# Impact of Different Nitrogen Fertilizers on the Diversity and Abundance of Bacterial and Fungal Communities in Grassland Soils: The Emerging Case of Ammonium Sulfate and Sulfammox Process

Bella Tsachidou, Magdalena Calusinska, Christophe Hissler, Jérôme Gennen, Benjamin Daigneux, Séverine Piutti, Alexandre Laflotte, Isabelle George, Philippe Delfosse

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Bella Tsachidou, Magdalena Calusinska, Christophe Hissler, Jérôme Gennen, Benjamin Daigneux, et al. Impact of Different Nitrogen Fertilizers on the Diversity and Abundance of Bacterial and Fungal Communities in Grassland Soils: The Emerging Case of Ammonium Sulfate and Sulfammox Process. Global Change Biology - Bioenergy, 2025, 17 (7), pp.e70050. 10.1111/gcbb.70050. hal-05126492

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RESEARCH ARTICLE

OPEN ACCESS

# Impact of Different Nitrogen Fertilizers on the Diversity and Abundance of Bacterial and Fungal Communities in Grassland Soils: The Emerging Case of Ammonium Sulfate and Sulfammox Process

Bella Tsachidou<sup>1,2</sup> | Magdalena Calusinska<sup>2</sup> | Christophe Hissler<sup>2</sup> | Jérôme Gennen<sup>3</sup> | Benjamin Daigneux<sup>4</sup> |
Séverine Piutti<sup>5</sup> | Alexandre Laflotte<sup>6</sup> | Isabelle George<sup>1</sup> | Philippe Delfosse<sup>7</sup>

<sup>1</sup>Laboratoire d'Ecologie des Systèmes Aquatiques, Université Libre de Bruxelles, Bruxelles, Belgium | <sup>2</sup>Luxembourg Institute of Science and Technology, ERIN, Belvaux, Luxembourg | <sup>3</sup>Agra-Ost, St. Vith, Belgium | <sup>4</sup>Ferme du Faascht, Attert, Belgium | <sup>5</sup>Laboratoire Agronomie & Environnement, INRAE, Université de Lorraine, Nancy, France | <sup>6</sup>Domaine de la Bouzule, ENSAIA, Vandoeuvre-lès-Nancy, France | <sup>7</sup>University of Luxembourg, Esch-sur-Alzette, Luxembourg

**Correspondence:** Bella Tsachidou (bella.tsachidou@list.lu)

**Received:** 6 March 2025 | **Revised:** 16 May 2025 | **Accepted:** 20 May 2025

**Funding:** This work is part of the PERSEPHONE Project, a Transfrontier Programme for European Territorial Cooperation that is co-financed by the European Union—European Regional Development Fund 2014–2020 INTERREG VA “Greater Region”, the Ministère de l'Environnement, du Climat et du Développement durable (MECDD) of Luxembourg, and the Luxembourg Institute of Science and Technology (LIST).

**Keywords:** 16S rDNA sequencing | ammonium sulfate | anaerobic digestion | biogas residues | climate change | ITS2 internal transcribed spacer region | metagenomics | soil bacteria and fungi | sulfammox

## ABSTRACT

Improving agricultural productivity to meet the growing food and feed demand via nitrogen fertilization comes with trade-offs such as environmental pollution and biodiversity loss. Biogas residues (BRs) being a relatively new biofertilizer aiming at substituting chemical nitrogen fertilizers, have raised questions regarding their biosecurity and environmental footprint. In this study, we explored and compared the effect of repeated application of different nitrogen fertilizers on the bacterial and fungal α-diversity, relative abundance, β-diversity, and taxonomic composition in grassland soils over a period of two years. Given the paramount importance of arbuscular mycorrhizal fungi in sustainable agriculture and climate change, we examined the relative abundance of Glomeromycota and their response to the different nitrogen fertilizers. Finally, the soil microbial community was scanned for the most prominent pathogens that are often detected in BRs and are the main concern related to their application on agricultural soils. Microbial communities in the soil were identified and quantified via high-throughput sequencing of the 16S rDNA marker gene for bacteria, and the ribosomal DNA Internal Transcribed Spacer (ITS2) region for fungi. Overall, the results suggest that the soil bacteriome is more sensitive than the mycobiome to nitrogen fertilization. Specifically, ammonium sulfate application appears to negatively impact bacterial alpha-diversity, while also altering the relative abundance of Glomeromycota, prompting us to question the potential involvement of the sulfammox process in the loss of soil microbial diversity. Notably, the application of biogas residues did not alter the diversity or abundance of soil microbial communities, nor harbored any significant pathogens; therefore, advocating for their safety and encouraging further research to validate their safe nature and beneficial properties.

This is an open access article under the terms of the [Creative Commons Attribution](https://creativecommons.org/licenses/by) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *GCB Bioenergy* published by John Wiley & Sons Ltd.
Agriculture aims to increase the yield of crops to provide suffi-
cient quantities of food, feed, and non-food bio-based products
for a population that is expected to reach nearly 10 billion peo-
ple by 2050 (Searchinger et al. 2019). To keep up with this chal-
lenge, farming systems have to overcome limiting factors such
as nutrient-poor or depleted agricultural soils by adding large
amounts of synthetic reactive nitrogen (Nr). However, excessive
and long-term application of synthetic N fertilizers leads to the
leakage and emission of Nr compounds into the environment,
causing groundwater pollution and surface water eutrophica-
tion (Huang et al. 2017), soil acidification (Tian and Niu 2015),
loss of soil biodiversity (Bai et al. 2010), reduction of soil fertility
(Sun et al. 2020), and ultimately a decrease in soil productivity
(Egan et al. 2019; Yuan and Peng 2017). As N fertilizer input is
expected to further increase to meet the global population de-
mand, maintaining a balance among food production, nutrient
input, and agroecosystem function and stability is critical.

Grasslands are important agricultural production systems cov-
ering close to one-third of the Earth's terrestrial surface (Suttie
et al. 2005) and 34% of the European Union's agricultural area
(Schils et al. 2022). Besides being the basis for livestock farm-
ing, grasslands provide important ecosystem services, including
nutrient cycling, water purification, and carbon sequestration,
while also supporting biodiversity and pollination (Velthof
et al. 2014; Zhao et al. 2020). To sustain or improve pasture and
forage production, grassland systems have been widely intensi-
fied through nitrogen fertilizer inputs (Stott and Gourley 2016).
In the EU, most of the grass originates from intensively fertil-
ized grasslands, and in some European countries (e.g., Belgium,
Luxembourg, and the Netherlands), the average rates of syn-
thetic N fertilizer applied to permanent grasslands exceed the
rates applied to croplands (Einarsson et al. 2021). In general,
management intensification may influence the provision of sev-
eral major grassland ecosystem services by affecting the struc-
ture and function of the primary drivers of ecosystem processes,
such as soil bacteria and fungi (Chomel et al. 2022). These micro-
organisms constitute a great portion of the biomass in grassland
systems (Fierer et al. 2009), play a key role in biogeochemical
cycles, and are very sensitive to environmental changes, with
specific functional groups of microbes, including N-fixing bacte-
ria, ammonia oxidizers, and mycorrhizal fungi, often being the
most affected (Egerton-Warburton et al. 2007; Fan et al. 2019;
Wessén, Nyberg, et al. 2010). Therefore, anthropogenic distur-
bances, including long-term nitrogen input, can alter microbial
community composition, function, and interactions, and greatly
influence carbon (C) and nitrogen (N) cycling, decomposition,
release of associated greenhouse gases (GHG) and agricultural
productivity (Dai et al. 2018; Herzog et al. 2015). Conversion
of grasslands to arable land, management intensification, and
pollution from fertilizers and pesticides threaten European bio-
diversity, as well as the ecosystem functions related to biodiver-
sity. Many soil biota species are more abundant in permanent
grasslands than in temporary grasslands or arable fields, while
microbial biomass can be 50% higher in permanent grasslands
compared to arable land (van Eekeren et al. 2010). The exten-
sive use of chemical fertilizers can dramatically alter grassland
ecosystems and decrease both below-ground and above-ground
biodiversity (Klimek et al. 2007; Yao et al. 2018). High nitrogen

availability can differentially favor bacterial populations, often at
the expense of (arbuscular) mycorrhizal fungi and even disrupt
the relationship between arbuscular mycorrhizal fungi (AMF)
and plants (Bradley et al. 2006). Also, intensive management of
grasslands and nitrogen addition can change plant species com-
position and reduce diversity (Gallego-Zamorano et al. 2022; Liu
et al. 2020), especially as nitrogen fertilizer levels rise (Humbert
et al. 2016; Midolo et al. 2019); hence, altering the rhizosphere
microbial communities (Ren et al. 2020) and soil physical and
chemical properties (Han et al. 2021). This in turn affects N cy-
cling, availability, and residence times in soil N pools due to the
differential abilities of plants to use N, their preference for dif-
ferent nitrogen forms (Britto and Kronzucker 2013) and the role
of the different rhizosphere microorganisms in the uptake and
transformation of nutrients in the roots (Ren et al. 2020). As part
of a global strategy to maintain the world's biodiversity, there is
a need to ensure that grasslands are not negatively impacted by
human intervention.

Maintaining the species diversity of grasslands is crucial for ecosystem functioning, leading to ecosystem stability and greater economic value (Binder et al. 2018; Pulungan et al. 2019). Nitrogen input has been found to exert significantly unfavorable effects on all indicators for grassland biodiversity, among other indicators for ecosystem services (Schils et al. 2022). Soil bacteria and fungi, as the two main groups of soil microorganisms that account for over 90% of the total soil microbial biomass (Six et al. 2006), could respond differently to N addition. Previous studies have indicated that fertilization changes the diversity, community structure, and activity of soil microorganisms (Chen et al. 2019; Guo et al. 2018). Bacteria are the most abundant and diverse group of soil microorganisms (Roesch et al. 2007), and they play vital roles in organic matter decomposition, nutrient transformation, degradation and stabilization of pollutants, and maintenance of soil productivity and ecosystem sustainability (Suman et al. 2022). Prokaryotes in general are more intensely studied than fungi, but arguably fungi play major roles as symbionts, pathogens, or decomposers in natural and managed ecosystems (Bálint et al. 2014). Because of distinct physiological properties, substrate preference, and survival strategies, soil bacteria and fungi exhibit different community dynamics when the microenvironment changes, and therefore, it is likely that they will respond differently to N addition (Ai et al. 2018; Sun et al. 2017). Soil bacterial communities are generally expected to have higher N requirements and sensitivity since they have a narrower range of nutrient composition, higher growth rate, and shorter turnover time than soil fungi (Chen, Yin, et al. 2022; Sun et al. 2017). However, in recent studies, it was demonstrated that the soil fungal community reacted more sensitively to N addition than the bacterial community (Yang et al. 2020). These varying results suggest that the responses of different soil microbial groups to nitrogen enrichment might be the outcome of multiple interconnected driving factors. Numerous recent studies have used sequencing technology to examine the effects of fertilization on the diversity and structural composition of soil microbial communities (McHugh and Schwartz 2015; Ren et al. 2020). Nevertheless, given the emerging new technologies and alternative N sources, there is relatively little research on the impact of different types of N fertilizers on the bacterial and
fungal communities in grassland soils. Therefore, there is an imperative need to understand how different nitrogen fertilizers may influence the soil bacteriome and mycobiome and their capacity to deliver multiple key ecosystem services (FAO 2010).

Since agronomic practices across the planet are becoming largely unsustainable, substituting synthetic N fertilizers with organic alternatives has been proposed as a strategy for decreasing the use of synthetic N in agricultural systems (Fan et al. 2021). Biogas residues, also known as digestate, are a relatively new but promising candidate to replace synthetic N fertilizers. They are a valuable co-product of the anaerobic digestion process (AD, biomethanisation), and their different fractions have been utilized as biofertilizers in agriculture (Nkoa 2014) with beneficial environmental and agricultural effects (Chiew et al. 2015; Sapp et al. 2015; Tsachidou, Daigneux, et al. 2019; Tsachidou, Scheuren, et al. 2019; Tshikalange et al. 2020). They contain essential macro and micronutrients, including mineral and organic-N fractions, for plants and soil organisms, and their composition may vary according to the type of feedstock utilized and operational conditions during the biomethanisation process (Lamolinara et al. 2022). Biogas residues can be derived from the anaerobic digestion of several organic wastes or bioresidues, including manure, energy crops, municipal organic waste, and agro/food industry by-products (Dincă et al. 2022).

Irrespective of the nitrogen source, application of different nitrogen fertilizers and management regimes may alter the compositional and functional response of soil microbial communities (Kaiser et al. 2016) affecting in turn the overall soil food web (Velthof et al. 2011). For example, the quality of soil organic matter (which may be shaped by the fertilizer input) may alter bacterial/fungal dominance, with labile organic substrate (low C/N) favoring bacteria and recalcitrant organic matter (high C/N) favoring fungi (Waring et al. 2013). Although most fertilization experiments report controversial findings, numerous long-term experiments (among them grassland experiments) comparing different organic and chemical fertilizers have shown the positive effects of the former in terms of microbial biomass, diversity, and activity, as well as the negative impact of the latter on all the above (Velthof et al. 2011; Wang, Liu, et al. 2018). Agricultural management research has primarily focused on how soil microbial communities are affected by organic and inorganic fertilizers, but scientific evidence for the newly emerging substitutes, such as biogas residues, is still scarce. Therefore, the evaluation of the ecological footprint of the biogas residues is essential for their valorization as a substitute and sustainable crop nutrition solution.

Besides understanding the fertilizer value of biogas residues, the effect of repeated application on soil microbial communities is of paramount importance. The objective of this study was to explore and compare soil bacterial and fungal diversity, community composition, and abundance in one newly established and two permanent grasslands of the Greater Region following the repeated application of different nitrogen fertilizers, using the Illumina MiSeq sequencing technology. Based on our recent findings (Tsachidou et al. 2021), we hypothesize

that biogas residues would increase the bacterial and fungal richness and diversity and enhance the abundance of Glomeromycota, as opposed to the chemical fertilizers tested. We also aimed to assess the differences driven by short-term and long-term N fertilizer application, as well as the seasonal and annual variability over a period of two years. Finally, we aimed to demonstrate that the repeated application of biogas residues does not introduce pathogens to agricultural soils and therefore does not pose a high risk for contamination. Based on the outcome of this large-scale comparison of the impact of different N treatments on soil microbial diversity, our overarching purpose is to increase political awareness in Europe over grassland soil biodiversity loss due to perpetual non-sustainable agricultural practices such as the utilization of chemical nitrogen fertilizers and to firmly establish biogas residues as a vital component of sustainable agriculture.

## 2 | Materials and Methods

### 2.1 | Study Site and Experimental Design

The experiment was conducted across one temporary and two permanent grasslands in the Greater Region, which is a Euroregion composed of five regional authorities located in four European states. One of the permanent grasslands is located in the commune of Attert, Belgium, and is adjacent to the biogas plant of "Ferme du Faascht" (49.745665° N; 5.812498° E). The climate in this region is classified as Cfb with a mean annual rainfall that amounts to 938 mm and an average annual temperature of 8.5°C. The two other grasslands are located in Champenoux, France, and are adjacent to the biogas plant of "Ferme de La Bouzule" (48.4421° N; 6.1934° E). The climate here is also classified as Cfb with a mean annual rainfall of 753 mm and an average annual temperature of 9.5°C. The mean monthly temperature and cumulative monthly precipitation recorded in 2018 and 2019 are presented for both sites in Figure 1. The grassland located in Attert (hereafter referred to as Faascht) and the grassland located in Champenoux (hereafter referred to as Bouzule) are permanent grasslands, while the second grassland in Champenoux is a newly established grassland (converted from cropland) since 2018 (hereafter referred to as Bouzule New). The two grasslands in Champenoux will be hereafter collectively referred to as the Bouzule sites. The profile and chemical characteristics of all three grassland soils are summarized in Table 1 and a representative aerial photograph of one of the sites (Bouzule New) is shown during a soil sampling campaign in Figure 2. An experimental setup that tests various nitrogen fertilization treatments for their polluting potential and agricultural performance has been in place since 2013 in Faascht and since 2014 in Bouzule, while the first fertilization in Bouzule New was performed in 2018.

Seventeen different fertilization treatments in Faascht and nine different treatments in each Bouzule site, classified into five and four schemes/types respectively (biogas residues (BR), manure (MNR), chemical (CF), combinatorial (BR_CF) and post-treated fertilizers (PT)) (Table 2), were evaluated and compared to an unfertilized control and to each other. The biogas residues used are a product of the biomethanisation plants on each site (Ferme du Faascht and La Bouzule), and
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Cumulative monthly rainfall (bars) and average monthly air temperature (dots and lines) at the Faascht site (as recorded at the nearby meteorological station in Useldange, Luxembourg) and the Bouzule site (as recorded at the nearby INRAE meteorological station in Champenoux, France) during 2018 and 2019.</figcaption></figure>

<table><caption>**TABLE 1** | Soil characteristics of the three experimental grasslands measured in the 0–30 cm soil layer.</caption><thead><tr><th rowspan="2">Parameters</th><th colspan="3">Site</th></tr><tr><th>Faascht</th><th>Bouzule</th><th>Bouzule new</th></tr></thead><tbody><tr><td>% Sand</td><td>41</td><td></td><td>36</td></tr><tr><td>% Silt</td><td>9</td><td></td><td>37</td></tr><tr><td>% Clay</td><td>50</td><td></td><td>28</td></tr><tr><td>% C<sub>org</sub></td><td>1.8</td><td>3.5</td><td>2.7</td></tr><tr><td>% N<sub>total</sub></td><td>0.3</td><td>0.4</td><td>NA</td></tr><tr><td>% CaCO<sub>3</sub></td><td>ND</td><td>45</td><td>NA</td></tr><tr><td>pH H<sub>2</sub>O</td><td>6.3</td><td>7.6</td><td>7.9</td></tr><tr><td>pH KCl</td><td>5.2</td><td>7.1</td><td>7.5</td></tr></tbody></table>

*Note:* The two Bouzule sites are adjacent and have the same particle composition. The N<sub>total</sub> and CaCO<sub>3</sub> content of the Bouzule New site was not determined (NA) at the time of grassland establishment. ND, under detection limit.

the substrates utilized are mainly liquid and solid bovine manure, crop residues, and organic wastes from the food industry. The biogas plant in La Bouzule has an installed power generation capacity of 40 kW<sub>el</sub> and is fed annually with cattle liquid manure (1740 m<sup>3</sup>), cattle solid manure (1590 t), and whey (60 t). Additional organic wastes might sporadically enter the process for research purposes. The plant consists of

one digester (450 m<sup>3</sup>) and one post-digester (1600 m<sup>3</sup>). At the time of the experiments, the biogas plant in Faascht showed an installed power generation capacity of 910 kW<sub>el</sub> and incorporated cattle manure (2000 t), grass and corn silage (4000 t), green waste, and agro-food waste (14,000 t). The digester line in Faascht consists of two fed digesters (750 and 2400 m<sup>3</sup>), two sequential post-digesters (750 and 4,000 m<sup>3</sup>), and a final storage tank (4,500 m<sup>3</sup>). The chemical characteristics of the biogas residue fractions and raw manure were measured on applied batches using standard analytical methods, and the fertilizer amount was adjusted based on their nitrogen content (RD: N 6.30 mg g<sup>-1</sup>; RM: N 2.7 mg g<sup>-1</sup>) prior to field application. The solid and liquid fractions were obtained with the use of a screw press separator equipped with a 500 µm sieve (FAN Separator PSS 3.2-520, GmbH, Germany), while a belt dryer (DORSET, The Netherlands) was used to dry the raw digestate to produce granules of 5–7 mm. The two post-treated biogas residue fractions were obtained through the fractionation of raw digestate by dynamic nanofiltration (NF) and reverse osmosis (ROSM), which are valuable techniques for nutrient and water recovery from biogas residues (Adam et al. 2018). The chemical fertilizers tested were ammonium nitrate (13.5% NH<sub>4</sub> + -N, 13.5% NO<sub>3</sub> - -N), ammonium sulfate (21% NH<sub>4</sub> + -N, 24% S), potassium nitrate (17.4% K, 13.7% NO<sub>3</sub> - -N), calcium nitrate (15.5% N, 26.5% CaO) and urea (46% N). The fertilization treatments were applied in a randomized block design with four biological replicates (plots) per treatment. The specifics of the fertilization treatments tested at each site are presented in Table 2. All solid fertilizers were weighed and evenly distributed over the plots by hand. The raw and liquid biogas residue fractions were dispersed with a watering can with a mounted shovel-shaped deflector. The nitrogen rates of the fertilizers tested were 230 and 350 kg<sup>-1</sup> N ha<sup>-1</sup> yr<sup>-1</sup> in accordance with the maximum admissible doses in the Greater Region (Grant et al. 2011) and the average annual N inputs applicable in Belgium (280 kg ha<sup>-1</sup> in the form of chemical fertilizer and manure from application and grazing) (Jensen et al. 2011). Each year, the total nitrogen dose was divided into instalments, and the fertilization was performed during the permissible period of application, from March to October, allowing the adjustment of fertilizer input to plant demand and therefore reducing N losses to the environment. Treatments applied at the rate of 230 kg N ha<sup>-1</sup> were divided into three instalments, while those applied at the rate of 350 kg N ha<sup>-1</sup> were divided into four instalments.

## 2.2 | Soil Sampling

Soil sampling was performed in 2018 and 2019, three times a year at a 0–10 cm depth of the topsoil. More specifically, each year, the first sampling campaign took place before fertilization and while the soil microbial communities were considered to be less active and dormant under the low temperatures of winter, and approximately one month after fertilization in spring and autumn. A hand auger of 3 cm diameter was used, and five soil cores were collected from each plot sampled in a W pattern. The soil cores from the same plot were collected in the same bag and sieved on site immediately after sampling. The sieved soil was homogenized by mixing, and five soil aliquots per plot were instantly frozen in a liquid nitrogen tank. Finally, the samples were stored at –80°C until further analysis.
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | The site of BouzuleNew in Champenoux during a soil sampling campaign. The representative plot arrangement is visible in this aerial photograph (LIST).</figcaption></figure>

<table><caption>**TABLE 2** | Treatments selected at the two experimental sites and the total nitrogen units applied during the management period per year since the installation of the experiments.</caption><thead><tr><th rowspan="2">Fertilization treatments (identification code)</th><th colspan="2">Total N input (kg ha<sup>-1</sup>)</th></tr><tr><th>Faascht</th><th>Bouzule/BouzuleNew</th></tr></thead><tbody><tr><td>Control (CTR)</td><td>0</td><td>0</td></tr><tr><td>Raw biogas residues (from Faascht & Bouzule plants)</td><td></td><td></td></tr><tr><td>(RD230)/(RD350)</td><td>230/350</td><td></td></tr><tr><td>(RDF230)/(RDB230) & (RDB350)</td><td></td><td>230/230 & 350</td></tr><tr><td>Liquid digestate (LD230)</td><td>230</td><td>—</td></tr><tr><td>Granulated digestate (GD230)</td><td>230</td><td>—</td></tr><tr><td>Solid digestate (SD230)</td><td>230</td><td>—</td></tr><tr><td>Raw manure (RM230)</td><td>230</td><td>230</td></tr><tr><td>Ammonium nitrate (AN230)/(AN350)</td><td>230/350</td><td>230</td></tr><tr><td>Ammonium sulfate (AS350)</td><td>350</td><td>—</td></tr><tr><td>Urea (UR350)</td><td>350</td><td>—</td></tr><tr><td>Calcium nitrate (CaN350)</td><td>350</td><td>—</td></tr><tr><td>Raw biogas residues + Ammonium nitrate (RD + AN230)</td><td>—</td><td>170 + 60<br>(230)</td></tr><tr><td>Raw biogas residues + Ammonium sulfate (RD230AS120)/<br>(RD170AS60)</td><td>230 + 120<br>(350)</td><td>170 + 60<br>(230)</td></tr><tr><td>Raw biogas residues + Urea</td><td>230 + 120</td><td>170 + 60</td></tr><tr><td>(RD230UR120)/(RD170UR60)</td><td>(350)</td><td>(230)</td></tr><tr><td>Raw biogas residues + Potassium nitrate (RD230PN120)</td><td>230 + 120<br>(350)</td><td>—</td></tr><tr><td>Reverse osmosis (ROSM230)</td><td>230</td><td>—</td></tr><tr><td>Nano filtrate (NF230)</td><td>230</td><td>—</td></tr></tbody></table>
## 2.3 | Amplicon Library Construction and Sequencing

A total of 1200 soil samples were collected over the two-year soil sampling campaigns and prepared for high-throughput sequencing of the bacterial 16S rRNA marker gene and fungal Internal Transcribed Spacer region (ITS2). DNA was extracted using the 96-well plate PowerSoil DNA Isolation kit (Qiagen, CA, USA) using an aliquot of 0.25g fresh weight (FW) of each soil sample as per the manufacturer's instructions. The DNA extract was checked by electrophoresis, quantified using a NanoDrop 1000 Thermo Fisher Scientific (Wilmington, DE) spectrophotometer, and normalized to a standard concentration (1.66 ng/µL) to serve as template DNA. Bacterial communities were analyzed through amplification of a fragment of the V6-V8 variable region of the 16S rRNA gene using the universal primers S-D-Bact-0909-a-S-18 (5'-ACT CAA AKGAA TWG ACGG) and S*-Univ-1392-a-A-15 (5'-ACG GGC GGT GTG TRC) (Klindworth et al. 2013), according to the previously optimized protocol (Calusinska et al. 2018). Fungal communities were analyzed through amplification of the ITS2 region (a marker considered suitable for revealing the operational taxonomic richness and taxonomy specifics of fungal communities (Yang et al. 2018)) using the primers ITS3F (5'-GCATCGATGAAGAACGCAGC) and ITS4R (5'-TCCTCCGCTTATTGATATGC) (Raja et al. 2017). The first-step 25-µL PCR reactions were carried out in triplicates and contained (1) the Q5 Master Mix (2X HotStarTaq Plus Master Mix) (Qiagen, CA, USA), (2) 1 µL of bacterial template DNA or 5 µL of fungal template DNA, (3) 10 µM of each primer, and (4) bovine serum albumin (BSA) (Sigma) at the final concentration of 1 mg/mL. For bacteria, the reaction mixtures were subjected to an initial denaturation at 98°C for 30 s, followed by 22 cycles at 98°C for 10 s, annealing at 58°C for 30 s, and elongation at 72°C for 30 s, and a final elongation step at 72°C for 2 min. For fungi, the reaction mixtures were subjected to an initial denaturation at 96°C for 3 min, followed by 30 cycles at 96°C for 1 min, annealing at 60°C for 1 min, and elongation at 72°C for 1 min, and a final elongation step at 72°C for 5 min. After the first-step PCR, the amplicons generated were checked on a 2% agarose gel to determine the amplification success and the relative intensity of the bands. PCR products were purified using AMPure magnetic beads (Beckman Coulter, IN, USA). The concentration of the amplicons was adjusted to 1.66 ng/µL, and 1 µL of each library was used as a template in a second-step 50-µL PCR reaction (Index PCR) where 5 µL of each Nextera XT barcode (Nextera XT Index Kit V2, Sets A thru D) and the Illumina adapters necessary for hybridization to the flow cell were added. PCR amplification was performed with the same enzyme and cycling conditions as described above, with the total number of cycles reduced to eight and an annealing temperature of 55°C for bacteria, while for fungi, only the total number of cycles was reduced to eight. The resulting amplicons were once more purified with the AMPure magnetic beads and pooled in equimolar concentrations. The final concentration of the library pool was determined with a KAPA SYBR FAST Universal qPCR Kit (Roche, IN, USA), according to the manufacturer's instructions. Libraries were mixed with Illumina-generated PhiX control library, denatured with fresh NaOH, diluted to a final concentration of 4 pM, and sequenced with

the MiSeq Reagent Kit V3-600 cycles (Illumina, San Diego, USA). In each sequencing run, 192 samples were pooled and sequenced on an Illumina MiSeq sequencing platform (LIST, Luxembourg).

## 2.4 | Bioinformatic Analysis of Amplicon Sequence Data

There were 253 M and 337 M sequences generated for bacteria and fungi, respectively. The amplicon sequence reads were demultiplexed and extracted (fastq.gz files) from the BaseSpace Sequence Hub (BSSH) with the use of the BaseMount command line tool and Linux command line interface (CLI). The USEARCH11 bioinformatic pipeline was used to process the raw sequences (Edgar 2010). The forward and reverse paired-end sequences were merged using *-fastq_mergepairs*, resulting in 59M merged pairs for bacteria and 140M merged pairs for fungi. Then both forward and reverse primers of size 22 and 15, and 24 and 20 for bacteria and fungi, respectively, were removed with *-fastx_truncate*. Merged 16S rRNA reads were filtered and reads below 400 bp were discarded (55M filtered reads remained), while ITS2 reads were not filtered to a specific length, as this region is subject to intraspecific variability (Nilsson et al. 2008; Zhou and Fong 2021). Unique sequences were detected with *-fastx_uniques*, and singletons were removed using the parameter *-minsize 2*, resulting in 1.9M sequences (55M seqs, 9.6M uniques, 7.6M singletons (80.0%)) for bacteria and 29M sequences (140M seqs, 128M uniques, 98M singletons (76.7%)) for fungi. UPARSE algorithm was implemented to remove chimeras and cluster 16S rRNA reads at a 3% divergence level (97% similarity) to obtain operational taxonomic units (OTUs). UNOISE3 algorithm was implemented to denoise ITS2 reads (at 100% identity) and obtain exact biological sequences or amplicon sequence variants (ESV or ASVs or "zero noise OTUs"). This approach was selected based on previous observations in our laboratory, showing that clustering fungi in OTUs can underestimate their diversity, while clustering bacteria in ASVs may have the opposite effect, leading to inflated diversity. Bacterial taxonomy was assigned by alignment with reference sequences using the non-redundant SILVA SSU ribosomal database v.138 (Release 132; http://www.arb-silva.de), while the fungal taxonomy was assigned using the UNITE database (Release 8.2; http://unite.ut.ee/index.php), and both were implemented in MOTHUR (Schloss et al. 2009). The sequence reads have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1263551 (Tsachidou 2025).

## 2.5 | Statistical Analyses

Sequences matching other than bacterial and fungal taxa accounted for approximately 6% of the dataset and were removed. In addition, only sequences that were assigned taxonomy with 99% and 100% confidence were maintained for downstream analyses. Overall, there were 28,619 OTUs (bacterial reads) and 31,306 ASVs (fungal reads) before rarefaction, to which we will collectively refer to as (z)OTUs hereafter. To standardize the sampling effort for the statistical analyses of reads and reduce the false discovery rate (FDR), rarefaction curves
were plotted, and abundances of each sample were rarefied and subsampled to 18,135 and 25,612 reads for bacteria and fungi, respectively. Samples with total counts below the defined threshold were excluded, leading to a trade-off between sampling depth and the number of samples evaluated (only treatments with an even number of observations (n = 4) were evaluated). Following processing and rarefaction, the dataset comprised 27,157 bacterial OTUs and 26,232 fungal ASVs (i.e., zOTUs). To further minimize the probability of retaining sequences resulting from sequencing errors that could lead to overestimation of rare (z)OTUs in the community analysis and to discard (z)OTUs with small mean and trivially high coefficient of variation, we checked for spurious (z)OTUs (microbiome Utilities::plot_taxa_cv) and applied a filter to remove (z)OTUs that do not appear more than once in at least 20% of the samples.

General data manipulation, statistical analysis, and graphical representation were performed using R (version 4.2.1). Alpha diversity (α-diversity) measures (Chao1, Pielou, and Shannon diversity index) were estimated using the *estimate_richness* function of the phyloseq package and the *evenness* function of the microbiome package. Alpha statistics were calculated with the Aligned Rank Transform (ART) procedure for nonparametric factorial analyses of repeated measures design data using the ARTool package (Wobbrock et al. 2011), followed by the ART-C procedure to conduct post hoc pairwise comparisons (Elkin et al. 2021). Differences with *p* ≤ 0.05 were deemed to be statistically significant. Beta diversity (β-diversity) was determined using Bray-Curtis dissimilarity, implementing the distance function of the phyloseq package (McMurdie and Holmes 2013), and visualized with PCoA (principal coordinates analysis) ordination plots using the plot_ordination function (phyloseq). Permutational multivariate analysis of variance (PERMANOVA) with 9999 permutations was performed to test whether specific groups are significantly different (*p* ≤ 0.05) from each other using the adonis function from the vegan package (Oksanen et al. 2013). Furthermore, to test if the dispersions (variances) of one or more groups are different, we performed the multivariate homogeneity of groups dispersions (average distance to centroids) analysis using the betadisper function (vegan). Similarity percentage (SIMPER) analysis was performed with the simper function (vegan) to identify the individual (z)OTUs/species that most contribute to the overall Bray-Curtis dissimilarity and cumulatively explain 70% of the variation between each group comparison. After merging the (z)OTUs with the same taxonomy using the tax_glam function (phyloseq), abundance maps were drawn to assess the changes in taxa composition between samples, followed by heatmaps (plot_taxa_heatmap) displaying the Z-score transformed relative abundance of bacterial and fungal taxa. Finally, to statistically test the differences in relative abundance of organisms at a specific taxonomic rank, we implemented a multivariate test for differences in the overall composition between groups of samples using the Xdc.sevsample function from the HMP package (La Rosa et al. 2012). This test was followed by the Wilcoxon rank sum test to reveal the significantly different bacterial and fungal taxa (e.g., phyla and genera) across treatments in the different sites using the wilcox.test function while decreasing the false

discovery rate for multiple comparisons using the Benjamini-Hochberg Procedure.

## 3 | Results

### 3.1 | Effect of N Fertilizers on Bacterial and Fungal Alpha-Diversity

The community α-diversity measures revealed the bacterial OTU and fungal ASV Chao1 richness, Pielou evenness, and Shannon diversity in the three different grassland soils, across three different seasons and two years, while also demonstrating the changes attributed to the different N fertilizers tested. Overall, the bacterial and fungal community richness, evenness, and diversity did not vary drastically between the three sites, with the control treatments as a reference indicating slightly higher bacterial and fungal richness in Faascht than in the Bouzule sites (Figures 3 and 4). Also, there was no clear seasonal or annual effect on the α-diversity measures, nor a trend observed in any of the three sites. The effect of the different N treatments was more pronounced on the bacterial community in Faascht, while in the sites of Bouzule and BouzuleNew, there were no statistical differences between the various N fertilizers regarding α-diversity measures. The Chao1 richness index was affected the most, followed by Shannon diversity, while Pielou evenness was not particularly affected (Figure S1 through S4). More specifically, the plots treated with ammonium sulfate (AS350) in the Faascht site showed consistently the lowest bacterial alpha-diversity measures (Chao1 and Shannon) among all N treatments and the unfertilized control across seasons and years. On the other hand, plots treated with urea (UR350) demonstrated a tendency for the highest bacterial alpha-diversity (Chao1), followed by calcium nitrate (CaN350), and both demonstrated statistical differences with AS350, particularly during the second year. Also, the NanoFiltrate (NF230) fraction was among the fertilizers with a tendency for higher alpha-diversity (Chao1) and exceeded in all instances its Reverse Osmosis (ROSM230) counterpart, while it statistically differed from AS350, particularly during the second year (Figure 3A). Furthermore, AS350 differed significantly from one or both of the solid biogas residue fractions (GD230 and SD230) in most instances. Finally, the Shannon diversity and Pielou evenness indices were considerably lower for AS350 compared to all other treatments in Faascht in all seasons and both years, but there were no statistical differences recorded (Figures S1 and S2). Regarding fungal alpha diversity, overall, α-diversity indices were not affected significantly by any of the N treatments in any of the three grasslands and showed only a slight but not significant variation across the seasons and years, mostly in the Bouzule sites. The unfertilized control treatment in all sites performed in the same range as most of the N treatments, with biogas residues following close and, in some cases, slightly exceeding the α-diversity values of the corresponding control treatment and several other treatments contained in different fertilization schemes (Figure 4). As opposed to bacteria, fungal Shannon diversity and Pielou evenness were affected more than the Chao1 richness index by the N treatments in the Faascht site, particularly during 2019;
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Legend on next page.</figcaption></figure>
**FIGURE 3** | Bacterial (OTU) Chao1 richness in the grassland soils of (A) Faascht, (B) Bouzule, and (C) BouzuleNew following the application of different N treatments across three different seasons during two consecutive years. Each color represents a type of N fertilizer (BR, biogas residues; BR\_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure), with each treatment being represented by four biological replicates (n=4). Letters indicate statistical differences between treatments (p<0.05). For treatment abbreviations, refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

however, no statistical differences were recorded (Figures S3 and S4).

## 3.2 | Bacterial and Fungal Beta-Diversity

For beta diversity, principal coordinate analysis (PCoA) at the (z)OTU level revealed strong clustering of bacterial (Figure 5A) and fungal (Figure 5B) communities among the experimental sites, with PC1 explaining 59 and 41% of the variation in bacterial and fungal species composition, respectively. A greater distance between two points implies a lower similarity, whereas similar (z)OTUs cluster together. The three different sites were clearly distinguished. The segregation observed among the sites was statistically verified by PERMANOVA (p < 0.001) and by a dispersion (variance) analysis to assess the variation of the sites (Figure S5). There was no clustering of bacterial or fungal communities observed at the (z)OTU level based on season, year, or treatment (except for the treatment of ammonium sulfate and all other treatments in the Faascht site). To further investigate the driving force of beta diversity results, we drew a Venn diagram showing the numbers of unique and shared (z)OTUs between sites, considering only (z)OTUs present in at least 50% of the samples of each group (Site). For bacteria, the Faascht site demonstrated a higher presence of unique OTUs (381 OTUs or 19%) while the Bouzule sites had more shared OTUs (355 OTUs or 18%), indicating a higher similarity with each other (Figure S6A). Similarly, for fungi, Faascht demonstrated a higher presence of unique ASVs (357 zOTUs or 35%) while the Bouzule sites had more shared ASVs (187 zOTUs or 18%), indicating a higher similarity with each other (Figure S6B). In addition, 46% of the bacterial OTUs were common for all three grasslands, while only 13% of fungal zOTUs were common for all grasslands, indicating the presence of more diverse fungal rather than bacterial communities among the sites. Finally, a Similarity Percentage analysis (SIMPER) was conducted to identify the (z)OTUs/taxa that most contribute to beta diversity measures and cumulatively explain 70% of the variation between each comparison. Only the (z)OTUs that had statistically different (p < 0.05) individual contributions to the community variation over 0.5% have been visually represented (Figure S7). SIMPER analysis detected 37 OTUs and 54 zOTUs that significantly contributed to the community variation of each grassland.

## 3.3 | Bacterial and Fungal Community
Composition and Abundance

The amplicon sequencing of soil bacterial 16S rRNA and fungal ITS2 region—post-filtering—resulted in the identification of a total of 1971 OTUs and 1069 ASVs representing 28 Phyla and 427 Genera of bacteria, and eight Phyla and 172 Genera of fungi, respectively, including only taxa identified

with 99% and 100% certainty. After merging the (z)OTUs representing the same taxa, combined abundance bar plots of all bacterial Phyla and Genera with abundance above 1%, and all fungal Phyla and Genera with relative abundance above 1% were drawn to assess the changes in species composition between treatments in the three different sites. Bacterial communities in all treatments and all three sites were dominated by Actinobacteria, followed by Proteobacteria and Acidobacteriota, and together comprised about 70 to 75% of the overall phyla abundance (Figure S8). The most abundant bacterial genera in Faascht were an unidentified genus of the order Vicinamibacterales (Acidobacteriota), the genus 67–14\_ge of the Solirubrobacterales order (Actinobacteriota), and the genus KD4-96 (Chloroflexi) (Figure S10A). In the Bouzule site, the most abundant bacterial genera were Gaiella (Actinobacteriota), an unidentified genus of the order Vicinamibacterales (Acidobacteriota), and the genus 67–14 of the Solirubrobacterales order (Actinobacteriota) (Figure S10B). In BouzuleNew, the most abundant genera were an unidentified genus of the order Vicinamibacterales, as well as the genus Vicinamibacteraceae (Acidobacteriota), and the genus 67–14 of the Solirubrobacterales order (Actinobacteriota) (Figure S10C). Fungal communities in all treatments and all three sites were dominated by Ascomycota, followed by Zygomycota and unclassified Fungi, and together comprised about 85 to 90% of the overall phylum abundance (Figure S9). The most abundant fungal genera in Faascht and BouzuleNew were Mortierella (Zygomycota), Fusarium (Ascomycota), and unclassified Fungi (Figure S11A,C). In Bouzule, the most abundant fungal genera were Mortierella (Zygomycota), Fusarium (Ascomycota), and the genus Preussia (Ascomycota) (Figure S11B). The abundances of several bacterial and fungal phyla and genera were significantly altered under different fertilization treatments in all three grasslands. The impact of the nitrogen fertilizers was more pronounced on the relative abundance of bacteria compared to fungi.

The multivariate test (HMP) for differences in the overall composition between groups of samples rejected the null hypothesis of no difference in the phylum- and genus-level abundance (p < 0.01). The Wilcoxon test that followed, comparing the relative abundance of bacterial and fungal taxa (e.g., phylum and genus), revealed the significantly different abundance of several bacterial and fungal taxa between treatments in each site. Regarding bacteria, in Faascht, the relative abundances of most bacterial phyla and genera were significantly different between ammonium sulfate and all other treatments (Figures 6A and 7A). More specifically, AS350 in Faascht increased the relative abundance of Actinobacteriota by 5%, especially the pathogenic genus *Mycobacterium*, the acidophilic genus *Acidothermus*, an unclassified genus of the *Solirubrobacteraceae* family, and an unclassified genus of the Gaiellales order, the relative abundance of genus *Pseudolabrys* (*Proteobacteria*), and the relative abundance of Firmicutes,
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

**FIGURE 4** | Legend on next page.
**FIGURE 4** | Fungal (ASV) Chao1 richness in the grassland soils of (A) Faascht, (B) Bouzule, and (C) BouzuleNew following the application of different N treatments across three different seasons during two consecutive years. Each color represents a type of N fertilizer (BR, biogas residues; BR\_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure), with each treatment being represented by four biological replicates (n=4). Letters indicate statistical differences between treatments (*p*<0.05). For treatment, abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

compared to the unfertilized control but also to the rest of the treatments (*p* < 0.001). Also, it decreased the relative abundance of ten out of fourteen phyla in total, with Acidobacteriota, Chloroflexi, Bacteroidota, Myxococcota, and Planctomycetota being affected the most (*p* < 0.05) (Figure 6A). In addition, the treatment of UR350 in Faascht increased the relative abundance of Nitrospirota and Methylomerabiliota compared to all other treatments (*p* < 0.05). In the site of Bouzule, AN230 increased the relative abundances of Acidobacteriota and Chloroflexi and decreased Bacteroidota and Myxococcota (Figure 6B), while in BouzuleNew, the treatment of AN230 increased Proteobacteria and decreased Methylomirabilota, and RD170AN60 increased Planctomycetota, Verucomicrobiota, Latescibacteriota, and Acidobacteriota, and decreased Actinobacteriota (*p* < 0.05) (Figure 6C). Overall, biogas residue fractions did not alter the relative abundance of bacteria at the phylum level in any of the grasslands, except RDB230, which decreased Verucomicrobiota, and RDB350, which increased Firmicutes and slightly decreased Actinobacteriota compared to the rest of the treatments in Bouzule.

Regarding fungi, in Faascht, the relative abundance of phyla Chytridiomycota, Glomeromycota, and Rozellomycota was decreased in AS350 and differed statistically mainly with UR350 (*p* < 0.05) (Figure 8A). On the genus level, the relative abundance of Ascomycotan genera *Preussia*, *Podospora*, and an unclassified genus of Sordariomycetes class in AS350 was decreased the most compared to the rest of the treatments (*p* < 0.05) (Figure 9A). On the other hand, the relative abundance of Ascomycotan genera *Metarhizium*, *Fusarium*, and other unclassified Ascomycota, as well as the Basidiomycotan genus *Cryptococcus* of the order Tremellales, was significantly increased compared to all other treatments (*p* < 0.05) (Figure 9A). The relative abundance of Chytridiomycota and Rozellomycota in UR350 and Rozellomycota in NF230 was significantly increased and differed greatly from the rest of the treatments (*p* < 0.05). Compared to AS350, the relative abundance of *Fusarium* and *Cryptococcus* (order Tremellales) in UR350 was decreased and was among the lowest observed (Figure 9A). Also, CaN350 led to a significant increase in the genus *Trichosporon* of the phylum Basidiomycota compared to all other treatments (*p* < 0.01). In the Bouzule sites, the treatments of MNR230 and AN230 had the most profound impact on the relative abundance of most taxa (Figures 8 and 9). In Bouzule, the relative abundances of Ascomycota and Basidiomycota were significantly altered in MNR230 and AN230 treatments compared to each other and to the rest of the treatments (Figure 8B,C) while the relative abundance of Ascomycota was also increased in BouzuleNew under AN230 as compared to all other treatments (*p* < 0.05). The treatment of MNR230 showed consistency in significantly increasing the relative abundance of the Ascomycotan genus *Preussia* in both Bouzule sites (*p* < 0.05), while the AN230 treatment in Bouzule and the treatments AN230 and RD170AN60 in Bouzule

New significantly increased the relative abundance of genus *Xylaria* (Figure 9B,C). In this case as well, biogas residue treatments did not alter substantially the relative abundance of the fungal phyla or genera except RDB230, which increased the relative abundance of genus *Thermomyces*, an unclassified genus of the family Microascaceae, and an unclassified genus of the order Sordariales compared to the rest of the treatments in the BouzuleNew site.

## 3.4 | Glomeromycota Response to Nitrogen Fertilization

The sequencing of the soil fungal ITS2 region—post-filtering—resulted in the identification of a total of 46 Glomeromycotan zOTUs, which corresponded to the orders of Glomerales, Diversisporales, and Paraglomerales (and an unclassified class of Glomeromycetes) and five distinct genera (three for Glomerales, one for Diversisporales, and one for Paraglomerales). At this point, it should be noted that the low number of Glomeromycotan zOTUs detected may be due to the fact that the primers used in this study are not AMF-specific and may amplify only a restricted number of Glomeromycotan taxa. A compositional bar plot was plotted to illustrate the relative abundance of the Glomeromycota on the order and genus levels and their response to the different nitrogen fertilizers tested in the three grasslands (Figure 10). Even though the orders and genera identified are the same in all three sites, their composition and mean relative abundance are different among the sites. Overall, in Faascht and Bouzule, the relative abundance of the order Glomerales is between 70% and 77%, while in BouzuleNew it is between 40% and 55%. The relative abundance of the genus Glomus of the order Glomerales was increased following the application of N fertilizers in Faascht, except for UR350 and NF230, while it was not altered in the Bouzule sites. In Faascht, the genus *Funneliformis* of the order Glomerales was particularly increased in the plots treated with UR350, NF230, and CaN350, comprising 46% of the overall relative abundance of the Glomeromycota present in UR350, 37% in NF230, and 25% in CaN350, while only 7% is present in the unfertilized CTR treatment (Figure 10A). The Paraglomerales order was more abundant in Faascht and BouzuleNew, with genus *Paraglomus* reaching up to 20% in BouzuleNew and unclassified Paraglomerales reaching 25% in certain treatments in Faascht, as opposed to the site of Bouzule, where the order Paraglomerales did not exceed 4% regardless of treatment. Furthermore, in Faascht, the order Paraglomerales increased significantly in AS350 and RD230AS120 treatments, comprising about 25% of the overall Glomeromycotan abundance in each of these treatments, while the unfertilized treatment of CTR contained only 4.5% (Figure 10A). The *Entrophospora* genus of the Diversisporales order was present in all sites and treatments, with a relative abundance between 5% and 25%. Finally, unclassified Glomeromycetes are present
<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Principal coordinate analysis (PCoA) ordination plot based on Bray-Curtis distances calculated using richness and abundance of the (z)OTU for (A) bacterial and (B) fungal communities. Each color represents a fertilization treatment, with each treatment being represented by four replicates (n=4). Each symbol (dot, triangle, square) corresponds to a different site. Each point represents the total microbial community of that sample. Points closer together have more similar microbiotas, while those farther apart have less similar microbiotas. For the treatment community variation of each grassland. Abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 6 | Legend on next page.</figcaption></figure>
**FIGURE 6** | Heatmap displaying the relative abundances of bacterial phyla with more than 1% relative abundance under different N fertilization treatments in (A) Faascht, (B) Bouzule, and (C) BouzuleNew. The abundance of taxa was calculated as the mean of four biological replicate plots measured over two years and three seasons (n = 24) and then standardized into a z-score. The colour scale shows the taxa abundance standard deviations from the mean, with red for a high abundance level (positive Z-scores) and blue for a low abundance level (negative Z-scores). Each color code represents a type of N fertilizer (BR, biogas residues; BR_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure; PT, post-treated biogas residue fractions). For treatment abbreviations, refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

in all three sites, with the relative abundance reaching over 25% in some of the treatments in Faascht and BouzuleNew, but do not exceed 9% in Bouzule. Biogas residue fractions did not alter the composition and abundance of any of the Glomeromycota as compared to the unfertilized control, except for the nanofiltration concentrate (NF230), which substantially increased the relative abundance of the genus *Funneliformis*.

## 4 | Discussion

### 4.1 | Impact of Different Nitrogen Fertilizers on the Soil Microbial Community

Although we cannot completely control all non-target factors, and at the same time, it is difficult to attribute changes to specific factors, we provide a holistic view of the soil microbial diversity in three different grasslands of the Greater Region treated with various types of nitrogen fertilizers. Generally, detecting differences in soil microbial communities requires long-term studies (Shen et al. 2010). Differences in soil organic C content (Soong et al. 2020), N availability (Cui et al. 2022), C/N ratio (Ramirez et al. 2010; Wan et al. 2015), soil pH (Rousk et al. 2010; Wang et al. 2019), soil moisture and temperature (Lauber et al. 2013) could drive the differences in microbial communities, in addition to the fertilization practices implemented and the duration of application (Yang et al. 2020). In this study, the response of community diversity measures (richness, diversity, and evenness) and the composition and abundance of bacterial and fungal communities did not change significantly across different seasons and over the years, nor revealed a clear trend. However, the composition and abundance of both bacterial and fungal communities, particularly at the OTU and ASV levels, varied greatly among the three grasslands. Furthermore, our results confirm that the soil bacteriome is more sensitive than the mycobiome in response to nitrogen fertilization and suggest that enrichment with N may influence soil bacteria and fungi through different mechanisms (Chen, Yin, et al. 2022).

Bacterial alpha diversity indices were affected substantially by certain treatments, such as the chemical fertilizers of ammonium sulfate, urea, and calcium nitrate, also observed by other authors (Habteselassie et al. 2022; Hallin et al. 2009; Wessén, Hallin, et al. 2010). The treatment of ammonium sulfate also altered the mean relative abundance of the most dominant bacterial phyla and genera in Faascht, while the rest of the treatments did not affect the relative abundance of any of the bacterial phyla or genera. The negative impact of ammonium sulfate on the bacterial α-diversity is probably attributed to the decrease of pH and nitrate accumulation in the soil, among other factors (Chen, Yin, et al. 2022). On the contrary, fungal alpha diversity indices

were not affected by any of the treatments in any of the sites. However, the relative abundances of several bacterial and fungal genera were significantly altered in all three grasslands. The relatively mild impact of N fertilization on the overall fungal community could possibly be explained by the low number and abundance of N metabolism-related genes in fungi as opposed to bacteria (Li et al. 2020). Another possible reason is that fungi typically have a wide pH optimum, and therefore they may have higher tolerances to pH decrease due to N enrichment compared to bacteria (Chen, Yin, et al. 2022; Rousk et al. 2010). Also, soil bacteria may appear more sensitive than fungi because they have much shorter turnover times than fungi and respond faster to changes in soil nutrients (Tian et al. 2018; Zhalnina et al. 2018). Different responses of bacterial and fungal communities have been reported in most long-term fertilization experiments (Eo and Park 2016; Francioli et al. 2016; Hartmann et al. 2015; Lentendu et al. 2014). Beta-diversity revealed grouping of microbial communities into clusters, with different (z)OTU and taxa contributions to community variation that well reflected the origin of the samples, with a distinct inherent composition of both bacterial and fungal communities among the different grassland soils, regardless of N fertilization. The diversity of the *soil microbial community* and the community structural composition are crucial to the performance of soil functions and the stability of the ecological environment (Pu et al. 2022). At the same time, the ecological environment can adversely affect the *soil microbial community* structure and function (activity) as a result of anthropogenic intervention, such as nitrogen fertilization. Achieving or maintaining microbial diversity and functionality in soils is one of the main approaches for ecological intensification and sustainable agriculture (Bertola et al. 2021). Since fertilization changes the properties of agricultural soils and affects the microorganisms living in them (Galantini and Rosell 2006; Leff et al. 2015; Pan et al. 2014), it is important to understand how soil microbial communities change as agricultural soils are pushed to new equilibria by the practice of nitrogen fertilization (Li et al. 2020).

Numerous recent studies have utilized the sequencing technology to examine the effects of fertilization on the diversity and structural composition of soil microbial communities (Bertola et al. 2021; McHugh and Schwartz 2015) in various agricultural lands, and have indicated that long-term application (Ge et al. 2008; Ren et al. 2020), as well as increased N loading of chemical fertilizers (Gu et al. 2021), can significantly alter soil microbial biomass, diversity, structure, and activity of soil microorganisms (Chen et al. 2019; Guo et al. 2018). In a long-term grassland experiment, Li et al. (2019) showed that high levels of chemical N significantly modified both the bacterial and fungal community composition, having far-reaching impacts on SOC turnover and nutrient acquisition, while Leff et al. (2015) demonstrated shifts in the taxonomic
<figure><img src="image_12.png" /><figcaption>**FIGURE 7** | Legend on next page.</figcaption></figure>
**FIGURE 7** | Heatmap displaying the relative abundances of bacterial genera with more than 1% relative abundance under different N fertilization treatments in (A) Faascht, (B) Bouzule, and (C) BouzuleNew. The abundance of taxa was calculated as the mean of four biological replicate plots measured over two years and three seasons (n = 24) and then standardized into a z-score. The colour scale shows the taxa abundance standard deviations from the mean, with red for a high abundance level (positive Z-scores) and blue for a low abundance level (negative Z-scores). Each color code represents a type of N fertilizer (BR, biogas residues, BR_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure; PT, post-treated biogas residue fractions). For treatment, abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

and functional traits of soil microbial communities in 25 grasslands across the world. Several studies have shown that high doses of N addition can reduce soil bacterial and fungal diversity, while low rates of N addition may have no significant effect on soil microbial diversity (Lu et al. 2021; Wang, Liu, et al. 2018). Long-term fertilizer inputs inevitably have the effect of altering the soil pH, which is considered an important determinant of bacterial diversity and community structure (Ren et al. 2020; Song et al. 2022). Contrariwise, long-term N-free treatments have revealed a trend toward higher richness and diversity (Eo and Park 2016). In the current experiment, we contrasted two adjacent grasslands, a permanent grassland (Bouzule) that had been repeatedly fertilized for six years with different N fertilizers, and a newly established grassland (BouzuleNew) fertilized for the first time. The diversity indices and the composition of the microbial communities between the two grasslands did not reveal any differences that could be attributed to the duration of nitrogen fertilization. On the contrary, the diversity indices and abundance of bacterial and fungal communities in the second permanent grassland (Faascht) fertilized for over seven years with high loads of N appeared to be affected by nitrogen fertilization, particularly following the application of chemical N fertilizers. While contrasting the two grasslands that were fertilized for several years before sample collection (Bouzule and Faascht), we concur with the aforementioned authors that the high levels of chemical N fertilizers may be responsible for the shift in diversity and abundance of microbial communities observed in the Faascht grassland as opposed to the Bouzule grassland where the level of chemical N tested was much lower. Nonetheless, at this point, we should stress the importance of local context and highlight that extrapolating results obtained from individual sites to other soil ecosystems or soil types is not advisable.

In several long-term trials, urea and ammonium fertilizers were found to reduce soil pH, while the application of nitrate fertilizers had little effect on soil pH compared to unfertilized controls (Geisseler and Scow 2014; Malhi et al. 2000). For example, striking differences in the effects of ammonium versus nitrate fertilizers on soil pH were observed in a long-term trial at Ultuna, Sweden, where ammonium sulfate led the pH in the topsoil to drop from an initial 6.5 in 1956 to 4.2 in 2009 (Kirchmann et al. 1994). In contrast, in both the unfertilized and calcium nitrate plots, the pH had not changed much from the initial values (6.2 in 1956 and 6.7 in 2009) (Börjesson et al. 2012). The decreased richness and diversity of the bacterial community observed in our study following the long-term application of ammonium sulfate could be potentially in line with the aforementioned explanation (Malhi et al. 2000). However, following the application of urea (UR350) in Faascht, bacterial richness and diversity appeared to increase (or not be affected) as opposed to the treatment of ammonium sulfate. Unlike ammonium sulfate, urea hydrolysis results in an increase in soil pH, and only after the hydrolysis of urea, nitrification of

$NH_4^+$ into $NO_3^-$ results in soil acidification (Subbarao et al. 2006). In addition, urea hydrolysis may affect soil pH through the generation of bicarbonate ($HCO_3^-$), which is subsequently converted into carbonic acid ($H_2CO_3$) before it is eventually converted into $CO_2$. Another interesting aspect of the urea fertilizer, as opposed to the rest of the chemical fertilizers, is that it brings labile carbon into the soil, possibly favoring the growth of soil microorganisms (particularly fast-growing bacteria) (van Midden et al. 2023), which could be one of the explanations regarding its positive effect on $\alpha$-diversity. In the same manner, the trend toward higher richness and diversity following the long-term application of calcium nitrate observed in this experiment is in line with the findings of Kirchmann et al. (1994). Another finding of our study that supports the determinant role of pH in bacterial communities and verifies the decrease of soil pH under ammonium sulfate is the significant increase of the richness of *Actinobacteria* observed in Faascht under AS350 (Figures 6A and S8A). *Actinobacteria* are an indicator of decreased soil pH caused by long-term N fertilization (Ren et al. 2020), demonstrating the importance of soil pH in shaping the prokaryotic community composition. According to a global meta-analysis of 212 studies, N addition-induced acidification appeared to be the predominant contributor to the decrease of soil microbial diversity, affecting mainly the rare soil microbial species (Yang et al. 2020).

Regarding the type of N fertilizers, in various comparative studies between organic and chemical fertilizers, ranging up to 100 years, it was shown that organic fertilizers increase soil microbial biomass, abundance, and activity compared to chemical fertilizers (Krause et al. 2022; Lori et al. 2017). Jangid et al. (2008) found higher bacterial diversity after manure application and lower diversity with mineral fertilizers. A meta-analysis by de Graaff et al. (2019) summarized that bacterial diversity was increased when the synthetic N input rate was less than $150\,\text{kg ha}^{-1}\text{year}^{-1}$, with organic N applied for more than five years, suggesting that this was due to increased soil organic matter accumulation and retention. Regarding biogas residues, their effect on the soil microorganisms is still debated and needs to be clarified before they can be used at a large scale without any reservations. Half of the studies investigating the impact of biogas residues on soil microbiota show neutral effects, 7% show negative effects, while 25% show a positive effect on the soil microbial quality. Furthermore, studies have observed the positive effects of biogas residues—either alone or enriched with organic amendments such as biochar—on soil microbial abundance and activity (Garbini et al. 2022; Holatko et al. 2021). Following the application of different biogas residue fractions and raw manure in our experiment, we did not observe any significant alteration regarding bacterial and fungal richness and diversity, nor abundance, compared to the unfertilized control treatments in any of the grasslands. All the organic treatments tested remained within the same range
<figure><img src="image_13.png" /><figcaption>FIGURE 8 | Legend on next page.</figcaption></figure>
**FIGURE 8** | Heatmap displaying the relative abundances of fungal phyla with more than 1% relative abundance under different N fertilization treatments in (A) Faascht, (B) Bouzule, and (C) BouzuleNew. The abundance of taxa was calculated as the mean of four biological replicate plots measured over two years and three seasons (n = 24) and then standardized into a Z-score. The colour scale shows the taxa abundance standard deviations from the mean, with red for a high abundance level (positive Z-scores) and blue for a low abundance level (negative Z-scores). Each color code represents a type of N fertilizer (BR, biogas residues; BR_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure; PT, post-treated biogas residue fractions). For treatment, abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

as the corresponding unfertilized control treatments at each site, and in some cases, their alpha diversity indices slightly exceeded some of the chemical and combinatorial treatments. Despite experimental evidence showing how soil microbial groups respond to differences in substrate quality, it remains unclear how soil microbial communities might change in response to different resource combinations. Studies investigating the impact of the partial substitution of chemical fertilizers by biogas residues on the soil properties and microbial community have demonstrated overall soil and environmental benefits (Tsachidou et al. 2021; Tsachidou, Scheuren, et al. 2019) as well as improved microbial diversity (Tang et al. 2022). The combinatorial treatments tested in our experiment did not exert any positive or negative impact on the bacterial and fungal communities compared to the unfertilized control treatment or to the raw biogas residue alone. However, in the case of ammonium sulfate, substituting 230 units of the total 350 units of nitrogen by raw biogas residue led to an increase of bacterial richness and diversity compared to ammonium sulfate alone. This observation advocates for the potential of the raw biogas residue fraction to recover soil microbial diversity (Li et al. 2022), demonstrating its unique regulatory property. On the contrary, in the case of urea, substituting 230 units of the total 350 units of nitrogen by raw biogas residue decreased the bacterial richness and diversity compared to when urea was applied alone. This outcome may be partially due to the fact that urea is a source of more readily available carbon compared to the organic carbon of the raw biogas residue fraction. Therefore, the quantity and composition of the carbon in biogas residue fractions have to be considered when substituting chemical fertilizers since it can affect soil microbial communities and alter their diversity and abundance (van Midden et al. 2023).

Finally, even though the (DNA-based) bacterial and fungal communities appeared to be robust to most of the nitrogen fertilization treatments, and did not vary among different seasons and years, there is evidence that the active (RNA-based) microbial community has a higher sensitivity to environmental changes (Herzog et al. 2015). Therefore, while considering the stability of grassland ecosystems and how different factors act in combination to influence soil microbial communities, it is essential that metagenomic (rDNA-based) studies are performed in parallel to metatranscriptomic (mRNA-based) studies to obtain a holistic and accurate view of the impact of different nitrogen fertilizers on the soil microbial communities and their involvement in nitrogen cycling.

## 4.2 | Sulfamox: The Emerging Case of Ammonium Sulfate

In light of the ammonium sulfate impact on the α-diversity metrics and the relative abundance of the bacterial and fungal

communities, it seems imperative to address the emerging case of ammonium sulfate and the sulfamox process. Ammonium sulfate is widely used as a sulfur (S) fertilizer, constituting about 50% of global S use. Being a by-product of various industrial processes, in recent years, ammonium sulfate has become a major source of sulfur for fertilizer use. Global use of S fertilizer in 2015 was reported as 13.3 Mt. S (Powlson and Dawson 2022). Farmers apply ammonium sulfate primarily where they need supplemental N and S to meet the nutritional requirements of growing plants. Ammonium sulfate provides an excellent source of S, which supports or drives numerous essential plant functions, including protein synthesis. After addition to the soil, ammonium sulfate rapidly dissolves into its ammonium and sulfate components. If it remains on the soil surface, ammonium may be susceptible to gaseous loss in alkaline conditions. Therefore, incorporating the material into the soil as soon as possible, or applying it before irrigation or a predicted rainfall, is advised. In flooded soils such as rice fields, ammonium sulfate is applied often since nitrate-based fertilizers are a poor choice due to denitrification losses.

In warm temperatures, microbes in the soil rapidly convert ammonium to nitrate via the process of nitrification [2NH₄⁺ + 3O₂ → 2NO₃⁻ + 2H₂O + 4H⁺], releasing acidity [H⁺]. Ultimately, after repeated application of N in the form of ammonium, soil pH will decrease. Due to the nitrification process, ammonium sulfate has an acidifying effect on soil, not from the presence of sulfate (SO₄²⁻), which has a negligible effect on pH, but from the acid-producing potential of ammonium that converts to nitrate. The acid-producing potential of ammonium sulfate is greater than that of ammonium nitrate since all of the N in ammonium sulfate converts to nitrate, compared to only half of the N from ammonium nitrate that converts to nitrate (given the equal N application rate). In addition, under anoxic conditions, sulfate can be an electron acceptor for ammonium nitrogen oxidation, a potential microbial process known as sulfate-reducing ammonium oxidation (sulfamox) or sulfamox (Bi et al. 2020; Dominika et al. 2021), which occurs in natural environments and provides a novel link between the cycles of nitrogen and sulfur (Liu et al. 2021; Rios-Del Toro et al. 2018). The process was first reported by Fdz-Polanco et al. (2001) and the overall sulfamox reaction was described as [SO₄²⁻ + 2NH₄⁺ → S⁰ + N₂ + 4H₂O], but the exact pathway of sulfamox remains largely unknown, and gaps regarding functional bacteria and their metabolic pathways, and the main influential factors make it challenging to estimate its environmental significance (Liu et al. 2021).

The sulfamox process plays a meaningful role in the global scale of the biogeochemical N and S cycle. Although the mechanism of sulfamox is not well elucidated, some of the bacterial
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**FIGURE 9** | Legend on next page.</figcaption></figure>
**FIGURE 9** | Heatmap displaying the relative abundances of fungal genera with more than 1% relative abundance under different N fertilization treatments in (A) Faascht, (B) Bouzule, and (C) BouzuleNew. The abundance of taxa was calculated as the mean of four biological replicate plots measured over two years and three seasons (n = 24) and then standardized into a Z-score. The colour scale shows the taxa abundance standard deviations from the mean, with red for a high abundance level (positive Z-scores) and blue for a low abundance level (negative Z-scores). Each color code represents a type of N fertilizer (BR, biogas residues; BR_CF, biogas residues and chemical fertilizers; CF, chemical fertilizers; CTR, control; MNR, manure; PT, post-treated biogas residue fractions). For treatment, abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.

species that have been associated with the sulfamox process, mainly in anaerobic ammonia and sulfate removing reactor experiments, and marine environments (marine sediments), are *Brocadia*, *Anammoxoglobus*, *Sulfate*, *Bacillus*, *Benzoevorans* (Cai et al. 2010; Liu et al. 2008), and species belonging to phylum *Verrucomicrobia* (Rikmann et al. 2016) and *Proteobacteria* (Wang et al. 2017). Given that the novel process of sulfamox has not been studied in terrestrial ecosystems and particularly in agricultural soils, it would be beneficial to understand the impact of ammonium sulfate on the indigenous soil microbial communities overall and to discover the microbial communities mediating the sulfamox process and how they determine the fate of ammonium sulfate applied to agricultural soils. Environmental factors such as pH and temperature play an essential role in the composition, activity, and metabolism of the microbial sulfamox community (Cai et al. 2010), as well as in its role in nitrogen metabolism, and therefore, the variation and impact of these external factors should be assessed (Vuono et al. 2015).

As mentioned previously, following the application of ammonium sulfate, bacterial richness and diversity were significantly reduced (Habteselassie et al. 2022; Hallin et al. 2009; Wessén, Hallin, et al. 2010), and the relative abundance of most bacterial phyla and genera was significantly altered (Figures 6A and 7A). The relative abundance of ten out of the 14 bacterial phyla was reduced, with *Acidobacteriota*, *Planctomycetota*, *Myxococcota*, and *Chloroflexi* affected the most. On the other hand, the relative abundance of *Actinobacteriota* and *Firmicutes* was significantly increased compared to the unfertilized control and all other treatments tested. Other long-term continuous nitrogen fertilization studies made similar observations, with *Acidobacteriota* and *Actinobacteriota* showing significant positive and negative correlations, respectively, with soil pH, confirming that pH is a dominant abiotic driver of bacterial community structure (Dai et al. 2018; Ren et al. 2020). The main genera that contributed to the increased relative abundance of the phylum *Actinobacteria* were *Acidothermus*, *Gaiellales*, *Mycobacterium*, and *Pseudolabrys*, which increased only in the plots treated with AS350. Therefore, the abundance of acidophilic *Actinobacteria* can serve as an indicator of a decreased soil pH caused by long-term N fertilization or by nitrogen fertilizers that can decrease soil pH, such as ammonium sulfate. Interestingly, in our previous studies conducted on the same soil plots, where we investigated nitrate accumulation in the soil profile (Tsachidou, Daigneux, et al. 2019; Tsachidou, Scheuren, et al. 2019), ammonium sulfate was among the treatments that led to the highest accumulation of nitrate (possibly via $SO_4^{2-}$ reducing oxidation of $NH_4^+$ to $NO_3^-$) (Schrum et al. 2009), further explaining our findings in the current study. Another interesting observation was the significant decrease of the relative abundance of

*Planctomycetota*, which are known to carry the anammox process (van der Star et al. 2007). Since the sulfamox process may occur in conjunction with the anammox process, and the concentration of $SO_4^{2-}$ and ratio of $NH_4^+/SO_4^{2-}$ can influence sulfamox and anammox processes, according to anaerobic reactor experiments (Grubba and Majtacz 2020), it is imperative to understand how bacteria responsible for these two processes may respond to ammonium sulfate enrichment. In addition, since both sulfamox and anammox processes are responsible for the anaerobic oxidation of NH4- and given the phenomenon of soil microbial functional redundancy (Chen, Ma, et al. 2022), it would be of particular interest to investigate if ammonium sulfate enrichment could benefit one process over the other. These observations suggest that the process of sulfamox and the role of sulfamox functional bacteria in nitrogen metabolism and loss from the agricultural soils, following the application of ammonium sulfate—particularly since ammonium sulfate is often applied in flooded soils (meeting the anoxic conditions required) – should be further explored by combining metagenomic, metatranscriptomic, and stable isotope techniques.

## 4.3 | Introduction of Pathogenic and Beneficial Microorganisms in Soils Fertilized With Biogas Residues

The effects of different types of nitrogen fertilizers on the microbial composition with respect to beneficial and harmful bacteria and fungi in the soil have rarely been reported. If beneficial microbes can be supplemented rapidly, beneficial bacteria and fungi can become dominant in the soil, not only inhibiting the growth of pathogens but also promoting the growth and development of crops. While biogas residues have been utilized as a soil amendment due to their abundance of readily available nutrients, their great load of different types of microorganisms with agronomic (e.g., biofertilizer value) and environmental importance (Insam et al. 2015) is oftentimes neglected or undermined (Roopnarain et al. 2023). In anaerobic digestion systems, bacteria, together with fungi, are responsible for breaking down organic matter (Vinzelj et al. 2020). Due to the increasing number of different types of organic waste utilized in anaerobic digesters, biogas residues are expected to have different chemical properties (Nielsen et al. 2020) and microbial composition (Coelho et al. 2019). Although anaerobic digestion is well known for reducing microbial pathogens and antibiotic residues in organic wastes (Visca et al. 2021) Biosecurity is the main concern related to the application of biogas residues to agricultural land due to the presence of pathogenic microbes (Sahlström 2003) which may contaminate a range of environmental media, posing a potential threat to human and animal health (Nag et al. 2021). Therefore, the European Regulation (EC) No. 1069/2009 (The
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**FIGURE 10** | Relative abundance of dominant genera of the fungal phylum *Glomeromycota* in the topsoil under different N addition treatments. Depicted are all the Orders and Genera identified in (A) Faascht, (B) Bouzule, and (C) BouzuleNew. Shades of a color correspond to genera within the same order. Values are the means of four biological replicate plots measured over two years and three seasons (n = 24). For treatment, abbreviations refer to Table 2. The values at the end of each treatment or component (in combinatorial treatments) represent the amount of total nitrogen units applied in that form.</figcaption></figure>
European Parliament and Council 2009) and Regulation (EU) No. 142/2011 (The European Commission 2011) allow the safe use of biogas residues as organic fertilizers and soil improvers by setting the limits of the pathogenic loads at 1000 CFU/g⁻¹. Indicator organisms such as fecal coliforms, *Bacteroides*, *E. coli*, and *Salmonella* are often used to assess the adequacy and efficiency of the anaerobic digestion process in reducing pathogen loads in the final product (Lim et al. 2020). As opposed to this investigation, most of the fertilization studies conducted on biogas residues have either assessed a very limited number of biogas residues, been done only under controlled conditions, and/or spanned a rather short amount of time (Nielsen et al. 2020).

In this study, we scanned the bacterial and fungal taxonomy tables for some of the predominant pathogenic microorganisms present in biogas residues and untreated manure. More specifically, the bacterial indicator organisms targeted were *Bacteroides*, *Enterococcus*, *Cryptosporidium*, *Campylobacter*, *Mycobacterium*, *Escherichia*, *Listeria*, *Salmonella*, and *Clostridium* (Nag et al. 2021), while the fungi targeted were extremely thermotolerant pathogens known to survive mesophilic (37°C) and thermophilic (55°C) temperatures, such as *Thermoascus* and *Thermomyces*, and pathogens that produce mycotoxins causing allergic reactions, such as *Aspergillus*, *Penicillium*, and *Rhizomucor* (Schnürer and Schnürer 2006), and other major groups of pathogenic fungi of genera *Fusarium*, *Verticillium*, *Rhizoctonia*, *Pythium*, and *Phytophthora* (Fråc et al. 2018). It has been shown that application of anaerobically digested instead of fresh manure on soil might control the release of pathogens (Goberna et al. 2011). Even though anaerobic digestion can inactivate pathogens present in the raw feedstock, some spore-forming microorganisms, for instance *Clostridium*, that are resistant to high temperatures might survive the procedure and eventually end up in the biogas residue (Nag et al. 2021). Temperature is considered a strong selective factor for the growth and activity of microbial communities in the anaerobic reactors and in the soil (Alster et al. 2018). The exposure of obligate and facultative anaerobes to aerobic conditions of the soil surface and lower temperatures compared to the mesophilic anaerobic environment, where biogas residues and animal slurry come from, may have deleterious effects on their metabolism and make microbial adaptation and survival very difficult (Fenchel and Finlay 2008; Gerardi 2003). In this study, the biogas residues originated from mesophilic biogas plants operating between 37°C and 42°C, much higher temperatures than the average found in the soils of the region throughout the year (Figure 1). Based on our findings, following long-term application of various biogas residue fractions in real conditions, there were no detectable bacterial or fungal pathogens established in the soil, and soil communities have not been altered due to biogas residue application. Furthermore, there were no alterations observed in pathogen content in plots treated with raw manure either. More specifically, out of all bacterial and fungal pathogens that were examined, only the bacterial genera of *Mycobacterium* and *Clostridium*, and fungal genera of *Fusarium*, *Aspergillus*, and *Penicillium* were detected. *Mycobacterium* was detected only in Faascht, and its relative abundance was significantly increased only in plots treated with ammonium sulfate (AS350) compared to all the other treatments (Figure 7A). *Clostridium* was present in all three grasslands and all treatments, including the unfertilized control treatment, but its relative abundance

remained far below the 1% threshold. *Fusarium* was the most abundant fungal pathogen that was present in all three sites and all treatments. Yet again, it was significantly elevated only in the plots treated with ammonium sulfate (AS350 in Faascht) compared to all the other treatments, but not in the organic treatments of biogas residues and manure, as would be expected. One theory that could explain this observation may be in line with the findings of Coelho et al. (2020) who showed that the microbial DNA sequences detected in abundance in biogas residues were not found or only found in low abundance in the soil, an indication that dominant microorganisms present in biogas residues failed to establish in the soil and/or replace the native microbial populations there, possibly due to niche incompatibilities and competitiveness of indigenous soil microbes. This failure to establish may be considered a positive or a negative fact depending upon the microbe type; e.g., for a pathogen, non-establishment is desirable, but for a plant-growth promoter, it is not. Another explanation may be, as proposed by Ho et al. (2015), that the changes in microbial community are more likely due to the stimulation of soil-borne microbes (e.g., the pathogenic genera of *Fusarium*) by the chemical composition of a fertilizer applied rather than the introduction of microbes present in the fertilizer per se. Nevertheless, it must also be taken into account that soil microbial community analysis by DNA sequencing might include DNA from already dead microbes (Carini et al. 2016).

In the literature, fewer studies have focused on the biofertilizer potential of the microbial community present in biogas residues. Apart from pathogenic organisms, many other beneficial microorganisms with potential for agronomical benefit or positive environmental impact can be found in biogas residues, including nitrogen-fixing, denitrifying, and nitrifying bacteria, methanogens, resistant bacteria involved in antibiotic transformation and removal, plant-growth promoting bacteria (PGPB), saprophytic fungi, and arbuscular mycorrhizal fungi (Coelho et al. 2020; Garbini et al. 2022). However, currently, little is known about how the indigenous soil microorganisms respond to microorganisms found in different biofertilizers and how biogas residues may influence the soil ecological system (Zhang et al. 2020; Zhao et al. 2023). Many questions still need to be answered; for example, what are the main microorganisms in biogas residues with potential agronomic use or pathogenic potential, and whether they are present in large enough quantities to impact the soil and plant systems. Therefore, further investigation is required since the effects of supplying various microorganisms via biogas residue application on the native soil microbial community and agricultural biosecurity overall are not yet fully understood. In line with other studies showcasing the harmless nature of biogas residues (Suproniene et al. 2022), these findings comprise another step towards demonstrating their innocuous effect on the soil microbial community.

## 4.4 | Impact of Nitrogen Fertilizers on Glomeromycota and Arbuscular Mycorrhiza Formation Potential

Fungi are an important component of grassland ecosystems due to their biochemical activity, engagement in nutrient cycling, and their role as biological agents against pathogenic microorganisms (Dengler et al. 2014; Fråc et al. 2018). Methods of
increasing soil fungal diversity within agroecosystems, partic-
ularly of beneficial fungi populations, should be used in prac-
tice for more sustainable agricultural production systems and
increased crop yields, decreased use of chemical fertilizers,
and protection of the soil environment (Frac et al. 2018). The
most important group of beneficial fungi in agricultural soils
is fungi of the phylum *Glomeromycota*, and in natural condi-
tions, they are associated (except one genus) with at least 80%
of terrestrial plants, forming mutualistic associations with
the roots of the plants known as arbuscular mycorrhiza (Oehl
et al. 2011; Williams et al. 2017). The key benefits of symbiosis
with arbuscular mycorrhizal fungi include the improvement of
rooting and plant establishment, stimulation of nutrient cycling,
improvement of soil structure, enhancement of host tolerance
to stresses, increased soil resource capture, and uptake of low
mobility ions (such as P), and enhancement of plant community
diversity (Azcón-Aguilar and Barea 1997; Etesami et al. 2021;
Smith and Read 2008). In addition, AMF allow plants to uti-
lize water and minerals in exchange for carbohydrates, limit-
ing therefore their dependence on fertilizers, which in turn can
enhance agricultural sustainability. Therefore, the economic
and ecological importance of these plant symbionts is obvious
(Krüger et al. 2009). Indeed, numerous experiments have shown
the potential of using arbuscular mycorrhizal fungi, such as
the plant-beneficial antagonistic *Glomus* sp., in increasing the
yield of plants by simultaneously reducing damages caused by
pathogens (Dawidziuk et al. 2016; Thilagar and Bagyaraj 2015).
However, long-term N (and P) supplementation may signifi-
cantly alter the community structure and composition of ar-
buscular mycorrhizal fungi (Wang et al. 2020; Wu et al. 2011),
as well as high concentrations of nitrogen can decrease AMF
biomass, species richness, and diversity in some ecosystems
(Egerton-Warburton et al. 2007; Williams et al. 2017).

Even though in this study we witnessed a relatively small impact
of N fertilizers on the overall fungal community, several genera
of the taxonomic group of *Glomeromycota* detected appeared
to be significantly affected by the different nitrogen fertilizers
tested. For example, in the Faascht grassland, the relative abun-
dance of the genus *Funneliformis* was significantly increased in
soil plots treated with urea (UR350), calcium nitrate (CaN350),
and the post-treated biogas residue fraction of NanoFiltrate
(NF230) as compared to the unfertilized control and all other
treatments (Figure 10A). Also, the relative abundance of the
genus *Glomus* was substantially lower under the treatments of
urea and NanoFiltrate as compared to the rest of the nitrogen fer-
tilizers, but not the unfertilized control treatment. Furthermore,
the treatment of ammonium sulfate (AS350) and the combinato-
rial treatment of ammonium sulfate and raw digestate appeared
to increase the relative abundance of the order *Paraglomerales*.
Regardless of treatment, the composition and abundance of
AMF genera indicate site-specific differences, with the relative
abundance of the order *Glomerales* being considerably lower,
and the order *Paraglomerales* being substantially higher in the
newly fertilized grassland of BouzuleNew, compared to the
long-term fertilized grasslands of Bouzule and Faascht that were
dominated by *Glomerales* and contained negligible amounts of
*Paraglomerales* (mainly of genus *Paraglomus*).

Fungi have an important ecological and engineering value in
**anaerobic digestion** systems, but have received less attention

than prokaryotes (Yang et al. 2022). AMF are among the mi-
croorganisms with agronomic and environmental importance
that are found in high abundance in biogas residues and could
be incorporated into the indigenous soil microbial population
following the application of biogas residues on soil (Coelho
et al. 2020; Wang, Yu, et al. 2018). However, it would be of
interest to assess how the sanitizing property of the anaerobic
digestion process may affect their survival and proliferation
in the soil. In this study, none of the biogas residue fractions
applied to the grassland soils had a unique impact on the
composition or abundance of *Glomeromycota* at any taxo-
nomic level, except for the nanofiltration concentrate (NF230),
which substantially increased the relative abundance of the
*Funneliformis* genus. Also, NF230, along with urea, was the
only treatment that did not affect the relative abundance of
genus *Glomus* as opposed to all the other fertilizers. The high
content of nanofiltration concentrates on organic matter, or-
ganic nitrogen, and phosphorus (Adam et al. 2018) could pos-
sibly explain its unique effect on the Glomeromycotan taxa
but also on the soil microbial community overall, and would
require further investigation.

In brief, this first glance at the composition and abundance of
Glomeromycota, following the application of various biogas
residue fractions on the grassland soils, did not demonstrate
any substantial alterations that could cause potential impair-
ment of arbuscular mycorrhiza formation. However, it should
be emphasized that since the primers used in this study are
not AMF specific and may amplify only a restricted number of
Glomeromycotan taxa, a set of primers that is suitable specifi-
cally for amplifying AMF rDNA, or primers complementary to
the conserved regions, would be required to better characterize
the diversity of AMF in the soil and for more robust phylogenetic
analyses with species-level resolution.

5 | Conclusions

Even though all nitrogen fertilizers have their own way to ben-
efit soil and the microorganisms residing within it, biofertiliza-
tion with biogas residues has the potential to provide long-term
sustainability to agricultural soils. The outcomes of this study
suggest that the grassland soil bacteriome is more sensitive than
its mycobiome in response to nitrogen fertilization. The type of
nitrogen fertilizer affected to a greater extent the α-diversity in-
dices of the bacteriome compared to the mycobiome. We verified
the negative impact of ammonium sulfate on the α-diversity of
the bacterial community, but also on the relative abundance of
several bacterial and fungal taxa, and suggest further investi-
gation of the sulfammox process in agricultural soils. This is of
particular importance for the biogas sector since the treatment
of biogas residues involves, among others, the capture of ammo-
nia with the use of sulfuric acid during drying, consequently
returning the end product of ammonium sulfate to the fields.
The β-diversity revealed the clear grouping of the bacterial
and fungal communities into clusters that well reflected which
grassland each sample originated from, mainly due to relative
abundance variation rather than composition. Regarding ar-
buscular mycorrhizal fungi, even though several genera of the
taxonomic group of *Glomeromycota* appeared to be significantly
affected by the different nitrogen fertilizers, we did not observe
any substantial alterations that could cause potential impairment of arbuscular mycorrhiza formation. Finally, no major pathogens were detected in any of the grassland soils following the application of different biogas residue fractions or the application of raw manure, indicating possibly the sanitizing property of anaerobic digestion and/or the failure of allochthonous pathogens to establish in the soil. Overall, even though we did not observe a significant increase in biodiversity following the application of biogas residues on grassland soils as expected, our study suggested the safe nature of biogas residues and the divergent effect of nitrogen fertilization on the assembly of soil bacterial and fungal communities, emphasizing the need to gain a more complete understanding of ecological consequences of anthropogenic nitrogen enrichment. To obtain a holistic and accurate view of the impact of different nitrogen fertilizers on the soil microbial communities, it is essential that metagenomic studies are performed in parallel to metatranscriptomic studies. Furthermore, improved understanding of how different individual and co-occurring environmental and external drivers interact and affect soil microbiota and their functions is essential for employing sustainable intensification strategies seeking minimal harm to ecosystems, animals, and humans.

To further promote biogas residues in the market and advance the substitution of chemical fertilizers, additional research is needed to verify the biosafety of biogas residues originating from different digestion systems and to prove that there is no risk for pathogen contamination of the agricultural soils following repeated long-term application. Also, to label biogas residue utilization as a tool for sustainable agricultural practice and climate change mitigation, more targeted studies are required to identify beneficial microorganisms such as the plant growth-promoting bacteria and arbuscular mycorrhizal fungi contained in biogas residues and assess how the sanitizing property of the anaerobic digestion process may affect their survival and proliferation in the soil. Finally, in the face of the newly emerging strategy of biofortification, biogas residues may present an attractive solution to battle micronutrient deficiencies (often termed as *hidden hunger*) and should be further evaluated for their micronutrient- and microbe-mediated crop biofortification potential.

## Author Contributions

**Bella Tsachidou:** conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing – original draft, writing – review and editing. **Magdalena Calusinska:** methodology, writing – review and editing. **Christophe Hissler:** supervision. **Jérôme Gennen:** resources. **Benjamin Daigneux:** resources. **Séverine Piutti:** resources. **Alexandre Laflotte:** resources. **Isabelle George:** supervision, writing – review and editing. **Philippe Delfosse:** funding acquisition, supervision, writing – review and editing.

## Acknowledgements

The authors wish to thank Dr. Malte Herold for his valuable assistance in the submission of the sequence reads to the NCBI Sequence Read Archive.

## Conflicts of Interest

The authors declare no conflicts of interest.

# Data Availability Statement

The data that support the findings of this study will be openly available in NCBI upon paper acceptance for publication.

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**Supporting Information**

Additional supporting information can be found online in the
Supporting Information section.