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Journal ArticleDOI

Application of Bayesian statistics to estimate nitrous oxide emission factors of three nitrogen fertilisers on UK grasslands.

TL;DR: A Bayesian approach was used to calculate N2O emission factors (EFs) and their associated uncertainties from flux chamber measurements made after the application of nitrogen fertilisers at four grassland sites in the UK, indicating that more complex models may be needed, particularly for measurement data with high temporal resolution.
About: This article is published in Environment International.The article was published on 2019-07-01 and is currently open access. It has received 23 citations till now. The article focuses on the topics: Bayesian statistics.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors presented the first long-term N2O eddy covariance dataset measured from a working farm, which was used over a four-year period to measure fluxes of the greenhouse gas nitrous oxide (N2O) from an intensively managed grazed grassland, to which regular applications of ammonium nitrate or urea fertilisers were spread, for two years each at the field site.

33 citations

Journal ArticleDOI
TL;DR: A Bayesian approach improves uncertainty analysis of EFs and Microbial inhibitors reduce emissions of N2O from mineral fertilisers significantly.

23 citations

Journal ArticleDOI
TL;DR: Five gap-filling practices are outlined: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap- filling soil N2 O emissions.
Abstract: Nitrous oxide (N2 O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N2 O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N2 O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap-filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap-filling soil N2 O emissions. To facilitate the use of improved gap-filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap-filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.

22 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present an inventory framework for monitoring, reporting, and verifying emissions from agricultural soils, with a multi-stage survey to collect data on agricultural management and N2O fluxes that allow for development, parameterization and application of models to estimate national-scale emissions.

21 citations

Journal ArticleDOI
TL;DR: In this paper, the authors found that combined application of fertiliser (calcium ammonium nitrate) and urine significantly increased the cumulative NO emissions as well as the N2O emission factor.

20 citations

References
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Book
01 Jan 1995
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

16,079 citations

Journal ArticleDOI
02 Oct 2009-Science
TL;DR: In this paper, the ozone depletion potential-weighted anthropogenic emissions of N2O with those of other ozone-depleting substances were compared, and it was shown that N 2O emission currently is the single most important ozone-destroying emission and is expected to remain the largest throughout the 21st century.
Abstract: By comparing the ozone depletion potential-weighted anthropogenic emissions of N2O with those of other ozone-depleting substances, we show that N2O emission currently is the single most important ozone-depleting emission and is expected to remain the largest throughout the 21st century. N2O is unregulated by the Montreal Protocol. Limiting future N2O emissions would enhance the recovery of the ozone layer from its depleted state and would also reduce the anthropogenic forcing of the climate system, representing a win-win for both ozone and climate.

3,363 citations

Journal Article
01 Jan 2009-Nature
TL;DR: Nitrous oxide emission currently is the single most important ozone-depleting emission and is expected to remain the largest throughout the 21st century, and N2O is unregulated by the Montreal Protocol, which would enhance the recovery of the ozone layer from its depleted state and reduce the anthropogenic forcing of the climate system.

3,069 citations

Journal ArticleDOI
TL;DR: Improved process understanding, building on the increased use of isotope tracing techniques and metagenomics, needs to go along with improvements in measurement techniques for N2O (and N2) emission in order to obtain robust field and laboratory datasets for different ecosystem types.
Abstract: Although it is well established that soils are the dominating source for atmospheric nitrous oxide (N2O), we are still struggling to fully understand the complexity of the underlying microbial production and consumption processes and the links to biotic (e.g. inter- and intraspecies competition, food webs, plant–microbe interaction) and abiotic (e.g. soil climate, physics and chemistry) factors. Recent work shows that a better understanding of the composition and diversity of the microbial community across a variety of soils in different climates and under different land use, as well as plant–microbe interactions in the rhizosphere, may provide a key to better understand the variability of N2O fluxes at the soil–atmosphere interface. Moreover, recent insights into the regulation of the reduction of N2O to dinitrogen (N2) have increased our understanding of N2O exchange. This improved process understanding, building on the increased use of isotope tracing techniques and metagenomics, needs to go along with improvements in measurement techniques for N2O (and N2) emission in order to obtain robust field and laboratory datasets for different ecosystem types. Advances in both fields are currently used to improve process descriptions in biogeochemical models, which may eventually be used not only to test our current process understanding from the microsite to the field level, but also used as tools for up-scaling emissions to landscapes and regions and to explore feedbacks of soil N2O emissions to changes in environmental conditions, land management and land use.

1,871 citations

Journal ArticleDOI
TL;DR: In this paper, a rain-event driven, process-oriented model of nitrogen and carbon cycling processes in soils was used to simulate N2O and CO2 emissions from soils.
Abstract: Simulations of N2O and CO2 emissions from soils were conducted with a rain-event driven, process-oriented model (DNDC) of nitrogen and carbon cycling processes in soils. The magnitude and trends of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of these emissions from the range of soil types examined demonstrates that the DNDC will be a useful tool for the study of linkages among climate, soil-atmosphere interactions, land use, and trace gas fluxes.

1,243 citations