Long-term variability in N2O emissions and emission factors for corn and soybeans induced by weather and management at a cold climate site

Interactions Between Bacterivorous Nematodes and Bacteria Reduce N2O Emissions

Trophic interactions in micro-food webs, such as those between nematodes and their bacterial prey, affect nitrogen cycling in soils, potentially changing nitrous oxide (N2O) production and consumption. However, how nematode-mediated changes in soil bacterial community composition affect soil N2O emissions is largely unknown. Here, microcosm experiments are performed with the bacterial feeding nematode Protorhabditis to explore the potential of nematodes in regulating microbial communities and thereby soil N2O emissions. Removal of nematodes by defaunation resulted in increased N2O emissions, with the removal of Protorhabditis contributing most to this increase. Further, inoculation with Protorhabditis altered bacterial community composition and increased the relative abundance of Bacillus, and the abundance of the nosZ gene in soil. In vitro experiments indicated that Protorhabditis reinforce the reduction in N2O emissions by Bacillus due to suppressing competitors and producing bacteria growth stimulating substances such as betaine. The results indicate that interactions between nematodes and bacteria modify N2O emissions providing the perspective for the mitigation of greenhouse gas emissions via manipulating trophic interactions in soil.

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha-1) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long-term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

Changes in the Yield Effect of the Preceding Crop in the US Corn Belt Under a Warming Climate

Crop rotation has been widely used to enhance crop yields and mitigate adverse climate impacts. The existing research predominantly focuses on the impacts of crop rotation under growing season (GS) climates, neglecting the influences of non-GS (NGS) climates on agroecosystems. This oversight limits our understanding of the comprehensive climatic impacts on crop rotation and, consequently, our ability to devise effective adaptation strategies in response to climate warming. In this study, we examine the impacts of both GS and NGS climate conditions on the yield effect of the preceding crop in corn-soybean rotation systems from 1999 to 2018 in the US Midwest. Using causal forest analysis, we estimate that crop rotation increases corn and soybean yields by 0.96 and 0.22 t/ha on average, respectively. We then employ statistical models to indicate that increasing temperatures and rainfall in the NGS reduce corn rotation benefits, while warming GS enhances rotation benefits for soybeans. By 2051-2070, we project that warming climates will reduce corn rotation benefits by 6.74% under Shared Socioeconomic Pathway (SSP) 1-2.6 and 17.18% under SSP 5-8.5. For soybeans, warming climates are expected to increase rotation benefits by 8.36% under SSP 1-2.6 and 13.83% under SSP 5-8.5. Despite these diverse climate impacts on both crops, increasing crop rotation could still improve county-average yields, as neither corn nor soybean was fully rotated. If we project that all continuous corn and continuous soybeans are rotated by 2051-2070, county-average corn yields will increase by 0.265 t/ha under SSP 1-2.6 and 0.164 t/ha under SSP 5-8.5, while county-average soybean yields will gain 0.064 t/ha under SSP 1-2.6 and 0.076 t/ha under SSP 5-8.5. These findings highlight the effectiveness of crop rotation in the face of warming NGS and GS in the future and can help evaluate opportunities for adaptation.

Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector

Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%-1.22% in 2010 to 1.27%-1.34% by 2050, representing a relative increase of 4.4%-11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%-0.35% (relative increase of 11.9%-17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying-wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.

Nitrous oxide emissions from soybean in response to drained and undrained soils and previous corn nitrogen management

While corn (Zea mays L.)-soybean (Glycine max. Merr. L) is a predominant rotation system in the US Midwest the residual effect of nitrogen (N) fertilization to corn on the following year's soybean and N2O emissions under different soil drainage conditions has not been studied. Our objective was to quantify agronomic parameters and season-long N2O emissions from soybean as affected by N management (0-N and optimum N rate of 135 kg N ha-1 as single or split application) during the previous corn crop under drained and undrained systems. Urea was applied to corn, and residual N effects were measured on soybean the following year in a poorly drained soil with and without subsurface tile drainage. Drainage reduced N2O emissions in one of three growing seasons but had no effect on soybean yield or N removal in grain. Nitrogen management in the previous corn crop had no effect on soybean grain yield, N removal, or N2O emissions during the soybean phase. Even though soybean symbiotically fixes N and removes more N in grain than corn, N2O emissions were more than two times greater during the corn phase (mean = 1.83 kg N ha-1) due to N fertilization than during the soybean phase (mean = 0.80 kg N ha-1). Also, N2O emissions in the corn years were increased possibly due to decomposition of the previous year's soybean crop residue compared to corn residue decomposition in the soybean years. Tile drainage, especially where wet soil conditions are prevalent, is a viable option to mitigate agricultural N2O emissions.

Unraveling the drivers for interannual variabilities of N2O fluxes from forests soils across climatic zones

Forest soils are an important source of nitrous oxide (N2O), however, field observations of N2O emission have often exhibited large variabilities when compared with managed agricultural lands. In the last decade, the number of forest N2O studies has increased more than tenfold, but only a few of them have looked into the interannual flux variabilities from the regional scale. Here, we have collected 30 long-term N2O monitoring studies (≥ 2 years) based on a global database, and extracted variabilities (VARFlux) as well as relative variabilities (VAR%, in proportions) of annual N2O fluxes. The relationship of mean annual precipitation (MAP), mean annual temperature (MAT), and nitrogen (N) deposition with flux variabilities was examined to explore the underlying mechanisms for N2O emission on a long-term scale. Our results show that mean VARFlux is 0.43 kg N ha-1 yr-1 and VAR% is 28.68%. Across climatic zones, the subtropical forests have the largest annual N2O fluxes, as well as the largest fluctuations among annual budgets, while the tropics were the smallest. We found that the regulating factors for VARFlux and VAR% are fundamentally different, i.e., MAT and N input determine the annual fluxes as well as VARFlux while MAP and other limiting soil parameters determine VAR%. The relative contributions of different seasons to flux variabilities were also explored, indicating that N2O fluxes of warm and cool seasons are more responsible for the fluctuations in annual fluxes of the (sub)tropical and temperate forests, respectively. Overall, despite the limitation in interpretations due to few long-term studies from literature, this work highlights that significant interannual variabilities are common phenomena for N2O emission from different climatic zones forest soils; by unraveling the divergent drivers for VARFlux and VAR%, we have provided the possibility of improving N2O simulation models for constraining the heterogeneity of N2O emission processes from climatic zones forest soils.

Effect of nitrification inhibitor (DMPP) on nitrous oxide emissions from agricultural fields: Automated and manual measurements

Nitrogen fertilisation contributes significantly to the atmospheric increase of nitrous oxide (N₂O). Application of nitrification inhibitors (NIs) is a promising strategy to mitigate N₂O emissions and improve N-use efficiency in agricultural systems. This study investigated the effect of NI, 3,4-dimethylpyrazol phosphate (DMPP) on N₂O mitigation from spring barley and spring oilseed rape. Manual and automatic chamber methodologies were used to capture spatial and temporal variability in N₂O emissions. In a second experiment, we study the effect of N fertiliser levels without NI (0 %, 50 %, 100 %, 150 % and 200 % of recommended amount of N fertiliser), as well as 100 % of N with NI on N₂O emissions in spring barley. The automated chamber measurements showed dynamics of N₂O changes throughout the season, including positive and negative peaks that were unobservable with manual chambers due to low temporal resolution. Although not significant, application of NI tended to reduce N₂O emissions. The reduction was on average 16 % in spring barley and 58 % in spring oilseed rape in manual chamber measurements. However, N₂O reduction was 108 % in continuous automatic chamber measurements in spring barley. The N₂O EFs for the growing season were very low (0.025 % to 0.148 %), with a greater reduction in EF in spring oilseed rape (76 %) than in spring barley (32 %) with NI application. A positive correlation (R = 80 %) was observed between N fertiliser levels and N₂O emissions. Crop yield and crop N uptake were not significantly affected by the use of NI. This study highlighted that NI can reduce N₂O emissions, but the reduction effects are plot, crop and microclimate specific. Long-term experiments with continuous plot-scale measurements are needed to capture and optimise N₂O mitigation effect of NIs across wide variability in soils and microclimates in agroecosystems.

Higher than expected N2O emissions from soybean crops in the Pampas Region of Argentina: Estimates from DayCent simulations and field measurements

In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this study we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. We measured N2O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N2O emissions at the same plots. The DayCent model accurately simulated annual N2O emission for all crops as compared to measured data (RMSE = 1.4 g N ha-1 day-1). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N2O emissions ranged from 1.4 to 5.1 kg N ha-1 yr-1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha-1 yr-1, and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha-1 yr-1. IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha-1 yr-1, respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N2O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.