Climate variability and nitrogen legacy effects on corn yield and soil nitrous oxide emissions: a 40-year simulation study

Abstract

Climate change and nitrogen (N) application significantly influence agricultural productivity and soil greenhouse gas emissions. However, the interactive effects of interannual climate variability and N fertilization legacy on corn yield and soil nitrous oxide (N₂O) emissions remain inadequately understood. In this study, we employed the DeNitrification-DeComposition (DNDC) model to simulate corn yield and soil N₂O emissions over a 40-year period (1981–2020). We designed a series of experiments by adjusting climate year data to quantify interannual variability in corn yield and soil N₂O emissions, while also disentangling the contributions of climate variability and N legacy effects. The results revealed substantial interannual variability in both corn yield and soil N₂O emissions. Corn yield was primarily driven by changes in growing season precipitation, while soil N₂O emissions were influenced by precipitation, exchangeable ammonium N (NH₄⁺), and nitrification-denitrification processes. Severe drought strongly reduced corn yield, while soil N₂O emissions exhibited a gradual yet pronounced legacy effect of N application, increasing from 1.69 to 7.85 kg N·ha⁻¹ over the 40-year period. This study highlights the relatively weak influence of interannual climate variability compared to the stronger legacy effects of N application on crop yield and soil N₂O emissions, providing valuable insights for sustainable agricultural and environmental management.

Graphical abstract

Keywords

interannual variability / legacy effect / DNDC model / climate change / nitrogen application / yield / soil N₂O emissions

1 Introduction

With a growing global population, enhancing sufficient crop production to meet food needs requires significant advancements in agricultural productivity (Fischer et al., 2014; Muchhadiya et al., 2024). Meeting the global food demand of approximately 10 billion people by the mid-21st century will become increasingly challenging as climate change accelerates (Godfray, 2014; Rütting et al., 2018). Climate conditions play a crucial role in crop growth and yield, directly influencing agricultural output (Ray et al., 2015; Matiu et al., 2017). Corn, one of the world’s most important cereal crops, is a staple food in many countries. The United States (US), responsible for about 30% of global corn production, plays a key role in international exports (Xu et al., 2022; Erenstein et al., 2022). Previous studies indicate that corn yield is strongly influenced by air temperature, precipitation, environmental factors, and agricultural management practices (Kucharik and Ramankutty, 2005; Kukal and Irmak, 2018). Among these, temperature has been identified as the primary climate variable affecting corn yield in the US (Schlenker and Roberts, 2009; Xu et al., 2021a). To sustain high corn yield, annual nitrogen (N) fertilizer application is essential. However, to maximize production, farmers often apply more N than the crop requires. A global meta-analysis found that corn’s N recovery efficiency is only 34%–45%, indicating the majority of applied N either accumulates in the soil or is lost to the environment (Yu et al., 2022). Excessive N fertilizer use not only leads to environmental degradation but also contributes significantly to greenhouse gas (GHG) emissions, particularly soil nitrous oxide (N₂O) emissions (Tian et al., 2020; Hlisnikovský et al., 2023). Tenuta et al. (2019) reported that 50% of soil N₂O emissions result from fertilizer application. Therefore, balancing crop yield optimization with the reduction of soil N₂O emission has become a pressing concern for farmers and policymakers alike.

Interannual variability in plant productivity and ecosystem responses is a well-documented phenomenon across terrestrial ecosystems (Hui et al., 2003; Hui and Jackson, 2006; Zhang and Huang, 2012; Kukal and Irmak, 2018; You et al., 2024). Crop yield and GHG emissions also exhibit significant year-to-year fluctuations at both local and global scales, influencing farmers’ incomes and food stability (Thornton et al., 2014; Touch et al., 2024). Reducing yield variability is essential for stabilizing food supplies and preventing price spikes that disproportionately affect food-insecure populations (Ray et al., 2015; Ceglar et al., 2018). Interannual crop yield variability is primarily driven by climate variability, with temperature and precipitation being the most influential factors. While extensive research has examined the long-term effects of climate change on agriculture, the specific effects of interannual climate variability on crop yield and soil N₂O emissions remain insufficiently understood (Ray et al., 2015). For example, Ceglar et al. (2018) found that a combined drought and heat stress index accounts for about 53% of interannual corn yield variability in Europe. In the US, temperature and precipitation together explain up to 32% of the interannual variability in corn and soybean yields (Leng et al., 2016). Globally, climate variability is responsible for an estimated 32%–39% of observed yield variation (Ray et al., 2015). Soil N₂O emissions, a potent GHG and major environmental concern, are influenced by multiple factors, including microbial processes (nitrification and denitrification), N substrate availability, soil moisture, aeration, and temperature (Granli and Bøckman, 1994; Bremner, 1997; Mosier et al., 1998; Skiba and Smith, 2000; Jauhiainen et al., 2012; Deng et al., 2016; Kaur et al., 2023). These emissions are closely linked to soil water-filled pore space, which in turn, is strongly influenced by precipitation patterns (Li et al., 1992b; Deng et al., 2016). However, the effects of climate change variability on corn yield and soil N₂O emissions, particularly their interannual variabilities, remain poorly understood. A better understanding of these variabilities is crucial for assessing the vulnerability and resilience of food production systems (Xu et al., 2022).

Climate and management practices not only directly regulate crop yield and soil N₂O emissions but also have lasting impacts through legacy effects. Legacy effects refer to the prolonged influence of past environmental conditions or management practices on present and future ecosystem processes (Cuddington, 2012). These effects extend beyond the initial event, shaping long-term ecosystem dynamics. For example, Delgado-Balbuena and Arredondo (2019) found precipitation during the previous dry season influences the carbon balance of grassland by affecting carbon uptake and ecosystem respiration of the following growing season. Similarly, Gong et al. (2020) reported that precipitation from the prior year significantly impacts above-ground biomass in semi-arid grasslands. Fertilization also leaves a lasting imprint on crop yield and soil N₂O emissions through its residual effects on soil fertility. Lee et al. (2025) conducted laboratory incubations using soil with different fertilization histories from two croplands. Their findings showed that N fertilization significantly increases N₂O emissions in Miscanthus x giganteus soil but has no effect on soil N₂O emission in corn soils. Additionally, Tian et al. (2024) utilized a long-term field experiment to demonstrate that legacy effects can significantly amplify soil N₂O emissions. Specifically, they found that residual impacts from previous N applications can cause N₂O emissions to double during the first growing season following fertilization. Their results highlight that historical N inputs and elevated soil nitrate concentrations are the key drivers of current N₂O emissions.

The effects of interannual variability of climate change and legacy effects of management practices have been investigated through long-term observations and field experiments (Beier et al., 2012; Heckman et al., 2023; Zhang et al., 2023). Based on historical climate and crop data from 1980 to 2008, Zhang and Huang (2012) reported that corn yield is highly sensitive to warming, with yield reductions closely correlated with lower precipitation. Han et al. (2021) simulated precipitation change and legacy effects of precipitation on above-ground net primary productivity in a field experiment, revealing greater sensitivities of productivity to previous-year precipitation. Zhang et al. (2023) further demonstrated that a 30% reduction in precipitation significantly enhances soil N₂O emission, primarily due to stimulated nitrification process.

Due to the costs and technique difficulties associated with measuring interannual variability and legacy effects in field experiments, modeling techniques have become an ideal tool for evaluating their impacts on ecosystem productivity, crop yield, and soil GHG emissions (Ehrhardt et al., 2018; Zhang et al., 2021b; Abdalla et al., 2022; Kaur et al., 2023). Various ecosystem models have been developed to simulate N transformations and the subsequent responses of soil N₂O emissions to agricultural practices and climate change (Chen et al., 2019; Abdalla et al., 2022). For example, Shen et al. (2016) used a process-based model to simulate net ecosystem carbon fluxes in a semi-arid savanna ecosystem and found that net ecosystem productivity is predominately determined by previous-year precipitation. The DeNitrification-DeComposition (DNDC) model, a mechanistic process-based model, has been extensively applied to quantify the GHG emissions at both local and regional scales in different croplands (Li et al., 1992a, 1992b; Giltrap et al., 2010; Ingraham and Salas, 2019; Abdalla et al., 2022; Kang et al., 2025). For example, Deng et al. (2016) parameterized the DNDC model based on a three-year field experiment in a cornfield in Nashville, TN, and simulated the effects of various agricultural practices on corn yield and soil N₂O emissions. More recently, Kaur et al. (2023) simulated the responses of corn yield and soil N₂O emissions to changes in precipitation, finding asymmetric responses of corn yield but a relatively linear response in soil N₂O emissions. Given its robust simulation capabilities, the DNDC model is well suited for evaluating the impacts of interannual climate variability and the legacy effects of management practices on crop yield and soil N₂O emissions.

In this study, we extended our previous modeling efforts to examine the responses of corn yield and soil N₂O emission to interannual climate variability and the legacy effects of N application. Using a previously calibrated DNDC model (Deng et al., 2016; Zhang et al., 2021a; Kaur et al., 2023), we designed three experimental scenarios based on a long-term (40-year) climate data set to assess these impacts. The main objectives of this study were to 1) quantify the interannual variability of corn yield and soil N₂O emissions; 2) evaluate the impacts of interannual climate variability on corn yield and soil N₂O emissions; 3) investigate the legacy effects of N fertilization on corn yield and soil N₂O emissions; and 4) develop relationships between corn yield, soil N₂O emissions, climatic factors, and N availability and processes. Understanding how climate variability and management legacies influence crop yield and soil N₂O emissions is essential for both scientific understanding and agricultural practices. The findings can help develop strategies to enhance corn yield stability while mitigating soil N₂O emissions, contributing to more sustainable agricultural management.

2 Materials and methods

2.1 The DNDC model and model validation

We used the DNDC model (version 9) to simulate corn yield and soil N₂O emissions based on a long-term (40 years) climate data set. Originally developed to quantify soil GHG (CO₂, CH_4, N₂O) emissions in croplands (Li et al., 1992a, 1992b), the model has been extensively validated and applied across various cropland systems under different agricultural practices and environmental conditions (Uzoma et al., 2015; Deng et al., 2016; Abdalla et al., 2022). The DNDC model requires data on daily climate drivers (e.g., air temperature and precipitation), soil properties (e.g., soil texture, bulk density), crop type, planting and harvesting dates, and management practices (e.g., N application timing, type, and amount). In corn fields, N fertilizer is typically applied at seeding in the spring and again before the reproduction stage (Deng et al., 2015). The model simulates daily plant growth and soil GHG emissions, where crop yield is influenced by temperature and precipitation through plant water uptake and growth. Soil N₂O emissions are primarily driven by nitrification and denitrification processes, which are regulated by soil environmental conditions and substrate availability (e.g., inorganic N).

We have parameterized and validated this model using a three-year cornfield experiment conducted at Tennessee State University Agricultural Research and Education Center in Nashville, TN, USA (Deng et al., 2015, 2016). The simulated corn yield and soil N₂O emission closely matched field measurements (Deng et al., 2016; Kaur et al., 2023). This validated model has also been used to assess the impacts of precipitation patterns and N application on soil N₂O emission (Zhang et al., 2021a) and to examine the response patterns of corn yield and soil N₂O emission to precipitation changes (Kaur et al., 2023). In this study, we simulated the effects of interannual climate variability and the legacy of management practices on corn yield and soil N₂O emissions. For management practices, we focused solely on N application, applying 218 kg N·ha⁻¹ annually, a typical rate for middle Tennessee. No-tillage, irrigation, or mulching was included, ensuring that the legacy effects were attributable solely to N application.

2.2 Long-term climate dataset

The long-term climate dataset was obtained from NASA POWER CERES/MERRA2 Native Resolution Daily Data website. This dataset includes 40 years (1981–2020) of daily climate data for the study site in Nashville, TN, covering minimum and maximum air temperatures and precipitation. Growing season (April to September) temperature and precipitation were calculated. Mean annual temperature ranged from 13.21°C in 1989 to 16.23°C in 2007, with a mean of 14.75°C, and showed a trend of increase over 40 years (t = −43.575 + 0.029x Year, r² = 0.20, p = 0.004). Growing season temperature, annual total and growing season precipitation did not show a trend of change over years. Annual precipitation during this period ranged from 926 mm to 1682 mm, with a mean of 1325.4 mm. The wettest year was 2020, while the driest was 2007 (Kaur et al., 2023).

2.3 Experimental designs to simulate the effects of IAV and legacy effects of precipitation change on corn yield and soil N₂O emission

We conducted one baseline run and three simulation experiments to evaluate how corn yield and soil N₂O emissions respond to interannual climate variability and the legacy effects of N application. For the baseline run, we simulated corn yield and soil N₂O emission over 40 years with climate data from the long-term climate data set (40 years, 1981–2020). For the management practices, we set the same N application at 218 kg·N·ha⁻¹ for all simulations. This model run provided baseline results of corn yield and soil N₂O to reveal their interannual variability.

Experiment I: Interannual climate variability and N application legacy effect. In this experiment, we run 40 single-year simulations using the DNDC model with each year’s climate data from 1981 to 2020. The interannual variability in corn yield and soil N₂O emissions was solely attributed to climate variability. The difference between these simulations and the baseline (continuous 40-year simulation) revealed the cumulative legacy effects of N application.

Experiment II: Legacy effects of N application and climate change. To isolate the legacy effect of N application, we ran the DNDC model over 40 years using the same year’s climate data through the entire simulation period. For example, in one run, we used only the 1981 climate data for 40 years; in another, we used only the 1982 climate data. This was repeated for all 40 years, resulting in a total of 1600 simulation years. Since climate drivers remained constant for each 40-year simulation, any variation in corn yield and soil N₂O emissions were due to the cumulative legacy effects of N application. Differences among the 40 runs reflected the impacts of climate variability.

Experiment III: Climate rotation and Latin Square design. To test whether climate year rotation would introduce a legacy effect and to quantify both climate and N application legacies, we designed a Latin Square experiment. We rotated the starting climate year from 1981 to 2020 and ran 40-year simulations for each rotation. For example, in one simulation, the climate sequence followed 1981, 1982,…, 2020; in the next, it started with 1982 and proceeded to 2020 before looping back to 1981, and so on. In the final simulation, the sequence began in 2020, followed by 1981, 1982,…, and 2019. This resulted in 1600 total simulation years, forming a 40 × 40 Latin Square. Row effect reflected the legacy of climate conditions, column effects captured the legacy of N application, and year effect represented interannual climate variability.

2.4 Data analysis

Using baseline results, we analyzed trends in air temperature, precipitation, growing season climate conditions, simulated corn yield, and soil N₂O emissions over 40 years. We calculated the mean, range, standard deviation, and coefficient of variation (CV) for these variables. In experiment I, we quantified interannual variability in corn yield and soil N₂O emissions, assessed the legacy effects of N application, and quantified the cumulative legacy effect of N application using linear regression. In experiment II, we used boxplots to illustrate the legacy effect of N application and the impacts of interannual climate variability on corn yield and soil N₂O emissions. In experiment III, we performed an ANOVA based on the Latin Square design to evaluate the effects of climate legacy (row), N application legacy (column), and interannual climate variability (year) (Hui and Jiang, 1996). Boxplots were used to visualize these effects. To analyze relationships between corn yield, soil N₂O emissions, and environmental and soil variables, we conducted simple and multiple regression analyses based on 40 years simulations. Simple linear regression was applied in most cases, except for yield vs. precipitation and soil N₂O emission vs. denitrification, where second order polynomial equations were used. Stepwise multiple regression analysis was performed to account for potential correlations among explanatory variables, with entry and stay probabilities set at α = 0.05. All data analysis and visualizations were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

3 Results

3.1 Interannual variability of climatic variables, corn yield, and soil N₂O emissions

Air temperature and precipitation exhibited significant interannual variability from 1981 to 2020 (Figs. 1(a) and 1(b)). Over these 40 years, the mean air temperature was 14.75°C, while the growing seasons air temperature averaged 23.32°C (Table 1). The mean annual total precipitation was 1325.40 mm, with a growing season precipitation of 489.68 mm. Compared to air temperature, precipitation displayed greater interannual variability, with a coefficient of variation (CV) ranging from 3.85% for growing season air temperature to 22.03% for growing season precipitation.

Simulated corn yield and soil N₂O emissions using the DNDC model also exhibited substantial interannual variability (Figs. 1(c) and 1(d)). Corn yield ranged from the lowest value of 2243.1 kg C·ha⁻¹ in 2007 to the highest of 4414.6 kg C·ha⁻¹ in 2009, with a 40-year mean of 3804.02 kg C·ha⁻¹ (Table 1). Soil N₂O emissions varied from the lowest value of 1.69 kg N·ha⁻¹ in 1988 to the highest of 7.85 kg N·ha⁻¹ in 2010, with a mean value of 3.84 kg N·ha⁻¹. Soil N₂O emissions exhibited greater interannual variability compared to corn yield (CV = 36.58% for soil N₂O emissions vs. 9.87% for corn yield).

3.2 Impacts of N application legacy on corn yield and soil N₂O emissions

In experiment I, we simulated a single year’s worth of corn yield and soil N₂O emissions 40 times using climate data for one year each time from 1981 to 2020 (without N application legacy) and compared these results to baseline simulation incorporating the legacy effect (Figs. 2(a) and 2(b)). Over 40 years, mean corn yield increased by 11.0%, from 3426.9 kg·C·ha⁻¹ without N application legacy to 3804.0 kg·C·ha⁻¹ (Fig. 2(a)). But similar patterns of interannual variability were observed, with CV values of 10.53% and 9.87% for simulations without and with legacy effects, respectively. For soil N₂O emissions, mean values increased by 212.6%, from 1.97 kg·N·ha⁻¹ in the absence of N application legacy to 6.16 kg·N·ha⁻¹ (Fig. 2(b)). The interannual variability was considerably lower without the N application legacy (CV = 13.68%) compared to simulations incorporating legacy effects (CV = 36.58%).

The cumulative legacy effects of N application (calculated as the differences between the two simulations) on both corn yield and soil N₂O emissions increased over time (Fig. 2(c)). Using linear regression models, we quantified these annual legacy effects: corn yield increased by 11.24 kg·C·ha⁻¹ per year, while soil N₂O emissions increased by 0.10 kg·N·ha⁻¹ annually on average. Notably, the legacy impacts on corn yield exhibited greater interannual variability than those on soil N₂O emissions.

3.3 Quantifications of interannual climate variability and legacy effects using a constant climate experiment

In experiment II, we simulated corn yield and soil N₂O emissions over 40 years while maintaining constant climate data for each year. We conducted 40 simulation runs, each using one year of climate data from 1981 to 2020, yielding a total of 1600 simulation years. This approach allowed us to distinguish between the impact of N application legacy and interannual climate variability (Figs. 3 and 4). The variations in the boxplots of Fig. 3 reflect the impacts of interannual climate variability. The differences in mean values among years reflect the impacts of interannual climate variability (Fig. 4). The variations in the boxplot reflect the impacts of legacy of N application.

Mean corn yield exhibited a gradual increase over 40 years (Fig. 3), rising from about 3481.0 kg·C·ha⁻¹ in early years to about 4023.2 kg·C·ha⁻¹ by year 40. An interaction between N application legacy and interannual climate variability was evident, as corn yield variations progressively increased. Similarly, soil N₂O emissions followed a near-linear upward trend until 2008, after which they plateaued, researching approximately 5.09 kg·N·ha⁻¹ by the end of the simulation period (Fig. 3). The variation in emissions among different climate conditions also increased over time.

Corn yield varied considerably across different climate years (Fig. 4). Yield variations were relatively small under normal climate conditions, but severe drought significantly reduced corn yield, with the lowest yield occurring in 2007. Soil N₂O emissions showed smaller interannual variability compared to corn yield, but large variations in soil N₂O emissions were observed due to the cumulative legacy effect of N fertilization.

3.4 Quantification of climate and nitrogen fertilization legacy, and interannual variability using a Latin Square design

In experiment III, we partitioned the variability in simulated corn yield and soil N₂O emissions into three factors: row (legacy effects of climate), column (legacy effects of N fertilization), and year (interannual climate variability). ANOVA results showed that both interannual climate variability and N fertilization legacy had significant effects on corn yield and soil N₂O emissions, whereas the legacy of climate was negligible (Table 2).

Mean corn yield gradually increased over time (Fig. 5(a); see Supplementary Materials Table S1), with increasing variation among different climate years, as revealed by the boxplot analysis. Soil N₂O emissions also increased over time, doubling over 40 years (Fig. 5(b)). The rate of increase slowed down around 2007, following a server drought year. Large variations in soil N₂O emissions among climate conditions were also observed (Fig. 5(a)). While significantly lower corn yield was simulated in 2007 (a drought year), the difference between 2010 (a typical year) and 1989 (a wet year) was small (see Supplementary Materials Fig. S1(a)). Corn yield increased over 40 years for all three years, with large variations under the drought year. Soil N₂O emissions increased gradually, similar to the overall changes over time (Fig. S1(b)).

Corn yield was significantly lower during drought years, particularly in 2007, 1988, and 2000 (Fig. 6(a)). The variability in yield was smaller during drought years compared to normal and wet years, as shown in the boxplots (Fig. 6(b)). The lowest soil N₂O emissions were observed in 1988, while the highest emissions occurred in 2010. Unlike corn yield, soil N₂O emissions exhibited smaller interannual variations across different years, although significant interannual variations were observed within each year.

3.5 Drivers of interannual variability of corn yield and soil N₂O emissions

Univariate regression analysis revealed significant relationships between corn yield, soil N₂O emissions, and environmental and soil variables (Figs. 7 and 8). Corn yield had a negative linear relationship with growing season temperature but increased with precipitation following a second order polynomial equation. Soil N₂O emission linearly increased with temperature, exchangeable NH₄⁺, and nitrification, but increased with denitrification following a second order polynomial equation.

To account for potential correlations among explanatory variables, we conducted multiple regression analysis. Overall, corn yield decreased with increasing growing season air temperature and denitrification and showed a convex quadratic relationship with precipitation (Fig. 7). The optional regression model for corn yield was

(1)\begin{array}{rl}\mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}=& -{622.62+15.1842\times \mathrm{P}\mathrm{P}\mathrm{T}\mathrm{g}-0.0131\times \mathrm{P}\mathrm{P}\mathrm{T}\mathrm{g}}^2\\ & +3.5572\times \mathrm{N}\mathrm{i},{\mathit{R}}^2=0.71,\end{array}

where PPTg is growing season precipitation, Ni is nitrification, and R² is the coefficient of determination. Growing season precipitation had the greatest positive effect on yield (Standard regression coefficient, b_st = 4.36), followed by nitrification (b_st = 0.32). The squared precipitation term had a negative impact on yield (b_st = −3.76).

Soil N₂O emission increased linearly with mean annual air temperature, exchangeable NH₄⁺, and nitrification while exhibiting a convex quadratic relationship with denitrification. The optimal multiple regression model for soil N₂O emissions was

(2)\begin{array}{rl}{\mathrm{N}}_2\mathrm{O}& =-0.4908+0.00096\times \mathrm{P}\mathrm{P}\mathrm{T}\mathrm{g}+0.0107\times \mathrm{N}\mathrm{i}\\ & +0.9037\times \mathrm{D}\mathrm{e}\mathrm{n}\mathrm{i}-0.0329\times {\mathrm{D}\mathrm{e}\mathrm{n}\mathrm{i}}^2,{\mathit{R}}^2=0.98,\end{array}

where Deni is denitrification. Denitrification had the strongest influence on soil N₂O emission (b_st = 1.39), followed by nitrification (b_st = 0.26), and growing season precipitation (b_st = 0.07), with the squared denitrification term exerting a negative effect (b_st = −0.69).

4 Discussion

Using a process-based biogeochemical model and a long-term (40-year) climate data set, we simulated the impacts of interannual climate change and legacy effect of N application on corn yield and soil N₂O emissions. Several key findings emerged from this study: 1) both corn yield and soil N₂O emissions showed strong interannual variability, with soil N₂O emissions displaying greater fluctuations than corn yield; 2) significant cumulative N application legacy effects were observed for both corn yield and soil N₂O emissions, with an annual increase of 11.24 kg·C·ha⁻¹ in corn yield and 0.10 kg·N·ha⁻¹ in soil N₂O emissions over the 40 years; 3) while both corn yield and soil N₂O emissions were strongly influenced by climate and the legacy of N application, the legacy effects of climate were weak for both variables; and 4) precipitation and nitrification played an important role in regulating both corn yield and soil N₂O emissions, but denitrification was the dominant driver of soil N₂O emissions. These results enhance our understanding of the factors controlling crop productivity and soil GHG emissions, offering valuable insights for agricultural management and climate change mitigation.

4.1 Impacts of interannual climate variability and legacy effects of nitrogen application on corn yield and soil N₂O emissions

Interannual variability in simulated corn yield and soil N₂O emissions were influenced by both interannual climate variability and legacy of N application (Fig. 1). We ran the DNDC model with consistent management practices (i.e., N application) while varying climate conditions to isolate the impact of climate variability (Climate only, Figs. 2(a) and 2(b)). The results showed similar interannual variability in corn yield (CV = 10.53%) compared to the baseline simulation (CV = 9.87%) but much smaller interannual variability for soil N₂O emissions (CV = 13.68% vs. 36.58%).

To access the cumulative legacy effects of N application, we compared annual corn yield and soil N₂O emissions between single-year climate runs and baseline simulations (Fig. 2(c)). Our findings indicated that N application led to long-term increases in both variables. With 218 kg·N·ha⁻¹ applied annually, a portion of the N was not immediately utilized by plants and instead accumulated in the soil. Previous studies have estimated that about 15%–20% of applied fertilizer-N remains in the soil each year (Sebilo et al., 2013; Van Meter et al., 2016; Lei et al., 2025), contributing to a legacy effect of 11.24 kg·C·ha⁻¹·yr⁻¹ for corn yield over the 40-years period (Fig. 2(c)). The cumulative legacy effects on soil N₂O emissions were more pronounced (Fig. 2(b)), as soil N₂O emissions are directly influenced by available N. Over time, as excess N accumulated, both nitrification and denitrification rates increased, leading to elevated soil N₂O emissions. This pattern aligns with findings from Rosace et al. (2020), who reported that previous soil management practices strongly influenced N₂O fluxes in laboratory incubation experiments. Similarly, Tian et al. (2024) estimated that the legacy effect accounted for 23% of the annual N₂O emission (i.e., 0.18 kg·N·ha⁻¹) in the first growing season following N application. Studies by Jones et al. (2022) and LaHue et al. (2016) further highlighted that long-term N fertilization shifts soil microbial community structure, enhances potential denitrification, and increases soil N₂O production. These observations reinforce our conclusion that available N and denitrification are the primary driver of soil N₂O emissions in croplands.

4.2 Legacy effect of nitrogen application and interannual climate variability on corn yield and soil N₂O emissions

Environmental conditions in a given year influence soil N₂O emissions not only directly but also through legacy effects on both biotic and abiotic processes (Thilakarathna and Hernandez-Ramirez, 2021). By running the DNDC model under the same climate conditions across years, we isolated the legacy effect of N application. The gradual increases in corn yield and soil N₂O emissions over time, even under constant climate conditions, confirmed the importance of N application legacy (Figs. 3 and 4). Annually applied N may not be fully taken up by plants, microbes, or lost via leaching (Fig. S2(a)). Leaching NO₃⁻ gradually increased during the first decade but stabilized, showing only minor interannual fluctuations thereafter. Only one notable spike in leaching NO₃⁻ occurred following a severe drought in 2007. Instead, residual N persists in the soil profile (Fig. S2(c)), enhancing ecosystem productivity, corn yield, and soil N₂O emissions in subsequent years. Soil N₂O fluxes remained throughout most of the study period, except for two years when early spring ice melt might trigger sharp increases (Fig. S2(b)). Consistent with previous studies, our results demonstrated that continued N application leads to increased soil N₂O emissions, with peak fluxes occurring shortly after fertilization (Huang et al., 2014; Deng et al., 2015; Pearce, 2016). Using DayCent model, Pacifico et al. (2024) similarly showed that reducing N fertilization could result in lower crop yields in subsequent years due to reduced N availability. Moreover, our study suggests long-term, continuous N application in annual cropping systems leads to high N losses, with potential risks for environmental degradation. The initially sharp increases in soil N₂O emissions, followed by slow increases, suggest the presence of N saturation thresholds. Zhao et al. (2024) reported that prolonged N accumulation in soil may eventually lead to N saturation, increasing the risk of residual N loss. These results highlight the importance of nutrient management in mitigating soil N₂O emissions by regulating soil N availability (Canisares et al. 2021; Wang et al., 2021; Singh et al., 2024).

Our simulations showed that the legacy effects of climate change on corn yield and soil N₂O emissions were weak. This contrasts with grassland ecosystems, where climate legacies can persist for years (Sun et al., 2022). For example, extreme drought in grasslands can shift species composition, increasing the dominance of drought-adapted annual plants (Griffin-Nolan et al., 2018; Xu et al., 2021b). However, in annual croplands, our results showed that corn yield rebounded quickly after severe droughts, suggesting that current-year climate exerts a stronger influence than previous-year conditions. Similar findings have been reported in previous studies. Gazol et al. (2020) found that climate legacy effects are less prevalent than expected and have limited impacts while Wu et al. (2018) observed that post-drought legacies lasted only one year, comparable to our findings in croplands. Grassland production fully recovered within a year after drought, largely due to compensatory increases in dominant grasses replacing less abundant species (Hoover et al., 2014; Hofer et al., 2016). Some studies reported longer-lasting climate legacies in grasslands, likely due to species-specific responses that drive shifts in plant species composition (Sala et al., 2012; Arredondo et al., 2016; Bunting et al., 2017). In contrast, legacy effects in forest ecosystems tend to persist longer due to the slower turnover rates and greater structural complexity of woody vegetation (Anderegg et al., 2015). Post-drought legacies in deciduous forests often involve delayed leaf-out, reduced leaf area, and altered species interactions, which may affect productivity over several growing seasons (Kannenberg et al., 2020).

Interannual climate variability also had significant impacts on corn yield and soil N₂O emissions (Figs. 5 and 6). Severe drought reduced corn yield by limiting leaf photosynthesis (Deng et al., 2015; Kannenberg et al., 2019). Previous studies have found that precipitation and soil water content strongly regulate interannual variability in net ecosystem productivity (Zhang et al., 2021b) and that changes in temperature and precipitation explain substantial portions of corn yield variability (Zhang and Huang, 2012; Leng et al., 2016). Similarly, our study demonstrated that reductions in precipitation and temperature increases negatively impacted corn yield (Fig. 5).

Soil N₂O emissions exhibited lower interannual variability compared to the yield, with drought conditions slightly reducing soil N₂O emissions (Fig. 6). This finding aligns with meta-analyses showing decreased precipitation generally suppresses soil N₂O emission due to enhanced soil aeration, which inhibits denitrification (Homyak et al., 2017; Zheng et al., 2020). However, Zhang et al. (2023) reported increased soil N₂O emission under 30% precipitation reduction, likely due to enhanced root turnover and stimulated nitrification. Despite this discrepancy, most field studies support our findings that precipitation reduction decreases soil N₂O emissions by limiting N-cycling microbe activity (Borken and Matzner, 2009; Li et al., 2020a). Tigchelaar et al. (2018) projected that interannual variability of corn yield could intensify under future climate change scenarios, particularly with rising temperatures. Considering the combined effects of temperature and precipitation, future climate conditions may further amplify interannual variability in corn yield and soil N₂O emissions.

4.3 Relationship between corn yield, soil N₂O emissions and climate, nutrient, and soil nitrogen processes

Both corn yield and soil N₂O emissions were significantly influenced by precipitation and nitrification processes. Corn yield exhibited a quadratic relationship with precipitation, initially increasing as precipitation increased, reaching a peak, and then slightly declining when precipitation became excessive. These findings align with previous studies. For example, Ruane et al. (2013) found that yield changes strongly correlate with total rainfall during the growing season. Climate variations remain the dominant factor influencing seasonal corn yields. Lobell and Field (2007) estimated that approximately 30% of corn yield variability could be explained by mean growing season temperature and precipitation, while Leng et al. (2016) reported that climatic conditions during the growing season accounted for about 40% of yield viability.

Soil N₂O emissions in this study were also strongly associated with denitrification, in addition to precipitation and nitrification. In fact, denitrification emerged as the dominant factor controlling soil N₂O emissions. Our results align with findings from Thilakarathna and Hernandez-Ramirez (2021), who reported that denitrification contributes approximately 77.4% of the total N₂O emissions. Zhang et al. (2021a) and Kaur et al. (2023) also demonstrated that increased soil moisture induced by precipitation significantly enhances denitrification rates, leading to elevated soil N₂O emissions. When precipitation was low, soil N₂O emissions declined due to reduced solute transport and increased risks to soil microbial motility (Beare et al., 2009). Even under severe drought conditions, denitrifying pathways can still dominate N₂O emissions due to the accumulation of N-bearing organic matter on soil microaggregate surfaces (Harris et al., 2021). Additionally, we observed increased N₂O emissions following all rainfall events, further supporting the role of denitrification in driving emissions. Our regression analysis confirmed that denitrification plays a critical role in explaining the variability of soil N₂O emissions.

5 Conclusions

Using the DNDC model, we simulated interannual variability in corn yield and soil N₂O emissions based on a long-term precipitation data set and quantified the legacy effects of N application and climate variability. Our findings indicate that models like DNDC can effectively assess the impacts of climate change and N management on crop yield and GHG emissions, providing valuable insights for climate mitigation strategies. We found strong cumulative legacy effects of continued N application but weak legacy effects of climate. While N legacies can be significant with sustained applications, the climate legacy effect appears to be short-lived, with no significant long-term impact detected in this study. Future research should focus on improving the quantification of N legacy across different cropland systems under varying climate conditions and over extended periods. Additionally, efforts should be directed toward partitioning the relative contributions of current and legacy effects of N application and climate variability to develop strategies for enhancing corn production while minimizing environmental impacts.

In N-fertilized corn fields, we found that both crop yield and soil N₂O emissions can increase concurrently, rather than exhibiting a trade-off (Thilakarathna and Hernandez-Ramirez, 2021). As global food demand rises, future crop production will likely require greater N inputs, yet excessive N application poses serious environmental risks, threatening long-term sustainability. The goal of reducing soil N₂O emissions could further complicate future efforts to increase corn yield (Kucharik and Ramankutty, 2005). Therefore, strategies aimed at stabilizing crop production while minimizing environmental impacts should be prioritized to ensure sustainable agriculture in the long run. Achieving this balance – maintaining stable yields while reducing soil N₂O emissions – will be critical for ensuring food security and mitigating the environmental footprint of agricultural management in a changing climate.

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