParaphoma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eParaphoma sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePleosporales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNectriaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFusarium solani\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePlectosphaerellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePlectosphaerella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePlectosphaerella sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e6.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eAscochyta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAscochyta medicaginicola\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e10.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eIlyonectria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eIlyonectria macrodidyma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e6.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndicator species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh-productivity zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHelotiales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHelotiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCadophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eCadophora sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_5055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGlomeromycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGlomeraceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow-productivity zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSordariomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePenicillium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePenicillium sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_2740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAscomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLasiosphaeriaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.883\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_3375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.319\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_4318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.431\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU_1441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBasidiomycota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSerendipitaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSerendipita\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSerendipita sp.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.141\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Putative plant pathogens in soils and roots\u003c/h2\u003e \u003cp\u003eThe relative abundance of putative pathogenic fungi varied between soil and root samples. In soil, putative pathogens accounted for approx. 25% of the fungal community, with only minor differences between low-productive and productive zones. In contrast, root samples contained substantially higher proportions of putative pathogenic taxa, representing around 65% of the community, with no apparent effect of productivity or sampling year (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated whether persistent alfalfa productivity differences within an organically managed field could be explained by key soil chemical properties and/or root- and soil-associated fungal communities. Across both sampling years, biomass remained consistently higher in zones classified as high productivity, confirming that UAV-based NDVI mapping captured spatial patterns in plant performance correctly. Plants collected in the high-productivity zones produced about 55% more biomass than those in the low-productivity zones, yet plants in both groups appeared healthy and showed no signs of nutrient deficiency or disease. At the same time, strong year-to-year differences in yield in both productivity zones likely reflected stand establishment effects and warmer-than-average conditions in early 2022 combined with particularly low rainfall in March and May. Interestingly, the relative difference in biomass between zones decreased from 73% in 2022 to 49% in 2023. Together, these results indicate that strong year-to-year variation in weather influenced overall biomass production, while persistent within-field factors maintained the spatial yield gradient.\u003c/p\u003e \u003cp\u003eWhile soil phosphorus, potassium and especially magnesium levels were consistently higher in the high-productive zones for phosphorus and potassium the differences were lower in 2023 than in 2022. Overall, the absolute values in the low-productivity zones remained within ranges typically considered adequate for alfalfa production (Meyer et al. 2007; Franzen and Berti 2023; Baker et al. 2024). Phosphorus (14\u0026ndash;18 ppm) was within the recommended range (10\u0026ndash;20 ppm), and potassium (72\u0026ndash;108 ppm) was slightly below the optimal range for multi-cut systems (\u0026gt;\u0026thinsp;125\u0026ndash;150 ppm) but similarly low in both zones and therefore unlikely to explain the large biomass differences. Even magnesium (95\u0026ndash;186 ppm), which showed the largest contrast, was still within the lower end of the optimum range in low-productivity zones, as deficient soils are considered below 50\u0026ndash;100 ppm (Undersander et al. 2011). The switch of mineral N and C:N ratios with Nmin 21% higher and 18% lower in high- compared to low-production zones in 2022 and 2023, respectively overall is surprising as no external inputs were made and could be a reflection of more intensive biological N-fixation under lower N-conditions (Binacchi et al. 2023). The difference of soil pH by about one unit (6.20 in productive vs. 7.29 in low-productivity zones, respectively) was also unlikely to cause differences as both values were within the recommended range for forage legumes (6.0\u0026ndash;7.5), and neither approached levels reported to suppress nodulation or N fixation (Rice et al. 1977). With respect to fungal communities, if nutrients were the major driver of the microbiome structure, stronger and more consistent changes in α- or β-diversity would be expected, particularly in soils where nutrient effects are most clearly manifested (Berg and Smalla 2009; Xu et al. 2012; Wang et al. 2017; Bay et al. 2021). Instead, the community wide differences were minor: soil α-diversity was similar in both zones and productivity explained a small portion of β-diversity (R\u0026sup2; ~0.09). Overall, while soil fertility varied across zones, the magnitude of these direct and indirect effects mediated via shifts in soil fungal communities was likely too small to account for the ~\u0026thinsp;55% biomass contrast and therefore unlikely to be the primary driver of the observed productivity differences.\u003c/p\u003e \u003cp\u003eThe strongest structuring factor of the fungal communities was the compartment (soil vs. root), which explained the largest portion of variation (R\u0026sup2; ~0.28) and far more than either productivity zone or sampling year. This agrees with previous studies which showed that host selection is the dominant force shaping microbial community assembly (Berg and Smalla 2009; Li et al. 2022). The soil communities in 2023 showed consistently higher richness and evenness whereas root communities represented a selected subset of soil fungi in both years, a well-described pattern in legumes and other crops (Bainard et al. 2017; Bay et al. 2021; Li et al. 2022). Several phyla that were abundant in soil, including \u003cem\u003eMortierellomycota\u003c/em\u003e, \u003cem\u003eZoopagomycota\u003c/em\u003e and \u003cem\u003eRozellomycota\u003c/em\u003e, were strongly reduced or nearly absent in roots. In contrast, \u003cem\u003eAscomycota\u003c/em\u003e which constituted about half of the soil fungal communities, accounted for 70\u0026ndash;95% of the root communities across years and productivity zones. At the OTU level, soil samples were dominated by predominantly saprotrophic and free-living taxa such as \u003cem\u003ePenicillium\u003c/em\u003e spp. (OTU2364), \u003cem\u003eMortierella\u003c/em\u003e sp. (OTU3501) (Tamayo-V\u0026eacute;lez and Osorio 2018; Ozimek and Hanaka 2021), \u003cem\u003eSolicoccozyma\u003c/em\u003e (OTU2743) (Glushakova et al. 2021; Yang et al.) and \u003cem\u003eCladorrhinum foecundissimum\u003c/em\u003e (OTU3090) (Suzuki 2009; Barrera et al. 2019), fungi commonly reported to be abundant in agricultural soils. These were either absent or present only at very low rates in roots, reflecting their primary role as decomposers rather than root colonizers. In contrast, roots contained a distinct set of fungi that were either much more abundant or almost exclusive to the roots. Taxa such as \u003cem\u003eFusarium solani\u003c/em\u003e (OTU3557), \u003cem\u003ePlectosphaerella\u003c/em\u003e sp. (OTU4069) and \u003cem\u003eAscochyta medicaginicola\u003c/em\u003e (OTU4144) occurred in both soil and roots but consistently at higher rates in roots, whereas several other taxa, including \u003cem\u003eParaphoma\u003c/em\u003e sp. (OTU2458), \u003cem\u003eParaphoma radicina\u003c/em\u003e (OTU2521), \u003cem\u003eIlyonectria macrodidyma\u003c/em\u003e (OTU4294) and \u003cem\u003eOlpidium\u003c/em\u003e sp. (OTU4517) were predominantly or exclusively root-associated. These fungi are well adapted to living plant tissues and the nutrient-rich environment and are generally poor competitors in soil (Hornby 1998; Farr and Rossman 2025; Singh et al. 2025).\u003c/p\u003e \u003cp\u003eProductivity-related differences in the overall root associated fungal community structure were detectable but minimal. Only in 2022, α-diversity in roots was significantly higher in low-productivity zones, while overall richness, Shannon diversity and evenness were similar between productivity zones and years. The absence of a consistent pattern suggests that productivity had only a weak influence on root fungal diversity and that host-driven selection was the primary factor shaping root fungal community structure. The β-diversity in this study further supports this, as productivity explained only\u0026thinsp;~\u0026thinsp;9% of community variation, a minor effect compared with the influence of host selection (28%). While host selection is widely recognized as a key determinant of plant-associated microbiomes (Berg and Smalla 2009; Li et al. 2022), multi-year analyses in perennial forage crops remain scarce, as most agricultural microbiome studies addressed management effects and/or seasonal variability within a single year (e.g. Shi et al. 2015; Hilton et al. 2018; Banerjee et al. 2019; Bay et al. 2021).\u003c/p\u003e \u003cp\u003eIn our study, root-associated fungal communities contained both stable and variable components across years. Approximately one third of all OTUs detected within each productivity zone occurred in both 2022 and 2023, and 61 OTUs were shared across all four sample groups, representing a persistent portion of root-associated mycobiome. The α- and β-diversity patterns including the turnover analysis all confirmed that these persistent taxa represented the predominant portion of the community, whereas most year-to-year variation involved low-abundance taxa. Measures of α-diversity, including Shannon and evenness remained similar across years and zones showing that the overall balance between common and rare taxa remained stable. Likewise, year explained only a small proportion of β-diversity variation (R\u0026sup2; ~0.08), consistent with quantitative adjustments among rare taxa rather than shifts in dominant groups. This was further reflected in the turnover analysis, where persistent OTUs accounted for 85\u0026ndash;90% of total abundance, while year-specific taxa contributed only 10\u0026ndash;15%. These results suggest that alfalfa hosts a resilient and predictable fungal community dominated by taxa that remain stable across contrasting years. Given that productivity-related differences were subtle relative to the strong compartment effect, much of the observed structure reflects host selection acting on the local soil fungal pool. Multi-year temporal sampling of plant root microbiomes has been conducted far more extensively in annual crops (Mavrodi et al. 2018; Stopnisek and Shade 2021; Chen et al. 2022; Lupwayi et al. 2022; Quiza et al. 2023; Yang et al. 2025), whereas long-term studies in agricultural perennial crops remain limited and includes mostly fruit trees, shrubs and orchids (Zeng et al. 2021; Becker et al. 2022; Macaya-Sanz et al. 2023; Argiroff et al. 2024). These studies also reported a stable dominant microbiome with temporal turnover largely confined to low abundance taxa, suggesting that a persistent root mycobiome with a dynamic rare fraction may be characteristic of many perennial crops. Such fungi are likely to contribute to nutrient acquisition, stress tolerance and hormonal modulation, however, most existing studies remain descriptive and the specific functional consequences for the host require further investigation.\u003c/p\u003e \u003cp\u003eRoot communities contained a surprisingly high proportion of putative pathogenic fungi (~\u0026thinsp;65%), yet all sampled plants looked healthy with no symptoms of disease. It should be noted that FUNGuild assigns \u0026ldquo;putative pathogens\u0026rdquo; based on broad guild annotations, and many of these taxa may not be pathogenic to legumes, exhibit context-dependent lifestyles and commonly occur as endophytes and/or saprotrophs in roots. Therefore, while functional roles based on OTU data must be interpreted with caution, the repeated detection of these taxa across years and productivity zones points to a background reservoir of stress-responsive fungi that commonly inhabit healthy alfalfa plants. Many of the dominant (top 10) taxa, however, including \u003cem\u003eFusarium solani, Plectosphaerella\u003c/em\u003e sp., \u003cem\u003eAscochyta medicaginicola, Paraphoma\u003c/em\u003e spp., \u003cem\u003eIlyonectria macrodidyma\u003c/em\u003e and \u003cem\u003eOlpidium\u003c/em\u003e sp., are well known pathogens of legumes and other crops (Farr and Rossman 2025). Their abundances varied across years and productivity zones without a consistent increase in roots from low-productivity zones, indicating that these fungi alone are unlikely to explain the observed productivity gradient. Instead, their persistent presence in roots suggests that these fungi exist likely as tolerated endophytes rather than active disease agents. This is consistent with our earlier findings in small seeded and grain legumes which reported similar pathogen spectra in symptomatic and asymptomatic plants (Šišić et al. 2018b, a, 2022, 2025; Baresel et al. 2025). Such asymptomatic colonizations result from a balanced antagonism between host defense and fungal pathogenicity factors (Schulz and Boyle 2005; Liao et al. 2025) which results in disease often only after abiotic stresses occur. While pathogens can persist in root tissue without triggering visible disease, sustaining these interactions however, may require metabolic costs for the host. To what extent these interactions influenced plant growth in our study, and whether such \u0026lsquo;hidden costs\u0026rsquo; were greater in low-productivity zones and contributed to the biomass differences remains unknown and needs further study. An important issue is that we sampled plant groups in productivity zones. It is well possible that sampling on a per plant basis, taking the exact biomass produced by a given plant and the microbiome per plant could yield more specific results.\u003c/p\u003e \u003cp\u003eIn contrast to pathogens, differences in a small number of but functionally important taxa might have had a stronger influence on plant performance. Shifts in key fungal groups can affect nutrient uptake, stress responses and overall plant health without major changes in community-wide diversity patterns (Gu et al. 2022; Sena et al. 2024). Core and indicator taxa are particularly useful in this context (Zhou et al. 2024) because they identify fungi that occur consistently within a group and are therefore more likely to exert a stronger effect on the host than other members. However, in our study, core taxa patterns did not align with a simple presence\u0026ndash;absence explanation of the productivity gradient. Several taxa with known pathogenic potential were part of the core community in both zones, and even zone-specific core taxa included taxa commonly reported as pathogens. For example, \u003cem\u003eParaphoma radicina\u003c/em\u003e (OTU 2521) and members of \u003cem\u003eNectriaceae\u003c/em\u003e (OTU 3555) and \u003cem\u003eFusarium redolens\u003c/em\u003e (OTU 2871) were core taxa in productive roots, indicating that productivity differences cannot be explained by pathogen presence\u0026ndash;absence alone (Šišić et al. 2018b, 2022, 2025). In contrast, roots from non-productive zones contained a distinct set of core taxa including \u003cem\u003eOlpidium\u003c/em\u003e sp. (OTU 4517), which has been frequently associated with reduced plant performance (Farr and Rossman 2025), as well as \u003cem\u003ePenicillium\u003c/em\u003e sp. (OTU 2364), an unclassified \u003cem\u003eLasiosphaeriaceae\u003c/em\u003e taxon (OTU 2740) and \u003cem\u003eBeauveria pseudobassiana\u003c/em\u003e (OTU 2960). While these taxa are not classical root pathogens, their consistent occurrence may reflect differences in root physiological status and overall soil conditions in low-productivity areas. Indicator taxa associated with roots from high-productivity zones, namely \u003cem\u003eCadophora\u003c/em\u003e sp. (OTU 700), a \u003cem\u003eHelotiales\u003c/em\u003e member (OTU 3194) and a \u003cem\u003eGlomeraceae\u003c/em\u003e taxon (OTU 5055), points to a different (functional) pattern than that observed in low-productivity roots. These fungi have been reported to support nutrient uptake and improve plant tolerance to soil or water stress (Lee et al. 2013; Knapp et al. 2018; Yakti et al. 2019; Abdalla et al. 2023; Das and Sarkar 2024; Li et al. 2025; de Oliveira and Pereira 2025). Their presence may have been relevant during the dry establishment year in 2022, when water and nutrient acquisition were likely more limiting. While these associations do not demonstrate causation, they indicate that roots in high-productivity zones hosted fungal partners with a greater potential to support plant health than those found in low-productivity zones.\u003c/p\u003e \u003cp\u003eTaken together, our results show that the strong and spatially stable productivity differences in this field cannot be fully explained by variation in measured soil chemical properties or by broad shifts in fungal diversity or community structure. Soil nutrient levels in low-productivity zones were generally within ranges considered adequate for alfalfa, and productivity explained only a small proportion of fungal β-diversity compared with the strong compartment effect and host-mediated selection. Most dominant root-associated fungi persisted across years and productivity zones, forming a stable root mycobiome in which taxa classified as putative pathogens were common. The absence of visible disease symptoms suggests that alfalfa can maintain largely asymptomatic associations with these fungi under field conditions, likely reflecting a balance between fungal pathogenic potential and host control. Although core taxa did not provide a simple explanation for the productivity gradient, differences in indicator taxa and in a small subset of consistently associated fungi suggest that specific root-associated taxa may still contribute to plant performance, particularly through effects on nutrient acquisition and stress tolerance. However, given the weak zone effect on overall community structure, fungal communities are unlikely to be the primary driver of the ~\u0026thinsp;55% biomass contrast. Instead, the productivity gradient likely reflects a combination of additional edaphic and other biological factors not captured by the present study. To further clarify the mechanisms underlying persistent within field yield differences, future work should include measurements of soil physical constraints, root system architecture, rhizobial symbiosis efficiency and broader multi-trophic interactions, including bacteria, protists and nematodes.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank Jayan Wijesingha, Matthias Wengert and Alborz Saidi for conducting the drone flights and providing the remote-sensing data.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by the LOEWE priority program \u0026lsquo;GreenDairy \u0026ndash; Integrated Livestock-Plant-Agroecosystems\u0026rsquo; of Hesse\u0026rsquo;s Ministry of Higher Education, Research, and the Arts, grant number LOEWE/2/14/519/03/07.001-(0007)/80.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: Adnan \u0026Scaron;i\u0026scaron;ić, Maria R. Finckh, Andreas Gattinger and Jelena Baćanović-\u0026Scaron;i\u0026scaron;ić; Methodology: Adnan \u0026Scaron;i\u0026scaron;ić and Deise A. Knob; Investigation and data collection: Adnan \u0026Scaron;i\u0026scaron;ić and Jelena Baćanović-\u0026Scaron;i\u0026scaron;ić; Formal data analysis and visualization: Adnan \u0026Scaron;i\u0026scaron;ić and Leonard V. Theisgen; Writing \u0026ndash; original draft: Adnan \u0026Scaron;i\u0026scaron;ić, Leonard V. Theisgen and Jelena Baćanović-\u0026Scaron;i\u0026scaron;ić; \u0026nbsp;Writing \u0026ndash; review and editing: All authors; Funding acquisition: Adnan \u0026Scaron;i\u0026scaron;ić, Maria R. Finckh, Andreas Gattinger. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll relevant data are included within the article and its supplementary materials. The datasets generated during and/or analysed during the current study are also available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbarenkov K, Nilsson RH, Larsson KH, et al (2024) The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Res 52:D791\u0026ndash;D797. https://doi.org/10.1093/nar/gkad1039\u003c/li\u003e\n \u003cli\u003eAbdalla M, Bitterlich M, Jansa J, et al (2023) The role of arbuscular mycorrhizal symbiosis in improving plant water status under drought. J Exp Bot 74:4808\u0026ndash;4824. https://doi.org/10.1093/jxb/erad249\u003c/li\u003e\n \u003cli\u003eAkter S, Mahmud U, Shoumik BAA, Khan MdZ (2025) Although invisible, fungi are recognized as the engines of a microbial powerhouse that drives soil ecosystem services. Arch Microbiol 207:79. https://doi.org/10.1007/s00203-025-04285-4\u003c/li\u003e\n \u003cli\u003eAnderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecology Letters 9:683\u0026ndash;693. https://doi.org/10.1111/j.1461-0248.2006.00926.x\u003c/li\u003e\n \u003cli\u003eAndrews S (2010) FastQC: a quality control tool for high throughput sequence data\u003c/li\u003e\n \u003cli\u003eArgiroff WA, Carrell AA, Klingeman DM, et al (2024) Seasonality and longer-term development generate temporal dynamics in the Populus microbiome. mSystems 9:e00886-23. https://doi.org/10.1128/msystems.00886-23\u003c/li\u003e\n \u003cli\u003eBainard LD, Navarro-Borrell A, Hamel C, et al (2017) Increasing the frequency of pulses in crop rotations reduces soil fungal diversity and increases the proportion of fungal pathotrophs in a semiarid agroecosystem. Agriculture, Ecosystems \u0026amp; Environment 240:206\u0026ndash;214. https://doi.org/10.1016/j.agee.2017.02.020\u003c/li\u003e\n \u003cli\u003eBaker M, Yost M, Anderson C, et al (2024) Alfalfa nutrient management guide. Utah State University Extension\u003c/li\u003e\n \u003cli\u003eBanerjee S, Walder F, B\u0026uuml;chi L, et al (2019) Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J 13:1722\u0026ndash;1736. https://doi.org/10.1038/s41396-019-0383-2\u003c/li\u003e\n \u003cli\u003eBaresel JP, \u0026Scaron;i\u0026scaron;ić A, Pittam AR, et al (2025) Organic minimum tillage systems based on intensification of legume cover cropping: agronomic and phytopathological aspects. Agron Sustain Dev 45:35. https://doi.org/10.1007/s13593-025-01029-y\u003c/li\u003e\n \u003cli\u003eBarrera VA, Martin ME, Aulicino M, et al (2019) Carbon-substrate utilization profiles by \u003cem\u003eCladorrhinum\u003c/em\u003e (Ascomycota). Revista Argentina de Microbiolog\u0026iacute;a 51:302\u0026ndash;306. https://doi.org/10.1016/j.ram.2018.09.005\u003c/li\u003e\n \u003cli\u003eBay G, Lee C, Chen C, et al (2021) Agricultural management affects the active rhizosphere bacterial community composition and nitrification. mSystems 6:10.1128/msystems.00651-21. https://doi.org/10.1128/msystems.00651-21\u003c/li\u003e\n \u003cli\u003eBecker MF, Hellmann M, Knief C (2022) Spatio-temporal variation in the root-associated microbiota of orchard-grown apple trees. Environ Microbiome 17:31. https://doi.org/10.1186/s40793-022-00427-z\u003c/li\u003e\n \u003cli\u003eBenjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57:289\u0026ndash;300. https://doi.org/0035-9246/95/57289.\u003c/li\u003e\n \u003cli\u003eBerg G, Smalla K (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1\u0026ndash;13. https://doi.org/10.1111/j.1574-6941.2009.00654.x\u003c/li\u003e\n \u003cli\u003eBinacchi F, Niether W, Brock C, et al (2023) Demystifying the agronomic and environmental N performance of grain legumes across contrasting soil textures of central Germany. Agriculture, Ecosystems \u0026amp; Environment 356:108645. https://doi.org/10.1016/j.agee.2023.108645\u003c/li\u003e\n \u003cli\u003eBushnell B (2014) BBMap: A Fast, Accurate, Splice-Aware Aligner\u003c/li\u003e\n \u003cli\u003eChen H, Boutros PC (2011) VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12:35. https://doi.org/10.1186/1471-2105-12-35\u003c/li\u003e\n \u003cli\u003eChen J, Sharifi R, Khan MS, et al (2022) Wheat microbiome: structure, dynamics, and role in improving performance under stress environments. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.821546\u003c/li\u003e\n \u003cli\u003eChen W, Simpson J, Levesque A (2015) RAM: R for amplicon-sequencing-based microbial-ecology. R package \u0026nbsp; version 1 2 1 7\u003c/li\u003e\n \u003cli\u003eDas S, Sarkar S (2024) Arbuscular mycorrhizal fungal contribution towards plant resilience to drought conditions. Front Fungal Biol 5:1355999. https://doi.org/10.3389/ffunb.2024.1355999\u003c/li\u003e\n \u003cli\u003eDe C\u0026aacute;ceres M, Jansen F, Endicott S, Dell N (2010) indicspecies: relationship between species and groups of sites. 1.8.0\u003c/li\u003e\n \u003cli\u003ede Oliveira JA, Pereira OL (2025) Opening the black box of an underexplored and megadiverse group: history, taxonomy and functionality of dark septate endophytes (DSE). Fungal Biology Reviews 54:100457. https://doi.org/10.1016/j.fbr.2025.100457\u003c/li\u003e\n \u003cli\u003eDesta AG (2025) The role of alfalfa (Medicago sativa L.) in soil health and greenhouse gas mitigation in integrated crop-livestock systems: a review. Archives of Agronomy and Soil Science 72:1\u0026ndash;16. https://doi.org/10.1080/03650340.2025.2610090\u003c/li\u003e\n \u003cli\u003eFarr DF, Rossman AY (2025) Fungal Databases, Systematic Mycology and Microbiology Laboratory, ARS, USDA. http://nt.ars-grin.gov/fungaldatabases/.\u003c/li\u003e\n \u003cli\u003eFern\u0026aacute;ndez-Ortega J, Cantero-Mart\u0026iacute;nez C (2025) Influence of soil management and previous crop nitrogen fertilization on alfalfa productivity in Mediterranean agroecosystems. Field Crops Research 329:109963. https://doi.org/10.1016/j.fcr.2025.109963\u003c/li\u003e\n \u003cli\u003eFinckh MR, Yli-Mattila T, Nyk\u0026auml;nen A, et al (2015) Organic temperate legume disease management. In: Finckh MR, van Bruggen AHC, Tamm L (eds) Plant diseases and their management in organic agriculture. APS press, Saint Paul, Minnesota, USA, pp 293\u0026ndash;311\u003c/li\u003e\n \u003cli\u003eFranzen DW, Berti M (2023) Alfalfa soil fertility requirements in North Dakota soils. North Dakota State University\u003c/li\u003e\n \u003cli\u003eFuchs JG, Thuerig B, Brandhuber R, et al (2014) Evaluation of the causes of legume yield depression syndrome using an improved diagnostic tool. Applied Soil Ecology 79:26\u0026ndash;36. https://doi.org/10.1016/j.apsoil.2014.02.013.\u003c/li\u003e\n \u003cli\u003eGardes M, Bruns TD (1993) ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol Ecol 2:113\u0026ndash;118. https://doi.org/10.1111/j.1365-294x.1993.tb00005.x\u003c/li\u003e\n \u003cli\u003eGlushakova AM, Lysak LV, Kachalkin AV, et al (2021) Transformation of microbial Complexes in Components of Soil Constructions of Different Origin (Soil, Peat, Sand) during Freezing-thawing Processes. Microbiology 90:176\u0026ndash;186. https://doi.org/10.1134/S002626172102003X\u003c/li\u003e\n \u003cli\u003eGro\u0026szlig; J, Gentsch N, Boy J, et al (2023) Influence of small-scale spatial variability of soil properties on yield formation of winter wheat. Plant Soil 493:79\u0026ndash;97. https://doi.org/10.1007/s11104-023-06212-2\u003c/li\u003e\n \u003cli\u003eGu Y, Banerjee S, Dini-Andreote F, et al (2022) Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations. ISME J 16:2448\u0026ndash;2456. https://doi.org/10.1038/s41396-022-01290-z\u003c/li\u003e\n \u003cli\u003eHilton S, Bennett AJ, Chandler D, et al (2018) Preceding crop and seasonal effects influence fungal, bacterial and nematode diversity in wheat and oilseed rape rhizosphere and soil. Applied Soil Ecology 34\u0026ndash;46. https://doi.org/10.1016/j.apsoil.2018.02.007\u003c/li\u003e\n \u003cli\u003eHornby D (1998) Diseases caused by soilborne pathogens. In: Jones DG (ed) The Epidemiology of Plant Diseases. Springer Netherlands, Dordrecht, pp 308\u0026ndash;322\u003c/li\u003e\n \u003cli\u003eKnapp DG, N\u0026eacute;meth JB, Barry K, et al (2018) Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci Rep 8:6321. https://doi.org/10.1038/s41598-018-24686-4\u003c/li\u003e\n \u003cli\u003eLahti L, Shetty S (2017)\u003c/li\u003e\n \u003cli\u003eLee E-H, Eo J-K, Ka K-H, Eom A-H (2013) Diversity of Arbuscular Mycorrhizal Fungi and Their Roles in Ecosystems. Mycobiology 41:121\u0026ndash;125. https://doi.org/10.5941/MYCO.2013.41.3.121\u003c/li\u003e\n \u003cli\u003eLenth RV (2016) Least-Squares Means: The R package lsmeans. J Stat Soft 69:1\u0026ndash;33. https://doi.org/10.18637/jss.v069.i01\u003c/li\u003e\n \u003cli\u003eLi F, Jin Z, Wang Z, et al (2022) Host plant selection imprints structure and assembly of fungal community along the soil-root continuum. mSystems 7:e00361-22. https://doi.org/10.1128/msystems.00361-22\u003c/li\u003e\n \u003cli\u003eLi W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658\u0026ndash;1659. https://doi.org/10.1093/bioinformatics/btl158\u003c/li\u003e\n \u003cli\u003eLi Y, Shen Y, Zheng R, et al (2025) Arbuscular mycorrhizal fungus reshapes the rhizosphere microbiome of alfalfa in response to above-ground attack by aphids and a fungal plant pathogen. Functional Ecology 39:2149\u0026ndash;2169. https://doi.org/10.1111/1365-2435.70078\u003c/li\u003e\n \u003cli\u003eLiao C, Doilom M, Jeewon R, et al (2025) Challenges and update on fungal endophytes: classification, definition, diversity, ecology, evolution and functions. Fungal Diversity 131:301\u0026ndash;367. https://doi.org/10.1007/s13225-025-00550-5\u003c/li\u003e\n \u003cli\u003eLindahl BD, Nilsson RH, Tedersoo L, et al (2013) Fungal community analysis by high-throughput sequencing of amplified markers \u0026ndash; a user\u0026rsquo;s guide. New Phytol 199:288\u0026ndash;299. https://doi.org/10.1111/nph.12243\u003c/li\u003e\n \u003cli\u003eLupwayi NZ, Blackshaw RE, Geddes CM, et al (2022) Multi-year and multi-site effects of recurrent glyphosate applications on the wheat rhizosphere microbiome. Environmental Research 215:114363. https://doi.org/10.1016/j.envres.2022.114363\u003c/li\u003e\n \u003cli\u003eMa Y, Shen Y, Zhou X, et al (2024) Biological decline of alfalfa is accompanied by negative succession of rhizosphere soil microbial communities. Plants 13:. https://doi.org/10.3390/plants13182589\u003c/li\u003e\n \u003cli\u003eMacaya-Sanz D, Witzell J, Collada C, et al (2023) Core endophytic mycobiome in Ulmus minor and its relation to Dutch elm disease resistance. Front Plant Sci 14:. https://doi.org/10.3389/fpls.2023.1125942\u003c/li\u003e\n \u003cli\u003eMavrodi DV, Mavrodi OV, Elbourne LDH, et al (2018) Long-term irrigation affects the dynamics and activity of the wheat rhizosphere microbiome. Front Plant Sci 9:. https://doi.org/10.3389/fpls.2018.00345\u003c/li\u003e\n \u003cli\u003eMcMurdie PJ, Holmes S (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8:e61217. https://doi.org/10.1371/journal.pone.0061217\u003c/li\u003e\n \u003cli\u003eMcMurdie PJ, Holmes S (2014) Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput Biol 10:e1003531. https://doi.org/10.1371/journal.pcbi.1003531\u003c/li\u003e\n \u003cli\u003eMendiburu DF (2014) Agricolae: statistical procedures for agricultural research. R package version 1 2 4 1:1\u0026ndash;6\u003c/li\u003e\n \u003cli\u003eMeyer RD, Marcum DB, Orloff SB, Schmierer JL (2007) Alfalfa fertilization strategies. In: Summers CG, Putnam DH (eds). University of California Agriculture and Natural Resources Publication, Oakland, USA\u003c/li\u003e\n \u003cli\u003eNguyen NH, Song Z, Bates ST, et al (2016) FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241\u0026ndash;248. https://doi.org/10.1016/j.funeco.2015.06.006\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Brien SL, Gibbons SM, Owens SM, et al (2016) Spatial scale drives patterns in soil bacterial diversity. Environmental Microbiology 18:2039\u0026ndash;2051. https://doi.org/10.1111/1462-2920.13231\u003c/li\u003e\n \u003cli\u003eOksanen J, Kindt R, Legendre P, et al (2009) The vegan Package\u003c/li\u003e\n \u003cli\u003eOliver AK, Callaham MA, Jumpponen A (2015) Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. Forest Ecology and Management 345:1\u0026ndash;9. https://doi.org/10.1016/j.foreco.2015.02.020\u003c/li\u003e\n \u003cli\u003eOzimek E, Hanaka A (2021) Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11:7. https://doi.org/10.3390/agriculture11010007\u003c/li\u003e\n \u003cli\u003ePinheiro P, Douglas B, Saikat D, et al (2025) nlme: Linear and Nonlinear Mixed Effects Models\u003c/li\u003e\n \u003cli\u003ePutnam DH (2021) Factors Influencing Yield and Quality in Alfalfa. In: Yu LX, Kole C (eds) The Alfalfa Genome. Springer International Publishing, Cham, pp 13\u0026ndash;27\u003c/li\u003e\n \u003cli\u003eQuiza L, Tremblay J, Pag\u0026eacute; AP, et al (2023) The effect of wheat genotype on the microbiome is more evident in roots and varies through time. ISME Commun 3:32. https://doi.org/10.1038/s43705-023-00238-4\u003c/li\u003e\n \u003cli\u003eR Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria\u003c/li\u003e\n \u003cli\u003eRencher AC, Schaalje GB (2008) Linear models in statistics, 2nd ed. Wiley-Interscience, Hoboken, N.J\u003c/li\u003e\n \u003cli\u003eRice WA, Penney DC, Nyborg M (1977) Effects of soil acidity on rhizobia numbers, nodulation and nitrogen fixation by alfalfa and red clover. Can J Soil Sci 57:197\u0026ndash;203. https://doi.org/10.4141/cjss77-024\u003c/li\u003e\n \u003cli\u003eSchloss PD, Westcott SL, Ryabin T, et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537\u0026ndash;7541. https://doi.org/10.1128/AEM.01541-09\u003c/li\u003e\n \u003cli\u003eSchulz B, Boyle C (2005) The endophytic continuum. Mycological Research 109:661\u0026ndash;686. https://doi.org/10.1017/S095375620500273X.\u003c/li\u003e\n \u003cli\u003eSena L, Mica E, Val\u0026egrave; G, et al (2024) Exploring the potential of endophyte-plant interactions for improving crop sustainable yields in a changing climate. Front Plant Sci 15:. https://doi.org/10.3389/fpls.2024.1349401\u003c/li\u003e\n \u003cli\u003eShi S, Nuccio E, Herman DJ, et al (2015) Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6:10.1128/mbio.00746-15. https://doi.org/10.1128/mbio.00746-15\u003c/li\u003e\n \u003cli\u003eSingh BK, Jiang G, Wei Z, et al (2025) Plant pathogens, microbiomes, and soil health. Trends in Microbiology 33:887\u0026ndash;902. https://doi.org/10.1016/j.tim.2025.03.013\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;i\u0026scaron;ić A, Baćanović-\u0026Scaron;i\u0026scaron;ić J, Al-Hatmi AMS, et al (2018a) The \u0026lsquo;forma specialis\u0026rsquo; issue in \u003cem\u003eFusarium\u003c/em\u003e: A case study in \u003cem\u003eFusarium solani\u003c/em\u003e f. sp. \u003cem\u003episi\u003c/em\u003e. Scientific Reports 8:1252. https://doi.org/10.1038/s41598-018-19779-z.\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;i\u0026scaron;ić A, Baćanović-\u0026Scaron;i\u0026scaron;ić J, Karlovsky P, et al (2018b) Roots of symptom-free leguminous cover crop and living mulch species harbor diverse \u003cem\u003eFusarium\u003c/em\u003e communities that show highly variable aggressiveness on pea (\u003cem\u003ePisum sativum\u003c/em\u003e). PLOS ONE 13:e0191969. https://doi.org/10.1371/journal.pone.0191969.\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;i\u0026scaron;ić A, Baćanović-\u0026Scaron;i\u0026scaron;ić J, Schmidt H, Finckh MR (2022) Farming system effects on root rot pathogen complex and yield of faba bean (\u003cem\u003eVicia faba\u003c/em\u003e) in Germany. Frontiers in Plant Science 13:. https://doi.org/https://doi.org/10.3389/fpls.2022.1009906\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;i\u0026scaron;ić A, Baćanović-\u0026Scaron;i\u0026scaron;ić J, Schmidt H, Finckh MR (2025) Effect of management system and pedoclimatic factors on \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003eDidymella\u003c/em\u003e communities associated with pea (\u003cem\u003ePisum sativum\u003c/em\u003e) roots in Germany. Sci Rep 15:2653. https://doi.org/10.1038/s41598-025-86018-7\u003c/li\u003e\n \u003cli\u003eSreelakshmi Y, Gupta S, Bodanapu R, et al (2010) NEATTILL: A simplified procedure for nucleic acid extraction from arrayed tissue for tilling and other high-throughput reverse genetic applications. Plant Methods 6:3. https://doi.org/10.1186/1746-4811-6-3.\u003c/li\u003e\n \u003cli\u003eStopnisek N, Shade A (2021) Persistent microbiome members in the common bean rhizosphere: an integrated analysis of space, time, and plant genotype. ISME J 15:2708\u0026ndash;2722. https://doi.org/10.1038/s41396-021-00955-5\u003c/li\u003e\n \u003cli\u003eSuzuki A (2009) Propagation strategy of ammonia fungi. Mycoscience 50:39\u0026ndash;51. https://doi.org/10.1007/S10267-008-0453-1\u003c/li\u003e\n \u003cli\u003eTamayo-V\u0026eacute;lez \u0026Aacute;, Osorio NW (2018) Soil fertility improvement by litter decomposition and inoculation with the fungus Mortierella sp. in avocado plantations of Colombia. Communications in Soil Science and Plant Analysis 49:139\u0026ndash;147. https://doi.org/10.1080/00103624.2017.1417420\u003c/li\u003e\n \u003cli\u003eUndersander D (2021) Economic importance, practical limitations to production, management, and breeding targets of alfalfa. In: Yu LX, Kole C (eds) The Alfalfa Genome. Springer International Publishing, Cham, pp 1\u0026ndash;11\u003c/li\u003e\n \u003cli\u003eUndersander D, Cosgrove D, Cullen E, et al (2011) Alfalfa management guide. American Society of Agronomy, Inc, Madison, WI\u003c/li\u003e\n \u003cli\u003eVan Der Heijden MGA, Bardgett RD, Van Straalen NM (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters 11:296\u0026ndash;310. https://doi.org/10.1111/j.1461-0248.2007.01139.x\u003c/li\u003e\n \u003cli\u003eVergopolan N, Xiong S, Estes L, et al (2021) Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrology and Earth System Sciences 25:1827\u0026ndash;1847. https://doi.org/10.5194/hess-25-1827-2021\u003c/li\u003e\n \u003cli\u003eWang L, Ali G, Wang Z (2023) Deep soil water depletion and soil organic carbon and total nitrogen accumulation in a long-term alfalfa pasture. Land Degradation \u0026amp; Development 34:2164\u0026ndash;2176. https://doi.org/10.1002/ldr.4597\u003c/li\u003e\n \u003cli\u003eWang R, Liu J, Jiang W, et al (2022) Metabolomics and microbiomics reveal impacts of rhizosphere metabolites on alfalfa continuous cropping. Front Microbiol 13:. https://doi.org/10.3389/fmicb.2022.833968\u003c/li\u003e\n \u003cli\u003eWang Z, Li T, Wen X, et al (2017) Fungal communities in rhizosphere soil under conservation tillage shift in response to plant growth. Front Microbiol 8:. https://doi.org/10.3389/fmicb.2017.01301\u003c/li\u003e\n \u003cli\u003eWatson CA, Atkinson D, Gosling P, et al (2002) Managing soil fertility in organic farming systems. Soil Use and Management 18:239\u0026ndash;247. https://doi.org/10.1111/j.1475-2743.2002.tb00265.x\u003c/li\u003e\n \u003cli\u003eWeigelt L, Wengert M, Wachendorf M, Wijesingha J (2025) Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures. Precision Agric 27:8. https://doi.org/10.1007/s11119-025-10301-w\u003c/li\u003e\n \u003cli\u003eWhite TJ, Bruns T, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for pylogenetics. In: Innins MA, Gelfand DH, Sninsky JJ (eds) PCR protocols: a guide to methods and applications. Academic Press, San Diego, pp 315\u0026ndash;322\u003c/li\u003e\n \u003cli\u003eXu L, Ravnskov S, Larsen J, Nicolaisen M (2012) Linking fungal communities in roots, rhizosphere, and soil to the health status of Pisum sativum. FEMS Microbiol Ecol 82:736\u0026ndash;745. https://doi.org/10.1111/j.1574-6941.2012.01445.x\u003c/li\u003e\n \u003cli\u003eYakti W, Kov\u0026aacute;cs GM, Franken P (2019) Differential interaction of the dark septate endophyte Cadophora sp. and fungal pathogens in vitro and in planta. FEMS Microbiol Ecol 95:fiz164. https://doi.org/10.1093/femsec/fiz164\u003c/li\u003e\n \u003cli\u003eYang L, Zhao H, Wu K, et al Soil Microbial community composition and diversity response to varying farmland types. Land Degradation \u0026amp; Development 0: https://doi.org/10.1002/ldr.70131\u003c/li\u003e\n \u003cli\u003eYang M, Schlatter DC, LeTourneau MK, et al (2025) Eight Years in the Soil: temporal dynamics of wheat-associated bacterial communities under dryland and irrigated conditions. Phytobiomes Journal 9:173\u0026ndash;188. https://doi.org/10.1094/PBIOMES-02-24-0028-R\u003c/li\u003e\n \u003cli\u003eZeng X, Diao H, Ni Z, et al (2021) Temporal variation in community composition of root associated endophytic fungi and carbon and nitrogen stable isotope abundance in two Bletilla species (Orchidaceae). Plants 10:18. https://doi.org/10.3390/plants10010018\u003c/li\u003e\n \u003cli\u003eZhao J, Chen J, Beillouin D, et al (2022) Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers. Nat Commun 13:4926. https://doi.org/10.1038/s41467-022-32464-0\u003c/li\u003e\n \u003cli\u003eZhou Y, Liu D, Li F, et al (2024) Superiority of native soil core microbiomes in supporting plant growth. Nat Commun 15:6599. https://doi.org/10.1038/s41467-024-50685-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alfalfa (Medicago sativa), productivity gradients, plant–microbe interactions, core microbiome, putative pathogens","lastPublishedDoi":"10.21203/rs.3.rs-9161471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9161471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003ePerennial forage legume leys such as alfalfa are a cornerstone of organic production and integrated crop-livestock systems. Spatial yield variability is common in agricultural fields but the belowground factors contributing to this variation are poorly understood. This study examined how soil chemical properties and soil- and root-associated fungal communities correspond to persistent productivity gradients in an organically managed alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e L.) ley.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAlfalfa plants and rhizosphere soil were sampled from high- and low-productivity field zones over two consecutive years to assess plant biomass, root health and soil chemical properties. Fungal communities were profiled using ITS1 amplicon sequencing. Diversity, community composition, temporal stability and the distribution of core, indicator and putative pathogenic taxa were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePlant biomass in high-productivity zones was ~\u0026thinsp;55% higher although plants in both zones appeared healthy. Soil P and K levels were similar between zones, whereas Mg differed strongly but remained within sufficiency ranges. Soil pH differed by one unit and mineral N showed no consistent spatial pattern. Fungal communities were primarily structured by compartment. Plant productivity explained only\u0026thinsp;~\u0026thinsp;9% of β-diversity. About one-third of root-associated taxa persisted across years and productivity zones, forming a dominant and stable community enriched in putative pathogens. Core and indicator analyses identified only few zone-associated taxa, including a \u003cem\u003eGlomeraceae\u003c/em\u003e indicator detected in high-productivity roots.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSoil nutrients and broad fungal community patterns did not explain the persistent productivity gradient. Differences were confined to a small number of root-associated taxa, suggesting that additional edaphic and biological factors beyond fungi likely contribute to the observed biomass differences.\u003c/p\u003e","manuscriptTitle":"Root-associated fungal communities along productivity gradients in a perennial alfalfa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 08:40:58","doi":"10.21203/rs.3.rs-9161471/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9a3f1f9-7ac5-4871-a1cd-08758657073e","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject without review","date":"2026-05-08T12:22:01+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:49:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 08:40:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9161471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9161471","identity":"rs-9161471","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}