Process-based modeling of soil nitrous oxide emissions from United States corn fields under different management and climate scenarios coupled with evaluation using regional estimates

1 Introduction

Many studies attempt to quantify broad-scale agriculturally-related greenhouse gas (GHG) emissions (Mosier et al., 1998; Lokupitiya and Paustian, 2006; Yao et al., 2006; Li, 2007; Olander et al., 2013) and soil organic carbon (SOC) sequestration (Sleutel et al., 2003; Stockmann et al., 2013; Nayak et al., 2019; Paustian et al., 2019) to support policymaking and GHG accounting. Efforts have intensified in the United States with a recommitment to GHG reductions and the Paris Agreement (Rumpel et al., 2018; Guenet et al., 2021). In particular, nitrous oxide (N₂O) which has a global warming potential (GWP) that is 298 times greater than carbon dioxide (CO₂) (IPCC, 2006) has gained enormous attention that leads to research on mitigating N₂O emissions through adaptive management practices (Mosier et al., 1996; Ogle et al., 2014; Maaz et al., 2021) and quantifying environmental impacts of N₂O emissions in a future climate (Butterbach-Bahl and Dannenmann, 2011; He et al., 2018; Ma et al., 2018).

Improving the capacity to quantify agricultural N₂O emissions at the regional or national scales under various management and climate scenarios is a priority for agriculture which accounts for approximately two-thirds of the total N₂O emissions (IPCC, 2019). Broad-scale GHG inventories (Zheng et al., 2004; Rochette et al., 2008; Mazzetto et al., 2020) and life cycle analyses (LCA) have estimated soil N₂O emissions using IPCC emission factors (EFs), which is defined as the ratio between N₂O emissions and N input rates, at Tier-1 (global values) (Bouwman, 1996) or Tier-2 (regionally-specific values adjusted with covariates or classes) scales (IPCC, 2006; Hergoualc’h et al., 2019). Model-based estimates that better account for soil organic matter (SOM) dynamics and site-specific environmental and management factors (Li et al., 1992; Hansen, 2002; Gilhespy et al., 2014; Berardi et al., 2020; Del Grosso et al., 2020) are increasingly being used for valorization and GHG accounting (Crossman et al., 2013; Banger et al., 2017; Sela et al., 2017; Osmond et al., 2018). Spatially-gridded models coupled with scenario analysis provide an important opportunity to improve GHG inventories by revealing regional differences in N₂O loss resulting from site and management-specific interactions (Laville et al., 2011; Iqbal et al., 2018).

Several broad (Tesfaye et al., 2021) and national-scale studies have used process-based models (Table 1) to estimate spatial variability of soil-based N₂O emissions at fine (e.g., county) spatial resolutions in order to inform management at a meaningful spatial unit. Unfortunately, many of these studies only carried out model validation by comparison with field observations collected over a narrow range or individual site or using Tier-1 estimates, which yielded little spatial or temporal information. Considering that the high variability of growing season N₂O (N₂O_gs) contributes to the main challenge of N₂O quantification (Werner et al., 2007; Leip et al., 2011; Smith, 2017), Tier-2 estimates derived from strategically compiled covariates or class-based datasets are needed to generate EFs to evaluate regionalized estimates generated with process models (de Klein et al., 2020).

TABLE 1

TABLE 1. Process-based models applied to estimate soil N₂O emissions at the national or broader scale.

To effectively model N₂O emissions and the associated EFs, it is necessary to calibrate and evaluate crop growth, soil moisture and SOC (Del Grosso et al., 2011). At present, very few studies (Table 1) have calibrated key parameters or validated N-submodel outputs other than N₂O emissions. The performance of SOC-submodels has been improved by adjustment of crop growth and soil decay factors using field observations of crop yield and SOC (Meersmans et al., 2013; Kwon et al., 2017; Gautam et al., 2020). Few broad-scale studies of soil N₂O flux have evaluated soil moisture estimates or the influence of temporal weather variability on emissions even though these are key drivers of N₂O across scales (Weitz et al., 2001; Wagner et al., 2003; Butterbach-Bahl et al., 2013). Models typically rely on surrogates such as soil texture and SOM coupled with simple water budget-based method to simulate moisture patterns (Saxton and Rawls, 2006). Datasets like North American Soil Moisture Dataset (NASMD) (Quiring et al., 2016; Yuan et al., 2021) that report uniform moisture present an opportunity to evaluate regional and temporal estimates needed for Tier-2 modeling of N₂O.

Model performance is greatly influenced by data quality, and this is particularly problematic for N fertilizer input types and rates, which are one of the most influential management factors determining N₂O (Akiyama et al., 2006; Kros et al., 2012; Eagle et al., 2017). Unfortunately, most efforts use N data reported at the state or regional level (Li et al., 1996; Del Grosso et al., 2006; Del Grosso et al., 2010). Inadequacies in the data describing N fertilizer additions to croplands that account for as much as half of direct N₂O emissions from agricultural soils, has resulted in large uncertainty in Tier-1 and 2-based estimates of soil N₂O EFs (Tian et al., 2020). A newly available United States county-level dataset for corn fertilizer N application rates (Xia et al., 2021) significantly improves N input data resolution. In addition, estimates of N₂O_gs must also account for the form of N fertilizers applied because chemical structure and organic and inorganic materials differ in their influence on N₂O emissions (Petersen et al., 1996; Shcherbak et al., 2014; Liu S. et al., 2017) and such differences need to be quantified to inform decision-making regarding agricultural N management. Default fertilizer coefficients commonly used in N₂O models can also be verified using compilations of field observations. The same is true for model coefficients used by N₂O models to adjust rates based on agricultural management practices. Ideally models can account for spatial interactions that can produce variable effects of practices such as reduced tillage (Chatskikh and Olesen, 2007; Mei et al., 2018), residue incorporation (Vinther et al., 2004; Li et al., 2010), fertilization and irrigation (Grant et al., 2006; Del Grosso et al., 2008; Katayanagi et al., 2012), cover crops (Farahbakhshazad et al., 2008; Abdalla et al., 2019; Muhammad et al., 2019), and crop diversification on N₂O emissions (Petersen et al., 2006; Parihar et al., 2018). Model-derived and Tier-2 summary-based estimates of management-associated N₂O_gs emissions at the broad scale are critical to promoting the adoption or implementation of practices that can contribute to GHG mitigations.

Challenges for broad-scale modeling of N₂O_gs remain as we continue to strive to translate modeling concepts into practical tools using reproducible and transparent methods. These kinds of tools are needed to support policies like the United States. Renewable Fuel Standards (RFS) which was first launched in 2005 (Malmedal et al., 2007) and spurred development of environmental assessment and LCA of corn ethanol production (Wang et al., 2007; Hsu et al., 2010; Hong et al., 2015; Lewandrowski et al., 2020), which accounts for about two-thirds of the United States biofuel production (Birol, 2018) and has accounted for the majority of the biofuel volume required by the RFS (Bracmort, 2022). Moreover, the complexity of management impacts, as well as their interactions with weather conditions, suggest the need for broad-scale modeling to better account for scenario-based soil N₂O emissions that are representative of future climate conditions (Abdalla et al., 2010; Karimi et al., 2021; Miller et al., 2022). This work takes advantage of a ‘surrogate or parameterized’ version of the process-based CENTURY SOM model (_PCENTURY) developed by Kwon and Hudson (2010) to estimate N₂O emissions from corn-based management systems in the United States Corn Belt. The model capacity of _PCENTURY was expanded here to estimate county-level N₂O emissions by drawing on N and hydrology submodels from the daily version of CENTURY (DAYCENT) and improved N input datasets. The objectives of this study were to evaluate regional and temporal soil moisture estimates and compare management scenario-based estimates of N₂O_gs-emissions against Tier-2 meta-datasets representing dominant management practices (crop rotation, N input type, tillage) used by corn-based systems in major corn-producing states. The model was then used to simulate N₂O_gs-emissions under regionalized estimates of wet, dry, and average weather conditions because future climate scenarios are usually associated with pronounced changes in precipitation (Sillmann et al., 2013; Liu C. et al., 2017).

2 Materials and methods

2.1 Model expansion and simulation

This study expanded the _PCENTURY model (Kwon and Hudson., 2010; Kwon et al., 2013; Kwon et al., 2017) (SI: Supplementary Figure D1) by adopting the main structures and algorithms of the plant growth, water budget, soil temperature, nitrification and denitrification modules of DAYCENT (Parton et al., 1998) for N₂O modeling (SI: Supplementary Appendix SA). The resulting model simulates SOC stocks, crop biomass, soil moisture, plant available N, and soil N loss on a monthly time step. Land use and land management change scenarios were developed using a gridded SOC modeling framework developed by Kwon et al. (2020) to assess GHG emissions from corn biofuel production. The framework employs four phases of land use history described in SI: Supplementary Appendix SA. The model baseline for comparison consists of a corn-soybean rotation (CS), averaged tillage (USDA, 2019), and synthetic N fertilizer additions (USDA, 2018) under normal weather conditions defined by a 30-year average (1986–2015). Model inputs including soil, climate, crop, and management data (Table 2) and the model calibration process are described more fully in SI: Supplementary Appendix SB. Alternative rotation scenarios begin with the baseline conditions and consider continuous corn (CC) and diversified cropping systems that included a 2-year corn/rye-soybean/vetch rotation (CCvSCv) and a 3-year corn-soybean/winter wheat-ryegrass rotation (CSWCv) (Figure 1). Conventional and no-tillage scenarios were run within the baseline CS rotation. Several N input scenarios (anhydrous ammonia (AA), urea, animal manure) were run within diversified rotations. Finally, weather scenarios were run within the conventionally-tilled CS rotation substituted dry and wet average monthly weather conditions from the driest and wettest year observed between 1986 and 2015 at the county level. This created unique weather stress patterns for each county. The scenario-based simulation was carried out for a 6-year time period (2016–2021) in major United States corn-producing states with model inputs (Table 2) and calibration process described in SI: Supplementary Appendix SB.

TABLE 2

TABLE 2. Data sources for key _pCENTURY model input variables.

FIGURE 1

FIGURE 1. Management and weather scenarios used for _pCENTURY-based simulation of soil N₂O emissions for 2016-2021 from corn fields. The baseline rotation and tillage scenario marked in red is considered the most common practice within major corn growing states in the United States The normal weather condition is defined with a 30-year average (1986–2015) before the simulation period. Dry and wet weather conditions were simulated by replacing monthly weather data inputs with the driest and wettest months between 1986 and 2015.

2.2 Model evaluation

2.2.1 Preliminary evaluation of simulated soil moisture using NASMD observations

Model simulated soil moisture estimated within the CS scenario was compared to the NASMD dataset that includes observed volumetric water content in the 0-10 and 0–100 cm soil depths. Since _PCENTURY simulates soil moisture at 0-30 and 30–100 cm soil depths, a weighted average was used for 0–100 cm comparisons. The NASMD data were reported at the gridded scale (0.25°) by harmonizing soil moisture observations from 16 regional and national soil moisture monitoring networks (Xia et al., 2015a). The data were interpolated to the county-level for croplands identified by the National Land Use Dataset (Homer et al., 2007; Homer et al., 2015) to match with county-level simulations. To match with _PCENTURY time-step, monthly NASMD soil moisture was calculated by averaging daily values for each month during the 10-year period (2003–2012) for which records were available. The comparisons were carried within agroecological zones (AEZ) that group counties with similar climate conditions (Batjes et al., 1997). Model simulated monthly soil moisture during this time period were also compared directly against NASMD observations as an evaluation of model simulated temporal pattern. Correlation between _PCENTURY anomalies (_PCENTURY minus NASMD) and key modeling factors including corn and soybean yields, soil properties, and weather were used to explore differences between modeled and measured results.

2.2.2 Comparison between model simulated soil N₂O_gs and a meta-analytical summary

Modeled N₂O_gs-emissions under the normal weather condition were evaluated with the Tier-2 meta-analytical summary of 705 observations (treatment-year combinations) of field emissions made during the growing season within major corn-producing states. Data extraction, quality control, distribution of study sites, and detailed management information of the studies are presented in SI: Supplementary Appendix SC. Soil N₂O_gs-emissions were simulated in _PCENTURY based on the planting and harvest dates specified by USDA (2010) and assumed to be representative for annual N₂O emissions. Then, we compared the modeled emissions with the meta-dataset to evaluate _PCENTURY’s capacity to estimate changes in flux associated with management scenarios considered. The cropping comparisons include CC versus CS or CS-based rotations where the 2-year corn/rye-soybean/vetch simulation was compared with observations from CCvSCv systems, and corn-soybean/winter wheat-ryegrass simulation was compared with CSWCv systems. Input comparisons included synthetic versus organic (manure, effluent, composts, residues) and anhydrous ammonia versus urea. Finally, meta-data summaries of conventional versus reduced or no-tillage, were compared against model simulated results.

2.3 Estimating regional differences in soil N2O emissions from corn production

Considering future climate scenarios, growing season N₂O_gs-emissions were simulated for dry, normal, and wet weather scenarios at the county-level before linear regression was carried out in R (R Core Team, 2020) to describe regional differences in simulated N₂O_gs-emissions by AEZ and their relationships to key model inputs (N rate, precipitation, and yield) and outputs (soil moisture and plant available N).

Soil N₂O EF was calculated with Eq. 1 for different modeling scenarios and the simulation results of the baseline scenario were compared against IPCC Tier-1 estimates (both the 2006 and the revised 2019 versions) and Tier-2 meta-analytical summary. We considered the contribution of crop residue inputs for the calculation of N₂O EFs using both the modeling and the meta-analysis approach in order to align with the definition of the IPCC method.

$\text{EF}=\frac{{\text{N}}_2\text{O emissions under fertilized treatment}-{\text{background N}}_2\text{O emissions}}{\text{Total organic and inorganic N inputs}}\times 100\text{\%}(1)$

3 Results and discussion

3.1 Preliminary evaluation on model simulated temporal and regional soil moisture

Our attempt to evaluate soil moisture estimates of _PCENTURY is only preliminary considering that the model currently generated results at a monthly time-step which is much coarser than the actual soil moisture changes in time. However, since the evaluation of model simulated soil moisture has been largely ignored by previous broad-scale N₂O modeling work (Table 1), we hope that our preliminary efforts can lead to a more standardized, rigorous model calibration and validation scheme for future work aiming to provide spatial and temporal insights into soil N₂O modeling at the regional or national scale.

Monthly soil moisture patterns for NASMD and _PCENTURY estimates were similar during the growing season (R² = 0.68) when monthly averages were calculated based on a 10-year time period (SI: Supplementary Figure D2). This is consistent with Xia et al. (2015b), who found agreement between warm-season soil moisture estimates (NASMD) and land-surface model simulations. Our temporal match was slightly better for whole profile (0–100 cm) comparisons than those made for the near-surface. The tendency for NASMD estimates to be drier and less variable in surface depths during winter was observed for all AEZs. This differs from Abdalla et al. (2020) where DeNitrification-DeComposition (DNDC) significantly underestimated field-scale soil moisture. The observed difference is likely due in part to the fact that NASMD data represent the 0–10 cm and not the 0–30 cm depth simulated by _PCENTURY. In our study, model overestimation of soil moisture was observed during the cool season in soils with higher clay contents when a crop was not present (SI: Supplementary Table D1). Overestimation also increased with precipitation and temperature, although such trends would have been better reflected by finer simulation resolution (e.g., daily or hourly) than the current monthly time-step of the model. Greater ability to model evaporation, snow cover, and frozen status is likely to improve non-season moisture estimates (Chantigny et al., 2016).

The temporal variation in monthly soil moisture during the 10 years was moderately captured by _PCENTURY (Figure 2), with Pearson correlation between NASMD and _PCENTURY calculated to be 0.67 and 0.80 for the top and whole soil layers, respectively. Unfortunately, the correlation was very weak for the winter-spring non-growing season for the top soil layer (R = 0.3). It might be the case that our monthly _PCENTURY model was not able to fully capture the impact of precipitation or snow events in terms of their timing and duration on soil moisture dynamics. Soil moisture can be influential on inter-annual variations in soil N₂O emissions (Skiba and Smith, 2000; Smith et al., 2004; Oorts et al., 2007) so further improvement of model structure and calibration on parameters for calculating soil hydraulic properties (e.g., hydraulic conductivity, wilting point, field capacity) is needed to better simulate soil moisture dynamics. Our work did not directly compare the influence of soil moisture estimates on the accuracy of N₂O simulation. Ideally future work will investigate the influence of calibrating soil hydraulic parameters on simulated soil N₂O emissions against field measurements. Our preliminary results showed the need to focus on the evaluation using measures from non-growing season N₂O emissions because of the poor model performance on soil moisture estimates during this time period.

FIGURE 2

FIGURE 2. Comparison of monthly volumetric soil moisture from 2003 to 2012 estimated with _pCENTURY simulation and reported by the North American Soil Moisture Dataset (NASMD) for the (A) top and (B) whole soil layer. The moisture contents were calculated as corn planting area weighted-averages for the United States Corn Belt counties investigated in this study. Surface depth soil moisture was calculated at 0–30 cm for _pCENTURY and 0–10 cm for NASMD) observations and whole soil layer comparison was carried out at 0–100 cm depth. Pearson correlations between _pCENTURY and NASMD were calculated for the whole dataset and for the datasets containing corn growing season (April to September) and non-growing season observations, respectively.

Spatially, while _PCENTURY estimates are driven by characteristics of the dominant soil type within a county, the NASMD estimates capture field-to-field variability and likely provide a better representation of average conditions. Because county-level NASMD estimates are based on extrapolations of station-based observations that may have smoothed out spatial differences, the variances of monthly soil moisture vary less than _PCENTURY-based estimates. Moreover, the variance of interpolated datasets can be affected by the number of samples (Beguería et al., 2016) and by the number of counties within an AEZ (Hofstra et al., 2010). Soil moisture estimates in AEZs with fewer counties and/or measuring sites are expected to be less variable.

3.2 Evaluating simulated soil N₂O emissions influenced by management factors

3.2.1 Soil N₂O_gs-emissions influenced by crops and rotation

Both modeled and database-derived Tier-2 estimates of N₂O_gs-emissions were greatest in the corn phase (2.7 $\pm$ 0.6 kg N₂O ha⁻¹) of the rotation (Table 3). The greater N inputs used to produce corn increase direct soil N₂O_gs-emissions (Jarecki et al., 2009; Millar et al., 2010; Nan et al., 2016). The database-derived estimates of corn phase background soil N₂O emissions (1.16 kg ha⁻¹, Table 3) are consistent with other studies (Bouwman et al., 2002; Yan et al., 2003). Reported corn N fertilizer input rates (177 kg N ha⁻¹) were only 6% lower than rates used in _PCENTURY scenarios (187 kg N ha⁻¹), making it possible to covert corn N₂O emissions to associated EFs for comparison of the two methods. While our EFs calculated during the growing season likely underestimate annual emissions, data suggest the majority of losses occur within the cropping system. The meta-analysis of Shang et al. (2020) found annual EFs for corn fields were only slightly (0.03%) higher than growing season values. Nevertheless, regions with snowfall and multiple freeze-thaw cycles would likely differ more (Wagner-Riddle et al., 2017) and this ties to our preliminary results showing moisture in winter was not well simulated (Figure 2), in which case the modeling of winter soil N₂O emissions and subsequently annual EF can be associated with large uncertainty. In particular, the relationship between soil N₂O emissions and water filled pore space, which is highly influenced by soil moisture, has been modeled to be a non-linear curve that increases to a degree due to maximum denitrification before dropping down caused by N₂O consumption (Van Der Weerden et al., 2012; Rabot et al., 2015). This, coupled with low nitrate availability in winter favoring N₂O consumption (Davidsson and Leonardson, 1997; van Groenigen et al., 2015) pose challenges to accurate estimation of non-growing season N₂O emissions. Relatively few data are available for evaluation of non-growing season N₂O emissions since many studies reported either annual or growing season cumulative N₂O emissions. Ideally model validation can be carried out at a monthly or finer temporal resolution with new data describing relationship between N₂O and soil moisture in winter-spring.

TABLE 3

TABLE 3. Fertilizer N input rates and cumulative growing season soil N₂O emissions from _pCENTURY simulation and field measurements used for validation. The results are reported as model simulated county or field measured averages followed by standard deviations Different lower- and upper-case letters represent significant differences at p < 0.05 for corn and soybean phases, respectively.

Reported average N input rates were about 20% lower for corn within CS (161 kg N ha⁻¹) than CC (199 kg N ha⁻¹) rotations (Table 3). While data-derived estimates suggest N₂O_gs-emissions during the corn phase were reduced by 24, 41, and 51% within CS, CCvSCv, or CSWCv rotations compared to CC (3.7 kg N₂O ha⁻¹), differences among rotations were not statistically significant due to high variability in field observations (Table 3). This variability resulted from edaphic factors and variability in both the quantity and quality of residues returned by cover crops and organic amendments applied before corn. Lower corn phase N₂O emissions observed following soybean likely reflect adjustment of N inputs based on rotation and N credits (Sawyer et al., 2006; Castellano et al., 2018; Morris et al., 2018) that can reduce N₂O flux. The _PCENTURY-based corn N₂O EF for the CS baseline scenario was 0.91 $\pm$ 0.19% when results from all counties considered were pooled and weighted based on corn planting area. This estimate was slightly lower than database-derived Tier-2 estimates (1.07%) and the IPCC (2006) Tier-1 estimate for agricultural soils (1%). The _PCENTURY-based estimates for N₂O_gs-emissions from the corn phase of CS, CSWCv and, CCvSCv rotations were significantly greater than from the CC rotation (Table 3). Statistical separation is due to lower variability of simulated estimates and to the fact that N rates were not altered for different rotation scenarios. Discrepancies between the observed results and simulated results suggest simulations may exaggerate feedbacks that increase flux from diversified rotations associated with return of low C/N residues (Chen et al., 2013), increased organic matter (Varvel, 2006; Mitchell et al., 2013) and plant available N (Kuo and Sainju, 1998; Peyrard et al., 2016).

Non-corn phase N₂O flux was generally tied to N input rates. The magnitude of modeled and estimated N₂O_gs-emissions was almost identical for the wheat phase, but the variance was again greater for database-derived estimates (Table 3). While the relatively low estimates of flux from the soybean phase (<0.1 kg N₂O ha⁻¹) resulted from simulations that add no N fertilizer to that crop phase, field observations reporting use of N fertilizers or manures in 28% of observations produced estimates that were least ten-fold higher. Both direct fertilizer additions and N applied to the previous crop might contribute to enhanced N₂O emissions following corn (Iqbal et al., 2015).

The _pCENTURY simulated N₂O_gs-emissions for the cover crop phase were much lower (74%) than estimates derived from field observations (Table 3). Similarly, Jiang et al. (2020) also showed that soil N₂O emissions during the cover crop phases were modeled with much lower accuracy than those from the corn years. Our assumption that cover crops were unfertilized likely reduced model estimates. Additionally, the model may have overestimated cover crop N uptake and diminished associated N₂O loss. Alternatively, our simulations may have overestimated soil moisture during winter (Figure 2) and so created redox conditions that promoted full reduction to N₂ (Chapuis-lardy et al., 2007; Wu et al., 2013). Foltz et al. (2021) also found that the DNDC model failed to capture field-observed decreases in N₂O_gs-emissions caused by winter cover crops. Improvements to soil moisture estimates and temporal interactions are needed to successfully capture ‘hot’ moments registered in the field. Improvement in _PCENTURY modeling for non-corn crop phases will require more detailed data inputs and model calibration. Ideally, such data can be acquired from long-term cover crop experiments that measured yield, SOC changes, and the associated GHG emissions.

3.2.2 Soil N₂O emissions influenced by N fertilizer type

The simulated N₂O_gs-emissions from corn plots amended with inorganic and or organic fertilizers did not differ (p > 0.05) and were slightly lower than field observations. Means derived from the field observations were 16% greater after organic amendments were used (Figure 3A), but the difference between organic and inorganic sources was non-significant (p > 0.05). Elevated N₂O_gs- emissions are rate sensitive and expected where manure additions deplete oxygen by stimulating heterotrophic activity (Petersen et al., 1996; Decock, 2014). While our simulations used the same rates for fertilizer and manure treatments to compare the effects of N forms, the average N input rates used for field trials were 58% greater for organic (261 kg N ha⁻¹) than inorganic N (165 kg N ha⁻¹) applications. More information about farming systems might be needed to justify assumptions about manure application rates to ensure that the database-derived EFs for organic (1.16%) and synthetic fertilizers (0.92%) (Table 4) are meaningful for making management recommendations. Only 16% of field observations were measured from soils treated with organic fertilizers so more data must be collected from diversified cropping systems. For example, a meta-analysis by Skinner et al. (2014) found lower N rates were applied in certified organic farming systems than in their conventional counterparts and that could be translated into reduced N₂O emissions. Nevertheless, a different comparison result might be expected for concentrated feeding operations. N rate assumptions are also complicated by organic fertilizer availability that can significantly influence soil N₂O emissions over time (Pang and Letey, 2000; Eghball et al., 2002; Shakoor et al., 2021). Accurate manure activity data is needed to effectively model GHG emissions for specific sites (Walling and Vaneeckhaute, 2020). For broad-scale modeling and policy-making, we must be aware of the influence that rate assumptions may exert on outcomes.

FIGURE 3

FIGURE 3. Comparison of means and standard deviations of growing season cumulative soil N₂O emissions from corn phase under different (A) N input types (synthetic versus organic), (B) N fertilizer types (anhydrous ammonia (AA) versus urea), and (C) tillage practices (conventional (CT) versus no-till (NT)). The results are reported for _pCENTURY-based model simulation and summarized from the meta-dataset of field observations. Number of observations within each category of the field dataset used for validation are shown within the columns. Paired and unpaired t-tests were used to compare treatment differences with p values shown with labels on the X-axis. Different letters represent significant difference at p < 0.05 identified with the t tests.

TABLE 4

TABLE 4. Management and climate associated emission factors (EFs) of cumulative growing season soil N₂O emissions (N₂O_gs) calculated from _pCENTURY simulation and field measurements used for evaluation. The EF results are reported as model simulated county or field measured averages followed by standard deviations.

Both _PCENTURY and database-derived estimates showed N₂O_gs-emissions were greater after use of AA than urea used at similar rates (Figure 3B), but differences were only statistically significant for modeled results. The meta-analyses of Bouwman (1996) and Eagle et al. (2017) concur. This indicates that fertilizer-based scalers used by models like CENTURY effectively represent the mechanistic influences inorganic N forms exert on N₂O production rates as suggested by Breitenbeck and Bremner (1986) that AA is more commonly applied through the injection method which induces highly alkaline soil zones that can accumulate a large amount of NH₄⁺ as substrates for N₂O production. Another explanation is that the enhanced NO₂⁻ level under AA treatment could promote subsequent soil N loss (Venterea et al., 2010). Because N rates are similar between the AA and urea treatments according to the meta-database, both _PCENTURY and meta-analytical summary found higher EFs for AA (1.28 and 0.92%) than urea treatment (0.89 and 0.75%; Table 4). Since AA and urea are the two major sources of synthetic N inputs in the United States Corn Belt (Snyder et al., 2014), their different impacts on N₂O should be evaluated with other environmental consequences (e.g., CO₂ emissions, N leaching) in order to inform better management practices at the broad scale.

3.2.3 Soil N₂O emissions influenced by tillage

Model simulation suggested soil N₂O_gs-emissions are significantly greater from fields using conventional than no-tillage (p < 0.001, Figure 3C). This agreed with database-derived estimates showing average N₂O_gs-emissions were increased 37% by tillage. The metadata-derived EFs were also, on average, higher under conventional (1.24%) than no-tillage (0.71%) because N rates were shown to be similar between the treatments (Table 4). Again, differences in field-based comparisons were non-significant due to inter-field variability (Figure 3C). Increased N₂O flux has been shown to result from better aeration that permits more N₂O to escape the soil rather than being converted to N₂ (Chatskikh and Olesen, 2007). Use of no-till has variable effects on bulk density (Blanco-Canqui and Ruis, 2018) but can commonly increase N₂O_gs-emissions when used in poorly drained soils (Rochette et al., 2008). On the other hand, use of no-tillage or conservation tillage most often reduces N₂O emissions when practiced long-term where soil structure is improved to decrease anaerobic soil microsites conductive to N₂O production (Six et al., 2004; van Kessel et al., 2013). Because tillage can have mixed impacts on soil N₂O emissions depending upon how practices influence substrate availability, aeration, yield, and soil water status (Linn and Doran, 1984; Smith et al., 2001; Mei et al., 2018), development or use of a tillage-based N₂O scaler or coefficient could be unwise. Therefore, there is a need to highlight effective modeling of soil moisture and the soil redox environment to better predict tillage associated N₂O emissions.

3.3 Spatial N₂O emissions influenced by weather scenarios

Our regional assessment of N₂O_gs-emissions revealed variability within and across regions in the United States Corn Belt (Figure 4). While the _PCENTURY simulations for the normal weather scenario (Figure 4B) are similar to state-level N₂O emissions patterns reported by McNunn et al. (2020), we identify potential for notable variability within states using county-level weather records. Spatial differences considered using normal weather data found the highest N₂O emissions occurred in Iowa and Indiana (Figure 4), which is driven by the relatively high N input rates reported in Xia et al. (2021). Weather scenarios exploring dry and wet conditions suggested cumulative N₂O_gs-emissions from the corn phase of the CS baseline would be greater under dry (3.3 kg N₂O ha⁻¹) and similar under wet (2.7 kg N₂O ha⁻¹) weather conditions when compared to results from normal (2.8 kg N₂O ha⁻¹) weather scenarios (SI: Supplementary Table D2). The ranking for EFs remains dry (1.19%) > normal (0.95%) > wet (0.88%) for model simulation because we assumed the same N input rates (Table 4). In reality, N input rates can vary because corn yield would change according to climate change scenarios, in which case yield-scaled EF that considers both N inputs and crop productivity (Aguilera et al., 2013; Iqbal et al., 2018) may better reflect the interaction between climate change and human activities.

FIGURE 4

FIGURE 4. _pCENTURY-based county-level estimates for: corn growing season N₂O emissions under (A) dry, (B) normal, and (C) wet weather scenarios; annual precipitation under (D) dry, (E) normal, and (F) wet weather scenarios; corn yield under (G) dry, (H) normal, and (I) wet weather scenarios. The model estimates are based on baseline scenario (corn-soybean rotation, conventional tillage) for a 6-year (2016–2021) simulation period for the major United States corn states.

Regression-based exploration of the spatial distribution of N₂O_gs-emissions suggested increased flux with soil moisture, fertility, and plant available N and decreased emissions with precipitation, water filled pore space, and corn yield (p < 0.05) (Table 5). Precipitation, plant available N, and corn yields were found lowest in the dry years, and plant available N and crop yields were greatest in the normal weather years (SI: Supplementary Table D2). Lower yields that resulted in reduced N uptake by crops created hotspots for N₂O emissions. Average crop yield was also reduced by 13% in wet years compared to the normal weather but did not cause increased N₂O_gs-emissions. Loss of N₂O occurring outside of the growing season, the conversion of N₂O to N₂ under prolonged wet conditions, or increased leaching losses could all account for reduced level of available N in wet weather scenarios (Hernandez-Ramirez et al., 2009). In addition, the reduction in N₂O_gs caused by wet conditions could also be attributed to _pCENTURY using a monthly time-step, which could not capture rapid changes in water filled pore space and N₂O emissions following rainfall events (Schmidt et al., 2000; Beheydt et al., 2007; Xia and Wander, 2022). Soil N₂O changes can occur at a sub-daily time step so studies have attempted to capture high temporal resolution N₂O dynamics (Laville et al., 2011; Lognoul et al., 2017; Su et al., 2021) using data collected with advanced techniques such as automated chamber and micrometeorological measurements (Hensen et al., 2013; Bréchet et al., 2021). However, such calibration data would be too expansive to collect at the broad scale for N₂O modeling. In the future, the evolving networks such as GRACEnet (Del Grosso et al., 2013) and COntinuous SOil REspiration network COSORE (Bond-Lamberty et al., 2020) could be used for more detailed calibration of model nitrification and denitrification parameters (e.g., a maximum fraction of N₂O to nitrified N at field capacity, adjustment coefficient on effect of moisture on denitrification, maximum fraction of ammonia nitrified during nitrification) at a finer temporal resolution if data collection and reporting can be further standardized.

TABLE 5

TABLE 5. Linear regression model built on estimating _pCENTURY simulated growing season corn N₂O emissions using fertilizer N rate (FERT), growing season moisture (MOS) and water filled pore space (WFPS), corn-year average plant available N (PAN), annual precipitation (PPT), and corn yield (YLD). The N₂O emissions were estimated for normal, dry, and wet weather scenarios for corn-soybean baseline scenario during the simulation period of 2016–2021. The results are summarized by agroecological zone (AEZ).

The _pCENTURY derived N₂O EF for the CS scenario under normal weather conditions was approximately 60% lower than the revised IPCC (2019) Tier-1 estimate (1.6 $\pm$ 0.3%) proposed for wetter climate conditions. This may be partially explained by the fact that several counties within AEZs 7 and 8 do not fit within the wet climate category as their annual precipitation > potential evapotranspiration (Stocker et al., 2018). IPCC’s ‘dry’ value (EF = 0.5%) (Hergoualc’h et al., 2019) is much closer to our average for those AEZs (Table 4). Even though the definition of “dry” and “wet” conditions is different in IPCC and that used for our model simulation in that the IPCC definition is based on spatial variability under current climate conditions, both results support the implications of Shang et al.‘s meta-analysis (2020) that suggests regional N₂O EFs are needed.

The average EFs derived from our study (0.9% from model and 1.1% from meta-database) under normal weather conditions were lower than that (1.7% for well-drained soils and 3.9% for poorly drained soils) estimated by Lawrence et al. (2021) for the United States Corn Belt. This difference might be partially explained by the fact that Lawrence et al. (2021) investigated annual cumulative N₂O while ours focused on N₂O_gs-emissions. The difference in data coverage concerning both N₂O emissions and N input rates can also cause varied estimates of N₂O EFs. In addition, the method of estimating background emissions and organic N inputs based on rates and/or availability can result in uncertainty associated with reported EFs, which emphasizes the cautions needed to interpret direct soil N₂O EFs. Ideally comparison of EFs can be carried out at finer time-step.

3.4 Implications on future agricultural management and climate adaptations

The increased atmospheric N₂O concentration has been closely associated with food production system and fertilizer use (Kroeze et al., 1999; Mosier and Kroeze, 2000) and is projected to continue rising if no mitigation strategies are adopted to improve agricultural nitrogen use efficiency (Montzka et al., 2011; Kanter et al., 2016). Studies have shown significant differences in estimated atmospheric N₂O concentration based on IPCC’s representative concentration pathways (RCPs) representing scenarios from little mitigation efforts to aggressive goals (Davidson, 2012). Accurate estimation of N₂O emissions from different management practices is therefore critical for selecting and promoting practices that can alleviate climate change risks for the future. Our study showed an example of comparing management associated N₂O emissions and EFs at the broad scale through both process-based modeling and meta-analytical summary. Future work is needed to compare more management scenarios that are not included in this study but are known to influence soil N₂O emissions including irrigation (Scheer et al., 2013; Maharjan et al., 2014), drainage (Datta et al., 2013; Fernández et al., 2016; Grossel et al., 2016), and the timing and method for fertilizer incorporation (Smith et al., 1997; Yan et al., 2001; Ma et al., 2010).

The study of Tesfaye et al. (2021) showed that soil N₂O mitigation potential can be mostly fulfilled with the reduction of excessive N inputs. This is in line with our finding that N₂O_gs-emissions were mostly observed and simulated to be higher under crop rotations incorporating a large amount of N inputs. According to Xia et al. (2021), excessive corn N inputs were calculated to be prevalent in the United States Corn Belt, meaning that there is great potential for management associated N₂O mitigation within the region. Although our model assumed zero or only a small amount of N inputs in non-corn phases and therefore generated lower estimates of N₂O_gs-emissions, our meta-dataset demonstrated studies with excessive N inputs during the cover crop phases which can lead to greater N losses through N₂O and N leaching. However, if cover crops were managed properly, meta-analysis found that soil N₂O emissions would be reduced or not affected compared to non-cover crop treatments considering the balance between increased SOC and reduced nitrate contents (Basche et al., 2014; Kaye and Quemada, 2017). The key to N₂O mitigation in a diversified cropping system is, therefore, to avoid the overapplication of fertilizers by better estimating N balance that takes into account N credits from cover crops, which can be achieved through the use of remote sensing (Xia et al., 2020) and N calculators (Gaskin et al., 2019).

We investigated the impacts of N input, fertilizer type, and tillage on N₂O_gs-emissions but none of the comparisons were identified to be significant according to the meta-database due to high variability. This cautions the interpretation of management associated N₂O mitigation potential by considering only a single management factor. Likely the interaction of management practices can lead to various N₂O responses among different sites. Our model simulation illustrated significant differences in N₂O_gs-emissions caused by N input and fertilizer types, but such results are heavily influenced by the model coefficients derived based on previous studies. Ideally, the coefficients should be updated to reflect findings from meta-databases and developed to reflect interactions with site-specific management (fertilizer incorporation method) and soil factors (e.g., texture and soil drainage class).

Our modeling of N₂O_gs-emissions under various climate scenarios showed a potential of increased N₂O emissions under future drought conditions. We did not investigate the impacts of raised temperature and CO₂ concentration on N₂O emissions, but studies have reported increased N₂O emissions under such scenarios because of enhanced microbial activity and denitrification (Butterbach-Bahl and Dannenmann, 2011; Li et al., 2020; Wang et al., 2021). Increased N₂O emissions under projected future climate conditions, especially those with higher levels of RCP (Riahi et al., 2011), can then contribute to a feedback loop that aggravates climate change. The regional differences in _pCENTURY-modeled soil N₂O emissions under dry and wet weather scenarios could be tied to soil pH and drainage class, which were identified as key factors explaining the spatial variability of observed annual N₂O emissions (Lawrence et al., 2021). Likewise, the modeling study of Zhang et al. (2020) also emphasized regional differences in SOC changing trend under future climate scenarios that is influenced by soil properties and management in the United States Corn Belt. These findings suggest that management adaptations to a changing climate should consider regional differences, in which case it would be critical to use modeling tools to simulate site-based management and climate interactions.

4 Conclusion

A parameterized CENTURY model (_pCENTURY) was used to estimate direct soil N₂O emissions under various management and climate scenarios from United States corn fields. The _pCENTURY model adequately predicted the magnitude of growing season N₂O (N₂O_gs) emissions from corn (N₂O_gs = 2.68 kg/ha) and wheat (N₂O_gs = 1.44 kg/ha) crop phases based on comparison with a Tier-2 meta-analytical summary. Model estimated N₂O_gs emissions and emission factors (EFs) differed by N fertilizer type (AA > urea) and tillage practice (conventional > no-tillage), which matched with the comparison of average values under these treatments from the meta-database. Differences between modeled and measured results for scenarios including soybean or cover crops were tied to model assumptions of N inputs and the greater variety of inputs and practices summarized within categories contained in the meta-database. Comparison with Tier-1 values for corn suggests that broad-scale process-based modeling can successfully generate regionalized EFs that are representative of generalized management scenarios. Discrepancies between Tier-2 and _pCENTURY-derived EFs on synthetic versus organic N inputs revealed not only the importance of N input data but also data describing the quantity and quality of organic inputs for estimating N₂O emissions from non-corn phases and complex rotations. Weather scenarios determined that decreases in corn yields in dry years reduced crop N uptake and created N₂O hotspots. Yield reductions occurring in wet years did not increase N₂O flux. Regional differences in weather and soil N₂O flux justify need for model-based (Tier-3) EFs that consider interactions between management and climate; however, we find improvements in the model’s ability to accurately simulate soil moisture during the winter and capture crop N uptake are needed to successfully estimate soil N₂O emissions and develop regional EFs that can better inform management decisions in a future climate.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

YX, MW, and HK contributed to conception and design of the study. SQ and SY provided soil moisture database and critical information behind the database. YX and MW performed statistical analysis. HK performed process-based modeling of N₂O emissions from soils. YX and MW prepared several drafts of manuscript and all authors contributed to the final manuscript revision.

Funding

This research was supported by the National Institute of Food and Agriculture (NIFA) of the US Department of Agriculture under contracts No. 015-51106-24198 (Project No. ILLU-875-626) and Hatch 600122-875000-875986 and the Bioenergy Technologies Office (BETO) of Energy Efficiency and Renewable Energy of the US Department of Energy under contract DE-AC02-06CH11357.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The views and opinions of the authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof. Neither the U.S. Government nor anyagency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.971261/full#supplementary-material

Abbreviations

AA, anhydrous ammonia; AEZ, agroecological zone; CC, Continuous corn; CS, Corn-soybean rotation; CCvSCv, 2-year corn/cover crop-soybean/cover crop rotation; CSWCv, 3-year corn-soybean/winter wheat-cover crop rotation; EF, Emission factor; GHG, Greenhouse gases; GWP, Global Warming Potential; LCA, Life Cycle Analysis; N₂O_gs, growing season nitrous oxide; NASMD, North American Soil Moisture Dataset; _PCENTURY, Parameterized CENTURY/DAYCENT model; RFS, Renewable Fuel Standards; SOC, Soil organic carbon; SOM, Soil organic matter.

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