Intermediate soil acidification induces highest nitrous oxide emissions

Domain: earth_science
Published: 2024-03-27
Editorial outcome: minor_revision

Abstract
Global potent greenhouse gas nitrous oxide (N2O) emissions from soil are accelerating, with increases in the proportion of reactive nitrogen emitted as N2O, i.e., N2O emission factor (EF). Yet, the primary controls and underlying mechanisms of EFs remain unresolved. Based on two independent but complementary global syntheses, and three field studies determining effects of acidity on N2O EFs and soil denitrifying microorganisms, we show that soil pH predominantly controls N2O EFs and emissions by affecting the denitrifier community composition. Analysis of 5438 paired data points of N2O emission fluxes revealed a hump-shaped relationship between soil pH and EFs, with the highest EFs occurring in moderately acidic soils that favored N2O-producing over N2O-consuming microorganisms, and induced high N2O emissions. Our results illustrate that soil pH has a unimodal relationship with soil denitrifiers and EFs, and the net N2O emission depends on both the N2O/(N2O+N2) ratio and overall denitrification rate. These findings can inform strategies to predict and mitigate soil N2O emissions under future nitrogen input scenarios.

Introduction
Nitrous oxide (N2O) is the dominant anthropogenic ozone-depleting substance and is also a long-lived potent greenhouse gas. It has a global warming potential about 265–298 times that of carbon dioxide (CO2) and contributes approximately 7% to the overall global warming. Although the N2O concentration in the atmosphere is low at ca. 330 ppb, it is increasing at an accelerating rate of ca. 0.75~1.0 ppb per year because human activities have greatly increased the input of reactive nitrogen (N) in the environment. Agricultural N fertilization, in particular, dominates human-induced N2O emissions. Since the proportion of reactive N (Nr) emitted as N2O (i.e., the emission factor, EF) is relatively stable in neutral soils, the rate of fertilizer N applied has been considered a robust predictor of N2O emission. Therefore, the Intergovernmental Panel on Climate Change (IPCC) uses 1% as the default EF of soils at pH of 6.76 (i.e., IPCC default Tier-1) in estimating N2O emissions. However, both process-based models and atmospheric inversion studies have recently demonstrated that N2O EFs have significantly increased, which reflects accelerating global N2O emissions in recent decades. This suggests that N-application rates are not reliable predictors of N2O emissions. Increases in N2O EFs have been attributed to the non-linear response of soil N2O emissions to N input, building on the premise that high N input exceeds plant N needs and leads to surplus N for microbial N2O production. Nitrogen applications further induce a higher proportion of N losses via N2O in acidic soils and it is well established that acidity (pH < 5.0) in soil increases the product ratio of [N2O/(N2O+N2)] during denitrification. One hypothesized explanation is that pH interferes with the assembly of the N2O reductase. However, it was also recently shown that soil pH only exerts a control of denitrification product ratio in fertilized soils, while in unfertilized soils, biological controls were more important. Despite increases in the N2O/(N2O+N2) product ratio of denitrification at low pH, N2O emissions are often low under acidic conditions because acidity suppresses microbial processes that generate N2O. In general, raising soil pH through liming to near-neutral level (pH > 6.5) reduces N2O emissions, but raising pH in acidic soils (pH < 5.6) to moderately acidic levels (pH = 5.6–6.0) often increases N2O emissions. Taken together, these results suggest that soil pH exerts a critical, nonlinear control over N2O emissions, highlighting the urgency for a comprehensive, mechanistic understanding of pH effects on soil microorganisms and microbial processes that modulate N2O dynamics.

Soil N2O emissions originate mainly from two microbial processes, ammonia oxidation being the first step in nitrification, and denitrification, which is the reduction of nitrate to gaseous N. Although ammonia oxidation by ammonia-oxidizing archaea (AOA) and bacteria (AOB) control the rate-limiting step of nitrification in most terrestrial ecosystems, denitrification plays a more important role in soil N2O emissions. Since the denitrification process is modular with varying genetic capacities for the different reductive steps in the denitrification pathway among denitrifying microorganisms, the composition of the denitrifying community will control N2O emissions. Of special concern is the proportion of the denitrifying community harboring the nosZ gene coding for the N2O reductase that converts N2O to N2 as it is the only known sink for N2O in the biosphere. There are two phylogenetically distinct clades in the nosZ phylogeny: nosZI and the recently described nosZII. Non-denitrifying N2O reducers which often possess nosZII. The ratio of denitrification genes, especially nirK and nirS encoding the known nitrite reductases involved in denitrification, to the nosZ gene abundance is often used as an indication of soil N2O emissions, but its relationship with soil pH remains largely unexplored.

There is a lack of a unifying, conceptual framework of soil pH impacts on denitrifying microorganisms and N2O EFs, which critically limits our capacity to predict and mitigate N2O emissions. Here, we address this knowledge gap with two comprehensive, global meta-analyses of N2O emission fluxes and EFs in 539 fertilization experiments and of the relationships between soil pH, denitrification gene abundance estimates, and N2O flux data based on 289 field studies. In addition, three field experiments with acid additions were analyzed to further evaluate the effects of manipulating soil acidity to identify relationships between soil pH and N2O EFs and disentangle the linkages among soil pH, community composition, and activities of denitrifying microorganisms, and N2O EFs.

Results
We first investigated how soil N2O EFs related to soil pH and the quantity of N input via fertilization by conducting a meta-analysis based on 539 field fertilization experiments, including 5438 observations of N2O emission fluxes and 3786 EFs records. Data was collected from experiments distributed among croplands, grasslands, and forests across the globe, published between 1980 and 2019.

The soil pH in the field sites covered a range from 2.8 to 9.7, with 58% having a pH value of 5.5–7.5. The highest N2O EFs mainly occurred in weak to moderately acidic soils (pH of 5.6–6.5), with an average EF of 1.2%. While there was a weak but statistically significant, linear relationship between pH and N2O EFs, this regression only explained 2.0% of the variation in EFs. Soil N2O EFs had a hump-shaped relationship with soil pH, which reached its maximum at pH 5.6, and explained 4.0% of the variation in N2O EFs. However, once N2O EFs were averaged across soil pH in increments (0.1 each), the hump-shaped relationship became markedly more apparent and reached its maximum at pH 6.0 and explained 56% of the variation. These results suggest that interactions between EF and pH diverge around a pH threshold of 5.6–6.0. By contrast, there was no significant linear relationship between N2O EFs and the quantity of N input. Indeed, the averaged EFs gradually increased with N input and reached their highest around 500–600 kg N ha−1 (EF = 1.4%). However, the average EFs decreased and remained relatively low in studies with an N input over 600 kg N ha−1 (EF = 1.0%). These results are inconsistent with the common belief that high N input or soil N content induces high EFs and reconfirm that N quantity alone cannot sufficiently predict N2O EFs. Further, the N2O EFs were significantly higher in acidic tropical soils (pH = 5.5; EFs = 1.1%) than in neutral subtropical (pH = 6.7; EFs = 0.9%) and temperate (pH = 6.9; EFs = 0.8%) soils, despite significantly lower N input in tropical (170 kg N ha−1) than subtropical (223 kg N ha−1) and temperate (207 kg N ha−1) soils. Nevertheless, in tea plantations, all on acidic soils and with high N input (mean = 401 kg N ha−1), N2O EFs positively correlated with both soil pH and the quantity of N input, indicating that high acidity reduces N2O emissions. Additionally, our regression analysis showed that soil organic carbon (SOC) content was negatively correlated with soil pH, but SOC itself was not significantly related to N2O EFs, suggesting that SOC may only indirectly affect N2O EFs via soil pH. Moreover, although N2O EFs significantly correlated with mean annual precipitation (MAP), total soil nitrogen (TN), and sand and clay contents, these correlations only explained a low percentage (1–3%) of the variation in N2O EFs. Unlike the hump-shaped relationships observed between soil pH and EFs, our further analyses did not find any significant non-linear relations between N2O EFs and MAP, or sand and clay contents. There was a hump-shaped relationship between N2O EFs and TN, but it only explained 2% of the variation of N2O EFs. Taken together, these results indicate that although adequate N levels are required for N2O production, either by nitrification or denitrification, and that multiple soil and climatic factors may affect N2O emissions, soil pH exerts a dominant, non-linear control over N2O EFs.

To disentangle the potential microbial mechanisms governing effects of soil pH per se on N2O EFs, we conducted three field experiments in unfertilized grasslands in which acidity was manipulated. Since none of the experimental sites had received any significant reactive N input (neither N deposition nor N fertilizers), the selection pressure of human-derived N on soil N-cycling microorganisms was negligible. We examined how changes in soil pH (i.e., soil acidification) influenced soil available N, abundance of nitrifier and denitrifier functional groups, and soil N2O emission potential. These experiments were located in three grassland sites with different initial soil pH: a Tibetan alpine meadow (pH = 6.0) near Maqu County, Gansu Province, a Mongolian steppe (pH = 7.3) in the Xilin River Basin of Inner Mongolia, North China, and a Yellow Loess semi-arid grassland (pH = 8.0) near Guyuan, Ningxia in West China.

Acid addition consistently reduced soil pH, effectively generating a pH gradient at each site. Soil NH4+-N decreased, but NO3−-N increased with increasing soil pH. The abundances of AOA and AOB also increased with increasing soil pH across the three sites, indicating that soil acidification inhibited AOA and AOB, and nitrification. Similar to AOA and AOB, abundances of nirK-, nirS- and nosZI-type denitrifiers generally increased with soil pH at all three sites, although they were lower in the sandy, low-C Mongolian soil than other two sites. The nosZI-denitrifiers were relatively less sensitive to low soil pH than those with nirS or nirK, but were more abundant under high soil pH, particularly in the alkaline Loess soil. Soil pH significantly impacted N2O emissions, which were highest in weakly to moderately acidic soils (pH = 5.6–6.3). Across the pH gradients at the three sites, we observed hump-shaped relationships between soil pH and the (nirK + nirS)/nosZI ratio, and N2O emissions, which both peaked at pH = 6.0.

We further quantified the potential denitrification activity in the grassland soils under non-limited N- or C-conditions. Incubations with and without addition of acetylene to block the conversion of N2O to N2 by N2O reductase allowed us to assess the potential N2O emission and the direct effect of soil pH on N2O reduction. Acid additions in the field experiments reduced the denitrification potential in acidic soils but increased it in alkaline soils, leading to the highest denitrification rates in neutral soils. As expected, the N2O/(N2O+N2) product ratio of denitrification decreased as soil pH increased. Similar to the relationship between soil pH and the denitrifier community composition, and N2O emissions, we observed a hump-shaped relationship between soil pH and potential denitrification. However, the pH optimum for potential denitrification (pH = 6.7) was higher than that detected for N2O emissions (pH = 6.0). As denitrification rates are often higher under neutral to weak alkaline conditions, this difference suggests that decreased pH may have contributed to relatively higher net N2O emissions by weakening the N2O sink strength. Collectively, results from the three field experiments provide direct evidence that soil pH modulates the strength of the soil as a N2O source or sink, mainly because weak to moderate soil acidity promoted N2O emissions through favoring N2O-producing (nirK over N2O-consuming denitrifiers, as well as suppressing reduction of N2O to N2.

To further examine the generality of the relationship between soil pH and the relative composition of the denitrifying microorganisms identified in our acidity manipulation experiments, we conducted a second global meta-analysis to examine the relationship between soil pH and the abundance of denitrification genes in 289 field studies. Our dataset covers 3899 gene abundance estimates paired with N2O flux data in croplands, grasslands, and forests. Since we only found nine studies with data on nosZ clade II combined with N2O emission data from field experiments, only nosZ clade I was considered in the following analyses. A positive relationship between soil N2O emissions and the (nirK + nirS)/nosZI ratio across the 289 studies was observed, underscoring the importance of the relationship between microbial sources and sinks for net N2O emissions. The meta-analysis largely supported our manipulation experiments by showing a hump-shaped (unimodal) relationship between soil pH and the abundances of nirK- and nirS-type denitrifiers, which reached their maximum at pH = 6.0–6.3 and pH = 6.3–6.8, respectively. However, soil pH was not significantly correlated with either the coarse or averaged abundance of nosZI. Consequently, the (nirK + nirS)/nosZI ratio also showed a hump-shaped relationship with soil pH, reaching its maximum at pH of 6.0–6.1. These results illustrate that weak to moderately acidic soils generally favor N2O-producing over N2O-consuming denitrifiers and induce high N2O emissions across the global scale.

Methods
The data collection and analysis followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We conducted an extensive search for studies of N fertilization and soil N2O emissions published between 1980 and 2019 through the Web of Science, Google Scholar, and the China Knowledge Resource Integrated Database. The keywords were used: (i) “nitrogen addition” OR “nitrogen deposition” OR “nitrogen amendment” OR “nitrogen fertilization”; (ii) “soil” OR “terrestrial”; and (iii) “N2O” OR “nitrous oxide”. We also extracted data and re-evaluated all studies from the databases published by Stehfest and Bouwman, Liu and Greaver, Shcherbak et al., Liu et al., Wang et al., Charles et al., Deng et al., Maaz et al., Cui et al., and Hergoualc’h et al. In order to avoid selection bias, we extracted peer-review publications with the following criteria: (a) only field studies in which the control and N fertilization treatment sites were located under the same climate, vegetation and soil conditions were included; (b) only chamber-based field experiments conducted in croplands, forests and grasslands were included; (c) studies using nitrification inhibitors were excluded. This yielded a dataset of 5438 observations of N2O emission fluxes from 539 field studies that spanned 42 countries and 570 sites. Experiments were grouped into three regions based on absolute latitude: tropical (23.4°S–23.4°N), subtropical (23.4–35.0°S or °N), and temperate (>35.0°S or °N). For each study, soil properties (i.e., pH, clay, silt and sand content, organic carbon, and total nitrogen) and climate (i.e., mean annual precipitation (MAP) and temperature (MAT)) were directly obtained either from texts and/or tables or extracted from figures using the GetData Graph Digitizer software.

Nitrogen fertilization rates and soil N2O emissions obtained from the literature were converted into the unit of kg N ha−1, respectively. Fertilizer-induced N2O emission was then calculated as the difference in soil N2O emission between the fertilization treatment (EN) and the no-fertilizing control (EO). Then the emission factor (EF) of N2O emissions of each fertilization treatment was calculated as the percentage of N2O emission relative to the amount of N fertilization rate (see Eq. 1). This yielded a dataset of 3786 N2O EF values.

EF (%) = 100 × (EN − EO) / N (1)

To determine the impact of soil pH on N2O EF, pH was divided into 58 groups by 0.1 unit (pH: 2.8–9.7). Soil pH was measured in water in most studies, but it was measured in CaCl2 or KCl in solution in a small number of experiments. We converted soil pH values measured in CaCl2 or KCl into water-based soil pH values, following the method described by Henderson and Bui and Kabala et al., respectively. A few studies did not specifically state the reagent used, and we assumed that water was used there. Notably, soil acidity or alkalinity was divided into: ultra-acidic of pH < 3.5, extremely acidic of pH = 3.5–4.4, very strongly acidic of pH = 4.5–5.0, strongly acidic of pH = 5.1–5.5, moderately acidic of pH = 5.6–6.0, slightly acidic of pH = 6.1–6.5, neutral of pH = 6.6–7.3, slightly alkaline of pH = 7.4–7.8, moderately alkaline of pH = 7.9–8.4, and strongly alkaline of pH = 8.5–9.0, following the Soil Science Division Staff (2017).

One major issue in the method using the coarse EFs is that the pH increments with more data points are given higher weight than the pH increments with fewer data points. Consequently, the statistical analysis is highly skewed towards the pH increments with a large number of field experiments and measurements. However, this does not provide a fair assessment of the pH effect on N2O EFs. Therefore, we adopted the average method by averaging all the N2O EFs at each pH increment to obtain the mean EF and then giving all the pH increments equal weights. We followed the method used by Linquist et al. and Feng et al. to evaluate the mean EF for the different pH groups (Eqs. (2) and (3).

M = Σ (Yi × Wi) / Σ Wi (2)

Wi = n / o (3)

We used Eq. (2) to calculate the weighted mean values for each pH unit group. In Eq. (2), M is the mean value of EF. Yi is the observation of EF at the ith pH unit group. Wi is the weight for the observations from the ith pH unit group and was calculated with Eq. (3), in which n is the replicates in each field experiment for each study, and o is the total number of observations from the ith pH unit group. At a given pH increment, this approach of weighting assigned more weight to well-replicated field measurements, reporting more precise EF (%) estimates.

Reactive N input affects N-cycling microbes and N2O emissions directly by increasing N availability for nitrification and denitrification and indirectly by inducing soil acidification. In order to determine the direct impact of soil acidification, we manipulated soil pH through adding diluted acids to create a pH gradient in three grassland experiments in the Tibetan Plateau, Inner Mongolian Plateau, and the Yellow Loess Plateau in China. We choose grasslands for three reasons. First, we wanted to assess the effect of soil pH without confounding effects of N fertilization. Unlike most Chinese croplands that have received high amounts of N fertilization, these grasslands are located in remote areas where there was low ambient N deposition and no N fertilization assuring minimal impact of human-derived N on soil N-cycling microbes. Second, since none of the experimental grasslands had received any significant reactive N input (N deposition or N fertilizers), the selection pressure of human-derived N on soil N-cycling microorganisms was negligible. Third, we wanted to have field experiments on acidic, neutral, and alkaline soils that also have decent amounts of available soil N. Available soil N (particularly NO3−) in other unfertilized soils, like forest soils, is very low and likely constrains N-cycling microbes. Moreover, grasslands potentially contribute 20% of total N2O flux to the atmosphere at the global scale. A considerable proportion of global grasslands are under moderate to intensive management, and it is expected that more grasslands will be under fertilization, likely increasing N2O emissions.

The three acid addition experiments were established in three grasslands with distinct climatic and soil conditions. The first experiment was set up in an alpine meadow at Gansu Gannan Grassland Ecosystem National Observation and Research Station (33°59′N, 102°00′E, ca. 3538 m a.s.l.) in Maqu county, Gannan Prefecture, Gansu Province, China. Over the last forty years, the MAP and MAT at this site were at 620 mm and 1.2°C, respectively. The soil was categorized as Cambisol (FAO taxonomy) and moderately acidic with a pH value of ca. 6.0 with moderate pH buffering capacity. The second experiment took advantage of an existing study on a steppe ecosystem at the Inner Mongolia Grassland Ecosystem Research Station of the Chinese Academy of Sciences (43°38′N, 116°42′E, 1250 m a.s.l.) near Xilin city, Inner Mongolia, China. The MAT at this site was 0.3°C with the lowest in January (−21.6°C) and the highest in July (19.0°C). It has had a MAP of 346.1 mm with the majority (ca. 80%) occurring in summer (June to August). It had a dark chestnut soil (Calcic Chernozem according to ISSS Working Group RB, 1998) with a nearly neutral pH value (ca. 7.3) and with high sand content and low pH buffering capcity. The third experiment was in a semi-arid grassland at the Yunwu Mountains Natural Preserve (36°10′–36°17′N, 106°21′–106°27′E, 1800–2100 m a.s.l.) on the Loess Plateau, Guyuan, Ningxia, Northwest China. This site has a typical semiarid climate, and the mean annual rainfall was about 425 mm with about two-thirds (60–75%) falling in July-September. Over the last three decades, this site had a MAT of 7.0°C (the lowest in January at −14°C and the highest in July at 22.8°C). The soil was a montane gray-cinnamon type classified as a Calci-Orthic Aridisol or a Haplic Calcisol in the Chinese and FAO classification, and alkaline with a pH of 8.0 and high pH buffering capacity.

At each site, a single factor of acid (sulfuric acid) addition experiment was designed. To minimize any potential direct acid damage to living plants and soil organisms, the specific dose of concentrated sulfuric acid (98%) needed for each plot was first diluted into 60 L of tap water and then sprayed into each plot. Equal amounts of water only were added to the no-acid controls (A0).

At the Gannan alpine site, the acid addition experiment was established in 2016 with five levels of acid addition: 0 (the control, A0), 1.32 (A1), 5.29 (A2), 9.25 (A3), and 14.53 (A4) mol H+ m−2 yr−1. Twenty plots (2 m × 2 m each) were then arranged in a randomized block design including four replicate blocks separated by 1 m buffer zones. Diluted sulfuric acid solution was applied twice each year (half of the designed dosage each time) in early June and late September of 2016, late April and late September of 2017, and late April 2018. At the Inner Mongolia steppe site, the acid experiment was initiated in 2009 with seven gradients of acid addition: 0, 2.76, 5.52, 8.28, 11.04, 13.80, and 16.56 mol H+ m−2 yr−1. The experiment was randomly positioned in a block design with 5 replicate blocks, leading to a total of 35 field plots (2 m × 2 m each). Diluted acid solution at the designed concentration was added to each plot in early September 2009, early June 2010, and early September 2010. Soil pH in all treatments stabilized and no additional acid has been added since 2010. For this study, we randomly chose four replicate field plots of five treatments, 0 (the control, A0), 2.76 (A1), 5.52 (A2), 11.04 (A3), and 16.56 (A4) mol H+ m−2 yr−1, to investigate the impact of soil acidification on soil nitrifiers, denitrifiers and denitrification. The acid experiment at the Guyuan site was established in 2016 with 30 plots (2 m × 2 m each) using a randomized block design. It had five levels of acid additions with six replicate blocks separated by 1 m walkways. The five levels of acid additions were: 0 (the control, A0), 0.44 (A1), 1.10 (A2), 7.04 (A3), and 17.61 (A4) mol H+ m−2 yr−1, respectively. Diluted acid solution was applied twice each year (half each time) in early June and late September of 2016, late April and late September of 2017, and early May 2018.

In mid-August 2018 when plant biomass peaked, three soil cores (5.0 cm dia.) were collected at 0–10 cm depth from each plot at both Gannan and Guyuan sites, and then mixed to form a composite sample per plot. For the Inner Mongolia site, soil samples were collected in the same way in early September 2020. Composited soil samples collected in field were placed on ice in coolers, and sent by express mail to the laboratory in Nanjing, China. All soil samples were first sieved through a mesh (2 mm) to remove rocks and dead plant materials. A small subsample (ca. 50 g) of each field soil sample was immediately stored at −20°C for molecular analyses, and the remainder was kept at 4°C in the refrigerator for later chemical and microbial analyses that were all initiated within 2 weeks. Soil pH in a soil-to-water (1:5, w/w) slurry was measured on an Ultramete-2 pH meter (Myron L. Company, Carlsbad, CA, USA). Inorganic NH4+-N and NO3−-N were extracted with 0.5 M K2SO4, and their concentrations in the extracts were quantified on a continuous flow injection auto-analyzer (Skalar SAN Plus, Skalar Inc., The Netherlands). For each soil sample, 0.3 g (dry soil equivalent) frozen soil was used to extract total genomic DNA with PowerSoil DNA kits (MoBio Laboratories, Carlsbad, CA, USA). The DNA quantity and quality were determined by a Nanodrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The copy numbers of AOA-amoA, AOB-amoA, nirK, nirS, and nosZI genes were determined using the Real-Time quantitative PCR System (Applied Biosystems, Foster City, CA, USA). The primer sets of crenamoA23F/crenamoA616r, amoA-1F/amoA-2R, nirK876/nirK1040, nirSCd3aF/nirSR3cd, and norZ1f/norZ1R were used for the amplification of AOA-amoA, AOB-amoA, nirK, nirS, and nosZI gene, respectively. Each qPCR reaction (20 µL volume) was performed with 10 µL SYBR Premix Ex TaqTM (Takara, Dalian, China), 1 µL template DNA corresponding to 8–12 ng, 0.5 µL of each primer, 0.5 µL bovine serum albumin (BSA, 5 mg mL−1) and 7.5 µL distilled deionized H2O (ddH2O). The standard curve for determining the gene copy number was developed using the standard plasmids of different dilutions as a temperate. The standard plasmids were generated from the positive clones of the 5 target genes, which were derived from the amplification of the soil sample. The amplification efficiency of the qPCR assays ranged from 90 to 100% with R2 > 0.99 for the standard curves. We checked potential qPCR reaction inhibition via the amplification of a known amount of the pGEM-T plasmid (Promega) with T7 and SP6 primers, adding to the extracts of DNA samples or water. No amplification reaction inhibitions in the samples were detected.

We did not directly monitor soil N2O fluxes in the field, mainly because the field sites were remote. Instead, microcosm incubation experiments were conducted to determine potential soil N2O emissions. For each soil sample, field soil (20.0 g dry mass equivalent) was placed into a 125-mL dark bottle, and deionized water was added to adjust soil moisture to ca. 70% water-filled pore space (WFPS), creating a moisture condition conducive for denitrifiers and denitrification. The high soil moisture content favored anaerobic processes since O2 diffusion into the soil was restricted and effects of oxygen should be negligible. All bottles were loosely covered with fitting lids and incubated in a dark incubator at 20°C. It is worth mentioning that both nitrification and denitrification processes produce N2O, but optimum N2O emissions from denitrification often occur at 70–80% WFPS. Also, our results showed that soil pH had a linear relationship with soil nitrifiers and the high soil moisture suppressed nitrification. Thus, the design of the incubation experiments targeted N2O from anaerobic processes like denitrification, and N2O emissions from nitrification or other aerobic processes were not considered.

To determine the N2O emissions, gas samples were taken from the headspaces of the incubation bottles as described by Zhang et al. More specifically, all incubation bottles were flushed with fresh air (2 min each) prior to the gas sampling, then immediately sealed and incubated for 6 h in the dark. A gas sample of 15 mL was taken from the headspace of each incubation bottle and was immediately transferred into a vial for gas chromatograph (GC) measurement. After gas sampling, all incubation bottles were loosely covered until the next gas sampling to ensure minimum water loss. Gas sampling was conducted 5 times, respectively, at 12, 24, 48, 72, and 96 h after the incubation initiation. N2O concentrations in the sampling vials were determined within 24 h after the sampling collection on a GC equipped with an electron capture detector (ECD) (GC-7890B, Agilent, Santa Clara, CA, USA). The N2O fluxes were calculated using the formula:

F = ρ × V × ΔC × 273 / ((273 + T) × W) (4)

where F is the soil N2O gas flux rates (µg N kg−1 soil h−1), ρ is the standard state gas density (kg m−3), V is the bottle volume (L), ΔC is the difference in N2O concentration (ppm) between two samples (0 and 6 h), T is the incubation temperature at 20°C, and W is the dry weight of soil (kg).

We further determined soil potential denitrification activities (PDA), using the modified acetylene (C2H2) inhibition technique. For each field soil sample, two sub-samples (each 5.0 g dry soil equivalent) were respectively put into two 100 mL sterile serum bottles. Then, 8 mL of N- and C-containing solution (KNO3 at 50 mg NO3−-N g−1 dry soil, glucose, and glutamic acid, each at 0.5 mg C g−1 dry soil) was added to create a soil slurry conducive for denitrification. To measure the PDA, 10% C2H2 was injected into one bottle to inhibit N2O reductase activity so that the N2O produced was not reduced to N2. In the other bottle, no C2H2 was added so that all enzymes of denitrification remained active and the N2O detected was the net difference between the production and consumption of N2O. All serum bottles were incubated in dark at 25°C with agitation at 180 rpm. Gas samples (10 mL) were taken from the headspace at 2, 4 and 6 h after the beginning of the incubation for determination of N2O concentrations on a GC (GC-7890B, Agilent, Santa Clara, CA, USA).

Similar to Meta-analysis 1, the data collection and analysis were also carried out according to the PRISMA guidelines. We conducted an extensive search in Web of Science and Google Scholar for studies in which nirK-, nirS- and nosZ (clade I and II) had been quantified with the two sets of search terms: (1) nirK, nirS or nosZ gene, and (2) soil or terrestrial. In total, the search resulted in ca. 1539 article hits in December 2021. All articles were carefully read through to select those based on field studies, whereas those based on microcosm studies were excluded. There were 286 published papers that met our criteria. We also included the data from the three field acid addition experiments described above. Special attention was also directed towards checking whether nosZ clade I, nosZ clade II or both were quantified. Only 26 published studies quantified nosZII and, among these, only nine also reported soil N2O emissions in the field. Therefore, the gene nosZ in the dataset in this study only refers to nosZ clade I. Thus, the final dataset contained data from 501 sites reported by 289 studies, and included 1347, 1265, and 1287 abundance estimates of nirK, nirS and nosZI genes, respectively.

We extracted data either from tables, texts or from figures using the GetData Graph Digitizer software. For each article, we extracted the following information for our analysis: the abundance of nirK, nirS, nosZI and nosZII genes (copy numbers per g soil), soil pH, and depth of collected soils. Latitude, altitude, MAP and MAT of the experimental sites were also recorded. All information of N2O emissions (N2O emission rates and/or cumulative N2O emissions) was extracted. Because various publications reported the results of N2O emissions in different units, we converted all N2O emission rates into the unit of μg N m−2 h−1. Data were log-transformed to meet statistical tests assumptions (if necessary). In the literature, most data of gene abundances were presented in the form of log-transformed numbers, we first transformed them back to real numbers and obtained the average gene abundances for each pH increment, and then again log-transformed. Similar to Meta-analysis 1, we examined the relationships between soil pH and abundances of denitrifying microorganisms, using both coarse abundance and averaged abundance of each functional group of denitrifiers at each pH increment.

In Meta-analysis 1, we examined potential linear or quadratic relationships between N2O EFs and soil pH, MAP, MAT, soil sand, silt and clay content, SOC or TN. In Meta-analysis 2, we examined potential linear and quadratic relationships between soil pH and the abundance of nirK-, nirS-, nosZI-type denitrifiers, or the (nirK + nirS)/nosZI ratio. The model goodness of fit was evaluated with the Akaike information criterion (AICc) where a lower AICc value represents a model with a better fit. In general, differences in AICc higher than 2 indicate that models are substantially different. Information on the AICc index was obtained using the package MuMIn from R. Given the large number of samples included in the meta-analyses, we interpreted the statistical significance of individual predictors using a conservative α of 0.001 following model selection by AICc. In Meta-analysis 1, we did a non-parametric alternative of Kruskal–Wallis analysis together with Pairwise Wilcox test to determine the differences in soil pH, N2O EFs, and N fertilization rate among different climate zones.

For the field experiments, we used linear mixed-effects (LME) models to determine the effects of acid addition on the response variables at each site, treating the acid treatments as fixed effects and block as a random effect. One-way analysis of variance (ANOVA) followed by Duncan’s multiple-range tests were used to compare the means among acid addition levels across all response variables. Then, we examined the relationships between soil pH and N2O emissions, PDA, N2O/(N2O+N2) ratio or the (nirK + nirS)/nosZI ratio across all three field sites, using linear or quadratic regression. We used the Akaike information criterion (AICc) to evaluate the model’s goodness of fit. All analyses were conducted in R (version 4.1.1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Based on the meta-analysis and results from the pH manipulafion experiment, the authors in this\nmanuscript found that the pH as the master factor controlled the N2O EF, and very interesfingly, they\nfound that a hump shaped relafionship between pH and N2O index and also (nirS+nirK)/nosZI. Most\nimportant thing is that the authors challenged the well accepted knowledge that increasing the pH by\nliming is the main method for mifigate the N2O emission. Instead, they proposed a strategy for\nmifigafing the N2O emission from acidic agriculture soil by regulafing the N2O producing and consuming\nmicrobial guilds. The findings in this manuscript is important to comprehensively understand the pH\neffect on the N2O emission in the farm land soil.\nHere are some specific comments:\nSince that the nosZ II carrying bacteria often act as the super N2O sink and their N2O mifigafion effect\nare befter than the nosZ I carrying bacteria, Only discussed the (nirK+nirS)/nosZI rafio in the manuscript\nwould somehow mislead.\nSince that the pH opfima for most crops are ranged from 5.5 to 6.5, and this pH range also is the\nmaximum N2O EF being found. We may conclude that the N2O emission in the low pH acidic soil could\nbe aftributed to the inhibifion of N2O reductase by the low pH condifion. Is it possible to introduce the\nlow pH adapted N2O reductase carrying bacteria, which were reported in previous study (Lycus et al.,\nISME J. 2017 Oct; 11(10): 2219–2232.) to the low or moderately low pH agricultural soil for the purpose\nof N2O mifigafing ?\nIn the microcosm incubafion experiment, During the incubafion between the gas sampling, the vials\nwere not sealed to allow the gas exchange. However, the incubafion with oxygen may inhibit the growth\nand acfivity of N2O sink bacteria. Will this influence the results for N2O EF respond to the varied pH?\nIt is a liftle strange that the hump-shaped relafionship appeared for PDA and pH. What is the reason of\ndecrease of PDA along with the increase of pH in Guyuan semi-arid grassland soil? Should be the\nimproper measurement of N2O?", "reviewer_2": "The authors conducted two extensive global meta-analyses studying the effect of soil pH on N2O EFs\n(with >5400 observafions) and on denitrifier community (with >3800 observafions), along with field pH\nmanipulafion experiments on three grassland sites. The findings suggested that soil pH primarily governs\nN2O EFs and emissions by affecfing the denitrifier community composifion, with the highest N2O EF\nobserved in soils with moderately acidic pH. Their efforts are to be commended. The findings are crucial\nin managing opfimal pH levels in agricultural soils to ensure nutrient availability and overall soil health.\nHowever, several aspects need further clarificafion and considerafion.\nThe potenfial effects of other factors that could drive N2O emissions/EFs should be considered. For\nexample, it is well known that soil moisture affects nitrificafion, denitrificafion, and their contribufion to\nN2O emissions, and that soil moisture (also rainfall) is quite variable and could lead to sporadic N2O\nemissions. I wonder how these effects were factored into the relafionship between soil pH and N2O EFs?\nMoreover, the pH buffering capacity of soils, dependent on various soil properfies, should not be\noverlooked, as it could affect the rate of soil acidificafion in response to acid addifions.\nThe inclusion of the pH manipulafion experiments on three grasslands is valuable but inconclusive. It is\nunclear why only grassland sites were selected, especially when the two global meta-analyses included\ncropping and forest systems as well. The representafion of grasslands as compared to other ecosystems\nconcerning their response to acid addifion (soil acidificafion) and associated microbial response and N2O\nemissions warrants jusfificafion. In parficular, the authors stated that none of the grassland sites had\nreceived significant reacfive N input (neither N deposifion nor N ferfilizers) (lines 162-163). In this case,\nthe expected N2O emissions from fertilizer N would be minimal. This scenario differs greatly from\ncropping systems, which often receive much higher N input.\nThe authors projected a global increase in N2O emissions, aftribufing it to rising N2O EFs due to\nferfilizer-induced acidificafion, mainly in China, Europe, and the US (lines 253-255). However, this\ngeneralizafion may oversimplify the situafion. I would expect a diverse range of soil pH in these\ncountries. Based on the hump-shaped relafionship illustrated in this study, for acidic soils in these\nregions, further acidificafion would lead to lower N2O EFs (not necessarily higher as predicted by the\nauthors).\nPlease elaborate why the pafterns of coarse EFs and averaged EFs against soil pH were so much different\n(Fig. 1). Are there any underlying mechanisms that should be further explored?"}, "author_response": "Point by point responses to Reviewers’ Comments on Manuscript\nNature Communications (NCOMMS-23-36237A)\n(Comments in black and Responses in blue)\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nBased on the meta-analysis and results from the pH manipulation experiment, the\nauthors in this manuscript found that the pH as the master factor controlled the N2O EF,\nand very interestingly, they found that a hump shaped relationship between pH and N2O\nindex and also (nirS+nirK)/nosZI. Most important thing is that the authors challenged\nthe well accepted knowledge that increasing the pH by liming is the main method for\nmitigate the N2O emission. Instead, they proposed a strategy for mitigating the N2O\nemission from acidic agriculture soil by regulating the N2O producing and consuming\nmicrobial guilds. The findings in this manuscript is important to comprehensively\nunderstand the pH effect on the N2O emission in the farm land soil.\nReply: Many thanks for your supports.\nHere are some specific comments:\nSince that the nosZ II carrying bacteria often act as the super N2O sink and their N2O\nmitigation effect are better than the nosZ I carrying bacteria, Only discussed the\n(nirK+nirS)/nosZI ratio in the manuscript would somehow mislead.\nReply: Very insightful comments. Yes, we are aware that nosZII carrying bacteria can\nbe an important N O sink (Jones et al. 2014; Hallin et al. 2018). In the revision, we\n2\nattempted to expand our second meta-analysis by including studies that measured\nnosZII abundance. Unfortunately, however, most studies have so far only measured the\nabundance of nosZI; only 26 field studies reported nosZII abundance and only 9 of them\nreported both nosZII abundance and N O emissions in field (please see Supplementary\nto make any generalization about the linkages among soil pH, nosZII and N O\n2\nemissions. Therefore, the only option is to focus on the (nirK+nirS)/nosZI ratio, which\ncontains over 1,671 paired data points from 289 studies.\nSince that the pH optima for most crops are ranged from 5.5 to 6.5, and this pH range\nalso is the maximum N2O EF being found. We may conclude that the N2O emission in\nthe low pH acidic soil could be attributed to the inhibition of N2O reductase by the low\npH condition. Is it possible to introduce the low pH adapted N2O reductase carrying\nbacteria, which were reported in previous study (Lycus et al., ISME J. 2017 Oct; 11(10):\n2219–2232.) to the low or moderately low pH agricultural soil for the purpose of N2O\nmitigating?\nReply: Great question and suggestion. Yes, we believe that it is possible. In this revision,\nwe have now discussed how the N O reductase-carrying bacteria adapted to low pH\n2\nmight be used in the acidic soil to mitigate N O emission (see Line 291-293).\n2\n“For example, some N O reductase carrying bacteria have adapted to highly acidic\n2\nsoils with pH as low as 3.7 (Lycus et al. 2017) and it may be possible to introduce these\nbacteria into soil to mitigate N O emissions in highly acidic soils.”\nsampling, the vials were not sealed to allow the gas exchange. However, the incubation\nwith oxygen may inhibit the growth and activity of N2O sink bacteria. Will this\ninfluence the results for N2O EF respond to the varied pH?\nReply: Very good point. Yes, incubation with oxygen may inhibit the growth and\nactivity of N O sink bacteria. However, during our microcosm incubation, soil moisture\n2\nwas adjusted to ca. 70% water-filled pore space (WFPS) by adding deionized water,\ncreating a moisture condition conducive for denitrifiers and denitrification (Butterbach-\nBahl et al. 2013; Qiu et al. 2019). This high soil moisture content favored anaerobic\nprocesses since O diffusion into the soil was restricted and effects of oxygen should be\n2\nnegligible. We have now added more details to the Method section (see Line 471-474).\nIt is a little strange that the hump-shaped relationship appeared for PDA and pH. What\nis the reason of decrease of PDA along with the increase of pH in Guyuan semi-arid\ngrassland soil? Should be the improper measurement of N2O?\nReply: Yes, the hump-shape curve between pH and PDA did not explain a very high\nvariation. This is likely because denitrification is an electron-consuming and\nheterotrophic process, and available C (here soil organic C, SOC) is another major\ndriving factor of denitrification (Firestone & Davidson 1989). Denitrification is often\nfavored under neutral to weak alkaline conditions (Šimek & Cooper 2002). Although\nthe Mongolia soil has a near-neutral pH, it had significantly lower SOC than two other\nsites. Therefore, it did not have the highest PDA. In contrast, the Alpine acidic soil of\npH 6.0 had a much higher SOC (9.8%) (see Supplementary Table 1), likely contributing\nto the highest PDA.\nIn addition, Guyuan soil was an alkaline soil with pH around 8.0, and a small decrease\nin soil pH slightly increased PDA. Again, this increase was likely because denitrifiers\nwere favored under neutral and weak alkaline conditions.\nReviewer #2 (Remarks to the Author):\nThe authors conducted two extensive global meta-analyses studying the effect of soil\npH on N2O EFs (with >5400 observations) and on denitrifier community (with >3800\nobservations), along with field pH manipulation experiments on three grassland sites.\nThe findings suggested that soil pH primarily governs N2O EFs and emissions by\naffecting the denitrifier community composition, with the highest N2O EF observed in\nsoils with moderately acidic pH. Their efforts are to be commended. The findings are\ncrucial in managing optimal pH levels in agricultural soils to ensure nutrient availability\nand overall soil health. However, several aspects need further clarification and\nconsideration.\nReply: Thank you very much for your positive comments and encouragement. We have\nnow modified our manuscript following your advice and comments.\nThe potential effects of other factors that could drive N2O emissions/EFs should be\nconsidered. For example, it is well known that soil moisture affects nitrification,\ndenitrification, and their contribution to N2O emissions, and that soil moisture (also\nrainfall) is quite variable and could lead to sporadic N2O emissions. I wonder how\nthese effects were factored into the relationship between soil pH and N2O EFs?\nMoreover, the pH buffering capacity of soils, dependent on various soil properties,\nshould not be overlooked, as it could affect the rate of soil acidification in response to\nacid additions.\nReply: Excellent comments. We have now examined the relationships between N O\npH and SOC (Supplementary Fig. 6) described in the previous version. Results from\nthese new analyses are included in Results (see Line 150-155) and also in the\nSupplementary information (Supplementary Fig. 7). Results showed that although N O\nnitrogen (TN), and sand and clay contents, the correlations only explained a very low\npercentage of the variation in N O EFs (1-3%) (Supplementary Fig. 7).\nresistant to soil acidification. We have now considered the pH buffering capacity in the\nDiscussion as well as mentioned this in the Method section (see Line 268-271; Line\n393; Line 400; Line 407).\nSupplementary Fig. 7. Relationship between mean annual precipitation (MAP, a),\ntemperature (MAT, b), soil sand (c), silt (d) and clay content (e), and total soil nitrogen\ncontent (TN, f), and N O EFs in the meta-analysis of fertilizer N effects on N O EFs.\n2 2\nThe shaded area presents the 95% confidence intervals around the linear regression line.\nStatistics (R2 and P values) for linear regression are indicated.\nThe inclusion of the pH manipulation experiments on three grasslands is valuable but\ninconclusive. It is unclear why only grassland sites were selected, especially when the\ntwo global meta-analyses included cropping and forest systems as well. The\nrepresentation of grasslands as compared to other ecosystems concerning their\nresponse to acid addition (soil acidification) and associated microbial response and\nN2O emissions warrants justification. In particular, the authors stated that none of the\ngrassland sites had received significant reactive N input (neither N deposition nor N\nfertilizers) (lines 162-163). In this case, the expected N2O emissions from fertilizer N\nwould be minimal. This scenario differs greatly from cropping systems, which often\nreceive much higher N input.\nReply: These are insightful comments and suggestions. For the field experiments, we\nchose the three grasslands as model systems for three major reasons (see Line 159-163,\nLine 374-386 and Supplementary Notes):\n1) The field manipulative experiments allowed us to examine how acidification (soil\npH) alone affects N O EFs and N O emissions. Input of N fertilizers (often in the form\n2 2\nNH +) directly affects N O EFs and emissions by increasing N substrate, but also\n4 2\nindirectly affects them through soil acidification, leading to the confounding effects of\nN and pH (Wang et al. 2018; Jones et al. 2022). Most croplands in China have received\nhigh amounts of N fertilization (Guo et al. 2010; Yu et al. 2022) and soil pH in the major\nChinese crop-production areas, on average, has declined significantly by ca. 0.5 unit\nfrom the 1980s to the 2000s (Guo et al. 2010). Also, plant harvest removes other\nnutrients from agricultural soils and agricultural practices (e.g., tillage and fungicides)\ndisturb soils, which could have some effects on N O EFs and emissions. Thus, isolation\n2\nof acidification from other human-induced activities on and N O EFs is a major reason\n2\nwhy we did not use agricultural soils.\n2) Applications of N fertilizers modify the community composition of N-cycling\nmicrobes (both nitrifiers and denitrifiers) (Ouyang et al. 2018) and the selective\npressure of N fertilizer inputs on the functional characteristics of soil microbial\ncommunities may mask the effects of soil pH on N-cycling microbes and N O\n2\nemissions (Jones et al. 2022). Since none of the experimental grasslands had received\nany significant reactive N input (N deposition or N fertilizers), the selection pressure of\nhuman-derived N on soil N-cycling microorganisms was negligible.\n3) We wanted to have field experiments on acidic, neutral and alkaline soils that also\nhave decent amounts of available soil N. Available soil N (particularly NO -) in other\n3\nunfertilized soils, like forest soils, is very low and likely constrains N-cycling microbes\n(Stark & Hart 1997).\nMoreover, grasslands cover about 40% of the global land surface and are widely used\nas pasture, supporting the livelihoods of more than 1.3 billion people through livestock\nproduction (O'Mara 2012; Buisson et al. 2022). Global grasslands potentially contribute\n20% of total N O flux to the atmosphere (Dangal et al. 2019; Chang et al. 2021).\nmanagement with fertilization and it is expected that more grasslands will be under\nfertilization in the future (Chang et al. 2021).\nThe authors projected a global increase in N2O emissions, attributing it to rising N2O\nEFs due to fertilizer-induced acidification, mainly in China, Europe, and the US (lines\n253-255). However, this generalization may oversimplify the situation. I would expect\na diverse range of soil pH in these countries. Based on the hump-shaped relationship\nillustrated in this study, for acidic soils in these regions, further acidification would lead\nto lower N2O EFs (not necessarily higher as predicted by the authors).\nReply: Great comment. We agree that our previous statement “We therefore forecast\ncontinuous increases in global N O emissions in the coming decades due to increasing\n2\nwill further induce acidification” were too simplistic. Because of variation in soil pH\nwithin each country or region, pH in a specific soil, rather than the average pH of the\nregion, will primarily affect N O emissions. Therefore, we have now reorganized the\n“In general, soil acidification has occurred in a large proportion of agricultural soils\nin China, US and Europe because of long-term N fertilization (Bowman et al. 2008;\nGuo et al. 2010; Chen et al. 2023). However, the degree of acidification varies locally,\nwhich can have different effects on soil N O emissions. According to our results, N\n2\nfertilization will induce increased acidification and N O EFs in soils with weak acidity\n2\n(pH = 6.0-6.7). Moreover, in several Chinese regions, a considerable proportion of\nagricultural soils are already highly acidic (4.5 < pH < 5.5), where low pH may indeed\ninhibit N O emissions (Fig. 6). However, the high acidity is suppressive to the growth\n2\nof crop plants, and farmers therefore increase soil pH through liming, which may\nincrease N O emissions (Wang et al. 2021). For neutral or alkaline soils (pH > 6.7),\n2\nparticularly those soils with high pH buffering capacity, N O emissions are likely less\n2\naffected because N fertilization may not significantly reduce soil pH over the short term.”\nPlease elaborate why the patterns of coarse EFs and averaged EFs against soil pH\nwere so much different (Fig. 1). Are there any underlying mechanisms that should be\nfurther explored?\nReply: This difference primarily stems from the fact that these two methods give\ndifferent statistical weight for each soil pH point. In the coarse EFs, the pH increment\nwith more data points (e.g., pH6.0, 153 data points) is given much higher weight than\nthe pH increment with fewer data points (e.g., pH4.5, 14 data points). Consequently,\nthe statistical analysis is highly skewed towards the pH increments with large number\nof field experiments and measurements, leading to a weak linear regression that can\nexplain a very low proportion of the data variation (Supplementary Fig. 4; also see\nWang et al. 2018). However, this does not provide a fair assessment of the pH effect on\nN O EFs. The averaged method solves this issue by averaging N O EF data points at\n2 2\neach pH increment to obtain one EF value and then giving all the pH increments equal\nweights. Our work is the first to address this issue and identifies the unique relationship\nbetween soil pH and N O EFs that has previously been overlooked.\nshowed a weak linear relationship (Wang et al. 2018) or no relationship (Cui et al. 2021).\nOne major reason may be due to the scale-issue in the diagram. Because some N O EFs\n2\nwere much higher than others, the Y axis in the coarse-pH diagram must be set much\nhigher than the average to cover all the data points, which could visually give a false\nimpression of linear relationship (Figure R1a, b). If we reduce the Y-axis scale in the\npH-coarse diagram to the same range for the pH-averaged diagram, we can visually see\nthat the pattern of the pH-coarse curve and the pH-averaged curve were indeed similar\n(see Figure R1c, d below). However, the explanatory power (R2=0.04 vs. R2=0.57)\ndiffered greatly because the coarse method gives too much weight to the pH points with\nmore measurements, as described in the previous paragraph.\nResponse Figure R1. Relationship between soil pH and coarse EFs or averaged EFs\nwith the same y axis range [a, b (from -7 to 20) or c, d (from -1 to 4)].\nTo further validate and demonstrate our elaboration/argument, we created a model\ndataset using different numbers of data points at each pH value. Then, we calculated\ncoarse and averaged EFs. From the diagram below, we can see that the curve patterns\nbetween pH and coarse EFs or averaged EFs were similar (Figure R2), but the two\ncurves are very different in their how much the variance of the dataset is explained\n(R2=0.04 for the coarse method but R2=0.75 for the average method). That is exactly\nwhat happened with our real dataset in our meta-analysis.\nResponse Figure R2. Relationship between soil pH and coarse EFs (a) or averaged\nEFs (b) with a model dataset."}, {"round": 2, "reviewer_comments": {"reviewer_1": "All my concern in last review has been properly responded. However, some minor revision is needed for\nthis manuscript before the publicafion.\nThe gene copy number of N2O source and sink is indeed related with the acfivifies of producfion and\nconsumpfion of N2O in the soil. but the quanfity of these genes was not the sole determining factor.\nEven with the same amount of nosZ containing bacteria, the reducfion of N2O is largely lower under low\npH (for instance, pH<6) than that in neutral pH due to the malfuncfion of NOR complex under low pH\ncondifion. I totally agree with the concept menfioned in this manuscript, \"the net N2O emission from\ndenitrificafion depends on both (i) the N2O/(N2O+N2) product rafio of denitrificafion and (ii) the overall\nrate of denitrificafion\". This should be recognized as the core idea running through the enfire arficle. It\nwould be befter if this also be demonstrated in some way in the abstract of this manuscript.\nsome specific comments:\nThe difference between figure 5f and 5g was neither descripted in the figure legend or in the text.", "reviewer_2": "I appreciate the authors’ effort in conducfing addifional analyses, which provide further informafion to\nsubstanfiate their findings. I have a couple of follow-up quesfions/suggesfions:\n1. In the new analysis of the correlafions between N2O EFs and climate/soil factors, the authors\nmenfioned that these correlafions explained only a very low percentage of the variafion in N2O EFs (1-\n3%) (lines 150-152) to support the asserfion that soil pH is a key factor. I seek further clarificafion since,\nas indicated in lines 128-130, soil pH explains only 4% of the variafion in N2O EFs, which is comparable to\nthe 1-3% range. In fact, the relafionship between soil pH and N2O EFs became evident only when\naveraged across soil pH in increments, where soil pH explained 57% of the variafion (lines 131-132).\nSlight rephrasing of the newly added sentences might prevent potenfial confusion or enhance clarity.\n2. The authors proposed introducing N2O reductase-carrying bacteria adapted to highly acidic soils as a\nmifigafion strategy for N2O emissions in such environments. However, earlier lines suggested that the\nissue primarily occurs under soil pH of 5.6-6.0 (slightly acidic). Considering this, would such a strategy\nsfill be effecfive for slightly acidic soils, given that bacteria adapted to highly acidic condifions may be\nstressed by a sudden change (increase) in pH when introduced?"}, "author_response": "Point by point responses to Reviewers’ Comments on Manuscript\nNature Communications (NCOMMS-23-36237B)\n(Comments in black and Responses in blue)\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nAll my concern in last review has been properly responded. However, some minor\nrevision is needed for this manuscript before the publication.\nReply: Many thanks for your supports. We have now revised our manuscript\nfollowing your suggestion.\nThe gene copy number of N2O source and sink is indeed related with the activities of\nproduction and consumption of N2O in the soil. but the quantity of these genes was\nnot the sole determining factor. Even with the same amount of nosZ containing\nbacteria, the reduction of N2O is largely lower under low pH (for instance, pH<6)\nthan that in neutral pH due to the malfunction of NOR complex under low pH\ncondition. I totally agree with the concept mentioned in this manuscript, \"the net N2O\nemission from denitrification depends on both (i) the N2O/(N2O+N2) product ratio of\ndenitrification and (ii) the overall rate of denitrification\". This should be recognized as\nthe core idea running through the en tire article. It would be better if this also be\ndemonstrated in some way in the abstract of this manuscript.\nReply: Insightful comment. We have now added the core point that the net N O\n2\nemission depends on both the N O/(N O+N ) ratio and overall denitrification rate in\n2 2 2\nthe abstract.\nsome specific comments:\nThe difference between figure 5f and 5g was neither descripted in the figure legend or\nin the text.\nReply: Thanks for pointing this out. We have now described Figure 5f and 5g both in\nthe main text and the Figure 5 legend (see Lines 221-222; Lines 863-875).\nReviewer #2 (Remarks to the Author):\nI appreciate the authors’ effort in conducting additional analyses, which provide\nfurther information to substantiate their findings. I have a couple of follow-up\nquestions/suggestions:\nReply: Thank you very much for your positive comments and encouragement. We\nhave now modified our manuscript following your suggestions.\n1. In the new analysis of the correlations between N2O EFs and climate/soil factors,\nthe authors mentioned that these correlations explained only a very low percentage of\nthe variation in N2O EFs (1-3%) (lines 150-152) to support the assertion that soil pH\nis a key factor. I seek further clarification since, as indicated in lines 128-130, soil pH\nexplains only 4% of the variation in N2O EFs, which is comparable to the 1-3%\nrange. In fact, the relationship between soil pH and N2O EFs became evident only\nwhen averaged across soil pH in increments, where soil pH explained 57% of the\nvariation (lines 131-132). Slight rephrasing of the newly added sentences might\nprevent potential confusion or enhance clarity.\nReply: Good point. We have now rephrased the sentences (see Lines 149-155) to\nexplain how the relationship between N O EFs and soil pH was different from those\n2\nbetween N O EFs and other soil and climate parameters.\n2\n2. The authors proposed introducing N2O reductase-carrying bacteria adapted to\nhighly acidic soils as a mitigation strategy for N2O emissions in such environments.\nHowever, earlier lines suggested that the issue primarily occurs under soil pH of 5.6-\n6.0 (slightly acidic). Considering this, would such a strategy still be effective for\nslightly acidic soils, given that bacteria adapted to highly acidic conditions may be\nstressed by a sudden change (increase) in pH when introduced?\nReply: Good question. However, whether those N O reductase carrying bacteria can\n2\nbe introduced into slightly acidic soils to effectively mitigate N O emissions warrants\n2\nfurther assessment. We have now revised the sentence (see Line 293-295)."}]

Decision letter