Vineyard mycobiota shows a local and long-term response to the organic mulches application

Vineyard mycobiota shows a local and long-term response to the organic mulches application David Labarga a , Andreu Mairata a , Miguel Puelles a , Jordi Tronchoni b , Ales Eichmeier c , María de Toro d , David Gramaje a , Alicia Pou a,* a Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Gobierno de la Rioja - Universidad de La Rioja, Logro˜ no 26007, Spain b Faculty of Health Sciences, Valencian International University, Valencia 46002, Spain c Mendel University in Brno, Faculty of Horticulture, Mendeleum - Institute of Genetics, Valticka 334, Lednice 69144, Czech Republic d Centro de Investigaci´ on Biom´ edica de La Rioja (CIBIR), Logro˜ no 26006, Spain ARTICLE INFO Keywords: Soil fungal microbiota Must fungal microbiota Grapevine trunk diseases Soil management Grapevine ABSTRACT Viticulture faces global warming challenge, prompting focus on sustainable practices. Organic mulches, an alternative to conventional practices, have shown the potential to enhance plant performance and soil quality. However, their impact on soil and must microbiota remains unexplored. Our three-year study, conducted in two vineyards located in Logro˜ no and Aldeanueva within the appellation of origin (DOCa) Rioja (Spain) aimed to assess the effects of five soil treatments—two conventional (Herbicide (H) and Tillage (T)) and three organic mulches (Grapevine Pruning Debris (GPD), Spent Mushrooms Compost (SMC) and Straw (S))— on soil and must fungal communities through a metataxonomic approach (ITS region). We hypothesized that mulches might modify soil and must fungal microbiota, thus influencing plant health and the winemaking process. Our findings revealed that soil and must fungal communities were primarily driven by location. While treatments did not significantly impact must microbiota, soil fungal communities varied with treatments in the third year, with notable disparities across locations. In Logro˜ no, GPD and H showed the highest diversity, while S exhibited the highest diversity in Aldeanueva. Besides, none of the mulches promoted the growth of pathogenic fungi associated with common vineyard diseases. Finally, the Saccharomycetaceae family was found in must and soil, indicating its presence in the soil prior to grape colonization. Overall, location emerged as the primary factor influencing soil and must fungi. Organic mulches demonstrated long-term effects on soil fungal diversity, although these effects varied across locations. This study pioneers a comprehensive assessment of organic mulches in shaping vineyard fungal communities across diverse soils, offering unprecedented insights into sustainable practices that enhance soil biodiversity and ecosystem resilience without elevating disease risk. 1. Introduction Organic mulches are any organic material used as a field treatment placed on the soil surface (Buesa et al., 2021; Pinamonti, 1998), with a wide range of potentially usable materials. Under the current framework of ecological transition adopted in agriculture and especially in viticulture (Montanarella and Panagos, 2021; OIV, 2020; Wine, 2021), these treatments have raised great interest. Many researchers have delved into their impact on plants and soil, showing clear benefits upon their application. Notable findings include heightened moisture retention (Pou et al., 2021), reduction of erosion and runoff (Shojaei et al., 2019) and effective suppression of soil weeds (Cabrera-P´ erez et al., 2023; Mairata et al., 2023). Additionally, these treatments have demonstrated enhancements in plant water status (Buesa et al., 2021) and substantial increases in grape yield and quality (Guerra and Steenwerth, 2012). All of the mentioned benefits are also of great interest in the face of the existing global warming scenario (Fraga et al., 2013; Hannah et al., 2013). Studies in microbial ecology have significantly advanced over the past decade due to the development of High-Throughput Sequencing (HTAS) techniques (Belda et al., 2017; Bokulich et al., 2018, 2012). Consequently, the existence of a plant-microbiota complex, known as “holobiont”, is now well recognized as crucial for plant health and development (Bettenfeld et al., 2022; Compant et al., 2019; Griggs et al., * Corresponding author. E-mail address: [email protected] (A. Pou). Contents lists available at ScienceDirect Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee https://doi.org/10.1016/j.agee.2025.109506 Received 18 June 2024; Received in revised form 17 January 2025; Accepted 17 January 2025 Agriculture, Ecosystems and Environment 382 (2025) 109506 Available online 21 January 2025 0167-8809/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/bync-nd/4.0/ ). 2021). Fungal communities, in particular, represent highly dynamic networks of interacting fungal species that perform essential ecosystem functions in the soil, all of which are vital for plant productivity and health (Frac et al., 2018; Powell and Rillig, 2018). Even minor changes in the microbial ecosystem surrounding the plant may have significant consequences on plant performance (Wei et al., 2022). Several factors, such as soil physicochemical characteristics (Zarraonaindia et al., 2015), altitude (Portillo et al., 2016), vintage (Bokulich et al., 2013) or climate (Burns et al., 2015), may trigger changes in the microbiota. Within vineyards, it has also been reported how overall crop management impacts the microbiota across different plant niches (Griggs et al., 2021; Signorini et al., 2021; Stefanini and Cavalieri, 2018). For instance, conventional, organic and biodynamic management directly affects soil (Karimi et al., 2020; Morrison-Whittle et al., 2017; Novello et al., 2017; Ortiz-´ Alvarez et al., 2021) and grape microbiota (de Celis et al., 2023; Martins et al., 2014; Setati et al., 2015, 2012). Exploring the influence of different agricultural practices, various studies have examined the effects of straw, plastic, manure or pellet mulches on microbiota in other crops such as maize (Dai et al., 2023; Xia et al., 2019), pear (Shi et al., 2023), blueberry (Lee et al., 2021), pea (Liu et al., 2020), strawberry (Mu˜ noz et al., 2022), cucumber (Tiquia et al., 2002) or cotton (Peng et al., 2022). Nevertheless, investigations into the impact of mulches on vineyard microbiota are scarce. Previous studies have considered the effect of biochar (Lehmann et al., 2011; Maienza et al., 2017), straw (Gupta et al., 2019) and grape pomace (Jacometti et al., 2007), among others. However no research have focused on other interesting substrates such as grapevine pruning debris or spent mushrooms compost. Furthermore, the consequences of agricultural practices, including mulching, remain unclear and variable, depending on factors such as location, soil properties and the specific plant niche under investigation (Burns et al., 2016; Giraldo-Perez et al., 2021; Zarraonaindia et al., 2015). Therefore, to the best of our knowledge, a comprehensive examination of the effect of these agricultural practices on soil and plant microbiota has not yet been conducted. Grapevine Trunk Disease (GTD) represent one of the principal challenges in modern viticulture, with causative fungi potentially residing in the soil or within plant debris. (Agustí-Brisach et al., 2013; Billones-Baaijens et al., 2018; Gramaje et al., 2018; Leal and Gramaje, 2024). Some of the most recurrent diseases include powdery mildew, botrytis, black foot, and esca. The introduction of plant material or external organic matter in the field may be a risky practice, as it may introduce diseases such as those mentioned above. Specifically, organic mulches containing pruning residues or dead plant material may serve as a source of inoculum (Elena and Luque, 2016). However, these sources have not yet been thoroughly studied, and their risk potential remains unknown. Therefore, assessing the pathogenicity of organic mulches in vineyards is mandatory. Some bacterial and fungal communities linked to plants are essential to crops. However, their relevance is even greater in vineyards since they are fundamental in winemaking, conferring unique desired and undesired characteristics to wines. Specifically, fungal communities include yeasts, which are responsible for the alcoholic fermentation of the must (Liu et al., 2017; Wei et al., 2022). Moreover, fungal diversity is vast and grapevines may host beneficial and pathogenic agents, affecting both the grapevine and the resulting wine (Gramaje et al., 2018; Jayawardena et al., 2018; Liu et al., 2017). The wide range of fungal life modes allows them to colonize diverse habitats, and fungi relevant to the fermentative process are likely to inhabit the soil for a certain time (Griggs et al., 2021). However, the connection between soil microbiota and must microbiota, as well as their effects on fermentation, remains unclear despite being addressed in various studies (Griggs et al., 2021; Mezzasalma et al., 2018; Zarraonaindia et al., 2015). The migration of microorganisms is challenging to study, and further research is needed to elucidate this phenomenon (Griggs et al., 2021; D. Liu et al., 2020a; Mezzasalma et al., 2018; Zarraonaindia et al., 2015). Additionally, the influence of soil physicochemical properties on this migration is not well understood, making it difficult to determine how such characteristics may affect the colonization of fungi. Considering all this information, it is necessary to characterize the impact of emerging agricultural practices, such as organic mulches, on plant-associated microbiota using the new tools available to study microbial ecology. This characterization is crucial to understand the effects of these practices and to optimize their implementation. Furthermore, bulk soil samples were selected over rhizosphere samples, as the direct contact of the mulching material with the soil surface is expected to exert a stronger effect on the bulk soil microbial communities which is indeed the main reservoir of the others plant compartments. In a previous three-year study, we assessed the impact of three organic mulches (grape pruning debris, spent mushrooms compost and straw) and two conventional treatments (herbicide and under-row tillage) on soil and must bacterial microbiota in two commercial vineyards within the DOCa Rioja region of Spain (Labarga et al., 2024). This study aims to extend our knowledge by examining the impact of the same treatments on soil and must fungal microbiota using a metataxonomic approach. We hypothesize that (i) soil-applied organic mulches shape the soil fungal microbiota by altering its diversity and composition; (ii) their impact on the must microbiota is minor, given that these are soil-specific treatments; and, (iii) due to the sporulation capacity of many fungi, the soil may serve as a niche for fungi associated with the winemaking process. 2. Materials and methods 2.1. Site characteristics and plant material The study was conducted over three consecutive growing seasons (2019, 2020, and 2021) in two separate commercial vineyards situated in Aldeanueva de Ebro (42º 11’ 58.344’’ N 1º 52’ 20.927’’ W) and Logro˜ no (42º 28’ 34.68’’ N 2º 28’ 55.775’’ W), La Rioja, Spain, with a geographical separation of 60 kilometers. These vineyards were part of the Protected Designation of Origin Rioja (D.O.Ca Rioja) (Wine, 2023) and exclusively cultivated the Tempranillo grape variety (Vitis vinifera L.). Both vineyards were surrounded by other commercial vineyards, since grapevines are the dominant crop in the region. The Aldeanueva vineyard was established in 2000, whereas the Logro˜ no vineyard dates back to 1985, both employing the 110-R (Richter) rootstock for vine grafting. The vine training system implemented was the vertical shoot positioning (VSP), which spur pruning following the bilateral Royat Cordon configuration. The pruning regime aimed to maintain six spurs per vine, each bearing 10–12 buds, with vine planting densities at 2.6 m x 1.2 m (3205 vines per ha) in Aldeanueva and 3 m x 1.2 m (2778 vines per ha) in Logro˜ no. The viticultural area is characterized by a warm-summer Mediterranean climate with continental influences, denoted as Csb according to the K¨ oppen-Geiger climate classification, and featured haplocalcid semi-arid soil types (Soil Survey Staff, 2022). Comprehensive weather data were collected by an automated agricultural climate station operated by the “Servicio de Informaci´ on Agroclim´ atica de La Rioja” (SIAR), conveniently located in proximity (5 km) to the vineyards (Table A. 1). The two vineyard locations selected for this study, Logro˜ no and Aldeanueva, belong to the same region (La Rioja) and were chosen based on their contrasting climatic conditions, soil types, and management practices. Logro˜ no is characterized by a cooler and wetter microclimate, while Aldeanueva experiences a drier and warmer environment. These differences are relevant for understanding the potential effects of organic mulches under varying environmental conditions. Besides, the soil types differ between the locations, with distinct physicochemical properties that may influence fungal community dynamics. In terms of vineyard management, Aldeanueva follows conventional agricultural practices, including the use of synthetic herbicides and fertilizers. In contrast, Logro˜ no implements practices aligned with organic agriculture, although it is not officially certified. This distinction provides an opportunity to evaluate the impact of organic mulching in D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 2 vineyards managed under different agricultural paradigms. 2.2. Experimental design As represented in Fig. 1, the experimental setup remained uniform across both locations, employing a randomized complete block divided into three (n =3) experimental units. Within each block, there were three organic mulches and two conventional treatments, each involving 40–50 plants. The organic mulches applied were: i) straw (S), sourced from wheat fields (Triticum sp.), ii) grapevine pruning debris (GPD) obtained after chopped the annual vineyard pruning, and iii) spent mushroom compost (SMC), a mixture comprising straw, animal manure, and urea. These mulches were applied uniformly along the vine rows (measuring 60 cm wide and 10–20 cm high) in January 2019, with annual renewal scheduled for the winter months. Conventional practices, including herbicide application (H) utilizing Terafit (25 % p/p. Flazasulfuron) and glyphosate (100 l ha^-1) (Fern´ andez-Alc´ azar, 2011), as well as under-row tillage (T) using a weeder blade at a depth of 15 cm, were conducted twice annually, following the methods outlined by Blanco-P´ erez et al. (2022) and Labarga et al. (2024). Additionally, buffer vines were interspersed among the treatments. In this study, tillage was strictly applied as a standalone treatment and was not conducted in any of the other four treatments, including those using organic mulches. The mulches remained on the soil surface throughout the experiment without soil mechanical incorporation. This approach preserves the distinct roles of the mulch treatments, enabling us to evaluate their surface-level effects on soil and microbial dynamics independently of the mechanical disturbance associated with tillage. Various factors influenced the selection of organic mulches. Straw, commonly used as an organic mulch, is widely adopted in the industry. The annual accumulation of pruning debris presents a significant opportunity for beneficial reuse. Similarly, the surplus of spent mushroom compost (SMC), a byproduct of Agaricus sp. cultivation in the region, offers an attractive option due to its ample availability. The choice of these organic mulches was also motivated by their distinct biochemical properties (Table A. 2). S and GPD both feature high organic matter content, low levels of nitrogen, phosphorus, and potassium (NPK), and elevated carbon-to-nitrogen (C/N) ratios, resulting in slower mineralization rates. In contrast, SMC has relatively lower organic matter content, with higher NPK values and a lower C/N ratio, supporting a more rapid mineralization process. This variation in nutrient release and decomposition kinetics provides an opportunity to assess how differing organic mulch compositions influence soil nutrient availability and microbial activity dynamics over time. Moreover, the historical reliance on herbicide application and tillage practices in the region’s vineyards informed their inclusion as conventional methods in this study. Soil physicochemical proprieties changes triggered by treatments applications are shown in the Table A. 3 where the soil analysis of 2018 (before treatments application) and 2021 (last year of study) are indicated. The vineyard plant protection performed by the grower in the experimental vineyards is described by Blanco-P´ erez et al. (2022) and summarizing in the Table A. 4. No fertilization was applied in the vineyards during the experiment. 2.3. Soil properties analysis Soil samples were collected from each replicate in October of each study year, avoiding the mulches, using an auger as described by Blanco-P´ erez et al. (2022) and Mairata et al. (2023). Briefly, in each replicate, soil samples (0–30 cm) were a composite collected from 10 points along the row. Samples were homogenized in bags and sieved (4 mm pore diameter) in the laboratory. 200 g of each sieved homogenate was dried at 40◦C for one week. Summerizing, a total of 90 samples were collected and analyzed (3 years ×2 locations ×5 treatments ×3 replicates). Subsequently, the Regional Laboratory of the Government of La Rioja (La Grajera, Logro˜ no, Spain) analyzed the following parameters: pH (Millennia and Markewitz, 2004), electrical conductivity, organic matter (Walkley and Black, 1934), macro-nutrients (NPK), oligo-nutrients (Mg, Ca, and SO 4 ), micro-nutrients (Fe, Mn, Zn, Cu, Al, and B), and other elements (Na and Pb) (Mehlich, 1984). Besides, soil texture (sand, silt, and clay percentages) (Bouyoucos, 1936) was also analyzed in each sample. The total carbon used in the C/N ratio was derived from oxidizable organic matter using a conversion factor of 1.3 (Walkley and Black, 1934). Nitrogen was analyzed as total nitrogen, while phosphorus, potassium, and other macro-, oligo-, and micronutrients were determined in their available forms. Percentage of sand, silt, clay, carbonates and active limestone were measured. Active limestone includes the portion of calcium carbonate (CaCO 3 ) that is chemically reactive, contributing to the soil’s buffering capacity and affecting its pH, as well as providing a source of calcium ions for plant nutrition. 2.4. Bulk soil and grape sample collection and DNA extraction Samples collection aligned with the harvest, differing according to Fig. 1. The scheme shows the samples used at each depth of analysis. Each year of analysis has 15 samples (3 replicates of each treatment). Besides, the experimental design performed at each location and year are shown. D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 3 both the year and location, as detailed in Table A. 5. For each treatment and year, we collected three sampling points of bulk soil from each of the three replicates under the mulch. These three sampling points were combined to form a composite sample representing the replicate of the treatment. Each replicate sample was collected with sterile gloves and 50 mL sterile tubes to avoid cross contamination at a depth of 5 cm. These samples were immediately refrigerated in the field and later stored at −80◦C for subsequent analysis. Therefore, a total of three DNA samples were analyzed from each treatment, corresponding to the three distinct replicates. Additionally, 5 kg clusters were harvested from randomly selected plants within each treatment replicate and processed in the lab. Then, clusters were crushed in a Maxicator 3500 mLand 50 mL of the resulting must undergo centrifugation and was stored at −80◦C. In summary, a total of 180 samples were collected and analyzed in the study, calculated as follows: 3 years ×2 locations ×5 treatments ×3 replicates ×2 sample types. DNA extraction was carried out from 0.25 g of sieved soil (soil DNA) and the resulting must pellet (must DNA) using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany). Then, DNA concentration and quality assessment were conducted using a Qubit 3.0 fluorometer (dsDNA HS Assay kit, Thermo Fisher Scientific, MA, USA) and a Fragment Analyzer (genomic DNA 50 kb Kit, Agilent, USA) 2.5. Library preparations The Genomics and Bioinformatics Core Facility from "Centro de Investigaci´ on Biomedica de La Rioja" (CIBIR) processed DNA samples, initiating with library production from 12.5 ng of DNA, as quantified by Qubit 3.0 (ThermoFisher). PCR conditions were as follows: 94º-5’; [94º30’’ +60º-30’’ +72º-30’’]×35; 72º-7’. The primers utilized for the Internal Transcribed Spacer 2 (ITS2) region may be found in Table A. 6 (Egidi et al., 2019; Ihrmark et al., 2012). Libraries underwent quality assessment via capillary electrophoresis utilizing Fragment Analyzer (dsDNA Reagent Kit (35–5000 bp), Agilent Technologies), while their concentration was determined using Qubit (dsDNA HS Assay kit, Invitrogen). The resulting libraries were pooled equimolarly and sequenced on an Illumina MiSeq (Ilumina), employing a 300-cycle paired-end run at a concentration of 12 pM, with a 10 % PhiX internal control included. Additionally, an internal control comprising commercial mock communities was integrated into the final run, processed like the remaining samples: Mycobiome Genomic DNA Mix (MSA-1010, ATCC Microbiome Standards). 2.6. Bioinformatic and statistical analysis The sequencing data quality was evaluated using FastQC (Andrews, 2023) version 0.10.1. Subsequent data processing was executed with SEED 2.0 (Vˇ etrovský et al., 2018). The raw forward and reverse sequences from each sample were combined into paired-end reads utilizing the fastq-join tool (version 1.1.2) from the eatools suite (Aronesty, 2011). Following this, sequences were subjected to quality filtering with a Q threshold of 30 and trimmed to a minimum length of 250 bases, removing any ambiguous bases. Sequences were then sorted based on barcode motifs and tagged with their respective sample names. Fungal ITS sequences were extracted using ITSx version 1.0.11 (Bengtsson-Palme et al., 2013). The sequences were clustered into operational taxonomic units (OTUs), and chimeric sequences were eliminated using Usearch-UPARSE version 8.1.1861 (Edgar, 2013), applying a 97 % pairwise identity threshold against the UNITE fungal dynamic database (Abarenkov et al., 2010). Representative consensus sequences were derived from the clusters using MAFFT version 7.222 (Katoh et al., 2009). Finally, OTU identification was conducted using blastn, tblastx, and makeblastdb version 2.5.0 +(https://blast.ncbi.nl m.nih.gov/Blast.cgi). The data obtained were normalized (Total Sum Scaling (TSS)) and analyzed by calculating refraction curves, alpha-diversity and betadiversity using MicrobiomeAnalyst (Chong et al., 2020; Dhariwal et al., 2017; Lu et al., 2023) and Rstudio 4.3.0. Rarefaction curves were represented to assess sample quality. Besides, Chao1 richness and Shannon diversity (alpha-diversity) were calculated in MicrobiomeAnalyst, and statistical differences between groups were studied by ANOVA test (p <0.05) using ‘agricolae’ package in Rstudio. Tukey’s post-hoc test found differences between groups. The relationship between fungal communities was investigated by calculating Bray-Curtis metrics (beta-diversity), which were then subjected to Permutational Multivariate Analysis of Variance (PERMANOVA) using “vegan” package and visualized employing the ordination-based method of Principal Coordinate Analysis (PCoA) plots. The “dplyr” and “tidyr” packages were used to manipulate the abundance data and to compare the soil and must results. All the plots were represented using “ggplot2” package and GraphPad Prism 8.0.1. Finally, the Venn diagrams were constructed using the ‘eulerr’ package. All the analyses were carried out at the ASV level, except for the abundance’s examination, which was conducted at the family level to enhance interpretability. The statistical analysis were perfomed from three replicates per treatment for microbial and physicochemical samples, which was collected as mention in the Sections 2.3 and 2.4. Finally, co-occurrence network analysis was performed from soil samples to find positive, negative or neutral interactions between taxa for each treatment applied. We used the integrated Network Analysis Pipieline (iNAP) (Feng et al., 2022) with the following conditions: p-value threshold of 0.05, 120 permutations and a correlation threshold of 0.3. The networks obtained were visualized and represented using Cytoscape 3.10.1 (Shannon et al., 2003). 3. Results 3.1. Soil physicochemical proprieties Soil chemical characteristics from both locations and all treatments are shown in Table A. 7. The treatments did not affect C/N, Ca, Fe, Mn, Cu, Al and Pb in Aldeanueva nor Ca, Al and Pb in Logro˜ no. The remaining parameters responded differently to the treatments in both locations. SMC exerted the greatest influence on the soil. In Aldeanueva, SMC resulted in higher levels of OM, EC, N, P, K, Mg, Zn and SO 4 and lower pH compared to other treatments. In Logro˜ no, SMC also resulted in higher levels of OM, EC, N, P, K, Zn, B, NH 4 and SO 4 and lower pH than those of the other treatments (see Table A. 7). The two studied locations differed in all parameters except P, Cu and SO 4 . Logro˜ no showed higher levels of organic matter (OM), C/N, EC, N, K, Mg, Ca, Fe, Zn, Al, B and NH 4 than Aldeanueva. Specifically, B, OM and N were 2.73, 2.35 and 2.31 times higher in Logro˜ no. Moreover, Aldeanueva exhibited higher levels of pH, Na, Mn, and Pb. The most significant differences were observed in Mn and Pb, which were 1.46 and 1.27 times higher in Aldeanueva, respectively. The soil’s physical characteristics varied notably between the two examined locations (Table A. 8). Logro˜ no exhibited elevated levels of silt, clay, carbonates, and activated carbon, whereas Aldeanueva displayed more significant proportions of sand. Nevertheless, despite the observed distinctions, according to the United States Department of Agriculture (USDA), both sites fall under the classification of loam soils. 3.2. Sequence analysis In the study, 180 samples comprising both soil and must were subjected to analysis. The process involved paired-end alignments, rigorous quality checks, and the removal of both chimeric sequences and singletons, culminating in the generation of 14,925,510 ITS sequence reads from fungi, which were further categorized into 951 distinct fungal ASVs. Rarefaction curves were represented to assess the sampling depth D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 4 as depicted in Figure A. 1. We observed that, with the exception of a few must samples from the first year (H2 and GPD2 in Logro˜ no and T3 in Aldeanueva), all samples reached a saturation point, indicating an adequate sampling depth (Figure A. 1 and Table A. 9). The variability in the size of the samples was notable. Specifically, the count of sequencing reads for soil samples ranged between 19,458 and 264,941 (Table A. 9). Conversely, the must samples showed a considerably lower range of sequencing reads, from 7972 to 114,202 (Table A. 9). This significant disparity in the volume of sequencing reads between the two types of samples led to conducting analyses on them separately, ensuring the integrity and specificity of the findings. The data were analyzed at three levels for soil and must (Fig. 1). The main objective was to determine the organic mulches impact on the fungal community in soil and must and to establish their significance compared to conventional systems. The first level (depth: global) involved a comprehensive analysis across the three years. The second level (depth: location) consisted of an individual analysis of each location. Lastly, the third level (depth: treatment) involved studying each year and location separately to explore the effects of different treatments. These sequence data have been submitted to the GenBank databases under accession number PRJNA1110149, BioSample (soil: from SAMN41326892 to SAMN41327071; must: from SAMN41326982 to SAMN41327071) and SRA (soil: from SRR29030274 to SRR29030335; must: from SRR29212773 to SRR29212860). 3.3. Soil fungal communities are affected by organic mulches in different ways depending on the location The main goal of analyzing soil samples was to determine the influence of the applied treatments in the vineyard on the existing fungi. For this purpose, all samples were studied by calculating the Chao1 and Shannon richness alpha-diversity indexes; as well as the Bray-Curtis index to study the beta-diversity of the samples. Soil samples from the two locations studied were analyzed separately, considering the differences in physicochemical characteristics (section 2.1.1) and alfa and beta diversity (Figure A. 2 and Table A. 10). At the first level of study (depth: global), the primary driver of the soil fungal samples was the location, dividing the samples on axis 1 (Aldeanueva left and Logro˜ no right) and explaining 24 % of the distance between them (Figure A. 2b). However, neither year nor treatment displayed apparent effects and did not cluster the samples according to the factors studied (Figure A. 2 a and c). Individual location analysis (depth: location) aimed to discover the effect of year and applied treatments on soil fungi. The PCoAs based on the Bray-Curtis index for each location indicated that year and treatment did not modify the fungal community composition (beta diversity) at any location, although statistically significant p-values were found, since no clusters were observed for either factor (Figure A. 3 and Table A. 12). Therefore, fungal community composition remained unaffected by year or treatments. Additionally, in the analysis of the Chao1 and Shannon richness and diversity (alpha-diversity) indexes for each location, a significant interaction between the year and treatment factors was detected (Table A. 13). Consequently, each of the three years was analyzed separately. In both locations, the treatment effect was not observed until the third year of study for both indexes studied and the impact varied depending on the location analyzed (Fig. 2 and Table A. 14). In Aldeanueva, S had the highest Chao1 richness, while H achieved the lowest Shannon diversity. The remaining treatments were higher than H and equal to each other. In Logro˜ no, GPD showed the highest Chao1 and Shannon diversity together with H. T, SMC and S reached lower values for both indexes. Fig. 2. Chao1 and Shannon indexes in each location studied. The results obtained in each year for the treatments H (orange), T (purple), SMC (blue), GPD (red) and S (green) are shown. Different letters show statistical differences between treatments in separate years (no letters: not significant). Asterisks show statistical differences between location means (*, p ≤0.05; ***, p ≤0.001). D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 5 When analyzing each location and year individually (depth: treatment), no consistent influence on fungal community composition was found by studying the Bray-Curtis diversity metric (Figure A. 4 and Table A. 15). GPD impacted it in the second year at both locations, while GPD and T affected in Logro˜ no in the third year. Nevertheless, no further clustering of treatments was observed in the remaining years and locations. The most abundant fungal phyla in the soil were Basidiomycota (30 ±12.13 %), Ascomycota (24.30 ±10.04 %), Mortierellomycota (10.70 ±5.88 %), Glomeromycota (6.81 ±7.09 %) and Chytridiomycota (2.23 ±3.72 %), which accounted for 74.04 % (Figure A. 5 and Table A. 16). Remarkably, 24.96 % of the taxa were not assigned to any phyla. 3.4. The organic mulch treatments do not impact the must fungal community The must samples were analyzed similarly to the soil samples, by calculating the Chao1 and Shannon richness and diversity indexes and the Bray-Curtis index. The main objective remained the detection of alterations in the must fungi resulting from the applied treatments in the vineyard soil. Must samples were analyzed at each location based on two main factors. First, the substantial differences between the soil properties of the two locations (section 2.1.1). Second, beta diversity results revealed that the location was the main driver of must fungi (Figure A. 6, Table A. 10 and Table A. 11). This factor separated the samples on axis 1 and explained 62.9 % of the distance between them. Thus, the must samples from Logro˜ no were clustered to the right and those from Aldeanueva to the left. The year and treatment factors did not affect must fungi when analyzing all must samples in the study (depth: general). Individual analysis of each location (depth: location) showed the year effect on the richness and diversity of must fungi in both locations Table A. 13. However, treatments were not significant at this level or when the years were analyzed separately (depth: treatment) (Table A. 14). In this case, treatments did not affect the richness, diversity or fungal community composition. Therefore, only the year effect but not the treatment effect (at any level of study) was observed on the must fungal communities. The most fungal-abundant phyla in the must were Ascomycota and Basidiomycota, which accounted for 98.03 % (Figure A. 5 and Table A. 16). 3.5. Organic mulches differently affect co-occurrence networks Co-occurrence networks analysis showed similar positive and negative soil fungal correlations in all treatments at the two locations studied (Figure A. 7 and Figure A. 8). However, the correlations present in the soil mycobiome of each treatment differed between treatments and between locations. Thus, Aldeanueva always had fewer fungal connections than Logro˜ no for all the treatments studied. In Aldeanueva (Figure A. 7), S was the treatment with the highest correlations between fungal microorganisms (535 positive and 483 negative). Besides, H and I were the treatments with the lowest number of positive and negative correlations, 189 and 194, and 173 and 211, respectively. Finally, GPD and SMC presented intermediate values close to 200 for both fungal correlations. In Logro˜ no (Figure A. 8), the two treatments with the highest number of fungal correlations were GPD (821/669) and H (636/625), followed by S and T, which had intermediate correlations (around 400). SMC was the treatment with the fewest positive and negative fungal correlations (312 and 225). Fig. 3. Oenological fungal families of interest representation. Abundances (%) mean ±standard error are represented in a bar chart and the presence-absence in a heat map of the oenological fungal families of interest. a) abundances and heatmap of the oenological families of interest in soil and must (soil: light green; must: dark green). b) abundance and heatmap of the oenological fungal families of interest in soil for the different treatments applied (H (orange), T (purple), SMC (blue), GPD (red) and S (green)) The red dashed line represents the mean abundance of all taxa present in the samples. Asterisks show statistical differences between groups (no letters: not significant; *, p ≤0.05; ***, p ≤0.001). Different letters show specific statistical differences between groups. D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 6 3.6. Oenological and pathogenic fungal taxa in soil and must 3.6.1. Oenological fungal taxa of interest In soil and must (Fig. 3 and Table A. 17) Candidaceae, Saccharomycetaceae, Sporidiobolaceae, Tremellaceae and Aspergillaceae were present. Metschnikowiaceae and Saccharomycodaceae were exclusive to must, and Schizosaccharomycetaceae only appeared in soil. Pichiaceae was not present in the soil or must samples. Between the nine families of interest, the most abundant (%) in must samples (Fig. 3 and Table A. 17) were Saccharomycetaceae, Sporidiobolaceae and Aspergillaceae with abundances of 31.02, 2.72 and 1.48, respectively, followed by Candidaceae, Tremellaceae and Saccharomycodaceae which reached values of 1.16, 0.01 and 2.41E-04. The Metschnikowiaceae family exhibited very low abundance (1.87E-05). Soil abundances were generally lower than in must. Saccharomycetaceae, Aspergillaceae and Sporidiobolaceae had the highest soil abundances (0.29, 0.22 and 0.02). Tremellaceae, Candidaceae and Schizosaccharomycetaceae had lower abundances (3.50E-04, 2.97E-05 and 8.37E-06). The analysis of soil families of interest across different treatments Fig. 3 and Table A. 17) revealed that only Saccharomycetaceae, Sporidiobolaceae and Aspergillaceae were consistently present. The Saccharomycetaceae family was the most abundant (%) in the T treatment (1.12), followed by S (0.11), SMC (0.09), H (0.08) and GPD (0.07). Sporidiobolaceae abundances, ranked highest in S (0.03), followed by GPD (0.03), T (0.01), H (0.01) and SMC (0.01). SMC (0.36) showed the highet Aspergillaceae abundance followed by H (0.26), GPD (0.2), S (0.15) and T (0.13). Tremellaceae was present in H, GPD and S with abundances of 1.7E-03, 1.7E-04 and 8.12E-05, respectively, while Candidaceae and Schizosaccharomycetaceae only appeared in GPD (1.5E04 and 4.19E-05 respectively). The families Metschnikowiaceae and Saccharomycodaceae, and Pichiaceae were absent in the soil. 3.6.2. Fungal taxa with pathogenic risk The results are shown in Fig. 4 and Table A. 18. The families Nectriaceae, Phaeomoniellaceae, Togniniaceae, Diatrypaceae and Erysiphaceae were consistently present in all treatments but varied in abundance. Nectriaceae was the most abundant (%) family, with SMC (1.554) showing the highest abundance, followed by H (1.459), S (1.301), T (1.264) and GPD (0.934). The other four families presented lower abundances in comparison. In the analyzed treatments, the abundance distribution of Phaeomoniellaceae was as follows: T (0.043), SMC (0.008), H (0.007), GPD (0.007) and S (0.006). For Togniniaceae the respective abundances were H (0. 034), T (0.009), SMC (0.006) S (0.005), and GPD (0.004). Diatrypaceae exhibited varying abundances in different treatments: SMC (0.017), T (0.013), S (0.009), H (0.04), and GPD (0.02). The abundance order for Erysiphaceae across treatments was GPD (0.002), T (0.001), S (0.001), H (3.60E-04) and SMC (2.71E-04). Botryosphaeriaceae was found in H, GPD and T with abundances of 2.25E-04, 1.95E-04 and 1.36E-04 respectively, while Ploettnerulaceae was only detected in GPD (6.36E-05). The families Celotheliaceae and Sclerotiniaceae were not detected in the soil. 4. Discussion The main goal of this study was to assess the impact of three organic mulches and two conventional treatments on the soil and must fungal community. Due to the positive (Liu et al., 2017; Wei et al., 2022) and negative (Gramaje et al., 2018; Jayawardena et al., 2018) influence that fungal communities may have on viticulture, understanding how they are affected by emerging treatments is crucial prior to their implementation. Thus, we utilized metataxonomic approaches to analyze fungal communities in both niches (soil and must). However, it is essential to acknowledge the limitations associated with High-Throughput Sequencing (HTAS) analyses. As highlighted by Belda et al. (2017), critical factors such as DNA extraction methods, primer selection, amplified region, sequencing methods, and databases may lead to results disparity. Significant barriers are found in the specific case of studying fungi associated with the ITS2 region, as noted by Martínez-Diz et al. (2019). The ITS2 region presents challenges in detecting certain fungal taxa (e.g., cryptic species, taxa poorly represented in databases, or those with limited variability in the ITS2 region), complicating the identification of cryptic species, and it remains unclear which region and primer pair are most suitable for addressing fungal diversity studies. One notable limitation of fungal HTAS studies is the frequent inability to achieve species-level identification, with even genus-level assignments often proving unreliable. Consequently, we focused our analyses at the family level, where taxonomic resolution is more consistent and interpretations remain robust across varied datasets. In this context, we have conducted an exhaustive literature review to select the techniques that we consider most appropriate for studying fungal diversity, considering all these restrictions and the limitations of this kind of studies. The present study encountered difficulties in identifying a significant number of fungal taxa in soil. This finding aligns with similar results reported in other studies examining fungal communities, which could be linked to HTAS barriers mentioned earlier. For instance, Morrison-- Whittle et al. (2017), Martínez-Diz et al. (2019) and Li et al. (2022) reported an inability to identify around 40 %, 30 % and 20 % of fungal taxa respectively in bulk soil, rhizosphere and must, similar to our study. Therefore, it must be taken into account that it is complex and challenging to study the fungal community using a single method, and conclusions drawn from such studies must be treated with caution. 4.1. Location shape soil and must fungal communities Despite the presence of a shared core microbiome across different locations (Gobbi et al., 2022; Mezzasalma et al., 2018), soil fungal communities are strongly influenced by location, commonly emerging Fig. 4. Fungal families with pathogenic risk representation. Abundance (%) mean ±standard error in a bar chart and the presence-absence in a heatmap of the fungal families with pathogenic risk in soil for the different treatments applied (H (orange), T (purple), SMC (blue), GPD (red) and S (green)). The red dashed line represents the mean abundance of all taxa present in the samples. Asterisks show statistical differences between groups (no letters: not significant; *, p ≤0.05; ***, p ≤0.001). Different letters show statistical differences between groups (n.s. or no letters: not significant). D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 7 as the primary influential factor (Belda et al., 2017; Gobbi et al., 2022; Liu et al., 2020b; Morgan et al., 2017; Morrison-Whittle and Goddard, 2018; Zarraonaindia et al., 2015). Biogeographic patterns play a crucial role in shaping the soil fungal composition in vineyards and may determine the types of yeasts that are present and, ultimately, influence winemaking outcomes (Dutra-Silva et al., 2021; Knight et al., 2015). This study found that location was the main driver of soil fungal communities, while the year of study had a weak influence. Although year can affect fungal communities to some extent (Liu et al., 2020b), its impact is minor and often tied to the specific climatic conditions of each vineyard (Stefanini and Cavalieri, 2018). In this study, the considerable differences in soil types between both locations could explain the biogeographic patterns found. Other studies have shown how fungal and bacterial communities respond differently to the existing biotic and abiotic factors (Holland et al., 2016; K¨ oberl et al., 2020; Likar et al., 2017). Our findings, along with those of Labarga et al. (2024) in bacteria under identical field experimental conditions, show that while bacterial communities are significantly affected by vintage, fungal communities have a stronger response to biogeographic patterns. Tian et al. (2024) demonstrate in maize systems how management practices, such as the application of mulch residues, distinctly influence soil microbial and faunal groups. Climate, soil properties, nutrient and water availability or crop-specific selection are key factors that shape soil microbiota (Bokulich et al., 2013; Chou et al., 2018; Egidi et al., 2019; Griggs et al., 2021; Taylor et al., 2014). However, since the conditions were identical in the compared studies, it suggests that fungal communities may be more resilient to disturbances such as those related to annual climatic differences. In must, we observed similar results to those in soil, with location being the main driver of the fungal community and year the secondary driver. The study of the grape fungal community has raised even greater interest than soil since it is directly involved in must fermentation. The "terroir" effect is enhanced by the finding of geographical fungal patterns in grapes and must (Bokulich et al., 2016; de Celis et al., 2023; Li et al., 2022; D. Liu et al., 2020b; Pinto et al., 2015; Taylor et al., 2014; Wang et al., 2021). A significant effect of the year on this niche has also been observed in grapevine (Bokulich et al., 2013; Grangeteau et al., 2017). Variations in the microbiota between vintages may result in wines with different qualities, making it an essential factor to consider. Our results agree with all these previous studies, showing that even in regions separated by 60 km, biogeographical patterns play a critical role in must composition and could determine the wine organoleptic characteristics of each area. 4.2. Treatment effect is location-dependent Soil management has a direct effect on soil and must microbiota. Previous studies have focused on characterizing conventional, organic, and biodynamic practices worldwide in vineyards soils (Colautti et al., 2023; Karimi et al., 2020; Morgan et al., 2017; Morrison-Whittle et al., 2017; Ortiz-´ Alvarez et al., 2021; Signorini et al., 2021), as well as in must and wine (Cordero-Bueso et al., 2011; de Celis et al., 2023; Grangeteau et al., 2017; Martins et al., 2014; Setati et al., 2015). These studies demonstrated a decrease in fungal microbial diversity in conventional compared to organic and biodynamic practices, along with a weakening of fungal networks associated with conventional practices. In this study, soil diversity and co-occurrence networks were consistently different between Logro˜ no and Aldeanueva, with higher correlations observed in Logro˜ no compared to Aldeanueva. These disparities can be attributed to significant differences in management history between locations. Aldeanueva has been under conventional management since the establishment of the vineyard, while Logro˜ no has been employing organic practices for 10 years (they are under certification process, but they still have not the corresponding certification). The legacy of soil management history on microbiota is detectable in the long-term (Karimi et al., 2020), which could significantly influence the observed diversity and networks patterns, together with soil physicochemical differences. In this context, "long-term" refers to the prolonged timeframe required for the effects of soil management practices to fully manifest and stabilize, particularly as soil microbiota respond gradually to sustained interventions over time. It can be inferred that the legacy of soil management practices is also a significant factor in explaining the observed disparities in soil characteristics, particularly the doubled values of organic matter (OM) and nitrogen (N) between the two locations. This historical context highlights the long-term impact of agricultural practices on soil health and microbial communities, underscoring the importance of sustainable management strategies in viticulture. The specific treatments used in this study primarily targeted the soil. While conventional practices involved product application (herbicide) and mechanical soil modification (tillage), organic mulches focused on applying substrates on the soil. Besides, they were not applied during ripening to prevent potential disturbance of the soil microbiota which could transfer to the must during sampling. Therefore, the lack of treatments effect found on the must microbiota was expected and is consistent with the mode of application of the treatments under study. Similarly, Chou et al. (2018) found no effect of soil-applied treatments such as tillage, herbicide and cover crops on grape microbiota. However, a discernible effect on soil microbiota was observed in our study, particularly in the third year of the study, consistent with similar research on soil bacteria in the vineyard (Labarga et al., 2024). Organic mulches and other sustainable practices have been reported to have a long-term effect (Hartmann et al., 2015; Karimi et al., 2020), agreeing with the present study. Besides, previous studies conducted within this same trial have demonstrated significant improvements in soil quality parameters, such as increased organic matter and nutrient content, further emphasizing the long-term benefits of organic mulches (Mairata et al., 2023). Our results showed that while S treatment resulted in the highest richness and diversity, in Aldeanueva, GPD and H exhibited this trend in Logro˜ no. The effect of management differs between regions and soil types (Burns et al., 2016). In our study, the locations differed greatly in soil type and management, as previously mentioned. It could be the reason why treatments have specific effects in each location, highlighting the importance of considering such factors when deciding the vineyard practices. Few studies have analyzed the effects of organic mulch on vineyard microbiota. Among them, straw is one of the most studied substrates and has been reported to increase soil fungal diversity (Dai et al., 2023; Gupta et al., 2019; Thomson and Hoffmann, 2007; Xia et al., 2019), mainly due to its effects on soil moisture and temperature regulation (Pou et al., 2021). Our data show increased diversity in Aldeanueva, suggesting that its effects might be enhanced in vineyards with bare soil, where increased humidity and temperature regulation are crucial. Other researchers have used organic matter-based substrates similar to SMC, also showing an increase in fungal richness and diversity (Burns et al., 2016; Lehmann et al., 2011; Maienza et al., 2017; Shi et al., 2023; Thomson and Hoffmann, 2007). We did not find similar effects with SMC, suggesting that this organic matter-based mulch had no detectable effect on soil fungal microbiota although it did have a direct effect on plant physiology (data not shown). Finally, the effect of GPD on microbiota, a substrate susceptible to containing high fungal diversity including fungi responsible for trunk diseases (Elena and Luque, 2016; Martínez-Diz et al., 2019), remains unclear. In this study, GPD increased soil fungal diversity in both locations and fungal richness in Logro˜ no. It could show a detrimental effect of this type of substrate due to the possible content of trunk diseases, although an increase in diversity could also enhance other beneficial fungal species. Anyway, a deeper analysis is needed to ascertain its specific impact. Understanding the presence of specific taxa under this substrate could facilitate better sustainable management of grapevine pruning debris. Tillage practices belonging to conventional practices decrease D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 8 diversity and negatively affect fungal community networks and correlations (Burns et al., 2016; Chou et al., 2018; Karimi et al., 2020; Mathew et al., 2012; Xia et al., 2019). Our study confirms these findings, since tillage treatment resulted in the lowest richness and diversity in both locations. Similarly, a negative effect of herbicides on soil fungal community diversity and composition has also been reported (Chou et al., 2018; K¨ oberl et al., 2020). However, in our study, H only reduced diversity and richness in Aldeanueva, coinciding with its conventional management history. In contrast, H treatment in Logro˜ no, was among the richest and most diverse together with GPD. Soil quality and type may be responsible for the differential effect of this treatment. The networks in environments with ecological treatments are stronger and less susceptible to external changes and disturbances (de Celis et al., 2023). Therefore, herbicide application would have a slighter effect in Logro˜ no than in Aldeanueva. Furthermore, herbicide application in Logro˜ no was limited during the years before the study, while in Aldeanueva, it was not, further influencing treatment effects. 4.3. Soil could be a reservoir of oenological fungal of interest Soil is a reservoir of microorganisms crucial for the plant and represents the most diverse niche within the viticulture ecosystem (Cureau et al., 2021; Deyett and Rolshausen, 2020; Martins et al., 2013; Mezzasalma et al., 2017). Hence, lots of taxa present in roots, bark, leaves and grapes are shared with soil (Cureau et al., 2021; Dutra-Silva et al., 2021; Guzzon et al., 2023; Mezzasalma et al., 2018; Morrison-Whittle and Goddard, 2018; Stefanini and Cavalieri, 2018; Zarraonaindia et al., 2015). Therefore, it is expected that soil fungi may migrate and influence the winemaking process. However, the enormous contrast in conditions between soil, grape and must complicate the survival of fungal taxa relevant to winemaking in all niches, and their implication in wine fermentation remains unclear (Casta˜ neda and Barbosa, 2017; Griggs et al., 2021; Mezzasalma et al., 2018). Our study focused on nine families of oenological interest: Candidaceae, Metschnikowiaceae, Saccharomycetaceae, Saccharomycodaceae, Schizosaccharomycetaceae, Sporidiobalaceae Tremellaceae, Aspergillaceae and Pichiaceae. While the presence of families containing species of oenological interest in soil and must was noted, some were found in very low abundances, such as Candidaceae, Sporidiobalaceae and Tremellaceae. This event has already been described in lactic and acetic bacteria and fermentative yeast (Casta˜ neda and Barbosa, 2017; Labarga et al., 2024). Others, such as Metschnikowiaceae, Saccharomycodaceae and Pichiaceae, were absent in soil. However, Saccharomycetaceae, the main family of oenological interest (Liu et al., 2017; Wei et al., 2022) which includes the species Saccharomyces cerevisiae, was detected in great abundance in soil and must. Different viticulture regions have soil-specific S. cerevisiae strains, contributing unique sensory organoleptic characteristics to the wine (Knight et al., 2015). Some spoilage genera also belong to the Saccharomycetaceae family being Brettanomyces the most relevant (Liu et al., 2017; Wei et al., 2022). Similarly, the family Aspergillaceae was found in high abundances across both studied niches. Among the fungi detrimental to winemaking are species of the genus Pichia (Pichiaceae), which produce volatile acids that disrupt the sensory profile and the formation of surface film on wine. Additionally, fungi from the genera Aspergillus and Penicillium (Aspergillaceae) are known producers of mycotoxins, including aflatoxins and ochratoxin A, both of which compromise the quality and safety of wine (Liu et al., 2017; Wei et al., 2022). Although Pichiaceae was not present in soil and must, Aspergillacea was one of the most abundant families in both niches. Some fungal species could fall to the ground after fruit drop and sporulate, surviving in the soil (Griggs et al., 2021; D. Liu et al., 2020b). Subsequently, they could migrate to grapes and become part of their microbiota, participating later in the fermentative process positively or negatively. Several migration ways have been described, including wind, rain, animal vectors or human practices (Burns et al., 2015; Griggs et al., 2021; Martins et al., 2013; Zarraonaindia et al., 2015). Recently, the microorganism migration, including yeasts, through plant sap has also been proposed (D. Liu et al., 2020b; Martins et al., 2013; Pacifico et al., 2019), althought Liu et al. (2020b) only detected non-fermentative yeasts. In this line, our results suggest that some members of oenological interes could migrate from soil to must, affecting the fermentative process and providing the wine with native site-specific characteristics. Soil treatments did not specifically affect fungal families of oenological interest in soil. Differences between T-GPD and SMC-T were found only in the Saccharomycetaceae and Aspergillaceae families, respectively. The greater aeration and disruption, and consequently higher soil dispersion caused by tillage, could cause the higher abundance found (Kladivko, 2001; Ventorino et al., 2019). However, neither organic mulching nor conventional practices positively or negatively affected consistently the presence of the rest of the families in the soil. Therefore, the implications of organic mulches on the winemaking process seem not to be relevant. 4.4. Organic mulches do not enhance fungal disease in soil The Fungi kingdom is widely diverse, and species belonging to Ascomycetes and Basidiomycetes exhibit potential pathogenicity associated with diseases such as Grapevine Trunk Disease (GTD) (Gramaje et al., 2018). GTD is one of the main problems affecting viticulture, as the fungal lifestyle and their high pathogenicity make their management extremely difficult (Leal and Gramaje, 2024). Hence, it is essential to assess the impact of novel field treatments on the targeted fungi causing these diseases to determine their potential implications. Particularly, substrates that can be used as mulches and are susceptible to containing pathogenic fungi, such as pruning debris, should be studied in depth to determine their pathogenic effects. This study focused on analyzing ten families containing species known to cause GTD (Nectriaceae, Ploettnerulaceae, Phaeomoniellaceae, Togniniaceae, Botryosphaeriaceae, Celotheliaceae, Diatrypaceae) and other recurrent vineyard fungal diseases, including powdery mildew (Erysiphaceae), black rot (Botryosphaeriaceae in the sexual phase) and Botrytis bunch rot (Sclerotiniaceae) (Gramaje et al., 2018; Leal and Gramaje, 2024). Fungi responsible for Black foot inhabit the soil and infect the plant by penetrating the roots (Agustí-Brisach et al., 2014; Agustí-Brisach and Armengol, 2013; Hrycan et al., 2020). However, not all GTD fungi are capable of living in soil. For example, the genera Phaeomoniella related to Botryosphaeria dieback and Petri disease, respectively, have been found in the rhizosphere and on dead plant material (Agustí-Brisach et al., 2013; Elena and Luque, 2016; Sacc` a et al., 2018). Besides, species of the Botryosphaeriaceae family, such as Botryosphaeria dothidea and Neofusicoccun parvum and Diplodia seriata, are more commonly found in plant debris, from where they infect the plant through wounds (Billones-Baaijens et al., 2018; Gramaje et al., 2018). Other species, such as Botrytis cinerea, and Erysiphe necator may be dispersed through the air and infect plant aerial organs. Thus, their presence in soil, separated from plant debris is also uncommon (Fern´ andez-Gonz´ alez et al., 2019, 2013; Gonz´ alez-Fern´ andez et al., 2021; Thomas et al., 1987). Nevertheless, after applying each treatment, we analyzed all of them in the soil to reveal if mulches, with particular emphasis on GPD, serve as a source of these pathogenic fungi, thereby enhancing their growth. All families were not found in soil. Among the detected families, only Nectriaceae, responsible for Black foot, was found in near-average abundance. The families Phaeomoniellaceae, Togniniaceae and Diatrypaceae were also detected but at lower abundances. These results indicate the presence of taxa responsible for Black foot, Petri disease and Eutypa dieback in soil but not for the rest of the studied diseases. Moreover, all of them were found in the five treatments studied without showing differences. At the family level, we did not find any mulches impacting the abundance of the analyzed taxa. Even GPD, a substrate susceptible to containing high abundances of pathogenic fungi (Elena D. Labarga et al. Agriculture, Ecosystems and Environment 382 (2025) 109506 9