Impacts of Soil Management and Sustainable Plant Protection Strategies on Soil Biodiversity in a Sangiovese Vineyard

Abstract

Vine growing is one of the most economically important sectors of Mediterranean agriculture, but its cultivation practices are highly detrimental to the environment and the associated biota. The application of both natural products inducing endogenous plant defense mechanisms and natural soil management systems represents a potential solution for the preservation and improvement of soil health and biodiversity. The Life Green Grapes project aimed at evaluating the effects of different natural and sustainable vine protection strategies and soil management on vineyard edaphic communities. Soil TOC, TN, C:N ratio, CaCO₃ content and pH were measured. Microbial communities (bacteria and fungi) were characterized through NGS, while nematodes and microarthropods were isolated and identified. Obtained data highlighted a relationshipbetween bacteria, fungi and nematodes with soil chemistry, and an effect of the different soil management on the single and total edaphic communities. Network analysis evidenced a positive effect of the application of sustainable soil managements on the relationships among the different investigated soil trophic levels, suggesting that more natural soil managements allow a better interaction between soil organisms. In conclusion, this work confirms the importance of the application of sustainable and natural soil management practices in agricultural ecosystems, with the aim of conserving and improving soil biodiversity.

1. Introduction

Vine growing is one of the most economically important sectors of Mediterranean agriculture. However, its cultivation practices are highly detrimental to the environment and the associated biodiversity, mainly due to the extensive use of pesticides aimed at preventing cryptogamic infections, often responsible for production losses with economic damage to growers [1,2]. The use of pesticides, together with other agricultural practices (e.g., plowing or tillage, mineral fertilization, irrigation and drainage systems and weed management), represents a strong threat to soil biodiversity in vineyard soils [2,3], affecting its role as a key component of soil quality [4] and impairing the functions and services it provides [5] (i.e., plant productivity, nutrient cycling, organic matter decomposition, pollutant degradation and pathogen control) [6].

In organic farming, cryptogamic diseases are mainly controlled with the use of copper and sulfur products, which are very effective against downy mildew and powdery mildew, respectively, and account for up to 70% of the total amount of fungicides used in viticulture [7]. However, the extensive use of copper over the years has led to its over-accumulation in many vineyard soils due to its high persistence in the surface layer of the soil [8,9], and thus, the use of copper in agriculture is strictly limited by current legislation (Regulation (EU) 2018/1981 of 13 December 2018). On the other hand, viticulture Integrated Pest Management (IPM), which has been mandatory in the EU since 2014 following the adoption of the Directive on the Sustainable Use of Pesticides, is based on an innovative approach that aims to increase the resilience of the vineyard and reduce its environmental impact. According to the principles of IPM, infections can be controlled through multiple approaches aimed at reducing the use of synthetic fungicides as much as possible [1].

In addition to the overuse of pesticides, excessive tillage and the use of chemical fertilizers also contribute to the degradation of the vineyard ecosystem. Indeed, it was observed that tillage negatively affects most soil microbes and their functioning [10], leading to a decrease in soil fungi and bacterial diversity. Anthropic interference particularly due to deep plowing, together with soil humidity and soil compaction are the conditions that most influence soil microarthropods [2,11,12], which are significantly affected by soil alterations and can react in different ways to unfavorable changes with massive alterations in the composition of their communities, up to the disappearance of the most sensitive species. Moreover, such vineyards are characterized by low nematode abundance indicating a limited biological activity in this environment reasonably due to scarce organic carbon content [13,14].

In order to comply with the recent directive and to improve the sustainability of viticulture, it is necessary to implement new approaches to protect vineyards from diseases, evaluating their real effectiveness and possible synergies or impacts on soil biota. Some studies have reported evidence of the efficacy of natural substances consisting of living micro-organisms (bio-fungicides), and bacterial or botanical derivatives against pathogen attacks on grapevines [15,16]. These natural substances have been shown to act as elicitors, simulating host-pathogen interactions and thus stimulating the plant’s endogenous defense mechanisms [17,18] and the physiological processes of absorption and assimilation of nutrients. Concerning soil management techniques, methods such as herbaceous cover crops and green manures represent strategic solutions for maintaining soil quality and health. Grass cover reduces soil erosion, nutrient leaching and soil compaction caused by mechanical operations, improving habitat complexity and increasing the abundance and diversity of natural enemies of pathogens, which can provide strong biological control [19,20]. The maintenance of permanent cover crops also increases soil microbial biomass and diversity and may be more advantageous for promoting microbe-mediated ecosystem services [2,21]. In addition, the green manure crops that are buried contribute to the nutritional enrichment of the soil [22,23].

In the context of understanding and addressing the concerns related to viticultural management practices, as well as options for protecting soil biodiversity [2], the demonstrative project Life Green Grapes had the aim to increase the sustainability of viticulture along the entire supply chain and to preserve and improve the vineyard-associated biodiversity [24]. To evaluate the environmental and soil health benefits using long-term monitoring data [25], a field trial was set up over three growing seasons (2018–2020) using innovative plant protection products and sustainable soil management procedures.

A preliminary evaluation of the effect of the “Green Grapes” approach on downy mildew development, plant yield, grape composition and total soil copper distribution was carried out by Storchi et al. [26]. Obtained results suggested the effectiveness of this approach under low downy mildew pressure, showing improved vineyard productivity, preserved grape quality and an increase in soil biological quality indices. However, an in-depth assessment of its impact on soil biodiversity and on the relationships among the different trophic levels is still lacking.

Indeed, soil organisms live within complex soil food webs, forming ecological clusters of strongly co-occurring phylotypes [27]. These ecological assemblages, guided by ecological processes (e.g., niche overlap or segregation, predation, cooperation, competition) [28], have important implications for ecosystem functioning [29]. Conventional agricultural practices are known to represent one of the most important threats to the conservation of soil biodiversity [30,31] and might interfere with this web of relationships, potentially impairing community functioning [6,32,33]. As such, a comprehensive evaluation of the impacts of any treatment on the soil biota should not exclude such an assessment. Notable examples of research investigating multiple soil trophic levels and their relationships with vineyard agricultural practices are represented by the studies performed by Puig-Montserrat et al. [34] and Ostandie et al. [35], on the effects of organic farming on different taxonomic groups, from plants to birds, arthropods and microorganisms. Nevertheless, these are two of the very few examples available [2]. The need to overcome this limitation in the study of vineyard soils has already been highlighted in literature; as stated by Paiola et al. [3] “multi-taxon studies should be encouraged, as they are promising for exploring the simultaneous responses of different organisms to the same drivers and to clarify the role of biological interactions”. In addition, to the best of our knowledge, the few available multi-taxon studies tend to consider the different levels of soil biota separately; therefore, a huge information gap lies in the absence of the evaluation of the possible interrelations and ecological associations among the different soil trophic levels in the vineyard.

With the purpose of shedding light on the biota multi-level interrelationships in vineyard soils, and how these are related to different vineyard managements, the present work aims at evaluating the effects of different vine control strategies (including integrated management, organic management and some variants based on the use of elicitors as a partial or total replacement of copper or other chemicals) on multiple vineyard edaphic communities. The different treatments were evaluated in association with two widespread vineyard soil management systems, permanent grassing and green manure. More specifically, we investigated the dynamics of the microbial (bacterial and fungal), micro-fauna (nematodes) and meso-fauna (microarthropods) community structure, diversity and trans-kingdom connections, with a focus on the possible interactions among the different trophic levels.

2. Materials and Methods

2.1. Study Area, Vineyards Treatments and Soil Samplings

The present study was conducted in a vineyard of about 6 ha owned by “Castello di Gabbiano—Beringer Blass Italia” farm, located in the DOCG Chianti Classico area (San Casciano in Val di Pesa, Florence, Italy) (Figure 1). This area belongs to the Mediterranean North environmental zone; it is a grapevine-growing area located on a plateau with an average altitude of around 200 m a.s.l. According to the Köppen climate classification, the climate in this area is Csa (Mediterranean climate with hot summers). The 10-year-old study vineyard is located between 155 and 185 m a.s.l., with east exposure and a slope of 21%. This vineyard of the cv. Sangiovese (V. vinifera), grafted on 110R rootstock, was planted with a spacing of 0.7 m (between vines) × 2.6 m (between rows). The vines were not irrigated and were grown using standard cultural practices. The vineyard soil averaged a clay texture (40% clay, 35% silt, 25% sand) with high carbonate content (20% total equivalent-CaCO₃) and a moderately alkaline reaction (pH = 8.1). The ordinary vineyard management was based on inter-row tillage (ST) and integrated pest management (IPM).

Within this vineyard, the “Green Grapes” strategy was applied for three years (2018–2020) [24]. The experimental design consisted of five 10,000 m² plots, each receiving one of the following plant protection treatment: (i) ordinary integrated pest management (IPM) as control; (ii) IPM with 50% of pesticides replaced by defense-inducing activity products, here referred to as Green Grapes elicitors, composed from seaweeds, yeast and plant extracts (IPM_50_GG) (for further information on the elicitors composition, please refer to the project Operational Handbook [36]); (iii) organic (copper-based) plant protection management (BIO); (iv) BIO with 50% of copper replaced by Green Grapes elicitors (BIO_50_GG); (v) 100% Green Grapes elicitors (GG). In addition, two different soil management strategies were applied for each plot: green manure (GM) and permanent cover crop (PCC), as reported by Storchi et al. [26]. GM consisted of growing specific crops in all the inter-rows (i.e., 35% Vicia faba, 25% Hordeum vulgare, 20% Avena sativa, 10% Vicia sativa, 5% Sinapsis alba, 5% Trifolium incarnatum), which were sown in autumn and cut and buried in early spring. PCC was natural and mostly represented by grasses. Each vine treatment was applied to 28 consecutive rows within each plot, 14 managed with GM and 14 with PCC.

In order to investigate soil chemical features and biodiversity (bacteria, fungi, nematodes and microarthropods) under the influence of the different treatments, one soil sample per plot was collected before the beginning of the trial (October 2017—baseline) and then six samples per plot (3 for GM and 3 for PCC) at the end of the study period (October 2020). Each sample was composed of five sub-samples. To avoid the drift effect between neighbor treatments, samples were collected in the middle rows of the plots, from row 10 to row 19. The list of samples and their metadata are reported in Supplementary Table S1.

Daily temperatures and rainfall were recorded by a weather station (Netsens, Italy) located at the edge of the vineyard.

2.2. Soil Chemical Characterization

During sampling, soil samples at a depth of 0–20 cm were collected also for chemical characterization. Soil pH was measured potentiometrically on a 1:2.5 soil:water suspension. Soil total organic carbon (TOC), total nitrogen (TN), and total lime content (CaCO₃-equivalent) were determined by dry combustion with a Thermo Flash 2000 CN analyzer (Shimadzu, Kyoto, Japan). For this purpose, 30–40 mg of soil were weighed into a Sn capsule and analyzed for total C (organic + mineral C) and total N contents. Separately, 15–20 mg of soil was weighed into an Ag capsule, treated with 10% HCl until complete removal of carbonates, and then analyzed for total C content (organic C). Soil total equivalent CaCO₃ content was calculated from the difference between the total and the organic C content [37].

2.3. Assessment of Soil Microbial Biodiversity

Soil samples (0–20 cm depth) were transported to CREA laboratories conserved with freezer packs and then stored at −20° until DNA extraction. Total DNA extraction was carried out in the days following sampling, within a couple of weeks, using the FastDNA SPIN Kit for soil (MP Biomedicals, Irvine, CA, USA). The bacterial 16S rRNA gene V3-V4 hypervariable regions and the fungal nuclear ribosomal ITS region were sequenced using the primers 341F (CCTACGGGNBGCASCAG) and 805R (GACTACNVGGGTATCTAATCC) [38] and ITS1F (TCCGTAGGTGAACCTGCGG) and ITS4R (TCCTCCGCTTATTGATATGC) [39], respectively. Libraries were sequenced in a single run using Illumina MiSeq technology with a paired-end sequencing strategy (2 × 300 bp). PCR amplification, library construction and sequencing were performed by an external company (IGA Technology Services, Udine, Italy). Raw sequence files were deposited in the NCBI Sequence Read Archive (SRA) under the accession PRJNA1081348.

PCR primers were removed from all the sequences using Cutadapt [40] (version 3.5) with a maximum error rate of 0.15. For ITS sequences, the presence of the forward primer in the reverse reads, and vice versa was checked and reads were trimmed accordingly. Then, 16S sequences were clustered into ASVs (amplicon sequence variants) following the DADA2 pipeline (version 1.16) (described at https://benjjneb.github.io/dada2/tutorial.html, accessed on 13 December 2023) [41] using the R software version 4.2.3 [42]. ITS sequences were clustered into 98%-OTUs (operational taxonomic units) (preferred over ASVs as reported in Tedersoo et al. [43]) using the VSEARCH de novo clustering tool [44] into the Qiime2 environment [45]. Fungal OTUs were then filtered to discard singletons, OTUs present in only one sample, and OTUs with relative abundance <0.005% (according to Bokulich et al. [46]). The taxonomic annotation was performed using the Silva database version 138 [47] for the DECIPHER R package [48] and the UNITE database version 29.11.2022 [49], for 16S and ITS data, respectively. Representative sequences that were not assigned to domain Bacteria (for 16S data) and to kingdom Fungi (for ITS data) were removed from the dataset [50].

Both bacterial and fungal ASV/OTU abundance tables were collapsed at the genus level. In order to obtain a similar order of magnitude of bacterial/fungal taxa, with nematodes and microarthropods, the 100 globally most abundant genera were selected for each microbial community (see Supplementary Material S2). Both communities were then characterized using the following indices: richness (R), biodiversity indices (H, Shannon-Weiner index; J, Evenness), and in addition, for bacteria, the ratio between Proteobacteria (copiotrophs) and Acidobacteria (oligotroph) (P/A) which, according to Smit et al. [51], might be indicative of the trophic level of the soil.

2.4. Evaluation of Nematode and Microarthropod Edaphic Communities

To characterize soil nematode structure, a set of soil samples was collected using a hand auger (5 cm inside diameter) from the 30 cm deep top layer of bulk soil after removing surface residues. For each soil sample, six cores were randomly sampled and then mixed to form one composite sample. Each sample was then placed in a sterile plastic bag, labeled, and stored in a cold chamber at 4 °C. Within a week from sampling, nematodes were extracted from 100 mL of each soil sample using the cotton-wood filter method (48 h in extraction at room temperature, approximately 20 °C). Each nematode suspension was sieved through a 25 µm mesh. Nematodes were mounted on temporary slides and identified at higher magnification to genus or family level using keys from Mai and Lyon [52], Bongers [53], Marinari–Palmisano and Vinciguerra [54] (see Supplementary Material S2). Taxonomic families were assigned to a trophic grouping based on Yeates et al. [55] and Okada et al. [56]. Nematode communities were characterized using richness (R) determined at the family level, biodiversity indices (H, Shannon–Weiner index; J, Evenness index), Maturity (MI), Plant parasitic (PPI) indices by Bongers [57] and food web indicators (BI, basal index; EI, enrichment index; SI structure index; CI, channel index) by Ferris et al. [58].

Soil samples for microarthropod evaluation were collected close to the previous ones (see above) in each plot using a special 10 cm³ corer. The day after sampling, microarthropods were extracted using modified Berlese–Tullgren funnels following the standard methodology [59] (see Supplementary Material S2). The edaphic microarthropod communities were characterized using the: richness (R) determined at order level, (iii) biodiversity indices (H, Shannon–Weiner index; J, Eveness index), and soil biological quality, QBS-ar index, according to Parisi et al. [59] in order to evaluate the soil biological quality by organism adaptation to the edaphic habitat.

2.5. Statistical Testing

Statistical analyses on soil chemical data and biological communities were performed using the vegan [60], phyloseq [61], agricolae [62] and mass [63] packages with relative plugins into the R environment [42]. All the plots were obtained using the ggplot2 R package [64].

At first, a Mantel test of correlation was performed between the dissimilarity matrices obtained from the 16S and ITS abundance tables at the ASV/OTU level and that obtained from genus-collapsed ones, to make sure that this simplification would not affect the structure and composition of the microbial communities data. Then, to normalize the sequencing depth of 16S and ITS data, abundance tables were rarefied before the calculation of the diversity indices.

The effect of different descriptive variables (i.e., sampling year, vineyard plot, plant treatment and soil management) on chemical data and community diversity and ecological indices was evaluated using analysis of variance (ANOVA), with Tukey HSD (for chemical data) and Student–Newman–Keuls (for community data) post hoc tests.

Correlations between chemical parameters were evaluated using Pearson’s correlation. Then, chemical data were scaled, and the total effect of the descriptive variables was evaluated using analysis of similarities (ANOSIM) with Euclidean distance. The ordination analysis of the soil samples based on their chemical features was obtained using principal component analysis (PCA).

To inspect the edaphic community structure in its entirety, bacterial and fungal genera, together with nematode families and microarthropod orders were merged in a single abundance matrix, and the relative abundances of each taxon among the samples were calculated. Using ANOSIM with Bray–Curtis distance, the effect of the management, treatment, and year was investigated on the single and total communities. To perform pairwise comparisons and to evaluate the possible interaction between multiple descriptive variables, permutational ANOVA (PERMANOVA) analyses were applied. The distribution of samples on the basis of their community structure was investigated using non-metric multidimensional scaling (NMDS) with Bray–Curtis distance, and the possible relations between soil communities and soil chemical properties were assessed by applying a constrained correspondence analysis (CCA).

The possible ecological associations among the different soil communities were inferred using the SPIEC-EASI algorithm [65], through the SpiecEasi [66] and igraph [67] R packages, on the abundance matrix of the total edaphic community. To obtain a network for each soil management and vine treatment, samples were grouped accordingly and processed. In addition, to obtain networks derived from a comparable number of samples, the mean abundance per taxon was calculated for those samples subjected to the same vine treatment for both PCC and GM soil managements (n samples = 5, 6 and 5 for 2017 baseline, each 2020 vine treatment, and each 2020 soil management, respectively). The networks obtained were then compared, inspecting their shape and the hub scores and node degree distributions. Subnetworks were obtained by selecting all the taxa of a specific trophic level, and then selecting the first neighbors of the selected nodes and the connecting edges. Network visualization and subnetwork computing were performed using the Cytoscape software version 3.10.1 [68].

Similarity percentage (SIMPER) analysis was performed on the total relative abundance table, to assess which taxa were primarily responsible for the differences between soil managements. Taxa with a significant effect (p-value < 0.05) and an average contribution to overall dissimilarity higher than 0.004 were investigated.

3. Results

3.1. Climate during the Study Trial and Soil Chemical Properties

The temperature trends of the vineyard were very similar between the summers of the two study years (2017 and 2020) (Figure 2b). Also, the precipitations followed a similar trend (Figure 2a), even though the 2020 summer resulted in a little drier July and September and slightly wetter June and August. However, the average values of the entire summer period were very similar (Figure 2c,d), as confirmed by the paired T-test performed between the months, which resulted in non-significant p-values for both precipitations and temperature.

Total organic carbon (TOC), total nitrogen (TN), the carbon-nitrogen ratio (C:N), calcium carbonate content (CaCO₃) and soil pH are reported in Supplementary Table S2. In general, chemical values varied among the vineyard plots (ANOVA p-values < 0.001 for all parameters, except for CaCO₃ and pH with p-values = 0.047 and 0.016, respectively), remaining constant during the two years of the study. As evidenced from the ordination analysis reported in Figure 2e, soil samples strongly tended to group on the basis of their plot of origin. Performing a multivariate analysis considering all the soil chemical parameters together, the plotted variable showed a significant effect in the distribution of the samples (ANOSIM R = 0.47, p-value < 0.001), while neither the year nor the soil management affected the soil chemical features.

Generally, an increasing or decreasing trend of the chemical values was observed moving from plot 1 to plot 5 (Figure 2f). CaCO₃ and pH showed slightly higher values in the first plots, while TN and TOC showed an increase moving from the beginning of the vineyard until plot 5, characterized by the highest concentrations. Indeed, the most contrasting plots are 1 and 5, with a variation in TOC from 0.6% (very low) to 1.3% (medium), and an equally important variation in TN (Pearson’s correlation between the two variables: R² = 0.97, p-value < 0.001).

3.2. Composition and Structure of the Edaphic Communities

Bacterial, fungal, nematode and microarthropod soil communities composition was assessed as reported in Materials and Methods.

In profiling the microbial sequencing data, a total of 2,079,928 bacterial 16S rRNA gene tags and 2,176,603 fungal ITS sequence tags were generated after quality filtering steps. These high-quality sequences were correctly mapped into 12,677 bacterial ASVs and 693 fungal 98%-OTUs. Both bacterial and fungal ASV/OTU abundance tables were grouped at the genus level, and the 100 most abundant genera were selected for each one. The Mantel test between the distance matrices obtained from the starting abundance tables and from the collapsed ones resulted in significant correlations (p-values = 0.001) with high R values of 0.88 and 0.97 for 16S and ITS data, respectively, allowing the use of these reduced abundance tables for all the subsequent analyses. Nematodes and microarthropods were grouped at family and order level, respectively. Because of their low abundances (observations: 3413 nematodes in 16 families and 4379 microarthropods in 19 orders) compared to microorganisms, all recorded individuals were analyzed.

Inspecting the entire edaphic community, it was observed that both different vine protection treatment and soil management resulted to significantly affect the soil community (p-values < 0.001), showing ANOSIM R values that suggested a dissimilarity among groups (treatments R = 0.38, managements R = 0.28). The PERMANOVA pairwise comparisons highlighted a significant difference among all three soil managements; comparing the different vine treatments, the IPM and the GG were the only ones that resulted significantly different from all the others. The possible effect of the interaction between treatment and management variables in defining the community structure was tested using PERMANOVA, highlighting a significant effect (p-value = 0.02) but an explained variance of only 12%. This aspect suggested the absence of a relation between these two variables in explaining the community structure.

The different soil management results significantly affected the bacterial (ANOSIM R = 0.25, p = 0.002), fungal (R = 0.25, p < 0.001) and nematode (R = 0.35, p < 0.001) communities when considered separately. The same communities were influenced also by the different vine treatments (p-values < 0.001, R = 0.33, 0.41, 0.14, respectively). On the other hand, the microarthropod community structure did not result in being influenced by either the management or the treatment.

The effect of management in structuring the edaphic communities was found to be very evident also in the ordination analyses, especially that of the entire community (Figure 3a). A good separation on the basis of the soil management resulted in being visible also for bacteria, fungi and nematodes alone, but not for microarthropods, in accordance with ANOSIM results (Figure 3b–e). At the same time, also the effect of the different treatments was clearly visible in the total community NMDS over the first ordination axis (NMDS1), where a shift of the composition starting from 2017 (leftmost), to the 2020 IPM treatment, through all the other 2020 treatments until the GG one (rightmost), is noticeable.

To investigate the possible relationships between soil chemical features and the soil biota, a CCA constrained on the chemical parameters (specifically, TN, TOC, CaCO₃ and pH) was performed on the soil communities (

Table 1. F-values and p-values of the chemical variables that resulted significantly related to the soil biological communities, obtained by the ANOVA analysis on the CCA model.

Biological Community	TN		TOC		CaCO3		pH
	F-Value	p-Value	F-Value	p-Value	F-Value	p-Value	F-Value	p-Value
Bacteria	2.79	0.003	-	-	-	-	-	-
Fungi	2.27	0.001	-	-	-	-	1.76	0.015
Nematodes	-	-	2.71	0.031	2.14	0.042	-	-
Microarthropods	-	-	-	-	-	-	-	-

). A significant relationship was observed between TN and bacteria and fungi. Fungi were also related to soil pH. A relation was also detected between nematodes and TOC and CaCO₃. On the other hand, microarthropods were not significantly related to any of the investigated chemical features.

3.3. Soil Biological Diversity Assessment

The effect of the different years, soil management, and vine treatment on biological diversity and ecological indices is reported in

Table 2. Average values and standard errors (±) of biodiversity indices, separated per group. The effect of the different variables was evaluated using ANOVA, and the significance of the Student–Newman–Keuls post hoc test is reported with letters.

Factor	Year		Soil Managements			Vine Protection Treatments
Subset	IPM		All			2020
Groups	IPM 2017	IPM 2020	ST 2017	PCC 2020	GM 2020	IPM	IPM 50 GG	BIO	BIO 50 GG	GG
Bacteria
R	98.40 ± 0.68	98.83 ± 0.31	98.4 ± 0.68	98.6 ± 0.34	98.13 ± 0.47	98.83 ± 0.31	98.83 ± 0.31	98.83 ± 0.65	98 ± 0.86	97.33 ± 0.8
H	4.02 ± 0.03	4.03 ± 0.02	4.02 ± 0.03	4.03 ± 0.02	4.06 ± 0.01	4.03 ± 0.02	4.09 ± 0.03	4.02 ± 0.03	4.08 ± 0.01	4.03 ± 0.02
J	0.88 ± 0.01	0.88 ± 0.00	0.88 ± 0.01	0.88 ± 0.00	0.89 ± 0.00	0.88 ± 0.00	0.89 ± 0.01	0.88 ± 0.01	0.89 ± 0.00	0.88 ± 0.00
P/A	2.80 ± 0.18	2.42 ± 0.10	2.80 ± 0.18	2.84 ± 0.18	2.92 ± 0.18	2.42 b ± 0.10	2.42 b ± 0.10	2.62 b ± 0.23	3.50 a ± 0.25	3.45 a ± 0.26
Fungi
R	73.6 ± 4.78	80.17 ± 0.91	73.60 c ± 4.78	84.53 a ± 1.07	79.33 b ± 0.72	80.17 ± 0.91	83.00 ± 2.29	80.17 ± 1.11	83.50 ± 2.16	82.83 ± 2.07
H	2.85 ± 0.13	3.03 ± 0.12	2.85 ± 0.13	2.97 ± 0.05	2.86 ± 0.07	3.03 ± 0.12	2.89 ± 0.11	2.89 ± 0.05	2.90 ± 0.08	2.86 ± 0.14
J	0.66 ± 0.02	0.69 ± 0.03	0.66 ± 0.02	0.67 ± 0.01	0.65 ± 0.01	0.69 ± 0.03	0.65 ± 0.02	0.66 ± 0.01	0.66 ± 0.02	0.65 ± 0.03
Nematodes
R	7.40 ± 0.40	6.50 ± 0.22	7.40 ± 0.40	6.47 ± 0.24	6.73 ± 0.25	6.50 ± 0.22	6.83 ± 0.31	6.67 ± 0.33	6.17 ± 0.40	6.83 ± 0.60
H	1.13 b ± 0.10	1.67 a ± 0.03	1.13 b ± 0.10	1.62 a ± 0.04	1.69 a ± 0.06	1.67 ± 0.03	1.65 ± 0.12	1.66 ± 0.05	1.59 ± 0.06	1.71 ± 0.10
J	0.42 b ± 0.02	0.82 a ± 0.02	0.42 b ± 0.02	0.80 a ± 0.02	0.82 a ± 0.04	0.82 ± 0.02	0.79 ± 0.08	0.80 ± 0.04	0.81 ± 0.03	0.83 ± 0.05
MI	1.22 b ± 0.04	2.40 a ± 0.14	1.22 b ± 0.04	2.23 a ± 0.07	2.32 a ± 0.04	2.43 ± 0.14	2.17 ± 0.05	2.32 ± 0.05	2.28 ± 0.10	2.17 ± 0.06
PPI	2.92 ± 0.11	2.33 ± 0.49	2.92 ± 0.11	2.80 ± 0.20	3.00 ± 0.08	2.33 ± 0.49	3.03 ± 0.03	3.08 ± 0.07	3.08 ± 0.08	2.97 ± 0.03
BI	11.20 ± 3.75	22.13 ± 6.31	11.20 b ± 3.75	18.51 b ± 2.29	30.51 a ± 4.08	22.13 ± 6.31	27.73 ± 3.83	33.33 ± 7.38	18.93 ± 4.57	20.40 ± 5.60
EI	96.52 a ± 0.94	52.43 b ± 9.44	96.52 a ± 0.94	68.60 b ± 2.94	42.38 c ± 4.66	52.43 ± 0.44	51.55 ± 5.86	51.87 ± 5.33	50.62 ± 11.31	70.98 ± 6.68
SI	48.94 b ± 5.00	64.80 a ± 3.89	48.94 b ± 5.00	66.19 a ± 2.80	58.04ab ± 2.35	64.80 ± 3.89	52.57 ± 3.55	61.22 ± 4.88	62.92 ± 2.92	69.08 ± 4.52
CI	2.84 b ± 0.93	36.28 a ± 10.82	2.84 b ± 0.93	13.15 b ± 2.75	46.23 a ± 5.82	36.28 ± 10.82	30.70 ± 6.64	34.55 ± 6.46	29.06 ± 16.32	17.87 ± 7.25
Microarthr.
R	5.80 ± 0.20	6.33 ± 0.84	5.80 ± 0.20	6.53 ± 0.46	6.53 ± 0.40	6.33 ± 0.84	6.33 ± 0.61	6.67 ± 0.56	5.50 ± 0.72	7.83 ± 0.31
H	0.99 ± 0.04	1.16 ± 0.09	0.99 ± 0.04	1.15 ± 0.06	1.06 ± 0.06	1.16 ± 0.09	1.14 ± 0.12	1.01 ± 0.10	1.17 ± 0.09	1.05 ± 0.04
J	0.52 ± 0.03	0.53 ± 0.04	0.52 ± 0.03	0.53 ± 0.04	0.47 ± 0.04	0.53 ± 0.04	0.53 ± 0.07	0.45 ± 0.08	0.62 ± 0.05	0.37 ± 0.03
QBS-ar	69.40 ± 3.11	92.83 ± 10.15	69.40 ± 3.11	87.07 ± 6.11	92.67 ± 5.31	92.83 ± 10.15	87.17 ± 7.88	85.17 ± 7.17	74.33 ± 8.50	109.83 ± 6.49

R: richness, H: Shannon index, J: evenness, P/A: Proteobacteria/Acidobacteria ratio, MI: maturity index, PPI: plant parasitic index, BI: basal index, EI: enrichment index, SI: structure index, CI: channel idex, QBS-ar: soil biological quality index.

. To assess the effect of the sampling year only the IPM treatment was considered, and to evaluate the effect of the vine treatment only 2020 data were considered.

Microarthropods diversity indices resulted to be influenced neither by the year nor by the management or the treatment. However, GM and PCC soil management showed similar QBS-ar values, both higher than conventional tillage applied until 2017, although not significantly. Similarly, bacteria and fungi were slightly influenced by these variables; for bacteria, only the Proteobacteria/Acidobacteria ratio resulted in being affected by the different treatments, separating the IPM, IPM_50_GG and BIO ones from BIO_50_GG and GG. Concerning fungal data, the different soil managements resulted in significantly differentiated fungal richness, with the highest values found associated with more natural and sustainable soil management techniques.

On the other hand, biodiversity and ecological indices of the nematode community were highly influenced by the considered variables. Specifically, both year and soil management showed an effect on nematode indices. The adoption of eco-sustainable soil managements such as GM and PCC significantly increased H, J, MI, SI and CI indices compared to ST applied in 2017, while EI significantly decreased in 2020. Few differences were found between GM and PCC. Specifically, EI and SI were significantly higher in PCC than in GM, whereas BI and CI were higher in GM than in PCC. No differences were found for richness.

3.4. Investigation of Trans-Kingdom Relations

The possible ecological associations among the different soil communities were inferred using the SPIEC-EASI algorithm [65] for co-occurrence network reconstruction. Within the obtained networks, each node corresponds to a specific taxon, and a link between any two nodes implies that the taxa abundances are not conditionally independent and that there is a (linear) relationship between them [65].

Concerning the network conformation (Figure 4), it was observed that the baseline network (2017; IPM) and all those obtained by grouping samples on the basis of vine treatment (2020; IPM, IPM_50_GG, BIO, BIO_50_GG, GG) had an overall similar structure, showing a set of taxa (often represented by microarthropods and/or nematodes, red or blue nodes) clearly separated from the rest of the nodes.

This was not true when grouping samples on the basis of the management, and results showed that the different soil management radically changed the network conformation moving from the ST of 2017 to 2020 GM and, especially, 2020 PCC. Indeed, the two 2020 networks showed a reduction in the clusterization of specific groups of taxa towards the periphery of the network in favor of greater integration of all the nodes within the network. This aspect was also confirmed by the analysis of the hub distribution (Figure 4, hub score is mapped to node dimension). In a network, a hub is a node showing many connections with other nodes. In the treatment networks, the hub nodes were all located at the edges of the network, not well integrated into the point cloud and mostly represented by microarthropod and nematode taxa. The average hub score of these networks was similar (Supplementary Figure S1a), being comprised of 0.04 (for BIO_50_GG) and 0.15 (for BIO, which resulted significantly different from the others in terms of average hub score). The PCC and GM networks, compared to the 2017 ST one, showed a good distribution of hub nodes within the network (Figure 4 and Supplementary Figure S1b) and an overall higher hub score (average hub scores: ST = 0.06, GM = 0.27, PCC = 0.23), resulting significantly different from ST (Tukey HSD p-values < 0.001) but also from each other (p-value = 0.01). The analysis of the degree distribution, i.e., the relative frequency of the degrees (connections) of the nodes, showed similar trends among the different treatments (Supplementary Figure S1d) while, concerning the soil management, PCC and GM showed different trends both between themselves and with respect to 2017 ST (Supplementary Figure S1e).

Based on all these aspects, the differences among the three soil managements were deemed more interesting from a network analysis approach point of view and analyzed in more detail.

From each of the three management networks, four subnetworks were derived, one for each trophic level of the soil community, to investigate how the links between the different taxa change as the management varies. The percentage of edges of the subnetwork with respect to the entire network (

Table 3. Relative abundances of subnetwork edges.

Subnetwork	ST 2017	GM 2020	PCC 2020
Bacteria	0.84	0.86	0.91
Fungi	0.94	0.89	0.96
Nematodes	0.18	0.19	0.20
Microarthropods	0.19	0.22	0.24

) highlighted how the total connections tended to increase for all four trophic levels (with the only exception of fungi in GM samples), moving from the standard tillage of 2017 to the 2020 green manure management and, especially, to the permanent cover crop strategy.

Then, to inspect the interactions between organisms of different trophic levels, the number of nodes retrieved in each subnetwork was reported (

Table 4. Number of members of the different trophic levels retrieved in each subnetwork. The percentage of increase or decrease in the number of members of the different trophic levels compared to 2017 is reported in brackets.

Subnetwork	Trophic Level Retrieved	ST 2017	GM 2020	PCC 2020
Bacteria	Fungi	92	87 (−5%)	95 (+3%)
	Nematodes	16	11 (−31%)	14 (−13%)
	Microarthropods	8	14 (+75%)	14 (+75%)
Fungi	Bacteria	93	89 (−4%)	94 (+1%)
	Nematodes	14	14 (+0%)	16 (+14%)
	Microarthropods	19	15 (−21%)	18 (−5%)
Nematodes	Bacteria	29	31 (+7%)	25 (−14%)
	Fungi	23	24 (+4%)	28 (+22%)
	Microarthropods	2	5 (+150%)	6 (+200%)
Microarthropods	Bacteria	10	22 (+120%)	25 (+150%)
	Fungi	18	32 (+78%)	34 (+89%)
	Nematodes	1	10 (+900%)	4 (+300%)

). The 2020 management increased the connections of nematodes and microarthropods with the other taxonomic groups, in particular nematodes with fungi and microarthropods, and microarthropods with the other three levels.

Finally, SIMPER analysis was performed to better investigate those taxa that mostly distinguish the communities associated with the three soil management (Figure 5). PCC and GM behaved similarly towards ST. Specifically, most of the taxa identified by SIMPER to distinguish between managements showed a higher relative abundance in ST; some of those (n = 5) were common to the comparisons PCC-ST and GM-ST, while others (n = 6) were retrieved only in the GM-ST comparison. Specifically, the nematode families of Trichodoridae (virus-vector), Paratylenchidae (plant-parasites) and Seinuridae (predators) were more abundant in ST than GM. In addition, the family Rhabditidae, belonging to nematode bacterial feeders and classified as colonizers, decreased its abundance both in GM and PCC. The pairwise comparison between PCC and GM highlighted, for the taxa that best discriminate among these two groups, higher abundances for PCC samples than GM ones, with the exception of an Ascomycota member. Only a few bacteria were retrieved in this analysis, belonging to Proteobacteria and Actinobacteriota. On the other hand, many Ascomycota members were identified, able to distinguish both the GM/PCC-ST pairs and the PCC-GM one. Nematodes and microarthropods also contributed to discriminating these two groups. In particular, GM reduced the abundance of the Meloidogynidae family (one of the most harmful plant-parasitic nematode family) and the microarthropod orders of Pseudoscorpiones, Pauropoda, Opilionids and Hymenoptera.

4. Discussion

In this study, we investigated the vineyard edaphic communities of the Castello di Gabbiano farm (Italy). During the three-year field trial, the vineyard was treated with innovative plant protection products and sustainable soil management procedures; the analysis of data collected before the beginning of the study (2017) and at the end of the trial (2020) is here reported and discussed.

Climate data were very consistent between the summers of 2017 and 2020, concerning both monthly average temperatures and cumulative precipitations. This stability of the climate conditions allowed us to hypothesize that the weather variable could have had only a minor effect in influencing soil biological communities when compared to other environmental variables such as soil management, vine treatment and sampling plot.

The vineyard soil revealed some spatial variability in chemical properties across the experimental plots, according to a stable pattern over-time. As typical of vineyard soils [69] this variability may have resulted from the interaction between pedogenic factors and soil management history. Most vineyards are established after deep tillage, in order to break and loosen the soil, and slope-reshaping practices (in hilly environments) to overcome slope limitations, involving soil movement from the upper to the lower slope positions [70]. Erosion dynamics can further contribute to increased spatial variability in sloping lands.

The influence of soil management and vine protection treatment on the structure of multiple levels of the soil biota was investigated. Indeed, the bacterial, fungal, nematode and microarthropod communities, which operate at different trophic levels, were explored in this study. It is known that only a few studies inspect multiple edaphic trophic levels [3], and explore their relationships with vineyard soil agricultural practices [2]. In this work, we add a further layer of complexity by evaluating the relations among the different soil biota levels, in the context of different agricultural practices. Specifically, we focused on investigating:

• The effect of the soil management and vine treatment on the structure of the edaphic communities (alone and joined in a single “meta-community”), and their possible relationships with soil chemical features (see Section 4.1);
• The effect of soil management and vine treatment on the diversity and ecological indices of the different levels of soil biota (see Section 4.2);
• The effect of soil management on the relationships among the different soil trophic levels (see Section 4.3). This comprehensive multi-level integration of soil biota data represents the focus of the present work, aiming to contribute to the filling of the current information gap on vineyard edaphic community associations and interactions.

4.1. Study of the Effect of the Soil Management and Vine Protection Treatment on the Structure of the Edaphic Community

Both different vine treatments and soil management influenced the vineyard edaphic communities. However, concerning vine treatments, results are difficult to interpret due to the chemical variability among the selected five plots. Thus, major consideration has been given to the effect of the different soil management procedures, which include samples from each of the five plots. Soil management affected bacterial, fungal and nematode communities and, as a result, the total community. On the other hand, microarthropods alone were not influenced by any of the tested environmental variables. This aspect is in line with the ecology of the investigated trophic levels. Bacteria, fungi and, to a lesser extent, nematodes are quite immobile organisms, and thus more subject to variations in the characteristics of the soil, i.e., chemical properties, oxygen, water and nutrient availability. Therefore, it is reasonable that external inputs, such as the application of a different soil management can influence and shape soil microbial and micro-fauna communities. Conversely, the edaphic meso-fauna members constantly move inside the soil matrix, in search of optimal conditions for them to thrive. For this reason, their distribution was much less influenced by external variables. In accordance with Gutiérrez-Lopez et al. [71], microarthropods were not related to the soil chemical features, whilst for bacteria, fungi and nematodes a significant relationship with soil chemistry was observed. Bacteria and fungi were related to soil total nitrogen content (TN), in accordance with evidence obtained from previous studies [72,73,74]. As reported by several authors, the composition and abundance of the nematode community were influenced by TOC; free-living nematodes, especially fungal feeders, were stimulated by organic matter supply, whereas plant-parasitic and virus-vector nematodes were suppressed [14,75,76,77]. Calcium carbonate was also related to soil nematode communities. Few studies reported the influence of calcium carbonate on the soil nematode community except on some cyst nematodes belonging to the Heteroderidae family [78].

4.2. Analysis of the Effect of Environmental Variables on Soil Biological and Ecological Indices

In line with the previous observations, the diversity indices of the microarthropod community showed low values indicating a scarce biological activity in this environment despite the adoption of soil sustainable management. Only the QBS-ar index increased in both PCC and GM compared to ST. Its values in PCC and GM almost reached good soil quality, set at the value of approximately 100 [59].

Bacteria and fungi were moderately affected by the same variables. Bacterial richness exceeded the fungal one, reflecting the generally higher bacterial diversity and occurrence [79,80]. As already observed by Hendgen et al. [80], the different management systems did not exhibit any impact on bacterial biodiversity. On the other hand, the vine treatment influenced the Proteobacteria/Acidobacteria (P/A) ratio between IPM, IPM_50_GG and BIO sample groups and BIO_50_GG and GG groups. According to Smit et al. [51], a positive selection for Proteobacteria and a reduced percentage of Acidobacteria might be related to nutrient-rich niches and, accordingly, BIO_50_GG and GG are the vineyard plots subjected to more natural and sustainable vine treatments, which could have allowed an increase in soil fertility. Nonetheless, the same plots (and especially plot n 5, treated with 100% GG elicitors) showed high levels of total organic carbon (TOC) and total nitrogen (TN) also before the beginning of the trial, with values conserved until the end of the study. TOC and TN are two of the major determinants for soil biological and chemical fertility [81], and this aspect makes it difficult to discriminate the effect of the plant protection treatment from the effect of the plot chemical features on the P/A ratio result.

On the other hand, biodiversity and ecological indices of the nematode community were highly influenced by year and soil management. The increment of Shannon and Evenness indices showed that the adoption of eco-sustainable soil management had a positive impact on soil biodiversity. This result was obtained in only three years, in contrast with previous studies in which these indices gave rather inconsistent results after a short period of organic matter application [14,82]. The ecological indices also confirmed the beneficial effects on soil quality of the adopted sustainable soil management. In fact, MI doubled from strongly degraded in 2017 to only disturbed by anthropic activity in 2020. By contrast, EI decreased from 2017 to 2020 indicating a minor abundance of r-strategy extreme bacterial feeders such as Rhabditidae, corresponding in the colonizer-persistent scale (ranging from 1 to 5) to guild cp 1. Contemporarily, an increased abundance of fungal feeders belonging to guild cp 2 determined the increment of this index. Interestingly, PPI remained quite constant. PCC and GM showed a different impact on the soil nematode community. PCC increased SI indicating an increase in k-strategy omnivores and predators, while GM favored BI and CI suggesting a shift from a dominance of bacterial decomposers to a more balanced degradation channel between bacteria and fungi.

4.3. Effect of the Application of Sustainable Soil Managements on Soil Organisms Relationships

The possible ecological associations among the different soil communities were investigated using network analysis. Since the effect of the plant treatment was hardly discernable from that of the sampling plot, and since the networks obtained from the different treatments were highly similar among themselves and compared to the baseline one, the attention was focused on the effect of the different soil management on the soil biota interconnections.

Indeed, the two networks obtained by the 2020 samples grouped on the basis of the management (GM and PCC), showed a greater integration of all the nodes within the network, a good distribution of hubs and an overall higher hub score compared to the standard tillage (ST) of 2017. The analysis of the possible interactions between the different trophic levels of the soil community highlighted that, for GM and PCC with respect to ST management, the number of connections among different levels tended to increase, especially for nematodes and microarthropods. This result allowed us to hypothesize that more natural soil management allows a better interaction between soil organisms of different trophic levels, with a higher effect on the micro- and meso-fauna. This agrees with previous observations, affirming that less intensive agricultural practices can lead to more elaborate soil food webs and to the establishment of complex belowground species associations [83,84,85], resulting in a lower fragility of the community and a higher resistance to potential perturbations [86].

The focus was maintained on those taxa that mostly distinguish the communities associated with the three soil managements. Nematodes, microarthropods and many members of Ascomycota could differentiate the groups of samples. On the other hand, only a few members of bacteria were involved in this sample separation, suggesting that the major differences among these groups are not related to the bacterial community. Instead, some taxa of nematodes and, to a lesser extent, microarthropods differentiated between the groups of samples based on soil management. In accordance with previous studies, the adoption of GM negatively affected some plant-parasitic nematodes [14,76,87]. Specifically, the virus-vector Trichodoridae and plant-parasitic nematodes belonging to the Paratylenchidae and Meloidogynidae families were reduced compared with ST. Moreover, as already mentioned above, the r-strategy extreme colonizers belonging to the Rhabditidae family decreased in both PCC and GM compared to ST. On the contrary, the two different eco-sustainable soil managements were differentiated by the microarthropod community. Pauropoda, Opilionida and Hymenoptera were more abundant in PCC than GM, demonstrating that PCC is a sustainable soil management with only a minor impact on this Phylum due to the absence of tillage. In fact, it is well known that tillage is the main cause of the loss of microarthropod biodiversity [11].

5. Conclusions

This work confirms the importance of the application of sustainable and natural soil management practices in agricultural ecosystems, with the purpose of conserving and improving soil biodiversity and the associated functions and services.

We provide evidence that vineyard soil biota at a multitrophic level and its network complexity are strongly related to soil management. Indeed, the application of natural and sustainable soil management techniques increased soil biota connectedness and biodiversity, fundamental for the maintaining of multiple functions in natural ecosystems. In addition, the application of sustainable management in viticulture can also promote soil biota resilience, a primary component of soil health.

A deeper investigation of these soil communities’ interconnections will help to better identify the relations between “positive” individuals, whose presence can improve the health of the plants and the environment and/or the provision of ecosystem services. The identification of microorganisms potentially favorable for the thriving of positive soil species might be exploited for the formulation of microbial-based products capable of promoting plant health/growth by intervening directly in the soil communities. A better comprehension of the relationships among soil communities might also help to entangle the current struggle in understanding the different effectiveness of the application of al-ready accepted microbial inoculants in different environmental contexts.