Evaluation of uncertainties in N₂O and NO fluxes from agricultural soil using a hierarchical Bayesian model

Abstract

[1] Agricultural soil is the major source of nitrous oxide (N 2 O) and nitric oxide (NO). However, N 2 O and NO fluxes from the soil show high spatial and temporal variability. Therefore, traditional statistical tools are insufficient for evaluating the strength of the emissions and determining the environmental and management factors affecting these fluxes. To compensate for the inherent variability of N oxide fluxes in situ, we proposed the application of a hierarchical Bayesian (HB) model based on a simple semi-mechanistic model with a lognormal probability distribution. We applied the HB model to the daily N 2 O and NO fluxes from an Andosol soil field to which a chemical N fertilizer was applied. In addition, we evaluated the responses of these fluxes to environmental factors and N application. The posterior inference revealed various sensitivities to the soil temperature and water-filled pore space (WFPS) among the N oxide gas fluxes. The N 2 O flux showed a higher temperature dependency compared with the NO flux. The estimated optimum WFPS of the NO flux (54.1% with credible interval (CI) 95%, from 47.1% to 79.4%) was lower than that of the N 2 O flux (75.8% with CI 95%, from 54.1% to 98.3%) in this soil sample. Although control plots without N fertilizer application are usually required to calculate the fertilizer-induced emission factor (EF), our HB model could estimate EFs and their uncertainties using posterior simulations (for N 2 O 0.077% with CI 95% high probability density, from 0.056% to 0.191%; for NO 0.875% with CI 95% high probability density, from 0.552% to 2.05%). Our HB model can be easily applied to the observations of the N 2 O and NO fluxes because it requires only several explanatory variables and can be used to evaluate the flux uncertainties and responses of the nonlinear N oxide fluxes to environmental factors.

Attribution of N<sub>2</sub>O sources in a grassland soil with laser spectroscopy based isotopocule analysis

“…Until now, there were no direct methods that allowed for the attribution of an emitted amount of N 2 O to a given process in the field (IPCC, 2007;Billings, 2008;Butterbach-Bahl et al, 1997. However, a detailed understanding of the temporal and spatial variations in N 2 O emissions and controlling processes is required to develop mitigation strategies and to better achieve emission reduction targets (Nishina et al, 2012;Cavigelli et al, 2012;Herrero et al, 2016;Decock et al, 2015).…”

Emissions of nitrous oxide (N2O) from soil surfaces and their historical changes in East Asia: a model-based assessment

“…Evaluation of the magnitude of N 2 O emissions requires a model of the N cycle, and the data needed for calibration and validation of such a model differ from those used to estimate CO 2 and methane (CH 4 ) emissions. Second, N 2 O emissions from soil, one of the major sources, are quite heterogeneous spatially and vary greatly temporally (e.g., Nishina et al 2012;Bellingrath-Kimura et al 2015). In soils, instantaneous N 2 O production occurs within microzones (i.e., hot spots) of high-microbial activity and bursts of N 2 O emissions often occur after rainfall and thawing of frozen soil (e.g., Kim et al 2012;Muhr et al 2008).…”

Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing

“…Then, the long-term and multipoint field observation data were parameterized with the Hamiltonian-Monte Carlo method. All of our model's explanatory parameters were remote-sensed by satellites, the nonremote sensible parameters were omitted, e.g., fertilization rate/species/timing, statistical data, soil bio-physicochemical parameters, and rice physiological parameters, and the multipoint ground observation flux data were reproduced; therefore, the representative values of CH 4 fluxes/emissions could be computed with only the transparent satellite data, despite the high spatial heterogeneity of the fluxes and environmental factors, e.g., electron accepters and organic matter in the soil at the field-plot scale [17,18,50,51]. Furthermore, since our model requires only satellite data and soil GIS maps as the data input, a high spatial resolution map is applicable by inputting high spatial resolution satellite data.…”

“…where LID is the ID of the flux measurement location and M is the number of flux data (M = 16,551); η (~Normal (1≤)) is the intercept parameter; the "subs.kine." function represents Michaelis-Menten kinetics (i.e., the CH 4 production rate is not regulated by the concentration when the fresh substrate concentration is high; however, the CH 4 production rate becomes constrained by the remaining substrate days after sowing has passed, and the amount of available substrate remaining becomes limited), and it is designed referring to the N fertilizer response function component of a N 2 O flux model reported in [17]. This model demonstrates the rapid CH 4 production/emission increase at the sowing date (i.e., immediately after the plowing/straw incorporation) due to the promotion of methanogenic enzyme activity and the gradual inhibition over time after the sowing following substrate depletion.…”

“…Ideally, local parameters that are difficult to precisely monitor on a regional scale (e.g., fertilizer/pesticide types and their application rate/timing/methods, carbon/nitrogen ratios of straw, specific rice physiological parameters, electron accepters in soils, and the distribution of rice stubble) should be avoided to maintain the high transparency of the method and to evaluate each parameter's uncertainty. GHG fluxes from agricultural soils are spatiotemporally highly heterogeneous, even at the plot scale [17,18]. The spatial distributions of rice stubble incorporated into soils and the soil surface produce highly heterogeneous CH 4 fluxes from rice paddy soils at the plot scale.…”