Nitrous oxide mitigation potential of biochar derived from agricultural and forest biomass: Effects of feedstock composition and pyrolysis temperature
Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico, USA
Agricultural Science Center, New Mexico State University, Clovis, New Mexico, USA
Department of Mathematical Sciences, Eastern New Mexico University, Portales, New Mexico, USA
Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, New Mexico, USA
Biosystems Engineering Department, Auburn University, Auburn, Alabama, USA
Abstract
Biochar application to soil has been promoted to mitigate climate change by reducing greenhouse gas (GHG) emissions, yet significant uncertainty exists in quantifying soil nitrous oxide (N2O) emissions from biochar‐amended soils. We evaluated soil N2O emissions from soils amended with biochar prepared from diverse agricultural and forest biomass and underlying biogeochemical mechanisms using long‐term soil incubations and empirical modeling. Biochars compared were pinewood pyrolyzed at 460°C (PB460), pinewood pyrolyzed at 500°C (PB500), pinewood pyrolyzed at 700°C (PB700), pine bark gasified at 760°C (GB760), cattle manure pyrolyzed at 500°C (CM500), pecan wood pyrolyzed at 500°C (PW500), hemp wood pyrolyzed at 500°C (HW500), and no biochar control (CTRL). Three nonlinear empirical models, first‐order kinetic model (FOKM), double exponential model (DEM), and first‐order logistic (FLOG) model, were tested to evaluate N2O emissions from various biochar‐amended soils. The PB700 was the most efficient in reducing N2O emissions, with 24% less total cumulative N2O emissions than CTRL. In contrast, the CM500 amendment resulted in 74% greater cumulative N₂O‐N emissions than CTRL (10.4 mg kg−1) and 102%–107% greater emissions than plant‐based agricultural biochars. Among models compared to study N2O emissions dynamics, the FLOG model best described the biochar N2O emissions irrespective of the biochar types. It showed the largest labile nitrogen pool (N l) in CM500 among all biochars, and the cumulative N2O emission was positively correlated with N l (r = 0.85; p < 0.001). Labile N content in biomass and pyrolysis temperature determined the N2O emissions mitigation potential in biochar‐amended soils.
Core Ideas
- Soil N2O emission was up to 24% less with plant residue biochar than with no biochar.
- The N2O emission was 102%–107% more with manure biochar than other biochar.
- The first‐order logistic model best described the N2O emissions from biochar added soils.
Article notes
Untitled section
Received 2025 Jan 18; Accepted 2025 May 27; Issue date 2025 Nov-Dec.
- CM500
- cattle manure pyrolyzed at 500°C
- DEM
- double exponential model
- FLOG
- first‐order logistic
- FOKM
- first‐order kinetic model
- GB760
- pine‐bark gasified at 760°C
- GHG
- greenhouse gas
- HW500
- hemp wood pyrolyzed at 500°C
- PB460
- pinewood pyrolyzed at 460°C
- PB500
- pinewood pyrolyzed at 500°C
- PB700
- pinewood pyrolyzed at 700°C
- PW500
- pecan wood pyrolyzed at 500°C
1.INTRODUCTION
Nitrous oxide (N₂O) is an important greenhouse gas (GHG) that has a global warming potential 273 times greater than that of carbon dioxide (CO₂) over a 100‐year time scale (Forster et al., 2021). About 60% of the global anthropogenic N2O emissions come from agriculture, primarily due to synthetic fertilizer and manure application (Frank et al., 2019). Release of N2O occurs during nitrogen (N) transformation as a result of different biological and chemical processes in the soil, such as nitrification, denitrification, nitrifier denitrification, chemo denitrification, and combined nitrification‐denitrification (Mosier et al., 1998; Seitzinger & Kroeze, 1998). Even modest reductions in N2O emissions from the soil by changing soil management could significantly reduce net GHG emissions and improve agricultural production. However, soil N2O emissions vary with agricultural systems, nutrient inputs, soil disturbance, and crop management (Foltz et al., 2019), and there is a significant knowledge gap on N2O emissions dynamics under diverse agricultural management systems. Improved knowledge of N2O emissions and their underlying mechanisms is critical for designing climate‐smart farming systems.
Biochar, a product of thermal breakdown (pyrolysis or gasification) of biomass, can potentially reduce N2O emissions by regulating both nitrification and denitrification. Pyrolysis and gasification processes during biochar preparation result in various aromatic and heterocyclic carbon (C) and nitrogen‐ring structures in biochar particles through dehydration, decarboxylation, demethylation, and cyclization processes, increasing its stability and nutrient retention capacity (Almendros et al., 2003; Baldock & Smernik, 2002). Amending soil with these biochars influences N dynamics (Almendros et al., 2003). Specifically, biochar application could significantly reduce soil NO3 −‐N and NH4 +‐N concentrations, thus affecting the total nitrification and denitrification rates and reducing N2O emissions (Harindintwali et al., 2021). In addition, it can regulate the capture and release of mineral N due to its highly porous structure, improving N use efficiency (Egyir et al., 2023; Hagemann et al., 2017). There is a high discrepancy in the response of biochar to N2O emissions, attributed to variations in pyrolysis temperature, biochar properties (C:N ratio, aromatic C contents), and environmental factors (soil environment, management practices) (Rehrah et al., 2014). For example, biochar prepared at lower temperatures (300°C–350°C) tends to have more dissolved organic carbon and N and induce N2O emissions when applied to the soil (Deng et al., 2021; Li et al., 2013). In contrast, biochar pyrolyzed at higher temperatures increases aromaticity, which is expected to increase N retention, reducing N2O emissions. Studies so far show reduced (Cayuela et al., 2014; Spokas & Reicosky, 2009), increased (e.g., Clough & Condron, 2010), or no change in N2O emissions after biochar amendment (e.g., Cheng et al., 2012). Therefore, more in‐depth studies are needed on how biomass type and pyrolysis temperature impact labile N content in biochar and N2O emissions to estimate the GHG mitigation potential of agriculture (Spokas & Reicosky, 2009).
Biomass type and composition affect the biochar pore structure and aromaticity of structural compounds. Since agriculture produces ∼5 Gt of residues (fresh weight), including straw, roots, old branches, and barks from trees, and so on, each year globally and ∼0.6 Gt in the United States alone (Bensten & Felby, 2010), utilizing agricultural residues in biochar production could be a sustainable solution to improve soil health and mitigate climate change. Tree fruit orchards and the rapidly expanding hemp industry in the western United States generate substantial amounts of agricultural residues (pruning, stem residues) each year, which can quickly decompose and release CO2 and N2O into the atmosphere. More than 14,218 million tons of forest residues in the world's productive forests (top 21 countries) and 2078 million tonnes in the United States require proper management to avoid unintended forest fires and burning agricultural residues (Baruya, 2015). Likewise, a substantial amount of manure produced from the dairy industry across the globe, including large dairies in the western United States, could be utilized to make biochar and reduce GHG emissions. This is critical in the United States because manure management alone contributes 9% of methane (CH4) emissions and 4% of N2O emissions into the atmosphere (US Environmental Protection Agency [EPA], 2023). Converting plant residues and manure into carbon‐rich biochar can help manage a substantial amount of agricultural residues that otherwise could cause environmental issues. However, applying agricultural biomass to soil can improve soil quality and mitigate GHG emissions Lehmann et al., 2021). The discrepancy in responses of biochar stems from the diversity in biomass types and pyrolysis conditions. Thus, characterizing various agricultural residues, estimating their C and labile nutrient contents, and understanding their impacts on nutrient transformation will help mitigate GHG emissions from agricultural systems.
Empirical models with suitable theoretical parameters could accurately describe soil processes, including nutrient transformation, especially when it is not possible to directly observe or manipulate multiple factors driving microbial transformations and physicochemical changes in soils. As long as the system is adequately described, the models have some predictive capability and allow evaluation of the system function under theoretical scenarios (Gertsev & Gertseva, 2004). Such models are used in estimating labile C and N fractions and decomposition kinetics of complex substances like biochar, in which pyrolysis conditions and biomass type determine the labile C and N contents and N transformation in biochar‐amended soils. For example, kinetic models are valuable in estimating the N mineralization dynamics and identifying the half‐life of labile N (Kuzyakov et al., 2014; Singh et al., 2012; Zimmerman, 2010). Utilizing locally available biomass to prepare biochar and characterizing their decomposition dynamics using empirical models could help identify low‐cost biochar technologies for sustainable agriculture.
The first‐order kinetic model (FOKM) (or one‐pool model) has been widely used to study the decomposition characteristics of organic compounds (Bai et al., 2013; Qayyum et al., 2012). This model assumes that all N decays occur at an average decay rate, irrespective of the chemistry of different N fractions. However, biochar has both labile and recalcitrant N fractions with varying degradation rates. Using more than one N pool may increase the model's fitness and calculation accuracy. The double exponential model (DEM) with two separate N pools or the first‐order logistic (FLOG) model with first‐order plus logistic functions could help better estimate labile N and N2O emissions dynamics. The DEM assumes that labile and slow pool N fractions decompose exponentially, while the FLOG model assumes that the degradation of complex N sources in biochar requires the growth of specialized microbes, and the N mineralization curve may not always follow an exponential trend (Gills & Price, 2016). The FLOG model could best describe the N dynamics of biochar‐amended soils because N dynamics in biocar‐amended soils are likely limited by the depolymerization of complex organic compounds, and the model can capture N2O emissions from easily degradable N compounds as well as emissions during the depolymerization of complex organic molecules (Schimel & Bennett, 2004). However, the model has not yet been tested to describe N2O emissions dynamics in biochar‐amended soils. This study is the first attempt to test the model in an independent dataset.
In this study, we aim to (a) evaluate biochar feedstock and pyrolysis temperature effects on N2O‐N emissions and (b) compare three nonlinear models for their prediction power, including the FLOG model, to characterize cumulative N2O‐N emissions, estimate the emissions dynamics in various biochars, and discuss the underlying biogeochemical processes. We hypothesized that N2O emissions may differ with biochar prepared using varying production methods (pyrolysis, gasification), feedstock, and/or pyrolysis temperatures, and the FLOG model could provide a more accurate and reliable representation of N2O emissions from soils amended with biochar.
Boxed Text
Core Ideas
- Soil N2O emission was up to 24% less with plant residue biochar than with no biochar.
- The N2O emission was 102%–107% more with manure biochar than other biochar.
- The first‐order logistic model best described the N2O emissions from biochar added soils.
2.MATERIALS AND METHODS
2.1.Soil and biochar
Soil for the laboratory incubation was collected from 0‐ to 10‐cm depth of a dryland field at the New Mexico State University Agricultural Science Center, Clovis. The soil is classified as Olton clay loam (fine, fixed, superactive, thermic Aridic Paleustolls) (Soil Survey Staff, 2023) and had a microbial biomass carbon of 462 mg kg−1, inorganic N of 18.6 mg kg−1, organic N of 0.83 g kg−1, and soil organic carbon of 10.8 g kg−1.
Seven biochars from various feedstocks and preparation conditions were compared in this study. Four biochars were prepared from pine (Pinus taeda L.) biomass: three from pinewood pyrolyzed at 460°C (PB460), 500°C (PB500), and 700°C (PB700), and one from pine bark gasified at 760°C (GB760). Three other biochars were prepared from pyrolysis at 500°C using cattle manure (CM500), pecan (Carya illinoinensis (Wangenh.) K. Koch) wood (pecan wood pyrolyzed at 500°C [PW500]) after pruning, and hemp (Cannabis sativa L.) wood/stalk (hemp wood pyrolyzed at 500°C [HW500]) after seed harvest as feedstock. The C and N composition of forest and agricultural biochar is presented in Table 1 and further explained in Sapkota et al. (2024, 2025), respectively. Elemental composition (C and H) of biochars was determined by ultimate analysis in Vario MICRO cube, Elementar, using the ASTM D5373–02 method.
| Biochar types | Treatments | Total carbon (g kg−1) | Total nitrogen (g kg−1) | C:N ratio |
|---|---|---|---|---|
| Forest | GB760 | 709 | 3.20 | 222 |
| PB460 | 885 | 1.90 | 466 | |
| PB500 | 784 | 2.00 | 392 | |
| PB700 | 872 | 2.10 | 415 | |
| Agriculture | PW500 | 748 | 10.2 | 170 |
| HW500 | 749 | 4.40 | 73.0 | |
| CM500 | 402 | 19.9 | 20.0 |
2.2.Experimental design, incubation procedure, and net nitrous oxide emissions
A laboratory incubation experiment was established on May 22, 2023, in a completely randomized design with eight treatments and four replications of each treatment, giving 32 experimental units. For every treatment, except for the control, ∼60 g of soil was mixed with 0.6 g of biochar (1% w/w) and was added to a specimen cup. The mixture was adjusted to a 23% v/v water content, approximately the field capacity moisture for the medium‐textured soils, and was placed into a 1‐L canning jar containing 5 mL of deionized water to maintain interior humidity. Three empty jars containing empty sample cups and 5 mL of deionized water were used as a control. To enable N2O extraction from the jars, a 1.5‐cm long butyl rubber stopper was inserted on the lid of each canning jar. The jars were kept at room temperature (23.5 ± 1°C) in a dark cabinet. Soil N2O fluxes were measured in 24 h, 72 h, 7 days, 14 days, and every week until 301 days after incubation started on May 22, 2023. A MIRA PICO N2O analyzer (AERIS Technologies Inc.) was used to measure N2O gas emissions during incubation. R. Ghimire and Khanal (2020) described the method for calculating CO2 emissions under long‐term laboratory incubations, which was modified to estimate N2O emissions. In short, a syringe attached to rubber tubing was placed into a septum on the lid of each jar, which was connected to the N2O analyzer. Lids of the incubation jar were opened following each measurement to allow the gas concentrations to re‐equilibrate to the room conditions through vacuum flushing. Specimen cups were weighed every 2 weeks to monitor the water loss from the incubated soils. If the weight of the cups dropped below the initial weight, deionized water was added to the soil to adjust its moisture content to 23% v/v. All samples were then re‐incubated until the subsequent measurement. Net N2O flux was computed by subtracting the N2O concentrations in the blank from the measured sample values. Cumulative emissions of N2O throughout the incubation period were calculated by linear interpolation of daily fluxes, followed by summing the emissions data for the entire study period. The N2O values recorded as negative were less than 5% of the total data and were considered zero based on Fidel et al. (2019).
2.3.Modeling nitrous oxide emissions kinetics
The N2O‐N emissions data were plotted against time (t) using three kinetic models: an FOKM (Equation 1), a DEM (Equation 2), and a first‐order plus logistic (FLOG) (Equation 3).
where N m is cumulative N2O–N evolved at time t (mg g−1), N l = biochar labile N pool size, and k l = mineralization rate of the labile N pool.
where N m is cumulative N2O–N evolved at time t (mg g−1). N l and N s are labile and slow N pool sizes, respectively. k l and k s are the labile and slow biochar N mineralization rates, respectively. t is expressed in days.
where N m is cumulative N2O–N evolved at time t (mg g−1), N 1 is labile biochar N pool, and N 2 is logistic delayed N pool of biochar. The k 1, k 2, and k 3 indicate mineralization rates of the labile pool, the time taken to mineralize half of the logistic pool or the inflection point's location (days), and the distance (days) between the inflection point and the three‐fourth maximum, respectively. The k 2 is assumed to be directly proportional to both the quantity of N2O–N released (which reflects organism growth) and the portion of N still present in the logistic pool (which indicates resource depletion), and k 3 characterizes the steepness of the logistic function and represents the time interval between the k 2 inflection point and when 75% of the maximum N2O‐N release from logistic pool occurs.
According to the FOKM (Equation 1), the N2O emissions are derived from one labile N pool. The N transformation (nitrification or denitrification) rate (k l) (Equation 1) of labile N, as estimated by the model, can be used to calculate the half‐life (t 1/2) of the labile N pool (Qayyum et al., 2012).
The DEM represents a biphasic pattern of mineralization of the added N (Equation 2). This two‐pool kinetic model describes the decomposition of a labile and slow N pool (Molina et al., 1980). The value of the slow turnover rate (k s) was utilized to calculate half‐life (t 1/2) for the slow N pool (N s) using Equation (4) by using the k s value instead of k l as above (Dodor et al., 2019).
The FLOG model (Equation 3) comprises an exponential and a logistic function, which denotes two different pools of potentially mineralizable N. It is assumed that the rate of N2O‐N evolution is directly proportional to the remaining N in the soil. The N 1 and k 1 are total mineralizable N, and the mineralization rate is constant, respectively. The logistic pool represents an N pool that will not mineralize until the secondary (specialized) microbial growth occurs (Gillis & Price, 2011). Therefore, the mineralization of this pool follows a bell curve that is not centered at t 0 but later at the time represented as k 2 in the model.
2.4.Statistical analysis
The treatment effect on cumulative N2O‐N emissions was analyzed using a one‐way analysis of variance (ANOVA) using the PROC MIXED model in the statistical analysis system (SAS) ver. 9.4 (SAS Institute). Treatment was considered a fixed factor, and replication was random. Statistical differences between treatment means were compared using the “lsmeans” procedure in SAS. In R statistical software (ver. 4.3.3), the observed cumulative N2O‐N in four replications of each treatment were pooled for the assessment of the model for each treatment by determining Pearson's correlation coefficient (r), root mean square error (RMSE), and normalized root mean square error (NRMSE). The RMSE was calculated as the mean square error between observed and predicted values, and NRMSE was obtained by normalizing the RMSE with the observed treatment means (Adhikari et al., 2024; B. Ghimire et al., 2017; Sapkota et al., 2024). A further analysis was conducted by fitting a nonlinear model to each replicate to compare estimated parameters across treatments using the ANOVA mixed model procedure in R. All statistical analyses were performed at a significant probability (p < 0.05). Before analysis, the normality of residuals was assessed for all the data using the Shapiro–Wilk test, and homogeneity of variance was assessed using Levene's test. Cumulative N2O‐N, labile (N l), and slow (N s) pools of N, which were positively skewed, were log‐transformed, and the mineralization rate of the slow pool, which was <0, was square root transformed to meet the ANOVA assumption. Back‐transformed means were presented in the results. Pearson's correlation (adjusted for multiple comparisons) was carried out to evaluate relationships between C:N ratio, N percentage of biochar, and cumulative N2O emissions, and to explore the relationships of the kinetic parameters of models with the soil and biochar properties.
3.RESULTS
3.1.Observed and model predicted nitrous oxide emissions trends
The observed emissions trend showed a rapid loss of N2O‐N in the first 2 weeks of incubation. Unamended soil (CTRL) released 71% of the total cumulative nitrous oxide during the 2‐week incubation period. Similarly, forest biochar released 63%–68%, and agricultural biochar released 54%–84% of the total cumulative N2O emissions in the first 2 weeks (Figure 1a,b). Among forest biochar, the N2O‐N emissions from PB700 and GB760 biochar were lower than those from the CTRL (Figure 1a). In contrast, PW500 and HW500 exhibited lower emissions among agricultural biochar, while CM500 biochar released greater cumulative N2O‐N than CTRL. The N2O emission increased considerably until 42 days and slowed down for the remaining incubation period (Figure 1a,b).
The FOKM captured the emissions trend well and indicated that N2O emissions reach a plateau around 42 days of incubation (Figure 1c,d). In contrast, the DEM predicted that emissions continue to increase even after 42 days, with no indication of plateauing thereafter (Figure 1e,f). Fitting the cumulative N2O‐N emissions data to all three kinetic models (FOKM, DEM, and FLOG) showed a high coefficient of determination. The r 2 values for the FOKM ranged from 0.91 to 0.99 (Table 2), whereas DEM and FLOG model had r 2 values of 0.99 for all treatments. The predicted values of all three models confirmed they can predict N2O emission dynamics in biochar‐amended soils, with the FLOG model with two N pools, followed by DEM, providing a better fit than the single‐pool FOKM (Tables 2 and 3). While the FLOG model and DEM had the highest and similar r 2 values, the FLOG model best described N2O emissions from biochars (Figure 1g,h), indicated by the lowest RMSE (0.10–0.36) and NRMSE (0.0012–0.0030) and highest r 2 values (0.99) (Table 2).
| Treatments | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Models | Parameters | GB760 d | PB460 | PB500 | PB700 | CTRL | PW500 | CM500 | HW500 |
| FOKM a | N 1 (mg kg−1) | 8.81ab | 11.05ab | 10.47ab | 7.52b | 9.75ab | 8.31b | 17.6a | 9.13ab |
| k 1 (mg kg−1 day−1) | 0.11ab | 0.08b | 0.08ab | 0.09ab | 0.12ab | 0.06b | 0.15a | 0.13ab | |
| t 1/2 (days) | 6.81ab | 13.1ab | 9.11ab | 9.81ab | 6.94ab | 12.5b | 4.77a | 6.44ab | |
| RMSE (mg kg−1) | 0.07 | 0.75 | 0.51 | 0.56 | 0.74 | 0.49 | 0.56 | 0.51 | |
| NRMSE (mg kg−1) | 0.0084 | 0.0070 | 0.0051 | 0.0108 | 0.0084 | 0.0080 | 0.0019 | 0.0077 | |
| r 2 | 0.93 | 0.95 | 0.97 | 0.93 | 0.91 | 0.97 | 0.98 | 0.95 | |
| DEM b | N 1 (mg kg−1) | 6.12b | 7.33ab | 8.10ab | 5.03b | 7.12ab | 5.67b | 16.20a | 6.88ab |
| N s (mg kg−1) | 3.24a | 3.68a | 3.11a | 3.08a | 3.57a | 5.32a | 2.34a | 2.76a | |
| k 1 (mg kg−1 day−1) | 0.316a | 0.218a | 0.234a | 0.268a | 0.375a | 0.369a | 0.188a | 0.303a | |
| k s (mg kg−1 day−1) | 0.013ab | 0.013ab | 0.010ab | 0.012ab | 0.010ab | 0.019a | 0.008b | 0.013ab | |
| t 1/2 (days) | 52.1ab | 55.9ab | 69.8ab | 58.7ab | 73.8ab | 45.9b | 106a | 56.2ab | |
| RMSE (mg kg−1) | 0.15 | 0.22 | 0.17 | 0.12 | 0.14 | 0.16 | 0.37 | 0.12 | |
| NRMSE (mg kg−1) | 0.0021 | 0.0021 | 0.0017 | 0.0024 | 0.0016 | 0.0026 | 0.0012 | 0.0018 | |
| r 2 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | |
| FLOG c | N 1 (mg kg−1) | 5.80b | 8.27b | 8.76b | 5.27b | 7.45b | 4.45b | 19.9a | 6.69b |
| N 2 (mg kg−1) | 3.75a | 3.24a | 2.54a | 2.98a | 2.82a | 2.53a | 2.02a | 2.64a | |
| k 1(mg kg−1 day−1) | 0.17a | 0.16a | 0.13a | 0.18a | 0.19a | 0.22a | 0.14a | 0.18a | |
| k 2 (day) | 30.0b | 84.9a | 74.1ab | 57.6ab | 79.9ab | 68.7ab | 53.0ab | 88.4a | |
| k 3 (day) | 57.4a | 28.6bc | 44.5ab | 49.6ab | 38.3ab | 32.2abc | 14.6c | 37.1ab | |
| RMSE (mg kg−1) | 0.12 | 0.18 | 0.13 | 0.11 | 0.12 | 0.19 | 0.36 | 0.10 | |
| NRMSE (mg kg−1) | 0.0017 | 0.0017 | 0.0013 | 0.0021 | 0.0014 | 0.0030 | 0.0012 | 0.0015 | |
| r 2 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | |
| Variables | N2O | N 1 | N 2 | k 1 | k 2 | k 3 |
|---|---|---|---|---|---|---|
| Total N | 0.52** | 0.67*** | −0.48* | −0.05 ns | −0.14 ns | −0.49* |
| Total C | −0.54** | −0.71*** | 0.47* | 0.17 ns | 0.25 ns | 0.36 ns |
| C: N | −0.26 ns | −0.34 ns | 0.22 ns | −0.06 ns | 0.09 ns | 0.30 ns |
| N2O | 1 | 0.85*** | −0.70*** | −0.36 ns | −0.02 ns | −0.43* |
The predicted parameter values across the three models were inconsistent among treatments (Table 2). In the case of forest biochar, the labile N fraction (N l) predicted by FOKM ranged from 7.52 to 11.1 mg kg−1, and the labile N fraction (N l) predicted by DEM ranged from 5.03 to 8.10 mg kg−1. Similarly, the FLOG model predicted that the labile N 1 pool ranged from 5.27 to 8.76 mg kg−1. The slow pool fraction (N s) predicted by DEM was similar across all forest biochar. Likewise, similar values were observed for the logistic delayed pool (N 2) estimated by the FLOG model. The FOKM predicted that forest biochars’ N mineralization rate ranged from 0.08 to 0.09 mg kg−1 day−1. Although nonsignificant, these values were slower than the CTRL (0.12 mg kg−1 day−1). The degradation rate (k s) for the slow pool in DEM ranged from 0.007 to 0.019 mg kg−1 day−1, with GB760 having the fastest and PB700 having the slowest mineralization among forest biochars.
The range of labile (N l) fraction among agricultural biochar according to the FOKM ranged between 8.31 and 17.6 mg kg−1, and for DEM, 5.67 and 16.20 mg kg−1. The FOKM, DEM, and FLOG model estimated labile N fractions for CM500 to be 17.7, 16.2, and 19.9 mg kg−1, respectively. According to the FLOG model prediction, the N l fraction in CM500 was 1.97–3.47 times greater than that of all other agricultural biochar and CTRL. The slow N pool across different treatments was similar to what the DEM and FLOG model predicted. The labile N (k l) decomposition rate significantly differed only in the FOKM. Labile N pool decomposition was significantly (2.62 times) faster for CM500 than PW500. In contrast, the DEM model predicted that slow N pool decomposition (k s) was considerably slower for CM500 (2.51 times) than for PW500 (0.0199 mg kg−1 day−1).
3.2.The half‐life of the labile and slow pools of nitrogen
The half‐life of the labile pool N (N l) among forest biochar ranged from 6.44 to 13.1 days, according to the FOKM. (Table 2). The FOKM predicted that soil with GB760 would lose N l fraction in 6.81 days. The FLOG model predicted that the time to decompose half of the logistic pool of N (k 2) was also the smallest for GB760 (30 days) and significantly different from k 2 in the PB460. Meanwhile, k 3, which explains the time from 50% to 75% decomposition of the logistic N pool, was highest in GB760.
Among agriculture biochars, the FOKM predicted the half‐life of the labile N pool (N l) ranged from 4.77 to 12.5 days (Table 2). The shortest half‐life was predicted for CM500 biochar (4.77 days), while the N l of PW500 biochar was the longest (12.5 days) (Table 2). The longest half‐life for the slow pool N (N s) was predicted for CM500 (106 days), and the shortest was for PW500, 5.9 days. The k 2 of the FLOG model was in the range of 53.0–88.4 days (Table 2). HW500 showed the most extended half‐life (88.4 days) of the logistic pool. Similarly, k 3 ranged between 14.6 and 37.6 days, with the lowest being for the CM500 among all other biochar types.
3.3.Cumulative nitrous oxide emissions at 301 days of incubation
The cumulative N2O‐N emission was numerically smaller for PB700 (7.98 mg kg−1) and GB760 (9.28 mg kg−1) compared to CTRL (10.4 mg kg−1), with reductions of 23.3% and 10.8%, respectively (Figure 2), but it was not significantly different. In contrast, N2O‐N emission was significantly different among agricultural biochar treatments. Particularly, soil amended with CM500 (18.1 mg kg−1) had 74.0% greater cumulative N₂O‐N emissions than CTRL (10.4 mg kg−1) and 126%–107% greater emission than plant‐based agricultural biochars PW500 (8.72 mg kg−1) and HW500 (8.93 mg kg−1) (Figure 2). The HW500 and PW500 biochar, although nonsignificant, had 16.1% and 14.1% lower cumulative N2O‐N emissions than CTRL (10.37 mg kg−1) (Figure 2). Moreover, manure biochar also had significantly greater emission than forest biochar (PB500, PB700, and GB760).
3.4.Correlation between kinetic model parameters and observed data
Cumulative N2O‐N emission was positively correlated with total N (r = 0.52; p < 0.01) and negatively correlated with total C (r = −0.54, p < 0.01) content of biochar, but we did not observe any significant correlation between the C:N ratio of biochar and N2O‐N emission (Table 3). The correlation analysis showed that the estimated labile N pool (N l) by the FLOG model was positively correlated with the N content of biochar (r = 0.67, p < 0.001) (Table 3). The FLOG model's projected N 2 and K 3 parameters had a strong negative connection with biochar N concentration (r = −0.48, p < 0.05) and (r = −0.49, p < 0.01). In the FLOG model, the N 1, N 2, and K 3 parameters showed significant correlations (r = 0.85, p < 0.001; r = −0.70, p < 0.001; and r = −0.41, p < 0.05, respectively) with cumulative N2O‐N, respectively (Table 3).
4.DISCUSSION
4.1.Contrasting effects of biochar from diverse biomass types on soil nitrous oxide emissions
Soil amended with CM500 biochar produced significantly greater cumulative N2O‐N than the control and the plant‐based biochars, likely related to the N composition and greater mineralization. High N content (19.9 g kg−1) and low C:N ratio (20:1) of CM500 biochar likely accelerated N transformation and loss in the form of N2O (Schouten et al., 2012). A positive correlation between cumulative N2O‐N emissions and N concentration of biochar also suggests that increasing N availability promotes N2O emissions. Manure‐derived biochar often has higher nutrient levels than biochar derived from lignocellulosic feedstocks (Gul & Whalen, 2016; Gul et al., 2015; Singh et al., 2010), leading to increased microbial activity and rapid decomposition, increased mineral N availability for nitrification and denitrification, and increased N2O emissions. In contrast, PB700 had 24% lower cumulative N2O‐N emissions than CTRL, likely due to (i) the non‐electrostatic sorption of NH4 + or NO3 − in the micropores of biochar and (ii) reduced availability of substrates for nitrification (Nelissen et al., 2014). The high discrepancy in emissions data was due to heterogeneity in biochar particles. Biochar generated at higher temperatures tends to have high NO3‐absorption capacity, decreases the availability of N substrates, and subsequently reduces soil N2O production (Clough et al., 2013). In contrast to earlier studies (Ameloot et al., 2013; Nelissen et al., 2014), our study did not detect any apparent effect of pyrolysis temperatures (700°C, 500°C, and 460°C) of pinewood biochars on cumulative N2O. In addition, previous studies have linked the cumulative N2O mitigation effect of biochar to N fertilizer applications, a scenario in laboratory conditions. Grutzmacher et al. (2018) conducted a laboratory study comparing the effects of biochar derived from sewage sludge, chicken manure, eucalyptus sawdust, and filter cake on N2O emissions in N‐fertilized and unfertilized soils. The Grutzmacher study observed that in the absence of N fertilizer, chicken manure biochar had higher soil N2O emissions than no biochar (unamended soil), while the other biochar treatments, with their N content ranging from 0.3% to 2.3%, did not significantly differ from the unamended soil. Among several different factors, the availability of carbon from biochar is a significant factor in efforts to reduce soil N2O emissions. Likewise, organic carbon can influence denitrification by providing an additional source of electron donors required for heterotrophic denitrification bacteria (Grutzmacher et al., 2018). However, since fertilizer was not applied, NO3 − and NO2 − may have been insufficient to promote significant N₂O emissions in unamended soil, limiting the biochar's effectiveness in mitigating emissions, as in the case of fertilizer. In addition, other factors affecting soil N2O emissions include soil type, temperature, moisture content, biochar particle size, and application technique. Future studies investigating how biochar particle sizes and external mineral inputs in soil incubation influence soil N2O emissions would enhance our knowledge for broader applications.
4.2.The first‐order logistic model best describes nitrous oxide emissions
Mathematical models are extensively used to explain soil N dynamics and soil organic matter decomposition (e.g., Adhikari et al., 2024; Stanford & Smith, 1972). The N dynamics assessments show rapid mineralization of the labile N pool, increasing the availability of NO3 − and NH4 +, which can enhance N2O emissions through nitrification and denitrification. The FOKMs are commonly used to describe N mineralization dynamics (Stanford & Smith, 1972). However, on complex organic matters such as biochar, microbial communities utilize N from less labile and recalcitrant pools once the readily available N is used. This will create inflection points in the N mineralization curve, and simpler models, such as first‐order and exponential models, cannot capture and describe these dynamics. The FLOG model incorporates a logistic component to explain how gradual changes in N availability over time and microbial feedback mechanisms influence N2O emissions. The FLOG model has previously been used to describe carbon mineralization in alkaline‐stabilized biosolids and has been chosen over FOKM and DEM (Gillis & Price, 2011), but it has never been used to explain the dynamics of N2O emissions. Therefore, our finding is the first to report that the FLOG model can best describe N2O emissions dynamics because it accounts for the delayed N pool that is initially unavailable for microorganisms and requires specific microbial development for degradation. Specifically, this model is optimal for describing N mineralization from biochar with more recalcitrant N content and in cases where N mineralization does not follow the exponential pattern. In our study, the FLOG provided a better fit over DEM and FOKM for explaining the N2O emissions dynamics. The FLOG model, with the concept of multiple N pools, had a similar R 2 value to that of DEM. This suggests that a two‐pool model can effectively capture the dynamics of N2O emissions in biochar‐amended soils over time (Singh et al., 2012). However, a comparison of the two models, DEM and FLOG, showed that the FLOG model had consistently lower RMSE and NRMSE values than the DEM for all biochar amendments and the control, indicating superior performance. In our study, the significant positive correlations of cumulative N2O‐N with the labile pool and negative correlation with slow N pools in the FLOG model also highlight its effectiveness in capturing N2O emissions dynamics. In the DEM, a significant positive correlation with N2O‐N was observed only for the labile N pool, with no correlation for the slow pool. This suggests the model may not fully capture N2O contributions from the less labile fraction of N in biochars. The significant correlation between the kinetic parameters (N 1 and N 2) and N2O emission further demonstrates the suitability of the FLOG model for describing N transformation in biochar‐amended soils. However, the model performance can vary with biochar types and soil environments and should be tested in different management scenarios to validate its broad applicability.
4.3.Feedstock composition and pyrolysis temperature regulate nitrous oxide emissions
Both laboratory incubation and kinetic modeling results show the vital role of biomass composition, specifically labile nutrient content, and pyrolysis temperature on soil N2O release. A single pool FOKM predicted a significantly higher mineralization rate of the labile N pool (k l) for CM500, along with a very short half‐life, compared to the slower mineralization and extended half‐life of the labile N pool in PW500. The DEM showed the difference in mineralization of the slow pool, that is, faster mineralization rate (k 2) in PW500 than in CM500. In addition, the shorter N l pool and longer half‐life of the stable N pool (N s) in CM500 suggest rapid mineralization of the biodegradable organic N pools (N l), leaving behind a relatively stable N pool with reduced decomposition. The large labile pools and rapid degradation of the labile pools in CM500 suggest that manure biochar is not suitable for N2O mitigation (Woolf et al., 2010). However, CM500 remains valuable as a soil amendment for crop growth due to its higher N contribution and slower rate of N release compared to inorganic N fertilizers. Although some N from the biochar (such as CM500) undergoes mineralization, this usually does not exceed 10%–20% of its total N content and may gradually supply N in the long term (Schouten et al., 2012). Plant‐derived biochar, on the other hand, was more suited for N2O mitigation due to its lower labile pool fraction and slower mineralization rate of both labile and (N l) and slow pool (N s). Compared to other plant‐derived biochar, the N pools (N l and N s) mineralization rate for PB500 and PB700 biochar was consistently slow. However, discrepancies in the N transformation time of the delayed pool, as estimated by FLOG, indicate the possibility of different microbial nutrient acquisition strategies. Biochar typically reduces soil NO3–N concentration, inhibiting N2O emission, but the relative impact varies with N composition in biomass because it affects N absorption (Tang et al., 2022), the abundance and composition of nitrifying and denitrifying microbial communities (Harter et al., 2016; Shi et al., 2019), and particularly the activity of denitrifying bacteria (Krause et al., 2018). This further highlights the importance of choosing the right model for accurately representing N dynamics. Long‐term field research and evaluation of N2O emissions dynamics in diverse soil types, temperature, and moisture regimes will help determine the best combination of biochar types with fertilizers to minimize N2O release and sustain crop production and soil fertility to achieve the dual goal of improving soil health and mitigating climate change. Overall, our experiment showed contrasting responses of agricultural and forest‐derived biochars on soil N2O emissions. The FLOG model accurately captured the emissions dynamics.
5.CONCLUSIONS
This study examined N₂O emissions from soil amended with biochar derived from agricultural and forest biomass through long‐term incubation and compared three nonlinear empirical models. Forest biochars PB700 and GB760 reduced N2O emissions compared to the control treatment, suggesting their potential to reduce global warming. Their N mineralization rates (for labile, slow, or delayed pools) were consistently slower than those of other biochars, suggesting the potential to reduce N₂O emissions in field conditions by applying pine biochar. Manure‐derived biochar (CM500) with high N, on the other hand, resulted in significantly higher cumulative N2O‐N emissions than the control and the other plant‐originated agricultural and forest biochars. Plant residue biochar (forest or agricultural) may help reduce GHG emissions more effectively than manure biochar, but the latter has a greater potential to enhance soil N content, improve nutrient cycling, and increase soil fertility. Model summary (r 2, RMSE, and NRMSE) indicated that the FLOG model can best describe N2O emissions. While kinetic parameters obtained from all three (FOKM, DEM, and FLOG) models could predict the N2O emissions, the FLOG model, which followed an exponential trend and had inflection points when microbes were deprived of more‐labile N and switched to less‐labile N sources, offered advantages over the other two models. The larger flux of N2O was associated with the labile N pool and higher rate constants and shorter half‐lives.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ACKNOWLEDGMENTS
This work was funded by the United States Department of Agriculture (USDA) Natural Resources Conservation Services (GR0007378) and partly by the USDA National Institute of Food and Agriculture (2023‐69016‐39062).
Untitled section
Sharma, B. , Ghimire, R. , Sapkota, S. , Shrestha, P. , Brewer, C. E. , & Adhikari, S. (2025). Nitrous oxide mitigation potential of biochar derived from agricultural and forest biomass: Effects of feedstock composition and pyrolysis temperature. Journal of Environmental Quality, 54, 1746–1758. 10.1002/jeq2.70054
DATA AVAILABILITY STATEMENT
Data will be available from the corresponding author on a reasonable request.
REFERENCES
Untitled section
References
- Adhikari, A. D. , Shrestha, P. , Ghimire, R. , Liu, Z. , Pollock, D. A. , Acharya, P. , & Aryal, D. R. (2024). Cover crop residue quality regulates litter decomposition dynamics and soil carbon mineralization kinetics in semi‐arid cropping systems. Applied Soil Ecology, 193, 105160. 10.1016/j.apsoil.2023.105160
- Almendros, G. , Knicker, H. , & González‐Vila, F. J. (2003). Rearrangement of carbon and nitrogen forms in peat after progressive thermal oxidation as determined by solid‐state 13C‐and 15N‐NMR spectroscopy. Organic Geochemistry, 11, 1559–1568. 10.1016/S0146-6380(03)00152-9
- Ameloot, N. , De Neve, S. , Jegajeevagan, K. , Yildiz, G. , Buchan, D. , Funkuin, Y. N. , Prins, W. , Bouckaert, L. , & Sleutel, S. (2013). Short‐term CO2 and N2O emissions and microbial properties of biochar amended sandy loam soils. Soil Biology and Biochemistry, 57, 4014–10. 10.1016/j.soilbio.2012.10.025
- Bai, M. , Wilske, B. , Buegger, F. , Esperschütz, J. , Kammann, C. I. , Eckhardt, C. , Koestler, M. , Kraft, P. , Bach, M. , Frede, H. G. , & Breuer, L. (2013). Degradation kinetics of biochar from pyrolysis and hydrothermal carbonization in temperate soils. Plant and Soil, 372, 375–387. 10.1007/s11104-013-1745-6
- Baldock, J. A. , & Smernik, R. J. (2002). Chemical composition and bioavailability of thermally altered Pinus resinosa (red pine) wood. Organic Geochemistry, 33, 10931–109. 10.1016/S0146-6380(02)00062-1
- Baruya, P. (2015). World forest and agricultural crop residue resources for cofiring . International Energy Agency Clean Coal Centre.
- Bentsen, N. S. , & Felby, C. (2010). Technical potentials of biomass for energy services from current agriculture and forestry in selected countries in Europe, The Americas, and Asia (Forest & Landscape Working Papers No. 54). Forest & Landscape.
- Cayuela, M. L. , Van Zwieten, L. , Singh, B. P. , Jeffery, S. , Roig, A. , & Sánchez‐Monedero, M. A. (2014). Biochar's role in mitigating soil nitrous oxide emissions: A review and meta‐analysis. Agriculture Ecosystems and Environment, 191, 5–16. 10.1016/j.agee.2013.10.009
- Cheng, Y. , Cai, Z. C. , Chang, S. X. , Wang, J. , & Zhang, J. B. (2012). Wheat straw and its biochar have contrasting effects on inorganic N retention and N2O production in a cultivated Black Chernozem. Biology and Fertility of Soils, 48, 941–946. 10.1007/s00374-012-0687-0
- Clough, T. J. , & Condron, L. M. (2010). Biochar and the nitrogen cycle: Introduction. Journal of Environmental Quality, 39, 1218–1223. 10.2134/jeq2010.0204
- Clough, T. J. , Condron, L. M. , Kammann, C. , & Müller, C. (2013). A review of biochar and soil nitrogen dynamics. Agronomy, 3, 275–293. 10.3390/agronomy3020275
- Deng, B. , Yuan, X. , Siemann, E. , Wang, S. , Fang, H. , Wang, B. , Gao, Y. , Shad, N. , Liu, X. , Zhang, W. , Guo, X. , & Zhang, L. (2021). Feedstock particle size and pyrolysis temperature regulate effects of biochar on soil nitrous oxide and carbon dioxide emissions. Waste Management, 120, 33–40. 10.1016/j.wasman.2020.11.015
- Dodor, D. , Amanor, Y. , Asamoah‐Bediako, A. , Maccarthy, D. , Dovie, D. , & Maccarthy, S. (2019). Kinetics of carbon mineralization and sequestration of sole and/or co‐amended biochar and cattle manure in sandy soil. Communications in Soil Science and Plant Analysis. 10.1080/00103624.2019.1671443
- Egyir, M. , Lawson, I. Y. D. , Dodor, D. E. , & Luyima, D. (2023). Agro‐industrial waste biochar abated nitrogen leaching from tropical sandy soils and boosted dry matter accumulation in maize. C, 9, 34. 10.3390/c9010034
- Fidel, R. B. , Laird, D. A. , & Parkin, T. B. (2019). Effect of biochar on soil greenhouse gas emissions at the laboratory and field scales. Soil Systems, 3, 8. 10.3390/soilsystems3010008
- Foltz, M. E. , Zilles, J. L. , & Koloutsou‐Vakakis, S. (2019). Prediction of N2O emissions under different field management practices and climate conditions. Science of the Total Environment, 646, 872–879. 10.1016/j.scitotenv.2018.07.364
- Forster, P. , Storelvmo, T. , Armour, K. , Collins, W. , Dufresne, J.‐L. , Frame, D. , Lunt, D. J. , Mauritsen, T. , Palmer, M. D. , Watanabe, M. , Wild, M. , & Zhang, H. (2021). The earth's energy budget, climate feedbacks, and climate sensitivity. In Masson‐Delmotte V., Zhai P., Pirani A., Connors S. L., Péan C., Berger S., Caud N., Chen Y., Goldfarb L., Gomis M. I., Huang M., Leitzell K., Lonnoy E., Matthews J. B. R., Maycock T. K., Waterfield T., Yelekçi O., Yu R., & Zhou B. (Eds.), Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change (pp. 923–1054).Cambridge University Press. 10.1017/9781009157896.009
- Frank, H. , Schmid, H. , & Hülsbergen, K. J. (2019). Modeling greenhouse gas emissions from organic and conventional dairy farms. Journal of Sustainable Organic Agricultural Systems, 69(1), 37–46. 10.3220/LBF1584375588000
- Gertsev, V. I. , & Gertseva, V. V. (2004). Classification of mathematical models in ecology. Ecological Modelling, 178(3–4), 329–334. 10.1016/j.ecolmodel.2004.03.009
- Ghimire, B. , Ghimire, R. , VanLeeuwen, D. , & Mesbah, A. (2017). Cover crop residue amount and quality effects on soil organic carbon mineralization. Sustainability, 9(12), 2316. 10.3390/su9122316
- Ghimire, R. , & Khanal, B. R. (2020). Soil organic matter dynamics in semi‐arid agroecosystems transitioning to dryland. PeerJ, 8, e10199. 10.7717/peerj.10199
- Gillis, J. D. , & Price, G. W. (2011). Comparison of a novel model to three conventional models describing carbon mineralization from soil amended with organic residues. Geoderma, 160, 304–310. 10.1016/j.geoderma.2010.09.025
- Gillis, J. D. , & Price, G. W. (2016). Linking short‐term soil carbon and nitrogen dynamics: Environmental and stoichiometric controls on fresh organic matter decomposition in agroecosystems. Geoderma, 274, 35–44. 10.1016/j.geoderma.2016.03.026
- Grutzmacher, P. , Puga, A. P. , Bibar, M. P. S. , Coscione, A. R. , Packer, A. P. , & de Andrade, C. A. (2018). Carbon stability and mitigation of fertilizer induced N2O emissions in soil amended with biochar. Science of the Total Environment, 625, 1459–1466. 10.1016/j.scitotenv.2017.12.196
- Gul, S. , & Whalen, J. K. (2016). Biochemical cycling of nitrogen and phosphorus in biochar‐amended soils. Soil Biology & Biochemistry, 103, 1–15. 10.1016/j.soilbio.2016.08.001
- Gul, S. , Whalen, J. K. , Thomas, B. W. , Sachdeva, V. , & Deng, H. (2015). Physico‐chemical properties and microbial responses in biochar‐amended soils: Mechanisms and future directions. Agriculture Ecosystems and Environment, 206, 46–59. 10.1016/j.agee.2015.03.015
- Hagemann, N. , Kammann, C. I. , Schmidt, H. P. , Kappler, A. , & Behrens, S. (2017). Nitrate capture and slow release in biochar amended compost and soil. PLoS One, 12, e0171214. 10.1371/journal.pone.0171214
- Harindintwali, J. D. , Zhou, J. , Muhoza, B. , Wang, F. , Herzberger, A. , & Yu, X. (2021). Integrated eco‐strategies towards sustainable carbon and nitrogen cycling in agriculture. Journal of Environmental Management, 293, 112856. 10.1016/j.jenvman.2021.112856
- Harter, J. , Weigold, P. , El‐Hadidi, M. , Huson, D. H. , Kappler, A. , & Behrens, S. (2016). Soil biochar amendment shapes the composition of N2O‐reducing microbial communities. Science of the Total Environment, 562, 379–390. 10.1016/j.scitotenv.2016.03.220
- Krause, H. , Hüppi, R. , Leifeld, J. , El‐Hadidi, M. , Harter, J. , Kappler, A. , Hartmann, M. , Behrens, S. , Mäder, P. , & Gattinger, A. (2018). Biochar affects community composition of nitrous oxide reducers in a field experiment. Soil Biology & Biochemistry, 119, 143–151. 10.1016/j.soilbio.2018.01.018
- Kuzyakov, Y. , Bogomolova, I. , & Glaser, B. (2014). Biochar stability in soil: Decomposition during eight years and transformation as assessed by compound‐specific 14C analysis. Soil Biology & Biochemistry, 70, 229–236. 10.1016/j.soilbio.2013.12.021
- Lehmann, J. , Cowie, A. , Masiello, C. A. , Kammann, C. , Woolf, D. , Amonette, J. E. , Cayuela, M. L. , Camps‐Arbestain, M. , & Whitman, T. (2021). Biochar in climate change mitigation. Nature Geoscience, 14, 883–892. 10.1038/s41561-021-00852-8
- Li, F. , Cao, X. , Zhao, L. , Yang, F. , Wang, J. , & Wang, S. (2013). Short‐term effects of raw rice straw and its derived biochar on greenhouse gas emission in five typical soils in China. Soil Science and Plant Nutrition, 59, 800–811. 10.1080/00380768.2013.821391
- Molina, J. A. E. , Clapp, C. E. , & Larson, W. E. (1980). Potentially mineralizable nitrogen in soil: The simple exponential model does not apply for the first 12 weeks of incubation. Soil Science Society of America Journal, 44, 442–443. 10.2136/sssaj1980.03615995004400020054x
- Mosier, A. , Kroeze, C. , Nevison, C. , Oenema, O. , Seitzinger, S. , & Van Cleemput, O. (1998). Closing the global N2O budget: Nitrous oxide emissions through the agricultural nitrogen cycle. Nutrient Cycling in Agroecosystems, 52, 225–248. 10.1023/A:1009740530221
- Nelissen, V. , Saha, B. K. , Ruysschaert, G. , & Boeckx, P. (2014). Effect of different biochar and fertilizer types on N2O and NO emissions. Soil Biology & Biochemistry, 70, 244–255. 10.1016/j.soilbio.2013.12.026
- Qayyum, M. F. , Steffens, D. , Reisenauer, H. P. , & Schubert, S. (2012). Kinetics of carbon mineralization of biochar compared with wheat straw in three soils. Journal of Environmental Quality, 41, 1210–1220. 10.2134/jeq2011.0058
- Rehrah, D. , Reddy, M. R. , Novak, J. M. , Bansode, R. R. , Schimmel, K. A. , Yu, J. , Watts, D. W. , & Ahmedna, M. (2014). Production and characterization of biochar from agricultural by‐products for use in soil quality enhancement. Journal of Analytical and Applied Pyrolysis, 108, 301–309. 10.1016/j.jaap.2014.03.008
- Sapkota, S. , Ghimire, R. , Bista, P. , Hartmann, D. , Rahman, T. , & Adhikari, S. (2024). Greenhouse gas mitigation and soil carbon stabilization potential of forest biochar varied with biochar type and characteristics. Science of the Total Environment, 931, 172942. 10.1016/j.scitotenv.2024.172942
- Sapkota, S. , Ghimire, R. , Brewer, C. E. , & Fernando, S. (2025). Contrasting effects of plant and animal residue biochars on soil health, carbon stability, and crop yield. Journal of Soils and Sediments, 25(3), 703–717. 10.1007/s11368-025-03968-1
- Schimel, J. P. , & Bennett, J. (2004). Nitrogen mineralization: Challenges of a changing paradigm. Ecology, 85, 591–602. 10.1890/03-8002
- Schouten, S. , van Groenigen, J. W. , Oenema, O. , & Cayuela, M. L. (2012). Bioenergy from cattle manure? Implications of anaerobic digestion and subsequent pyrolysis for carbon and nitrogen dynamics in soil. GCB Bioenergy, 4, 751–760. 10.1111/j.1757-1707.2012.01163.x
- Seitzinger, S. P. , & Kroeze, C. (1998). Global distribution of nitrous oxide production and N inputs in freshwater and coastal marine ecosystems. Global Biogeochemical Cycles, 12, 93–113. 10.1029/97GB03657
- Shi, Y. , Liu, X. , & Zhang, Q. (2019). Effects of combined biochar and organic fertilizer on nitrous oxide fluxes and the related nitrifier and denitrifier communities in a saline‐alkali soil. Science of the Total Environment, 686, 199–211. 10.1016/j.scitotenv.2019.05.394
- Singh, B. P. , Cowie, A. L. , & Smernik, R. J. (2012). Biochar carbon stability in a clayey soil as a function of feedstock and pyrolysis temperature. Environmental Science & Technology, 46, 11770–11778. 10.1021/es302545b
- Singh, B. P. , Hatton, B. J. , Singh, B. , Cowie, A. L. , & Kathuria, A. (2010). Influence of biochar on nitrous oxide emission and nitrogen leaching from two contrasting soils. Journal of Environmental Quality, 39, 1224–1235. 10.2134/jeq2009.0138
- Soil Survey Staff . (2023). Web soil survey . USDA. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx
- Spokas, K. A. , & Reicosky, D. C. (2009). Impacts of sixteen different biochar on soil greenhouse gas production. Annals of Environmental Science, 3, 179–193.
- Stanford, G. , & Smith, S. J. (1972). Nitrogen mineralization potentials of soils. Soil Science Society of America Journal, 36(3), 465–472. 10.2136/sssaj1972.03615995003600030029x
- Tang, Z. , Liu, X. , Li, G. , & Liu, X. (2022). Mechanism of biochar on nitrification and denitrification to N2O emissions based on isotope characteristic values. Environmental Research, 212, 113219–113219. 10.1016/j.envres.2022.113219
- US Environmental Protection Agency (EPA) . (2023). Inventory of U.S. greenhouse gas emissions and sinks: 1990–2021 . https://www.epa.gov/ghgemissions/inventory‐us‐greenhouse‐gasemissions‐and‐sinks‐1990‐2021
- Woolf, D. , Amonette, J. E. , Street‐Perrott, F. A. , Lehmann, J. , & Joseph, S. (2010). Sustainable biochar to mitigate global climate change. Nature Communications, 1, Article 56. 10.1038/ncomms1053
- Zimmerman, A. R. (2010). Abiotic and microbial oxidation of laboratory‐produced black carbon (biochar). Environmental Science & Technology, 44, 1295–1301. 10.1021/es903140c
Associated Data
Data Availability Statement
Data will be available from the corresponding author on a reasonable request.