R
R. W. Sneath
Researcher at University of Bedfordshire
Publications - 26
Citations - 863
R. W. Sneath is an academic researcher from University of Bedfordshire. The author has contributed to research in topics: Slurry & Greenhouse gas. The author has an hindex of 12, co-authored 26 publications receiving 836 citations.
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Development and application of a mechanistic model to estimate emission of nitrous oxide from UK agriculture
TL;DR: A mechanistic model of N 2 O emission from agricultural soil (DeNitrification-DeComposition-DNDC) was modified for application to the UK, and was used as the basis of an inventory of emissions from UK agriculture in 1990 as discussed by the authors.
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A UK inventory of nitrous oxide emissions from farmed livestock
TL;DR: A UK inventory of the nitrous oxide (N 2 O) emissions from farmed livestock was compiled to identify areas where potential abatement practices may be effective as mentioned in this paper, which included details of numbers of animals within each category of a species, animal liveweights, number of days housed, excretal rates and volumes of manures in stores.
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An inventory of nitrous oxide emissions from agriculture in the UK using the IPCC methodology: emission estimate, uncertainty and sensitivity analysis
L. Brown,S. Armstrong Brown,S. C. Jarvis,B. Syed,Keith Goulding,V. R. Phillips,R. W. Sneath,B. F. Pain +7 more
TL;DR: In this article, the authors used the IPCC default values of all emission factors and parameters to estimate nitrogen oxide emissions from UK agriculture to be 87 Gg N2O-N in both 1990 and 1995, with an overall uncertainty of 62%.
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A farm-scale study on the use of clinoptilolite zeolite and De-Odorase® for reducing odour and ammonia emissions from broiler houses
TL;DR: In this article, the authors compared the effectiveness of two abatement compounds, clinoptilolite and De-Odorase®, when used in broiler production and found no statistically significant reduction in odour concentration or odour emission rate for either of the additives used.
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Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data
Phillip A. Evans,Krishna C. Persaud,Alexander S McNeish,R. W. Sneath,Norris Hobson,Naresh Magan +5 more
TL;DR: A radial basis function artificial neural network was used to correlate sensor array responses with human grading of off-taints in wheat, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population.