Global Research Alliance N₂O chamber methodology guidelines: Summary of modeling approaches

Abstract

Measurements of nitrous oxide (N 2 O) emissions from agriculture are essential for understanding the complex soil-crop-climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N 2 O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models,

Global Research Alliance N2O chamber methodology guidelines: Recommendations for deployment and accounting for sources of variability

“…A recommended minimum set of ancillary measurements for N 2 O EF studies would improve the potential for subsequent meta‐analyses (de Klein, Alfaro, et al., 2020; López‐Aizpún et al., 2020). If the goal is to understand temporal patterns in N 2 O emissions, or for model development or verification, then a wider range of (frequent) ancillary measurements are necessary (de Klein, Alfaro, et al., 2020; Dorich et al., 2020; Giltrap et al., 2020).…”

Global Research Alliance N2O chamber methodology guidelines: Statistical considerations, emission factor calculation, and data reporting

“…Statistical or empirical modeling is often used for identifying factors that significantly influence N 2 O emissions, in order to estimate country‐ or region‐specific N 2 O emissions (Giltrap et al., 2020), or aid decisions on disaggregation of EFs for different N sources and/or soil, climatic, or landscape features (e.g., soil drainage class, season, or slope; Chadwick et al., 2018; Saggar et al., 2015; Shrestha et al., 2014). Statistical modeling can also be used to estimate missing data values in N 2 O time series datasets (gap filling).…”

“…Giltrap et al. (2020) includes an overview of some of the key points for three commonly used process‐based models.…”

“…(2014) found that a poor description of experimental and environmental data in published studies restricted their meta‐analysis of N 2 O emission data. Finally, the development and evaluation of process‐based models also relies on robust reporting of (meta)data and supporting information (Giltrap et al., 2020).…”

“…This in turn limits interstudy comparisons and assessment of the reliability and uncertainty associated with the results of the individual studies. This paper is part of a special section of the Journal of Environmental Quality that considers the challenges associated with all key aspects of chamber methodologies, including design (Clough et al., 2020), deployment (Charteris et al., 2020), gas analysis (Harvey et al., 2020), automated chambers (Grace et al., 2020), flux calculation methods (Venterea et al., 2020), modeling (Giltrap et al., 2020), and gap‐filling procedures (Dorich et al., 2020). These papers collectively provide guidance on best practices and factors that need to be considered when adopting N 2 O chamber methodologies.…”