Interactive Effects of Copper and Organic Matter on Dicyandiamide Efficacy in Suppressing Soil Nitrification and N₂O and CO₂ Emissions

Abstract

Nitrification is a key process governing nitrogen (N) loss and greenhouse gas emissions in agricultural soils, and its regulation is strongly influenced by both chemical inhibitors and soil properties. Copper (Cu), a metal cofactor that is crucial for the function of ammonia monooxygenase (AMO), plays an important role in ammonia oxidation, whereas dicyandiamide (DCD) suppresses nitrification and may interact with Cu to inhibit AMO activity. However, the extent to which Cu availability and soil organic matter (SOM) jointly regulate DCD efficiency remains poorly understood. In this study, an incubation experiment was conducted using tropical paddy soils with contrasting SOM contents to explore how varying Cu levels (10 and 200 mg Cu kg⁻¹ soil) impact DCD efficiency in regulating the nitrification process and controlling nitrous oxide (N₂O) and carbon dioxide (CO₂) emissions. Our results showed that DCD generally suppressed nitrification, as indicated by reduced NO₃⁻ accumulation and lower NO₃⁻/NH₄⁺ ratios. However, the response to Cu was strongly SOM-dependent. Under low SOM, Cu addition was associated with a partial restoration of nitrification activity, suggesting a potential reduction in DCD efficiency, whereas under high SOM, this effect appeared to be attenuated, likely due to Cu complexation and reduced bioavailability. Increasing Cu levels further weakened DCD inhibition, particularly in low SOM soils. DCD significantly reduced N₂O emissions, but this mitigation effect declined with Cu addition, suggesting a Cu-mediated influence on nitrification–denitrification pathways. On the other hand, CO₂ emissions were reduced under DCD application and appeared to be further reduced under Cu treatments. Changes in enzyme activities and nitrifier gene abundances supported these patterns, suggesting distinct responses of AOA and AOB communities under varying SOM and Cu conditions. This study provided evidence that the interaction of Cu availability and SOM may play an important role in governing the efficacy of nitrification inhibitors. This highlights the importance of considering soil-specific chemical environments when optimizing N management strategies to reduce environmental N losses.

1. Introduction

Nitrogen (N) is an essential element for all living organisms and one of the key factors determining crop productivity [1,2]. Consequently, N fertilization is crucial for maintaining high agricultural production [2]. However, a relatively large portion of applied N (20–70%) may be lost through gaseous volatilization and/or leaching, leading to negative agronomic and environmental impacts [3,4]. Ammonium (NH₄⁺) released during fertilization and derived from organic N mineralization can be oxidized to nitrate (NO₃⁻) via the nitrification process [5,6,7]. The high mobility and low adsorption capacity of NO₃⁻ make it more prone to leaching, runoff, and denitrification [8,9]. Therefore, understanding the factors regulating nitrification is essential for increasing N efficiency and minimizing N losses [10,11,12].

Copper (Cu) is an important micronutrient that participates in various enzymatic processes in soil microbiomes. One of these processes is ammonia (NH₃) oxidation, which is considered the first and rate-limiting step of nitrification. Specifically, NH₃ monooxygenase (AMO)—a Cu-containing enzyme [11,13,14,15,16,17]—catalyzes this process. Previously published research suggested that Cu availability can affect nitrification rates [18,19,20,21,22]. In general, lower Cu concentrations have been reported to stimulate nitrification by enhancing microbial enzymatic reactions [18,19,20], whereas higher Cu levels may inhibit microbial metabolism due to toxic effects [21,22]. Based on these studies, it appears that Cu may influence nitrification rates in a concentration-dependent manner—shifting from stimulatory to inhibitory effects.

At the same time, Cu’s effect on nitrification depends not only on total Cu concentrations but also on bioavailable Cu levels in soils. Soil organic matter (SOM) is a key factor in determining Cu bioavailability [23]. SOM shows a high affinity for Cu and can alter its bioavailability by complexing with metal ions via carboxyl and hydroxyl groups [24,25]. This mechanism can either reduce Cu toxicity or limit microbial access to Cu [26,27,28]. Furthermore, SOM plays a role in shaping other aspects of microbial activity and biochemical transformations, such as substrate provision and N transformation processes. Thus, SOM may affect nitrification both by regulating Cu bioavailability and by creating a suitable biochemical environment for these processes. Overall, these factors suggest that SOM may act as a potential regulator of Cu-dependent changes in nitrification processes, although this interaction has not been thoroughly studied.

To minimize N losses from agricultural soils, several measures have been implemented. Nitrification inhibitors (NIs) represent an effective group of compounds used in this process [29]. Since the introduction of NIs in agriculture, multiple advantages of these agents have been reported, including improving N use efficiency, crop production and the mitigation of greenhouse gas emissions [30,31]. Among various NIs, dicyandiamide (DCD) is frequently used as a nitrification inhibitor that can increase NH₄⁺ retention in soils while decreasing NO₃⁻ accumulation [32]. Several studies have shown that the addition of DCD is associated with increased NH₄⁺-N concentrations [33], decreased NO₃⁻-N concentrations [34], and reduced nitrous oxide (N₂O) and carbon dioxide (CO₂) emissions [35,36]. This was mainly due to its inhibitory effects on NH₃-oxidizing microorganisms, especially NH₃-oxidizing bacteria (AOB) [37,38,39,40].

However, the efficiency of DCD is not constant and may be influenced by soil properties such as SOM, pH, and texture [17,41,42]. Among these factors, the interaction between DCD and soil Cu has recently attracted considerable attention. It has been proposed that DCD may act, at least partly, through interactions with Cu associated with AMO activity, thereby potentially affecting nitrification processes [43]. Under this framework, Cu availability may play an important role in determining DCD inhibitory efficiency. Although previous studies have shown that DCD performance varies with soil properties (e.g., pH, SOM content, and clay percentage) [44], the extent to which Cu concentration directly affects the efficiency of a potentially Cu-interacting NI such as DCD remains unclear.

It is also important to note the importance of SOM in this interaction. By binding Cu and regulating its bioavailability, SOM may indirectly influence the Cu-DCD interaction. In addition, SOM can alter microbial activity, N transformation rates, and the chemical stability of DCD [17,41,42]. Therefore, SOM can simultaneously regulate Cu bioavailability and soil conditions that influence DCD efficiency. Although these factors are important for understanding DCD-Cu interactions, the role of SOM in modulating the interaction between Cu concentration and DCD efficiency has not yet been explicitly investigated.

This knowledge gap is particularly relevant given that Cu is not only involved in nitrification but may also play a role in broader N cycling processes and gaseous emissions. Apart from its involvement in nitrification, Cu is also an important component of enzymes involved in the denitrification process [45,46,47], and can therefore influence N₂O production and conversion. In addition, soil processes such as nitrification, denitrification, and microbial respiration are closely linked to CO₂ emissions, which can be influenced by both Cu availability and DCD application [33,41,48,49]. Thus, understanding the relationship between Cu concentration, DCD performance, and SOM content may provide important insights into the regulation of soil N transformation and gaseous emissions.

Thus, there are still some open questions concerning Cu’s influence on N transformation in soils. Specifically, how does Cu concentration affect DCD efficiency in regulating nitrification and greenhouse gas emissions? To what extent can SOM modulate these interactions? Addressing these questions is important for advancing our understanding of the effectiveness of NIs in different soils and for improving their use in sustainable agriculture practices.

Therefore, the aim of this study is to evaluate the effect of Cu concentration on DCD efficiency in regulating N availability and nitrification, as well as N₂O and CO₂ emissions in soils with differing SOM levels. The following hypotheses are proposed: (i) Cu availability may regulate nitrification in a concentration-dependent manner and consequently influence DCD inhibitory efficiency; (ii) SOM may regulate Cu-DCD interaction through its dual role in modulating Cu availability and soil biochemistry; and (iii) this interaction may influence N transformations and gaseous emissions from soils.

2. Materials and Methods

2.1. Soil Collection and Preparation

Soils were collected from two different locations. The soil with high SOM content was collected from Lincun (110°22′20″ N, 18°57′11″ E) and soil with low SOM content was collected from Shanniutian Village (110°22′29″ N, 19°0′7″ E), Wanning City, Hainan Province (Figure S1). Both sites are characterized by a tropical monsoon climate, with an average annual temperature, annual evaporation, and annual precipitation of 23.9 °C, 1872.7 mm, and 2400 mm, respectively. Soil samples were collected from a depth of 0–20 cm. Plant roots and visible organic residues were picked from the soil. Air-dried soil was sieved through a 2 mm mesh. The initial soil properties were analyzed (Table 1) following standard soil agrochemical analysis methods [50].

Table 1. The initial physical and chemical properties of soils.

Soil Property	Lincun	Shanniutian
Organic matter content (g kg−1)	44.8	22.2
Total organic carbon content (g kg−1)	26.0	12.9
Total N content (g kg−1)	0.33	0.35
Extractable NH4+-N (mg kg−1)	0.55	0.21
Extractable NO3−-N (mg kg−1)	4.40	0.21
Available potassium (mg kg−1)	67.1	37.0
pH	5.80	5.79
Available Cu content (mg kg−1)	6.77	0.11
Bulk density (g cm−3)	1.12	1.13
Cation exchange capacity (cmolc kg−1)	19.7	14.9
Clay content (g kg−1)	255	240
Silt content (g kg−1)	239	265
Sand content (g kg−1)	505	496
Texture	Sandy Clay Loam	Sandy Clay Loam

2.2. Experiment Design

Air-dried soil was pre-incubated at 60% of water holding capacity (WHC) at 25 °C for 7 days. Following the pre-incubation, urea (U), DCD, and Cu (applied as CuSO₄.5H₂O) were dissolved in distilled water, applied to soil and thoroughly mixed. Four treatments were applied on the two soils: U (200 mg N kg⁻¹ soil), U_{_}DCD (200 mg N kg⁻¹ soil + 20 mg DCD kg⁻¹ soil), U_{_}DCD_{_}Cu₁ (200 mg N kg⁻¹ soil + 20 mg DCD kg⁻¹ soil + 10 mg Cu kg⁻¹ soil), U_{_}DCD_{_}Cu₂ (200 mg N kg⁻¹ soil + 20 mg DCD kg⁻¹ soil + 200 mg Cu kg⁻¹ soil). The two Cu concentrations were selected based on established literature. The low Cu concentration (10 mg kg⁻¹) represents an elevated but environmentally common level in tropical agricultural soils, falling within the typical background range of 2–50 mg Cu kg⁻¹ reported for paddy soils and comparable to levels found in soils receiving Cu-based fungicides [23,27]. The high Cu concentration (200 mg kg⁻¹) represents a severe contamination level that exceeds the critical threshold of 150 mg kg⁻¹, above which significant nitrification inhibition has been reported [22]. This concentration has been used in a previous experiment to simulate Cu-contaminated conditions [21]. Thus, the two Cu rates allow distinction between Cu as an essential micronutrient (10 mg kg⁻¹) and Cu as a contaminant (200 mg kg⁻¹) in modulating DCD efficiency. Moreover, the application rate of DCD (10% of the N addition rate) was chosen because it represents a commonly used ratio in nitrification inhibition experiments and is deemed adequate in inhibiting nitrification without introducing unrealistically high amendment levels [41]. Each of the 4 treatments had 3 replicates. Treated soils were incubated in 1000 mL glass jars under controlled temperature of 25 °C in an automated dark chamber (HP-400S; Wuhan Ruihua Instrument & Equipment Co., Ltd., Wuhan, China) for 30 days. A thin plastic sheet containing small pin holes was placed on the jar tops to minimize moisture loss and provide gas exchange. Soil moisture content was checked every 2 days and maintained at 60% WHC by adding distilled water.

2.3. Measurement Indicators and Methods

2.3.1. Determination of Soil Mineral N, pH and Electric Conductivity (EC)

Soil extractable NH₄⁺-N and NO₃⁻-N and soil pH and EC were measured at various intervals (1, 5, 10, 15, 20, 25, and 30 days). Soil NH₄⁺ and NO₃⁻ were extracted using 2 mol L⁻¹ KCl at a soil-to-solution ratio of 1:5 (10 g soil: 50 mL KCl). The suspensions were agitated at 185 rpm for 60 min at room temperature and subsequently filtered through quantitative filter paper. The NH₄⁺ content was determined using the indophenol blue colorimetry method (625 nm), while NO₃⁻ content was determined by ultraviolet dual wavelength spectrophotometry (220 and 275 nm) spectrophotometry.

Soil pH and EC were tested at a 1:2.5 (soil: water) ratio. Soil pH was determined using a pH meter (HANNA HI2221, Villafranca Padovana, Italy), and soil EC was determined using an EC meter (DDS-11A, Shanghai Yueping Scientific Instrument Co., Ltd., Shanghai, China) [50]. Total organic carbon (TOC) and total N were measured using a Carlo Erba NA-1500 analyzer (Carlo Erba Instrumentazione, Milan, Italy), and SOM was calculated as TOC × 1.724. Available potassium in the soil was extracted using 1 M ammonium acetate solution (NH₄OAc; pH 7.0) and determined by flame photometry. Copper availability in the soil was determined using DTPA extraction followed by analysis using atomic absorption spectrophotometry (AAS). Soil bulk density was measured using the core method, where soil samples of known volume were collected, oven-dried at 105 °C until constant weight, and bulk density was calculated as the ratio of dry soil mass to core volume. Soil texture was determined using the hydrometer method based on particle size distribution (i.e., sand, silt, and clay fractions).

2.3.2. Determination of CO₂ and N₂O Emissions

Gas samples for CO₂ and N₂O emission measurements were collected on days 1, 5, 10, 15, 20, 25, and 30 after treatment application following the method described by [21]. Each 1000 mL glass incubation jar contained 100 g of dry soil, providing a headspace volume of approximately 900 mL. During gas sampling, jars were sealed airtight using rubber stoppers fitted with a three-way stopcock. Parafilm M was wrapped around the stopper to provide an additional seal. A thin plastic sheet with 5–8 small pin holes (diameter ~0.5 mm) was placed over the jar tops during incubation (non-sampling periods) to allow gas exchange while minimizing moisture loss. The jars were sealed for exactly 4 h (closure time, Δt) for each gas sampling event. This duration was chosen based on preliminary linearity tests, which showed that both N₂O and CO₂ accumulation remained linear over 0–6 h (R² > 0.95 for both gases). At 0, 2, and 4 h after sealing, 20 mL of headspace gas was extracted using a 25 mL gas-tight syringe (Hamilton, Company, Reno, NV, USA). The 20 mL sample volume represented less than 2% of the total headspace volume (>900 mL), minimizing pressure disturbance. A 10 mL aliquot of each gas sample was then injected into a gas chromatograph (GC-2014, Shimadzu Corporation, Kyoto, Japan) equipped with a ⁶³Ni electron capture detector (ECD) (Agilent Technologies, Santa Clara, CA, USA) for N₂O analysis and a flame ionization detector (FID) (Agilent Technologies, Santa Clara, CA, USA) with a methanizer for CO₂ analysis. The column and detector temperatures were set at 50 °C and 300 °C, respectively. High-purity N₂ was used as the back-flushing gas, while the carrier gas consisted of 95% argon and 5% methane at a flow rate of 25 mL min⁻¹. The N₂O and CO₂ emission rates (F) were calculated using the following equation [51]:

$$\mathrm{F}=\mathsf{\rho }\times \mathrm{V}/\mathrm{m}\times ∆\mathrm{C}/∆\mathrm{t}\times 273/(273+\mathrm{T})$$

(1)

where F is the emission rate (μg N₂O-N kg⁻¹ h⁻¹ or mg CO₂-C kg⁻¹ h⁻¹), $\mathsf{\rho }$ is the density of the corresponding gas at standard conditions (kg m⁻³), V is the jar volume (m³), m is the dry soil weight (kg), ΔC/Δt is the gas production rate during the 4 h closure period (μg kg⁻¹ h⁻¹ or mg kg⁻¹ h⁻¹), and T is the incubation temperature (25 °C). Cumulative gas emissions were calculated by integrating the emission rates over time using the trapezoidal integration method:

$$\mathrm{M}=\sum _{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{F}}_{\mathrm{i}}+{\mathrm{F}}_{\mathrm{i}+1})/2\times ({\mathrm{t}}_{\mathrm{i}+1}-{\mathrm{t}}_{\mathrm{i}})\times 24$$

(2)

where M is the cumulative emission (μg N₂O-N kg⁻¹ or mg CO₂-C kg⁻¹), Fᵢ is the emission rate at sampling time i, and t_i+1 − t_i is the time interval between sampling days. A blank jar (without soil) was included as a control to correct for ambient gas concentrations. All treatments were sampled in triplicate.

2.3.3. Determination of Soil Enzyme Activities

The four soil enzyme activities investigated were β-1,4-glucosidase (BG), N-acetyl-β-D-glucosaminidase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (AP) following [52,53]. BG belongs to the group of carbon-acquiring enzymes; NAG and LAP are N-acquiring enzymes, and AP is a phosphorus-acquiring enzyme. Soil samples (2.75 g) were suspended in 91 mL of sodium acetate buffer at pH 5.0 (50 mmol L⁻¹) in a 250 mL beaker and shaken vigorously for 30 min at 210 rpm and 25 °C. Afterward, 200 µL of substrate solution (200 µmol L⁻¹) and 800 µL of the soil extract were placed in each well of a 96-well black microplate. The substrates used for enzyme analysis were as follows: 4-methylumbelliferyl-β-D-glucoside for BG, 4-methylumbelliferyl-N-acetyl-β-D-glucosaminide for NAG, 4-methylumbelliferyl-phosphate for AP, and L-leucine-7-amino-4-methylcoumarin for LAP. As part of calibration, 200 µL of 4-methylumbelliferone (MUB) standard solutions (0–200 µmol L⁻¹) and 800 µL of soil suspensions were added to the corresponding wells of the microplate. The microplates were then tightly covered and incubated at 25 °C with continuous shaking for 4 h. The incubation time was selected based on preliminary investigation demonstrating that product formation remained linear within 2–6 h (all R² > 0.98). Next, the microplates were centrifuged at 4800 rpm for 3 min at 4 °C. From each well, 250 µL of supernatant was carefully transferred to the wells of another plate. Finally, fluorescence intensity was measured at excitation/emission wavelengths of 365 nm/450 nm using a microplate reader (Synergy HTX, BioTek Instruments, Winooski, VT, USA). All enzymatic activities were expressed as nmol g⁻¹ h.

2.3.4. Determination of AOA and AOB Gene Abundances

DNA was extracted from soil samples (0.5 g) using the FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s instructions. DNA quality and concentration were assessed using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA), and DNA integrity was confirmed by 1% agarose gel electrophoresis.

The AmoA gene copy numbers were quantified using quantitative real-time PCR (qPCR) analysis on an ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The primer sets used were AOA-23F/AOA-616R for AOA-amoA [54] and AOB-1F/AOB-2R for AOB-amoA [55]. The reactions were carried out in a 20 µL reaction mixture containing 10 µL of SYBR Green Premix Ex Taq II (Takara, Kusatsu, Shiga, Japan), 0.4 µL of each primer (10 µmol L⁻¹), 0.4 µL of ROX reference dye, 2 µL of DNA template (10 ng µL⁻¹) and 6.8 µL of RNase-free water. A total of 35 thermal cycles was performed; each consisting of denaturation at 95 °C for 30 s, annealing and elongation at 60 °C for 34 s, and extension at 72 °C for 30 s. The post-amplification step consisted of melting curve analysis (60–95 °C, with increments of 0.5 °C every 5 s).

Quality control and quantification were achieved through the generation of standard curves based on serial ten-fold dilutions (10¹–10⁷ copies µL⁻¹) of plasmid DNA containing amoA gene fragments. Plasmids carrying the amoA gene fragment were generated by cloning PCR products (629 bp for AOA-amoA; 491 bp for AOB-amoA) into the pMD19-T vector (Takara, Japan). Escherichia coli DH5α competent cells were transformed and positive clones were confirmed by Sanger Sequencing. Plasmid DNA was extracted using a Plasmid Mini Kit (Omega Bio-Tek, Norcross, GA, USA). Gene copy numbers were computed based on plasmid DNA concentration and molecular weight.

For AOA-amoA, the standard curve showed an R² value of 0.998 and amplification efficiency of 92.3% (slope = −3.52). For AOB-amoA, the R² value was 0.997 with an amplification efficiency of 95.1% (slope = −3.45). Melting curve analysis showed single peaks at 84.5 °C (AOA-amoA) and 86.2 °C (AOB-amoA), indicating high primer specificity. Cycle threshold (Ct) values ranged from 18 to 35 cycles. All reactions were performed in duplicate, and no-template controls (NTCs) were conducted to detect contamination. Gene copy numbers were reported in log₁₀ (copies g⁻¹ dry soil).

2.4. Statistical Analysis

The experimental data were organized using Microsoft Excel (version 2020), and statistical analyses were performed using SPSS version 18.0. All data were expressed as means ± standard error (SE). The Shapiro–Wilk test was performed to assess the normality of the data. Several variables did not meet the normality assumption despite the application of a square root transformation (Table S1). Nevertheless, homogeneity of variance was confirmed for all variables (Levene’s test, p > 0.05). Since ANOVA can tolerate moderate violations of normality, particularly in balanced experimental designs, the original data were used for further analysis. Data were analyzed using repeated measures ANOVA (mixed-effects model) to evaluate the effects of treatments over time. Post hoc comparisons were conducted using the least significant difference (LSD) test at a significant level of p < 0.05. Origin 2021 software (version 2021) was used to generate the graphs, and RStudio (version 4.5.2) was used to perform correlation analysis.

3. Results

3.1. Soil pH and EC

There was a significant influence of SOM on soil pH and EC (p < 0.001; Figure 1). Overall, soils with lower SOM content tended to exhibit higher soil pH, while soils with high SOM had higher soil EC. The effects of the treatments were also significant for both soil pH and EC (p < 0.001). In comparison with urea alone (U), application of DCD (U_DCD) and U + DCD + low Cu (U_DCD_Cu1) was associated with an increase in soil pH by approximately 0.2 to 0.4 units, regardless of SOM content. At the same time, these treatments were associated with lower soil EC compared with U alone. This pattern may be related to changes in soil ionic composition associated with nitrification inhibition. The response of the treatment with the highest Cu concentration (U_DCD_Cu2) differed from the other treatments. This treatment caused a significantly lower soil pH compared with all other treatments, regardless of SOM content (p < 0.05). Moreover, U_DCD_Cu2 was associated with a substantial increase in soil EC.

Figure 1. Effects of urea, dicyandiamide (DCD), and copper (Cu) application on soil pH and electrical conductivity (EC) under soils with high and low soil organic matter (SOM) content. (a) cumulative soil pH, (b) cumulative soil EC, (c) temporal changes in soil pH during the incubation period, and (d) temporal changes in soil EC during the incubation period. Treatments included urea alone (U), urea + DCD (U_DCD), urea + DCD + low Cu (U_DCD_Cu1), and urea + DCD + high Cu (U_DCD_Cu2). Values represent mean ± standard error (SE), and error bars may be smaller than the symbols in some cases. * and *** indicate significant differences at p < 0.05 and p < 0.001, respectively. Different lowercase letters indicate significant differences among treatments according to Tukey’s HSD test at p < 0.05, whereas the same lowercase letters indicate no significant difference.

In soils with high SOM content, U_DCD_Cu2 showed markedly higher soil EC than other treatments, being 20–30% higher than U. In low-SOM soils, EC under U_DCD_Cu2 did not differ substantially from the other treatments, although a tendency toward increasing EC over time was observed. In addition to differences in the magnitude of pH and EC between the treatments, temporal changes over the 30-day incubation period revealed specific patterns. U_DCD generally showed a consistent increase in soil pH under both SOM conditions, particularly during the early-mid-incubation periods. In contrast, U_DCD_Cu2 consistently exhibited the lowest soil pH throughout the experiment. The highest EC values were recorded under U_DCD_Cu2 during the incubation period, especially in soils with high SOM content, where soil EC remained above the initial level (day 0).

3.2. Soil Extractable NH₄⁺-N and NO₃⁻-N Concentrations and Their Ratio

There was a significant impact of SOM on the extractable concentration of NH₄⁺-N, NO₃⁻-N, and NO₃⁻/NH₄⁺ ratio in all tested conditions (p < 0.001; Figure 2). Generally, soils with high SOM tended to have higher NH₄⁺-N concentration (about 10–25%) compared with soils with low SOM; in contrast, soils with low SOM exhibited higher NO₃⁻-N concentration (by about 15–30%). The effects of different treatments were statistically significant for all parameters (p < 0.001). In both SOM soils, the urea-only application resulted in the highest NO₃⁻-N concentration and NO₃⁻/NH₄⁺ ratio, suggesting relatively higher nitrification activity. The treatment with DCD resulted in significant decreases of NO₃⁻-N concentration (by 40–60%) and the NO₃⁻/NH₄⁺ ratio, along with increased NH₄⁺-N concentrations compared with treatment U (p < 0.05), indicating effective nitrification inhibition. The presence of Cu in treatments with DCD also influenced these patterns. In high-SOM soils, the lowest NO₃⁻-N concentration and NO₃⁻/NH₄⁺ ratio were observed in the U_DCD_Cu1 treatment. By contrast, in low-SOM soils, the effect of Cu addition appeared to be less pronounced for NH₄⁺-N and NO₃⁻-N concentration but was associated with a gradual increase in the NO₃⁻/NH₄⁺ ratio (p < 0.05) as Cu concentration increased.

Figure 2. Effects of urea, dicyandiamide (DCD), and copper (Cu) application on soil extractable ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and their ratio (NO₃⁻/NH₄⁺) under soils with high and low soil organic matter (SOM) content. (a) cumulative NH₄⁺-N, (b) cumulative NO₃⁻-N, (c) NO₃⁻/NH₄⁺ ratio, (d) temporal changes in NH₄⁺-N, (e) temporal changes in NO₃⁻-N, and (f) temporal changes in NO₃⁻/NH₄⁺ ratio during the incubation period. Treatments included urea alone (U), urea + DCD (U_DCD), urea + DCD + low Cu (U_DCD_Cu1), and urea + DCD + high Cu (U_DCD_Cu2). Values represent mean ± standard error (SE). Different lowercase letters indicate significant differences among treatments within each soil type (p < 0.05). *** indicates significant differences at p < 0.001.

In all soils, NH₄⁺-N concentrations were generally higher under treatments U and U_DCD than under treatments with Cu additions (U_DCD_Cu1 and U_DCD_Cu2); however, there was no significant difference between the Cu concentrations. This suggests that Cu may partially counteract the effect of DCD on nitrification processes. During the 30-day incubation, all parameters showed trends consistent with the observations described above. There were consistently high NO₃⁻-N concentrations in soil under treatment U, whereas DCD-treated soils generally exhibited lower NO₃⁻-N concentrations. At the same time, NO₃⁻-N concentration tended to increase over time in soils treated with Cu additions, particularly under low SOM conditions.

3.3. Soil N₂O and CO₂ Emissions

The impact of SOM on the cumulative emissions of CO₂ was significant (p < 0.001), while its impact on the cumulative emissions of N₂O was not significant (p = 0.890); however, soil × treatment interaction was found to be significant for both gases (p < 0.001; Figure 3). Cumulative emissions of CO₂ under high SOM were substantially higher than under low SOM (approximately 2–3 times higher). In contrast, cumulative emissions of N₂O were generally higher under low SOM. Significant differences in emissions were noted among treatments for both N₂O and CO₂ (p < 0.001). Under high SOM conditions, the highest emissions of CO₂ were noted with treatment U, while emissions were reduced by approximately 5–10% following DCD addition (U_DCD). With further addition of Cu (U_DCD_Cu1 and U_DCD_Cu2), CO₂ emissions tended to decrease further, with the lowest values observed under U_DCD_Cu2 (about 10–15% lower than U; p < 0.05).

Figure 3. Soil N₂O and CO₂ emissions rate as affected by applications of urea, DCD, and Cu under soil with high and low SOM. (A) Soil CO₂ emissions rate with different times during the incubation ratio, (B) Soil N₂O emissions rate with different times during the incubation, (C) Accumulative data of soil CO₂ emissions rate, and (D) Accumulative data of soil N₂O emissions rate. Values represent mean ± standard error (SE). Error bars are shown for all measurements; however, in some cases, they may be smaller than the symbols due to low variability among replicates. Different lowercase letters indicate significant differences among treatments (LSD test, p < 0.05). Statistical results for soil type, treatment, and their interaction are presented within the panels. *** indicates significant differences at p < 0.001.

With regard to N₂O emissions, a DCD application was associated with reduced emissions compared with U under high and low SOM levels. However, the addition of Cu to DCD was associated with an increase in N₂O emissions, with emissions tending to increase progressively (p < 0.05) as Cu concentration increased from U_DCD to U_DCD_Cu2. A similar pattern was observed under low SOM conditions, U_DCD showed the lowest N₂O emissions, while Cu addition (especially U_DCD_Cu2) was associated with higher emissions. These observations suggest that, under low SOM conditions, Cu addition may reduce the inhibitory effect of DCD on N₂O emissions. Moreover, cumulative N₂O emissions were consistently higher under low SOM compared with high SOM conditions. In contrast, CO₂ emissions under low SOM showed a different pattern, with the highest emissions observed under U_DCD and the lowest under U_DCD_Cu2. No significant difference was found between U and U_DCD_Cu1, suggesting that the effect of Cu on CO₂ emissions under low SOM was less pronounced than under high SOM. As seen in temporal patterns, emissions of CO₂ under high SOM were highest in the initial measurement period and gradually decreased over time. On the other hand, N₂O emissions exhibited different temporal patterns depending on SOM conditions.

3.4. Soil Enzymatic Activities

SOM had a significant effect on soil enzyme activities, including BG, NAG, AP, and LAP; all activities increased significantly in soils with high organic matter content compared to those with low organic matter content (p < 0.001; Figure 4). The effects of treatments were significant for all studied enzymes under both high and low SOM conditions (p < 0.001). The treatment combining DCD with Cu (U_DCD_Cu1) was associated with the highest activities of BG, NAG, and LAP under high-SOM conditions, increasing enzyme activities by approximately 15–30% compared to the urea-only treatment. The lowest enzyme activities under high-SOM conditions were observed in the U treatment, where no nitrification inhibitor was applied. In low-SOM soils, a similar trend was observed, with U_DCD_Cu1 showing the highest activities of BG, NAG, and LAP. In the case of AP, the highest activity was observed under the U_DCD_Cu2 treatment, while the lowest activities of BG, NAG, and LAP under low-SOM conditions were observed under U and U_DCD treatments, respectively.

Figure 4. Effects of urea, dicyandiamide (DCD), and copper (Cu) application on soil enzyme activities under soils with high and low soil organic matter (SOM) content. (a–d) cumulative enzyme activities of β-glucosidase (BG), N-acetyl-β-D-glucosaminidase (NAG), acid phosphatase (AP), and leucine aminopeptidase (LAP), respectively; (e–h) temporal changes in BG, NAG, AP, and LAP activities during the incubation period. Treatments included urea alone (U), urea + DCD (U_DCD), urea + DCD + low Cu (U_DCD_Cu1), and urea + DCD + high Cu (U_DCD_Cu2). Values represent mean ± standard error (SE). Different lowercase letters indicate significant differences among treatments within each soil type (p < 0.05). *** indicates significant differences at p < 0.001.

Regardless of soil type, the effect of DCD alone on enzyme activities appeared to be less pronounced compared to the DCD–Cu combinations. These patterns suggest that Cu addition may enhance microbial activity associated with enzyme production. Enzyme activities increased over time, with different temporal dynamics under both high- and low-SOM conditions. The highest enzyme activities were observed during the first half of the incubation period, followed by a declining trend thereafter. The highest activities at any point during incubation were generally observed under the U_DCD_Cu1 treatment.

3.5. AOA-amoA and AOB-amoA Gene Abundance

The presence of SOM had a significant effect on the abundance of both amoA-archaea (AOA-amoA) and amoA-bacteria (AOB-amoA) genes (p < 0.001; Figure 5). In general, soils with lower SOM tended to exhibit higher abundances of these genes compared with high SOM samples. Similarly, treatment effects were also statistically significant for both AOA and AOB abundances (p < 0.001). In soils with high SOM, the highest abundances of AOA-amoA and AOB-amoA were recorded under urea alone (U), while the lowest abundances were recorded under U_DCD_Cu2, which resulted in a reduction of approximately 31–49% compared to U (p < 0.05), suggesting inhibitory effects on ammonia oxidizers under DCD and high Cu levels. However, in soils with low SOM, AOA and AOB showed different responses to treatment. The abundance of AOA-amoA tended to increase with Cu addition (U_DCD_Cu1 and U_DCD_Cu2), with the highest values being found in the latter case. In contrast, AOB-amoA abundance showed limited variation among treatments, although the highest values were observed under U, and DCD-containing treatments were associated with lower AOB-amoA abundance, particularly when combined with Cu (p < 0.05). As expected, in soils with both high and low SOM contents, the application of DCD (either without or with Cu) was associated with reduced AOB abundance compared with U. However, AOA responded differently depending on SOM level, with Cu application associated with increased AOA abundance in soils with low SOM, but reduced abundance in soils with high SOM, along with the DCD application. In low SOM soils, there were no significant differences in AOA abundance between U_DCD_Cu1 and U_DCD_Cu2 samples, suggesting a limited dose-dependent response to Cu under these conditions.

Figure 5. Effects of urea, dicyandiamide (DCD), and copper (Cu) on the abundance of ammonia-oxidizing archaea (AOA-amoA) and ammonia-oxidizing bacteria (AOB-amoA) in soils with high and low soil organic matter (SOM) content. The figure shows the gene abundance under different treatments in both soils. Cumulative abundances of AOA (a) and AOB (b). Temporal changes in the abundance of AOA (c,e) and AOB (d,f) during the incubation period in soils with low and high organic matter contents, respectively. Urea, urea + DCD, urea + DCD + low Cu, and urea + DCD + high Cu were used in both soils as treatments. The results are shown as mean ± SE. Different lowercase letters indicate significant differences between treatments within each soil group (p < 0.05). * and *** indicate significant differences at p < 0.05 and p < 0.001, respectively.

3.6. Relationships Among Studied Variables

The relationships between the variables under high and low SOM content are shown in Figure 6 and Tables S2 and S3. Different correlation relationships were observed for these soils, suggesting SOM-dependent differences in N transformations and microbial activities. Soil pH under high SOM content showed a positive relationship with CO₂ emissions (r = 0.608, p < 0.05) and a negative relationship with N₂O emissions (r = −0.734, p < 0.01). These relationships may reflect links between soil pH, microbial activity, and gaseous emissions. Soil NH₄⁺ concentration was strongly negatively correlated with NO₃⁻ (r = −0.827, p < 0.001) and positively correlated with enzyme activities such as BG (r = 0.678, p < 0.05) and AP (r = 0.734, p < 0.01), suggesting an association between N availability and microbial activity. Soil NO₃⁻ concentration was positively correlated with the abundance of AOB (r = 0.978, p < 0.001) and CO₂ emissions (r = 0.704, p < 0.05), indicating a potential linkage with nitrification-related processes and microbial respiration. The BG activity was negatively correlated with AOA abundance (r = −0.825, p < 0.001).

Figure 6. Pearson correlation analysis of soil properties, enzyme activities, and nitrification-related indicators under soils with high and low soil organic matter (SOM) content, generated using R software (version 4.5.2). Color gradients from red to blue represent positive to negative correlations, respectively, for high SOM (a) and low SOM (b) soils. Asterisks indicate statistical significance at * p < 0.05, ** p < 0.01, and *** p < 0.001.

In soils with low SOM content, the soil pH showed a positive correlation with NH₄⁺ concentration (r = 0.732, p < 0.01) and CO₂ emissions (r = 0.868, p < 0.001) and a negative correlation with N₂O emissions (r = −0.902, p < 0.001) and AOA abundance (r = −0.718, p < 0.01). Soil NH₄⁺ concentration showed negative relationships with NO₃⁻ (r = −0.980, p < 0.001), AOA (r = −0.986, p < 0.001), and AOB abundance (r = −0.765, p < 0.01), suggesting an association between NH₄⁺ availability and nitrification-related processes. Soil NO₃⁻ concentration revealed strong positive relationships with the abundance of AOB (r = 0.843, p < 0.001) and NO₃⁻/NH₄⁺ ratio (r = 0.980, p < 0.001). It should be noted that enzyme activities were affected by SOM differently. In case of low SOM, BG activity showed a positive correlation with the AOA/AOB ratio (r = 0.959, p < 0.001), and negative relationships with NH₄⁺ (r = −0.765, p < 0.01), NO₃⁻ (r = −0.627, p < 0.05), and AOB abundance (r = −0.750, p < 0.01). Similarly, AP activity had a positive relationship with the AOA/AOB ratio (r = 0.597, p < 0.05) and N₂O emissions (r = 0.730, p < 0.01), and a negative relationships with NH₄⁺ (r = −0.980, p < 0.001), CO₂ (r = −0.912, p < 0.001), and pH (r = −0.739, p < 0.01). NAG activity had positive relationships with AOA abundance (r = 0.621, p < 0.05) and the AOA/AOB ratio (r = 0.758, p < 0.01).

4. Discussion

As the primary source of N and the main component of plant nutrients, SOM is a key regulator of nitrification through its influence on substrate accessibility, composition of the microbial community, and soil physicochemical properties [56,57,58]. Based on the results obtained, an accumulation of NH₄⁺ ions and a simultaneous reduction of NO₃⁻ ions under conditions of high SOM indicate an inhibition of nitrification, whereas opposite trends are associated with an increase in the rate of nitrification. It should be noted that these patterns are not solely substrate-driven and may also reflect biological regulation, as higher abundances of AOA and AOB were observed under low SOM conditions. The negative correlation between NH₄⁺ and NO₃⁻, along with the positive one between NO₃⁻ and AOB abundance, indicates a close association between nitrification processes and NH₃-oxidizing microorganisms [59,60].

On a mechanistic level, SOM is an environmental factor affecting nitrification through several interconnected processes. Specifically, high SOM content is associated with increased microbial activity and the production of extracellular enzymes, such as BG, NAG, AP, and LAP, which showed positive correlations with NH₄⁺-N concentrations and negative correlations with NO₃⁻-N concentrations [61,62,63,64,65]. This suggests that immobilization and biochemical transformations of organic substrates favor the reduced form of N [3,66,67]. Secondly, SOM-related acidification may contribute to reduced activity of AOB, which are known to be sensitive to lower pH levels [68,69,70,71]. And thirdly, SOM can act as a competitive ligand for Cu ions, potentially reducing Cu available as a cofactor for AMO [9,72,73,74]. Thus, these findings suggest that SOM may regulate nitrification not only through direct effects on substrate availability and microbial activity, but also through modulation of metal–enzyme interactions that influence nitrification intensity [66,67,68,69,70,71,72,73,74,75]. The link between nitrification suppression and SOM is described earlier [75,76]; however, unlike previous works, the presented results provide insights into the potential underlying mechanisms.

One of the key mechanisms of the discussed phenomena may involve the interplay between DCD efficiency and Cu availability. Under low SOM conditions, low concentrations of Cu resulted in the improvement of the inhibitory impact of DCD on nitrification. It was demonstrated by a decrease in NO₃⁻ concentration and retention of more NH₄⁺. In other words, under low SOM, there is a synergic limitation of activity of ammonia-oxidizing microorganisms due to Cu and DCD action [14,72,73,77]. The effectiveness of this effect is supported by the observed decrease in AOB abundance in this experiment. Thus, Cu, being an inhibitor of nitrification in small quantities [18,22], turns out to be an enhancement factor for DCD under the specified conditions. The results suggest that Cu may act as an additional limiting factor, particularly under conditions of low SOM.

The described pattern was less pronounced under conditions of high SOM, which may be related to the intense binding of Cu by SOM, potentially reducing its bioavailability and limiting its interaction with DCD [72,73]. The presented results suggest that SOM may influence the efficiency of inhibition not only through direct biochemical mechanisms, but also via metal speciation. In this context, SOM may exert a dual role in regulating nitrification by acting as both a limiting and modulating factor [66,67,75,76].

High Cu concentrations result in decreased efficiency of DCD on nitrification under conditions of low SOM. At the same time, the decrease in the efficiency of the inhibitor may be associated with changes in the sensitivity of the AOB to Cu or partial restoration of the enzymatic activity of AMO [22,44,74]. Additionally, the observed reduction in enzyme activity and microbial indicators may be associated with the toxic effects of Cu on soil microbiota [78,79,80]. Therefore, a transition from Cu limitation at low concentrations to Cu toxicity is one of the factors determining the response of nitrification to Cu.

The influence of Cu on N₂O emissions revealed additional aspects of the problem. Similar to its effect on nitrification, DCD reduced N₂O emissions due to limiting substrate production [33,41]. However, the increase in Cu concentration was associated with an increase in N₂O emissions. Given that the content of denitrification functional genes was not measured, it can be hypothesized that Cu may influence the process at different steps. Since Cu is one of the essential components of N₂O reductase, Cu deficiency or toxicity may inhibit the last stage of denitrification [81]. In this context, the observed increase in N₂O emissions may be attributed to the decoupling of these processes. Additionally, incomplete inhibition of nitrification results in the accumulation of substrates, such as nitrite ions (NO₂⁻) and hydroxylamine (NH₂OH), causing an increase in N₂O emissions [22].

Unlike N₂O, CO₂ emissions showed a significant dependence on Cu concentrations. Specifically, DCD reduced CO₂ emissions, and its impact on this process increased with a rise in Cu concentration. It was found that a high concentration of this metal suppressed microbial activity, reducing the rate of microbial respiration [78,82]. The positive correlation between CO₂ emissions and enzyme activities suggests that the process is determined by microbial activity. Thus, under high Cu content, microbial activity was decreased, and the corresponding reduction in emissions occurred [22,78,82]. The results suggest that Cu may inhibit nitrification and potentially affect denitrifying microorganisms, which could contribute to increased N₂O emissions.

From the general viewpoint, the results indicate that the efficiency of NIs is greatly dependent on soil biochemical properties, and should not be assessed without taking into account SOM content and the availability of various elements. These findings suggest that a “one-size-fits-all” approach to N management in agriculture may be hardly applicable, given its strong dependence on site-specific soil and environmental conditions. To obtain maximum efficiency and minimize greenhouse gas emissions, it is necessary to conduct a site-specific assessment of SOM content and metal availability and microbial community composition.

Within this framework, the hypotheses proposed in this study can be critically evaluated. The first hypothesis, that Cu availability regulates nitrification in a concentration-dependent manner and consequently affects DCD inhibitory efficiency, is supported. The results indicate that Cu likely exerts a concentration-dependent effect, where low Cu enhanced DCD efficiency under low SOM conditions, while high Cu reduced its inhibitory effect, indicating that Cu availability could modulate the nitrification process and the performance of DCD. The second hypothesis, that SOM regulates Cu–DCD interaction through its dual role in modulating Cu availability and soil biochemistry, is supported by the observed trends. Our findings indicate that SOM may influence nitrification through microbial and enzymatic pathways, while also affecting Cu bioavailability via complexation, thereby modulating the interaction between Cu and DCD. This dual mechanism explains the contrasting responses observed between high and low SOM soils. The third hypothesis, that the interaction between Cu and DCD affects N transformations and gaseous emissions, is supported, although further evidence would be needed for full mechanistic confirmation. The combined effects of Cu and DCD significantly altered NH₄⁺ and NO₃⁻ dynamics, as well as N₂O and CO₂ emissions, suggesting that their interaction could play an important role in regulating nitrification processes and associated greenhouse gas fluxes.

These results have several implications for improving N management practices from a sustainability standpoint. First, while the application of NIs such as DCD has shown promise in mitigating greenhouse gas emissions, their effectiveness may vary depending on soil conditions, specifically SOM content and Cu availability. The findings indicate the importance of site-specific soil management approaches, including the adjustment of nutrient inputs based on soil conditions. For example, in soils with a low level of organic matter, an improvement in the efficiency of DCD by manipulating Cu concentration could enhance N utilization and minimize the loss of NO₃⁻, thereby preventing pollution. On the other hand, for soils with high SOM concentrations, the effectiveness of DCD in reducing greenhouse gas emissions may be limited due to enhanced microbial activity and substrate availability. Second, the study underscores the importance of considering multiple effects of various soil management practices. While the application of DCD and Cu significantly reduces CO₂ emissions due to the inhibition of microbial activity, it can also negatively impact the ability of DCD to suppress N₂O emissions, creating a trade-off between two processes. The findings suggest that soil management policies must consider multiple factors to ensure sustainability. Lastly, the research suggests that the concentration of trace metals and SOM in soil can affect the effectiveness of NIs and minimize soil NO₃⁻ losses.

While this study provides important insights, several limitations should be acknowledged. For example, the limited data on the speciation of Cu in soil hinders a more mechanistic approach to understanding the interactions between Cu bioavailability and DCD under varying levels of SOM. Future studies could therefore focus on characterizing Cu fractionation (e.g., exchangeable and organic-bound forms) and its dynamic interactions with NIs. Furthermore, it is important to point out that the two soils used in the experiment were sourced from different sites, and in addition to contrasting SOM contents, differed in other physicochemical properties, such as CEC and NO₃⁻-N content. Therefore, the observed differences may reflect the combined effects of SOM and inherent soil properties, making it difficult to fully isolate the effects of SOM alone. In addition, the absence of Cu only and U + Cu control treatments represents a limitation of the experimental design, as it constrains the ability to isolate the independent effects of Cu on N transformations. Thus, the reported response was mainly related to the interaction of Cu with DCD rather than the independent action of added Cu. Former studies indicated the independent influence of Cu on the nitrification process and microbial activity [21,22]; therefore, the lack of these control treatments may influence the interpretation of the results obtained in this study, especially when discussing the impact of Cu-DCD interactions on N transformations and gaseous fluxes.

5. Conclusions

This study suggests that Cu availability may play an important role in modulating the efficiency of DCD as an NI, particularly in interaction with SOM. The results indicate that under low SOM conditions, low Cu availability was associated with enhanced DCD performance in suppressing nitrification, whereas increasing Cu concentrations tended to reduce its inhibitory effect, suggesting a concentration-dependent and soil-specific response. In contrast, under high SOM conditions, the influence of Cu on DCD efficiency appeared to be less pronounced, likely due to reduced Cu bioavailability associated with SOM complexation. The interaction between Cu and DCD also appears to influence gaseous emissions. While DCD generally reduced N₂O emissions, the addition of Cu may diminish this mitigation effect under both SOM conditions, suggesting that Cu availability may influence processes controlling N₂O production and reduction. In contrast, CO₂ emissions were generally reduced under DCD application and appeared to be further suppressed at higher Cu levels, which may be associated with reduced microbial activity. Overall, these findings suggest that the effectiveness of DCD may not be fixed but may depend on soil chemical conditions, particularly SOM content and Cu availability. This suggests that soil-specific management strategies may be needed to better optimize the use of NIs for improving N use efficiency and reducing environmental impacts. However, it is important to note that this study was conducted under controlled incubation conditions. Factors such as soil moisture dynamics, temperature fluctuations, plant–soil interactions, and root activity in field environments may influence Cu availability, microbial processes, and the performance of DCD. Therefore, these findings should be interpreted with caution, and further studies under field-based systems are needed to validate these findings.