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C. Bache

Bio: C. Bache is an academic researcher. The author has contributed to research in topics: Bayesian statistics. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

Papers
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Journal ArticleDOI
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.

23 citations


Cited by
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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