How Benzoic Acid—Driven Soil Microorganisms Influence N₂O Emissions

Abstract

Urine-derived and plant-derived benzoic acid can accumulate within soil, and it likely changes the soil microbial community as well as N₂O emissions; however, its mechanism is not clear. This study conducted an incubation experiment to monitor N₂O emissions under low moisture (40% water-filled pore space (WFPS)) and high moisture (85% WFPS) conditions. Metagenomic sequencing and q-PCR methods were used to determine the link between N₂O emissions and the composition and functions of soil microbiota. Benzoic acid (BA) was found to significantly, yet dose-dependently, impact N₂O emissions; that is, low BA concentrations increased N₂O, whereas high BA decreased N₂O. However, this was only found under high moisture conditions. In contrast, BA had little impact on N₂O emissions under low moisture conditions. Under high moisture conditions, BA increased the gene copy number of bacteria and fungi, and decreased the ratio of bacteria to fungi. Similarly, BA significantly increased the abundance of denitrification functional genes, but reduced the (NirS + NirK)-to-NosZ ratio at the peak of emission. This is in agreement with the observation of the increased relative abundance of genes encoding N₂O reductase (EC 1.7.2.4) and NO₃⁻ heterotrophic reductase (EC 1.7.1.15, EC 1.7.2.2) in the metagenomic analysis. In summary, high concentrations of benzoic acid reduce N₂O emissions by promoting the reduction of N₂O. This study revealed the impact of BA on soil microbiota and highlighted the favorable conditions and underlying mechanism behind BA’s significant impact on soil N₂O emissions. This study’s novelty lies in the fact that it deepens our understanding of the complicated role of root exudates and metabolites of animals and plants in soil.

1. Introduction

The cumulative emission of atmospheric N₂O increased by approximately 20% in the last century, and it is still increasing at a rate of 0.2–0.3% per year [1,2]. Furthermore, global agriculture may be responsible for 40% of the anthropogenic N₂O emissions [3,4]. As a major contributor to the N loss from agricultural lands, denitrification in soil has caused large amounts of N₂O emissions from agriculture [5,6]; therefore, it needs to be managed with great caution.

Arable lands receiving organic and chemical N fertilizers and pastures receiving feces and urine from grazing ruminants are important N₂O sources owing to their high N loads [7,8,9]. For instance, a standard urine collection pad covering a surface area of 0.2 m² can accommodate approximately 2 L of urine, which contains nitrogen (N) at a concentration level of 10 g/L. This translates into a nitrogen application rate of 1000 kg of N per hectare, wherein between 50% and 90% of the nitrogen is present in the form of urea [10]. The subsequent nitrification and denitrification, mediated by various microbial processes under favorable conditions for N loss and N₂O emissions, could lead to heightened concentrations of N₂O originating from urine. Meanwhile, the rising numbers of ruminants, globally driven by the increasing demand for meat and dairy products, could further increase the total amount of urine-derived N₂O emissions. Therefore, there is an urgent need for mitigation strategies.

Multiple approaches have been suggested to reduce the emission of N₂O from cultivated fields and grazing lands. For example, previous studies have proposed mitigation strategies for soil N₂O emissions using animals’ diet to control ruminant urine composition [11,12,13]. A similar strategy, which uses precision fertilization to prevent excess fertilization in arable lands, has been thoroughly examined [14,15].

Recently, the use of natural or synthetic inhibitors for hydrolysis, nitrification, and denitrification has been considered a useful and effective method for reducing soil N₂O emissions. As an example, elevating the proportion of hippuric acid (HA) in urine from 3% to 9% can decrease the proportion of N₂O-N emissions from soil-applied urine-N from 8.4% to 4.4%. Similarly, Van Groenigen et al. [16] showed that the N₂O in soil decreased by more than 50% when the concentration of HA in synthetic urine increased from 0.4 to 5.6 mM/kg. This was attributed to benzoic acid (BA), the product of HA decomposition in urine, which has the potential to reduce N₂O emissions by suppressing microbial activity [16,17,18,19]. Currently, BA has been used as a feed additive in animal production, in regions such as the European Union, to reduce urinary tract infection and improve feed digestibility [20,21]. Regulation (EU) 2020/1031 issued by the European Union stipulates that when benzoic acid is used as a feed additive for fattening pigs, its minimum and maximum contents in complete feed with a moisture content of 12% shall be 3 g/kg and 10 g/kg, respectively. The European Food Safety Authority (EFSA) has further indicated that a content of 5 g/kg of benzoic acid in complete feed is considered safe for weaned piglets [22]. In those cases, approximately 65% of the BA ingested is discharged through urine when the addition of BA in feed is 5% [23,24]. Therefore, both the HA and BA in urine can be regulated by the amount of BA in the animal’s diet [25]. From this perspective, modulating the concentrations of HA and BA in urine could represent a viable approach to mitigating N₂O emissions in grazing lands.

Benzoic acid is also commonly found in plant root exudates and tissues as plant secondary metabolites [26,27]. Hence, serving as an additional source of benzoic acid (BA) in soil, BA holds a significant influence on the release of N₂O from cultivated fields. For instance, it was found that the decomposition of crop straw can produce BA [28]; thus, it is likely to inhibit the denitrification process as well as N₂O emissions. However, there is no study on the interaction between plant-derived BA and soil nitrogen (N). Previous studies have demonstrated that root exudates of important crop species, including sorghum, wheat, and rice, can specifically inhibit soil nitrification. In addition, B-proanthocyanidins in the tissue extracts of Fallopia spp. can inhibit denitrification [29]. Researchers have referred to the substances from plants that can inhibit soil nitrification and denitrification as biological nitrification inhibitors (BNI) and biological denitrification inhibitors (BDI) [30,31,32].

Although in some conditions, BA did not show significant mitigation of N₂O emissions from pastures owing to its low content or rapid breakdown in soils, it is believed that root exudates and plant secondary metabolites could shape nutrient cycles in many situations by influencing the plant–soil microbiota [33,34,35]. Compared with chemical inhibitors such as dicyandiamide and 3,4-dimethylpyrazole phosphate, biological inhibitors have the advantages of having low environmental pollution, long action time, and notable economic benefits [36,37]. The studies on BNI and BDI not only provide a new perspective for studying how plant-soil microbiota interactions affect N transformation, but they also improve the understanding of the plants’ ability to shape microbial soil functions. At the same time, a new sustainable agricultural path could be developed by reducing fertilizer input, soil N loss, and greenhouse gas (GHG) emissions, as well as by improving plant growth and yield.

To reveal the underlying impact of soil BA on the N cycle and N₂O emissions, this study performed a soil incubation experiment under high and low soil moisture conditions with chemically pure BA and NO₃⁻-N as experimental materials. This was done to explore the conditions under which BA can significantly influence N₂O emissions. This was combined with quantitative PCR (q-PCR) and metagenomic technology to clarify the mechanism by which BA influences soil microbial communities and denitrification processes. This study provides an insightful understanding of how soil BA derived from various pathways affects the agricultural N cycle and GHG emissions.

2. Materials and Methods

2.1. Soil Sampling

The soil samples were obtained from a cucumber greenhouse situated in Baoding County, Hebei Province, China, located at coordinates 115°47′ E and 38°87′21″ N. This region experiences a semi-humid climate, characterized by an annual average temperature of 13.4 °C and an annual precipitation of 550 mm, with the majority (>80%) occurring between June and October. The field in question has been under a rotation system involving cucumber and tomato crops for three decades. Based on the classification system of the World Reference Base for Soil Resources [38], the soil at the experimental site is categorized as meadow-cinnamon.

Soil samples were gathered from the topsoil layer (0–20 cm depth) and subsequently sieved to 2 mm to eliminate stones and plant debris following air-drying. The soil exhibited a pH-H₂O value of 7.05 and comprised 20.36 g kg⁻¹ of organic carbon (OC), 1.9 g kg⁻¹ of total nitrogen (TN), 49.4 mg/kg of plant-available phosphorus (PAP), and 9.96 g kg⁻¹ of total potassium (TK). Notably, BA was not detectable in the soil samples.

2.2. Experimental Design and Set-Up

A static chamber incubation system was used in this study. The sieved soil was compressed into polyvinyl chloride tubes, which were 20 cm in height and had an internal diameter of 10 cm, to achieve a depth of 10 cm and a bulk density of 1.25 g cm⁻³. The water-filled pore space (WFPS) of the soil was then adjusted to either 85% (designated as high moisture, HM) or 40% (designated as low moisture, LM). The incubation tubes were sealed using parafilm (PM996) to minimize the loss of moisture while allowing gas diffusion. They were then incubated in the dark at a temperature of 25 °C for a duration of seven days prior to the administration of the treatment.

Four experimental scenarios were evaluated (

Table 1. Summary of composition of benzoic acid (BA) applied for all treatments.

Treatment	CK	B1	B2	B3
NO3−-N (kg ha−1)	100	100	100	100
BA (mmol kg−1 dry soil)	0	1	2	4

), namely CK (control without BA application), B1 (with a BA application rate of 1 mmol kg⁻¹ soil), B2 (with a BA application rate of 2 mmol kg⁻¹ soil), and B3 (with a BA application rate of 4 mmol kg⁻¹ soil). Set 15 repetitions for each treatment (CK, B1, B2, and B3). The application rate of N (NO₃⁻-N) was 100 kg ha⁻¹. The soil index was destructively sampled in triplicate on five sampling dates.

2.3. Gas Analyses

Gas samples were obtained daily between 8:00 and 9:00 a.m. For the measurement of N₂O, the static chamber method was employed to collect gas samples, and a gas chromatograph (model Agilent 6820, manufactured by Agilent Technologies, Santa Clara, CA, USA) was utilized to analyze the N₂O concentrations in these samples. The rate of N₂O emission [mg m⁻² h⁻¹] was calculated using the following formula [39]:

$$\mathrm{F}={\displaystyle \frac{d{C}_t}{dt}}\times H\times {\displaystyle \frac{T_0}{\left(T_0+T\right)}}$$

(1)

where F denotes the temporal rate of concentration variation [mg m⁻² h⁻¹], dC_t/dt is the linear rate of change in gas concentration over time within the observation period (mg m⁻³ h⁻¹), H is the height of the headspace of the measurement chamber (m), T₀ is the absolute temperature of air in the standard state (273.15 K), and T is the actual air temperature (°C).

2.4. Soil Analyses

Destructive soil sampling was carried out on days 0, 3, 6, 12, and 18 of incubation for the 85% WFPS treatment, and on days 0, 1, 3, 5, 11, and 15 of incubation for the 40% WFPS treatment. This was for the subsequent determination of soil physical and chemical indexes.

The gravimetric moisture content of the samples was ascertained through drying them at 105 °C for a duration of 24 h. To determine mineral N, an extraction process involving 2 M KCl was employed (where 20 g of fresh soil was mixed with 100 mL of 2 M KCl and shaken for 1 h). Subsequently, the extracts underwent colorimetric analysis using the Smart Chem 200 instrument to quantify NH₄⁺-N and NO₃⁻-⁻N concentrations. The soil’s pH level was measured in a suspension containing soil and deionized water in a 1:2 ratio, utilizing a digital pH meter equipped with glass and calomel electrodes for precise readings.

The BA concentration in the soil samples was determined using HPLC after extraction with ethyl acetate [40]. Five grams of soil sample were weighed into 30 mL of ethyl acetate, and the mixture was shaken overnight at 160 r/min under a constant temperature of 30 °C. After centrifugation, the supernatant was rotary evaporated to dryness at 35 °C. The residue was re-dissolved in 1.5 mL of HPLC-grade methanol, and the solution was filtered through a 0.2 μm organic membrane before detection. The analysis was performed using an Agilent high-performance liquid chromatograph equipped with a Thermo C18 column (250 × 4.6 mm, 5 μm). The mobile phase consisted of a mixture of acetonitrile and 1% glacial acetic acid at a ratio of 1:3. During detection, the injection volume was set at 10 μL, the flow rate was 0.9 mL/min, the column temperature was maintained at 25 °C, and the detection wavelength was set at 230 nm.

2.5. Metagenomic Sequencing

The soil sample DNA extracts underwent fragmentation to attain an average fragment size of roughly 400 base pairs, employing the Covaris M220 system sourced from Gene Company Limited, Hong Kong, China. This fragmentation was a preparatory step for the construction of paired-end libraries, which was executed utilizing the NEXTflexTM Rapid DNA-Seq kit provided by Bioo Scientific, located in Austin, TX, USA. Following this, adapters carrying the entire complement of hybridization sites for sequencing primers were covalently linked to the blunt termini of the resulting fragments. The sequencing process involving paired ends was conducted on an Illumina HiSeq Xten instrument, manufactured by Illumina Inc. and situated in San Diego, CA, USA. This process was executed by Majorbio Bio-Pharm Technology Co., Ltd., a company located in Shanghai, China. The sequencing was carried out using HiSeq X reagent kits (Illumina, Inc., San Diego, CA, USA), following the detailed protocols provided by the manufacturer, which can be accessed at their official website (www.illumina.com, accessed on 15 July 2024).

2.6. Gene Prediction, Taxonomy, and Functional Annotation

Contigs were analyzed to identify open reading frames (ORFs) with the aid of the MetaGene tool (version 0.0.8) [41]. Subsequently, ORFs with a minimum length of 100 bp or greater were selected and converted into amino acid sequences, utilizing the translation table provided by the National Center for Biotechnology Information (NCBI), Bethesda, MD, USA.

A non-redundant inventory was generated by applying CD-HIT [42] with thresholds of 90% sequence identity and 90% coverage to ensure uniqueness. Post-quality control, the reads were aligned to this non-redundant gene inventory using SOAP aligner [43] with a 95% identity criterion. Subsequently, the abundance of each gene within the samples was assessed.

Taxonomic annotations for the representative sequences of the streamlined gene inventory were carried out using blastp implemented in DIAMOND v0.9.19, with an e-value threshold of 1 × 10⁻⁵, against the NCBI NR database. Additionally, Cluster of Orthologous Groups of Proteins (COG) annotations were performed for these sequences by querying the egg NOG database (version 4.5.1) with DIAMOND, employing an e-value cutoff of 1 × 10⁻⁵. The KEGG database was utilized for annotation purposes, employing DIAMOND with an e-value threshold set at 1 × 10⁻⁵ [44].

2.7. q-PCR

The gene copies of bacteria (16S), fungi (ITS), nitrite-reducing bacteria (NirS and NirK), and nitrous oxide-reducing bacteria (NosZ) were determined using real-time fluorescence q-PCR. Standard sample preparation steps were as follows: PCR amplification, PCR product purification, quality inspection, cloning vector connection, blue and white spot screening, plasmid extraction, and sequencing identification. The PCR amplification primers used are listed in

Table 2. PCR amplification primer.

Gene	Primer	Primer Sequence (5′–3′)	Gene Length	Quantitative PCR Reaction Procedure
16s	Eub338_Eub806	ACTCCTACGGGAGGCAGCAG	460	95 °C for 3 min × 1 cycle; 95 °C for 30 s, 58 °C for 30 s, 72 °C for 40 s × 35 cycles.
ITS	ITS1F_ITS2R	CTTGGTCATTTAGAGGAAGTAA	300	95 °C for 3 min × 1 cycle; 95 °C for 30 s, 58 °C for 30 s, 72 °C for 40 s × 35 cycles.
nirK	nirKF1a	GGMATGGTKCCSTGGCA	515	95 °C for 3 min × 1 cycle; 95 °C for 30 s, 56 °C for 30 s, 72 °C for 40 s × 35 cycles.
	nirKR3	GCCTCGATCAGRTTRTGG
nirS	nirSCd3	GTSAACGTSAAGGARACSGG	422	95 °C for 3 min × 1 cycle; 95 °C for 30 s, 58 °C for 30 s, 72 °C for 40 s × 35 cycles.
	nirSR3	GASTTCGGRTGSGTCTTGA
nosZ	nosZ-F	CGCRACGGCAASAAGGTSMSSGT	267	95 °C for 3 min × 1 cycle; 95 °C for 30 s, 56 °C for 30 s, 72 °C for 40 s × 35 cycles.
	nosZ-R	CAKRTGCAKSGCRTGGCAGAA

.

2.8. Statistical Analyses

Statistical analyses were performed using IBM SPSS Statistics version 25 (SPSS Inc., Chicago, IL, USA). A one-way ANOVA was conducted, followed by a Tukey’s honestly significant difference (HSD) post hoc test to further analyze the data. The threshold for statistical significance was set at p < 0.05. To assess the relationship between soil physicochemical properties and N₂O emissions, Pearson’s correlation coefficient analysis was employed. For statistical purposes, any data points falling below the detection limits were treated as zero. To assess differences in soil microbial community structures among treatment groups, permutational multivariate analysis of variance (PERMANOVA) was performed using the adonis2 function in the vegan package of R statistical software (Version 4.2.1).

3. Results

3.1. N₂O Emission

As shown in Figure 1A, BA strongly affected the emission of N₂O in soil under the condition of 85% WFPS, and there were significant differences among these treatments. For example, the emission patterns of the CK and B3 treatments showed “single peak” emissions, whereas those of the B1 and B2 treatments showed “double peak” emissions. During days 1–4 of incubation, the N₂O emissions of all treatments first increased and then decreased in all treatments, and the gas emission from CK was greater than that from the BA treatments. Thereafter, the gas emissions of the CK and B3 treatments gradually approached 0 mg m⁻² h⁻¹. Meanwhile, the gas emissions of treatments B1 and B2 increased and reached the second peak on days 8 and 6, respectively, and subsequently decreased to the same level as that of CK by days 11 and 9, respectively. The cumulative gas emissions during the first peaking period (days 1–4) of each treatment (CK, B1–B3) accounted for 99.0%, 34.3%, 73.2%, and 94.0% of their total emissions.

The cumulative N₂O-N emissions for CK over the duration of the experiment were 207.6 mg m⁻² compared with 476.7, 194.9, and 49.1 mg m⁻² for B1, B2, and B3 treatments, respectively (Figure 1B). From the information presented above, it can be seen that a low concentration of BA stimulated N₂O emissions, but with an increase in BA concentration, the effect on N₂O emissions gradually changed from significant promotion to inhibition. Under the condition of low water content, the N₂O emission flux and cumulative emissions were low (Figure S1A), and the cumulative N₂O emissions across the various treatments exhibited no statistically significant variations. (Figure S1B). This might be attributable to the faster degradation rate of BA in 40% WFPS soil than in 85% WFPS soil (Figure S1C).

3.2. Soil Parameters

3.2.1. Soil pH

Under HM conditions, soil pH initially declined with the addition of BA (Figure 2A). Compared with CK, the pH of the B1, B2, and B3 treatments was significantly 0.27, 0.46, and 0.56 units lower at the beginning of the experiment (Figure 2A). Thereafter, all treatments presented increasing pH values; however, the values of the treatments with BA appeared to be lower, especially those of B3, when compared with those of CK. Upon completion of the culture duration, the pH values of all treatments were similar. Under LM conditions of 40% WFPS, the BA-treated soils had lower pH values than at the early stage, compared with CK; nevertheless, similar pH values of these treatments were observed afterwards (Figure S1D).

3.2.2. Soil BA

Figure 2B shows that the BA content in soil decreased during the experiment under the HM condition. For instance, the content of BA in treatments B1 and B2 declined to below the detection limit after day 12, and the residual BA in B3 was 0.13 mmol kg⁻¹ at the end of incubation (Figure 2B). Under LM conditions, the BA content of each treatment fell below the detection limit after day 3, indicating that BA was more easily degraded under aerobic conditions (Figure S1C).

3.2.3. Soil Inorganic Nitrogen

Under the condition of 85% WFPS, soil NO₃⁻-N concentrations decreased from day 1 in all treatments at different rates (Figure 2C). For example, on day 3, the concentration of NO₃⁻-N in the CK treatment was 122.1 mg/kg, while those under the B1, B2, and B3 treatments were significantly higher than that in CK by 81.6%, 87.8%, and 33.3%, respectively. Nevertheless, post day 6, a notable elevation in NO₃⁻-N concentration was observed in the CK compared to the BA treatments, whereas no considerable discrepancies emerged among the NO₃⁻-N concentrations within the BA treatments. This is evident on day 6 of incubation, where the concentration of NO₃⁻-N in CK was 95.1 mg kg⁻¹ and that in the BA treatments was 43.4, 55.2, and 35.8 mg kg⁻¹, respectively. During days 12–18, the residual NO₃⁻-N in CK was in the range of 45–54 mg kg⁻¹, but that in the treatment with BA decreased to a much lower level (3.1–9.5 mg kg⁻¹). Therefore, BA had a strong impact on NO₃⁻ transformation under HM conditions. In contrast, although the content of NO₃⁻-N in CK was higher than that in the BA treatment, there was no significant difference among the treatments at the end of incubation. Thus, BA application had less of an impact on NO₃⁻ transformation under LM conditions (Figure S1E).

Meanwhile, the NH₄⁺-N concentration of CK increased during incubation under the HM condition (Figure 2D). In contrast, the NH₄⁺-N in the BA treatment decreased during the first 3 days, and then gradually increased until the end of the experiment. In addition, the concentration of NH₄⁺-N in the CK was significantly higher than that in the treatment with BA throughout the experimental period. Specifically, from day 6 to the end of incubation, the concentration of NH₄⁺-N in the B2 treatment was always higher than that in the B1 and B3 treatments. At the end of the experiment, the concentration of NH₄⁺-N in CK reached 66.68 mg kg⁻¹, and was 32.9–47.1% higher than that in the BA treatment.

The behaviors of soil NH₄⁺-N under LM conditions appeared to be complex. For example, concerning days 2–10 of incubation, soil NH₄⁺-N decreased rapidly to 10 mg kg⁻¹ in the first 3 days, and then increased to its peak on day 10 before again decreasing. There was no significant difference in the NH₄⁺-N content among the treatments at the end of the incubation period (Figure S1F).

3.3. Community Structure and Composition of Microorganisms

This study investigated the effect of the BA application (B3 treatment) versus the CK on the soil microbial diversity under 85% WFPS. BA application significantly increased the Shannon index of soil microorganisms over the incubation period, and increased the Simpson index at the end of incubation (day 18). However, it had little impact on the ACE and Chao1 indices (Table S1).

In addition, BA application had no significant effect on the microbial composition at the domain level, but significantly affected the relative abundance of dominant microorganisms at the phylum level (Figure S2). Principal coordinate analysis (PCoA) was performed to evaluate the impact of BA on the structure of the soil microbial community at the phylum level. For the bacterial community, the first two principal axes explained 70.8% (PC1) and 14.5% (PC2) of the change, respectively (Figure 3A). The data points of CK-3 and B3-3 were significantly separated along the PC2 axis (p < 0.01), and the treatments of CK-3, B3-3, CK-18, and B3-18 were significantly separated along the PC1 axis (p < 0.01). The results showed that BA application significantly affected the composition of bacterial and fungal community structures in the early stage of the experiment, but this influence mostly decreased at the end of incubation (day 18) (Figure 3B).

RDA analysis showed that the environmental factors accounted for 84.2% of the changes in the bacterial community in the soil, which were 78.09% (RDA1) and 10.73% (RDA2), respectively (Figure 3C). In addition, the selected environmental factors significantly affected the soil bacterial community structure, especially BA with an impact coefficient of 0.825, which was higher than that for pH (0.732) and NO₃⁻-N (0.591) and NH₄⁺-N (0.588) concentrations (

Table 3. Impact coefficients of environmental factors on soil bacterial and fungal community structures in redundancy analysis (RDA).

Items	pH	BA	NO3−-N	NH4+-N
Bacterium	0.732 **	0.825 **	0.591 *	0.588 *
Fungi	0.764 **	0.834 **	0.365	0.270

). In the fungal community (Figure 3D), the environmental factors can explain 59.6% of the changes in soil community structure, of which RDA1 was 50.4% and RDA2 was 9.2%. Among the selected environmental factors, BA and soil pH had significant effects on fungal community structure, with coefficients of 0.834 and 0.764, respectively (Table 3).

3.4. Functions of Microorganisms

The results of PCoA analysis of the samples at the COG functional level showed that the first two principal axes explained 85.8% (PC1) and 6.7% (PC2) of the functional changes in bacterial communities at this level (Figure S3). PERMANOVA results showed that there was no significant difference in microbial functional composition between the CK-3 and B3-3 treatments (R² = 0.584, p = 0.1), and between CK-18 and B3-18 (R² = 0.418, p = 0.2). The results of COG functional annotation showed that the annotated sequences were distributed into 23 functional classifications (Figure 4). Except for function N and function Z, the gene abundances of annotated functions in treatment B3 were lower than those in treatment CK, but only the gene abundances of W and Z showed statistically significant differences (Figure 4). Therefore, BA application had a weak impact on the functions of soil microorganisms.

3.5. The Degradation Pathway of BA

The degradation pathways of BA include the catechol, 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, gentian acid, and anaerobic degradation pathways. Except for the anaerobic degradation of BA, the pathways are aerobic degradation pathways. The key enzymes in different degradation pathways are marked in Figure 5A, and the functional genes encoding the key enzymes were annotated in combination with the KEGG database (Figure 5B–H).

On day 3 of incubation, the relative abundance of genes exhibited no notable variation encoding B12D, BCL, C12O, and 4H3H was found between CK and B3 (Figure 5B–D,G). In contrast, compared with CK, increased C23O and BCA (Figure 5E,F) and decreased P34O (Figure 5H) were observed. However, at the end of incubation (day 18), the relative gene abundance of B12D, BCL, C12O, BCA, and 4H3H in the B3 treatment was significantly higher than that in CK, with an increase of 42.0%, 105.5%, 21.8%, 208.5%, and 12.87%, respectively. Furthermore, there was no significant difference in C23O and P34O between CK-18 and B3-18. The different behaviors of the annotated gene expression corresponding to the BA degradation pathways might be attributable to their different responses induced by the BA application.

3.6. Pathways of Soil N Cycle: Metagenomic Analysis

Through KEGG pathway analysis (Figure 6), twelve functional genes associated with the nitrogen cycle were identified, corresponding to nitrate reductases (EC 1.7.7.2, EC 1.7.5.1, EC 1.9.6.1), nitrite reductase (EC 1.7.2.1), nitric oxide reductase (EC 1.7.2.5), nitrous oxide reductase (EC 1.7.2.4), heterotrophic nitrate reductases (EC 1.7.7.1, EC 1.7.1.15, EC 1.7.2.2), ammonia monooxygenase (EC 1.7.99.1), and hydroxylamine oxidoreductase (EC 1.7.2.6). These genes are categorized under the “Nitrogen metabolism” pathway (map00910) in the KEGG database, and their abundance variations directly reflect the functional potential of soil microbes in participating in nitrogen transformation.

On the 3rd day of culture, the addition of BA significantly increased the relative abundance of compiled nitrate reductase (EC 1.7.5.1), nitrous oxide reductase (EC 1.7.2.4), nitrate heterotrophic reductase (EC 1.7.1.15 and EC 1.7.2.2), that the rate of increase was 23.5%, 15.3%, 17.5% and 61.8% respectively. The findings indicated that BA exerted a notable influence on the soil nitrogen cycling process. Specifically, on the 18th day of incubation, the incorporation of BA led to a considerable decrease of 20.1% in the relative abundance of the nitrite reductase-encoding gene (EC 1.7.2.1), whereas no discernible impact was observed on the abundance of other enzymatic genes.

In addition, it was found that at the end of the incubation, the abundances of the genes encoding nitrate reductase (EC 1.9.6.1), nitric oxide reductase (EC 1.7.2.5), and hydroxylamine oxidoreductase (EC 1.7.2.6) in CK-18 significantly decreased by −24.5%, −11.9%, and −81.4% respectively, compared with that of CK-3 (Figure S4A). This was different in the B3 treatment, at the end of the incubation (B3-18 versus B3-3), where the enhanced reduction of NO to N₂O (16.9%) and NO₂⁻ to NH₄⁺ (203.5%) remained (Figure S4B).

3.7. Effects on Bacterial and Fungal Abundances

During the initial incubation period (day 3), the bacterial gene copy numbers in the B1, B2, and B3 treatments exhibited increases of 192.7%, 271.2%, and 56.4%, respectively, in comparison to the CK. Notably, the B2 treatment showed a statistically significant elevation compared to B1 (p < 0.05). (Figure 7A). On the sixth day of the experiment, no notable variations were observed among the different treatments. However, by the conclusion of the incubation period (day 18), the bacterial gene copy number in the B2 treatment was markedly elevated compared to both the CK and B1 treatments, with statistical significance at p < 0.05.

In addition, the gene copy numbers of fungi for the BA treatments were 258.8%, 496.9%, and 227.2%, respectively, which are significantly greater than those of the CK on day 3 of incubation (Figure 7B). Furthermore, among these treatments, B2 exhibited a statistically significant increase compared to the others (p < 0.05). This was also observed on day 6 of incubation, when the gene copy number of fungi in the B1, B2, and B3 treatments was 101.1%, 139.2%, and 25.7% greater, respectively, than that in CK; and that of B1 and B2 reached a significant level (p < 0.05). Upon completion of the cultivation, the fungal gene copy count in the B2 treatment alone demonstrated a statistically significant elevation compared to all other treatments (p < 0.05).

Additionally, the incorporation of BA was observed to decrease the bacterial-to-fungal ratio (Figure 7C). For example, on day 3 of incubation, the ratio of bacteria to fungi in B1, B2, and B3 was significantly reduced by 36.7%, 52.0%, and 61.7% compared with that in CK (p < 0.05). On days 6 and 18, only the ratio of bacteria to fungi in treatment B2 was significantly lower than that in treatment CK.

3.8. Effects on the Abundance of Denitrification Functional Genes

In this study, BA addition significantly affected the abundance of denitrification functional genes (Figure 7A–G). For example, on day 3 of incubation, the gene copy number of NirK in CK was significantly lower (p < 0.05) than that in B1 (152.9%) and B2 (51.5%) (Figure 7D). At the sixth day, the B2 treatment exhibited a statistically significant increase in the NirK gene copy number compared to all other treatments (p < 0.05), whereas no notable differences were observed among the remaining treatments. Upon completion of the incubation, the CK treatment showed a statistically significant elevation in the NirK gene copy number compared to all other treatments. In addition, the BA application showed a similar impact on the gene copy number of NirS, but with a greater increase (p < 0.05). Such increases included a 351.4–662.1% increase on day 3 and a 347.1–839.9% increase on day 6 in B1 and B2 compared with that of CK (Figure 7E). This was maintained until the end of incubation.

The gene copy number of NosZ first increased with the rate of BA application, and then decreased when the BA rate was 4 mmol kg⁻¹ (Figure 7F). On day 3 of incubation, the gene copy numbers of NosZ in the B1, B2, and B3 treatments were 7.3, 14.6, and 7.4 times, respectively, the value of that in CK. On day 6, the gene copy number of NosZ in the B1–B3 treatments decreased to 2.7, 3.5, and 1.8 times that in CK, respectively. This was maintained until the end of incubation. Specifically, B1 and B2 treatments always had more gene copies of NosZ than CK throughout the incubation period (p < 0.05).

More importantly, the ratio of (NirS + NirK) to NosZ is an important factor that directly affects N₂O production, where the lower the ratio, the higher the capacity of reducing N₂O to N₂. Compared with CK, the ratios of the B2 and B3 treatments were significantly lower, by 25.6% and 115.5%, respectively, than those in CK at the initial stage of incubation (Figure 7G). This might be responsible for the low N₂O emissions in B3 and B4. At the end of incubation, the ratio of B3 treatment was significantly higher than that of CK, B1, and B2 treatments by 96.1%, 196.7%, and 53.8%, respectively.

4. Discussion

4.1. Impact on Soil Microorganisms

Benzoic acid is a widely used antibacterial chemical. Therefore, it is of great importance to address its impact on soil health, in terms of the microbial composition and function of soil, owing to its response to changes in the external environment [45,46,47]. The increased gene copy number of soil bacteria and fungi during the incubation of a BA application demonstrated that BA might have been decomposed and used as a carbon source by microorganisms. This is consistent with the results of previous studies [27]. Meanwhile, the significantly reduced ratio of bacteria to fungi with BA application indicates that fungi might have a stronger positive response to BA under the conditions of this study. BA significantly affected the microbial community structure; nevertheless, in terms of the 23 functions annotated, only two functions (extracellular structure and cytoskeleton of microorganisms) were significantly influenced by BA application. This is consistent with how BA inhibits the growth of soil microorganisms by destroying the permeability of microbial cell membranes, thereby affecting the electron transport system of substrate transport and oxidative phosphorylation [19,48,49]. Overall, BA application had little impact on soil microbiota at the application rate investigated in this study.

4.2. Impact on Denitrification

This study found that BA application only inhibited NO₃⁻ consumption at the early incubation stage, but eventually promoted the reduction of soil NO₃⁻ under HM conditions. This is consistent with the performance of anti-bacterial chemicals, which involves suppressing the activity of soil microorganisms at the early stage, but enhancing the activity with the provision of carbon from the degradation of BA or the conversion of BA to BA compounds [50,51]. All treatments showed an increasing trend in NH₄⁺-N concentration, which may be related to the dissimilatory reduction of soil NO₃⁻ to NH₄⁺ under HM conditions [52,53]. The KEGG pathway of soil N in this study also showed that BA promoted the dissimilatory reduction of NO₃⁻ to NH₄⁺ by 61.8%, as well as promoted the transformation of NH₄⁺ to NH₂OH by 209.1%. Furthermore, the enhancement of the oxidation of NH₄⁺ to NH₂OH might be responsible for the lower NH₄⁺ increase observed in B3 (Figure 2D).

The enhanced reduction of NO₃⁻ to NO₂⁻ at the early stage may be responsible for the accumulation of NO₂⁻. Surprisingly, according to the KEGG pathway of the N cycle, which suggests that when the same substrate is consumed, the Nos activity level becomes higher, the more N₂O is converted to N₂ [54,55], the more the reduction of N₂O to N₂ is enhanced. This is in line with the q-PCR results, which showed that the copy numbers of functional genes such as NosZ, NirS, and NirK were significantly increased in the BA treatments. Most importantly, BA significantly reduced the ratio of (NirK + NirS) to NosZ during the early stage of incubation. Both the metagenomics and q-PCR results indicated that the mechanism of BA decreasing N₂O emissions might be due to the elevated reduction of N₂O to N₂.

This study also showed little change in soil NO₃⁻-N under LM conditions, which is due to weak denitrification [56,57,58,59]. Furthermore, the varied soil NH₄⁺-N may be the combined result of soil mineralization and nitrification [60,61] under these relatively dry conditions.

4.3. Implications of BA on the Soil N₂O Emission

Incorporating inhibitors serves as an efficacious approach to decrease soil nitrogen loss, enhance nitrogen use efficiency, and alleviate nitrous oxide emissions [62,63,64,65]. In recent years, with deepening research on root–plant–soil interactions, the concepts of BNI and BDI have been proposed. Biological nitrification inhibitor (BNI) and BDI, originating from plant root exudates and tissue extracts, can improve soil N utilization and reduce N₂O emissions by specifically inhibiting microbial activities related to N transformation [31,32,65,66]. B-Procyanidins, the only biological denitrification inhibitors reported to date, can inhibit the process of denitrification by specifically inhibiting the activity of membrane-bound NO₃⁻ reductase in a dose-dependent manner [31].

With this encouragement, a similar strategy of using BA for soil N₂O mitigation was also examined. However, contrasting results have been reported [16,67]. For example, Kool et al. [67] found that the content of HA increased from 3% to 9% of total N in synthetic urine, and the N₂O emissions decreased from 8.4% to 4.4% of the applied urine-N. Similarly, the study by van Groenigen et al. [16] showed that the N₂O decreased by more than 50% when the concentration of HA in synthetic urine was over 3.9 mmol kg⁻¹. Nevertheless, some studies have found the impact of BA application on N₂O emissions to be minuscule [18,19]. These inconsistencies among previous studies can be attributed to the following reasons.

4.3.1. Indoor Incubation Versus Field Application

Most studies that found that BA decreases N₂O emissions were based on the indoor soil column incubation method under controlled conditions [16,18,67]. Mechanical disturbances to soil, such as the pre-treatment processes of screening and drying, can provide homogeneous soil and greatly reduce the spatial variability associated with in situ tests; however, it is more likely to confirm the treatment effect. It is possible that this process also changed the size, structure, and function of microbial communities [68], which may have affected the metabolism and retention time of HA and BA in soil. In addition to the stronger N losses in the field, such as NO₃⁻ leaching, the varied soil moisture in the field may favor microbial activity for BA/HA degradation, and result in the rapid decrease of their concentrations, as observed in this study under LM conditions.

4.3.2. Soils with Different BA Tolerance Levels

The tolerance of soil denitrification to BA application could differ among various soil types for the following reasons. (1) Soil denitrification dominated by fungi may show greater tolerance to BA application because fungi with denitrifying functions may be slow in responding to BA [69]. (2) BA-degrading microorganisms may have already been established in soils frequently treated with urine and root exudates [70,71], which is in agreement with the enhanced gene expression for BA degradation observed in this study.

4.3.3. Soils Under Different Water Management

Under the LM condition, low water content may be less favorable for the denitrification process [57,58,72]. This can be combined with the greater degradation rate of BA, but eventually showed little impact on soil N₂O emission.

Overall, this study showed that BA may have promoted the transformation of NO₃⁻ and increased N loss. Moreover, most likely, the mitigation of N₂O may rely on the efficient reduction of N₂O to N₂. However, the aforementioned analysis showed that the impact of BA application on N₂O might be dose- and soil moisture-dependent. Hence, there is a pressing need to intensify efforts in assessing the influence of soil BA on nitrogen transformation processes and greenhouse gas emissions.

5. Conclusions

This incubation study demonstrated that soil BA had a strong impact on gas emissions only under HM conditions (e.g., >85% WFPS). Moreover, the fact that the increased N₂O emissions under low BA concentrations (<2 mmol/kg soil) still yielded decreased N₂O under high BA (>3 mmol/kg soil) showed that the influence of BA on soil N₂O is dose-dependent. High-throughput sequencing results revealed that BA application (or soil enrichment) increased the gene copy number of soil bacteria and fungi, but decreased the ratio of bacteria to fungi. Specifically, BA significantly increased the abundance of denitrification functional genes (NirS, NirK, and NosZ), but reduced the ratio of (NirS + NirK) to NosZ at the peak of emission. This is in line with the metagenomic finding that BA significantly increased the relative abundance of the genes encoding Nos reductase and Nir heterotrophic reductase. Overall, this study provides a novel understanding of the impact of soil BA on the N cycle and N₂O emissions.