Global Research Alliance N2 O chamber methodology guidelines: Considerations for automated flux measurement

Preconditions for Including the Effects of Urease and Nitrification Inhibitors in Emission Inventories

Urease and nitrification inhibitors can reduce ammonia and greenhouse gas emissions from fertilizers and manure but their effectiveness depends on the conditions under which they are used. Consequently, it is essential for the credibility of emission reductions reported in regulatory emission inventories that their effectiveness is assessed under real-world conditions and not just in the laboratory. Here, we specify the criteria we consider necessary before the effects of inhibitors are included in regulatory emission inventories.

Mix-method toolbox for monitoring greenhouse gas production and microbiome responses to soil amendments

In this study, we adopt an interdisciplinary approach, integrating agronomic field experiments with soil chemistry, molecular biology techniques, and statistics to investigate the impact of organic residue amendments, such as vinasse (a by-product of sugarcane ethanol production), on soil microbiome and greenhouse gas (GHG) production. The research investigates the effects of distinct disturbances, including organic residue application alone or combined with inorganic N fertilizer on the environment. The methods assess soil microbiome dynamics (composition and function), GHG emissions, and plant productivity. Detailed steps for field experimental setup, soil sampling, soil chemical analyses, determination of bacterial and fungal community diversity, quantification of genes related to nitrification and denitrification pathways, measurement and analysis of gas fluxes (N2O, CH4, and CO2), and determination of plant productivity are provided. The outcomes of the methods are detailed in our publications (Lourenço et al., 2018a; Lourenço et al., 2018b; Lourenço et al., 2019; Lourenço et al., 2020). Additionally, the statistical methods and scripts used for analyzing large datasets are outlined. The aim is to assist researchers by addressing common challenges in large-scale field experiments, offering practical recommendations to avoid common pitfalls, and proposing potential analyses, thereby encouraging collaboration among diverse research groups.•Interdisciplinary methods and scientific questions allow for exploring broader interconnected environmental problems.•The proposed method can serve as a model and protocol for evaluating the impact of soil amendments on soil microbiome, GHG emissions, and plant productivity, promoting more sustainable management practices.•Time-series data can offer detailed insights into specific ecosystems, particularly concerning soil microbiota (taxonomy and functions).

Denitrification and the challenge of scaling microsite knowledge to the globe

Our knowledge of microbial processes-who is responsible for what, the rates at which they occur, and the substrates consumed and products produced-is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are-by themselves-insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

Autonomous high-throughput in situ soil nitrogen flux measurement system

Nitrogen (N) behavior in soil is a major component of the global N cycle. Climate scientists seek to accurately measure N flux to the atmosphere, farmers want to maximize plant N uptake and reduce input costs, and industries land-applying wastewater must mitigate potential N leaching to drinking water supplies. The need to quantify denitrification rates of wastewater disposed of by vegetable processing and cheese making industries in Wisconsin drove the development of an autonomous high-throughput in situ sampling and analysis system for soil N flux. The system was deployed to six unique industry sites with different soil types for 7 days once per quarter and data collected continuously. Additional seasonal data collection allowed for the determination of system N mass balances. The system can deliver quality data under challenging conditions where staffing would be impractical and provide detailed information about soil gas emissions under a range of environmental conditions.

Nitrous oxide emissions from agricultural soils challenge climate sustainability in the US Corn Belt

Agricultural landscapes are the largest source of anthropogenic nitrous oxide (N2O) emissions, but their specific sources and magnitudes remain contested. In the US Corn Belt, a globally important N2O source, in-field soil emissions were reportedly too small to account for N2O measured in the regional atmosphere, and disproportionately high N2O emissions from intermittent streams have been invoked to explain the discrepancy. We collected 3 y of high-frequency (4-h) measurements across a topographic gradient, including a very poorly drained (intermittently flooded) depression and adjacent upland soils. Mean annual N2O emissions from this corn-soybean rotation (7.8 kg of N2O-N ha-1⋅y-1) were similar to a previous regional top-down estimate, regardless of landscape position. Synthesizing other Corn Belt studies, we found mean emissions of 5.6 kg of N2O-N ha-1⋅y-1 from soils with similar drainage to our transect (moderately well-drained to very poorly drained), which collectively comprise 60% of corn-soybean-cultivated soils. In contrast, strictly well-drained soils averaged only 2.3 kg of N2O-N ha-1⋅y-1 Our results imply that in-field N2O emissions from soils with moderately to severely impaired drainage are similar to regional mean values and that N2O emissions from well-drained soils are not representative of the broader Corn Belt. On the basis of carbon dioxide equivalents, the warming effect of direct N2O emissions from our transect was twofold greater than optimistic soil carbon gains achievable from agricultural practice changes. Despite the recent focus on soil carbon sequestration, addressing N2O emissions from wet Corn Belt soils may have greater leverage in achieving climate sustainability.

Diurnal variability in soil nitrous oxide emissions is a widespread phenomenon

Manual measurements of nitrous oxide (N₂ O) emissions with static chambers are commonly practised. However, they generally do not consider the diurnal variability of N₂ O flux, and little is known about the patterns and drivers of such variability. We systematically reviewed and analysed 286 diurnal data sets of N₂ O fluxes from published literature to (i) assess the prevalence and timing (day or night peaking) of diurnal N₂ O flux patterns in agricultural and forest soils, (ii) examine the relationship between N₂ O flux and soil temperature with different diurnal patterns, (iii) identify whether non-diurnal factors (i.e. land management and soil properties) influence the occurrence of diurnal patterns and (iv) evaluate the accuracy of estimating cumulative N₂ O emissions with single-daily flux measurements. Our synthesis demonstrates that diurnal N₂ O flux variability is a widespread phenomenon in agricultural and forest soils. Of the 286 data sets analysed, ~80% exhibited diurnal N₂ O patterns, with ~60% peaking during the day and ~20% at night. Contrary to many published observations, our analysis only found strong positive correlations (R > 0.7) between N₂ O flux and soil temperature in one-third of the data sets. Soil drainage property, soil water-filled pore space (WFPS) level and land use were also found to potentially influence the occurrence of certain diurnal patterns. Our work demonstrated that single-daily flux measurements at mid-morning yielded daily emission estimates with the smallest average bias compared to measurements made at other times of day, however, it could still lead to significant over- or underestimation due to inconsistent diurnal N₂ O patterns. This inconsistency also reflects the inaccuracy of using soil temperature to predict the time of daily average N₂ O flux. Future research should investigate the relationship between N₂ O flux and other diurnal parameters, such as photosynthetically active radiation (PAR) and root exudation, along with the consideration of the effects of soil moisture, drainage and land use on the diurnal patterns of N₂ O flux. The information could be incorporated in N₂ O emission prediction models to improve accuracy.

Hot moments drive extreme nitrous oxide and methane emissions from agricultural peatlands

Agricultural peatlands are estimated to emit approximately one third of global greenhouse gas (GHG) emissions from croplands, but the temporal dynamics and controls of these emissions are poorly understood, particularly for nitrous oxide (N₂ O). We used cavity ring-down spectroscopy and automated chambers in a drained agricultural peatland to measure over 70,000 individual N₂ O, methane (CH₄ ), and carbon dioxide (CO₂ ) fluxes over 3 years. Our results showed that N₂ O fluxes were high, contributing 26% (annual range: 16%-35%) of annual CO₂ e emissions. Total N₂ O fluxes averaged 26 ± 0.5 kg N₂ O-N ha^-1 y^-1 and exhibited significant inter- and intra-annual variability with a maximum annual flux of 42 ± 1.8 kg N₂ O-N ha^-1 y^-1 . Hot moments of N₂ O and CH₄ emissions represented 1.1 ± 0.2 and 1.3 ± 0.2% of measurements, respectively, but contributed to 45 ± 1% of mean annual N₂ O fluxes and to 140 ± 9% of mean annual CH₄  fluxes. Soil moisture, soil temperature, and bulk soil oxygen (O₂ ) concentrations were strongly correlated with soil N₂ O and CH₄ emissions; soil nitrate ( NO3- ) concentrations were also significantly correlated with soil N₂ O emissions. These results suggest that IPCC benchmarks underestimate N₂ O emissions from these high emitting agricultural peatlands by up to 70%. Scaling to regional agricultural peatlands with similar management suggests these ecosystems could emit up to 1.86 Tg CO₂ e y^-1 (range: 1.58-2.21 Tg CO₂ e y^-1 ). Data suggest that these agricultural peatlands are large sources of GHGs, and that short-term hot moments of N₂ O and CH₄ are a significant fraction of total greenhouse budgets.