Understanding soil quality and resilience: effects of perturbations and natural variations on nitrous oxide emission, water retention and structure (RRES)

Lead Research Organisation: Rothamsted Research
Department Name: Unlisted


The aim of this project is to provide a major advance in soil science which can be used for the promotion of Soil Quality and Soil Resilience while achieving minimum emission of nitrous oxide (N2O). We will do this by investigating Soil Quality in terms of a hierarchy of properties, from purely physical (soil void structure), through quasi-static fluid properties (water retention), dynamic fluid properties (conservative Br- tracer dispersion and breakthrough), dynamic fluid properties with chemistry (NO3- tracer dispersion and breakthrough), and dynamic fluid effects with chemistry and biology (N2O production). Properties within this hierarchy will be investigated by a range of experimental methods, all of which have been developed or improved recently. They are: X-ray CT scanning; water retention with an additional gravity drainage cell; conservative and reactive tracer breakthroughs on a new automated precision lysimeter; and N2O emission with a newly enhanced automated apparatus. The dominance of denitrification over nitrification will be checked by measurement of isotope and isotopomer ratios.The soil samples will cover a range or arable and grassland soils, with different organic carbon contents and age of ley, but with similar clay contents.The project will be underpinned by a new multiscale void-structure model, which will be used to generate the properties of two extremes of soil structure / a 'series' structure and a 'parallel' structure. Inputs to the model will be water retention curves, and macro-pore sizes from X-ray computed tomography. Outputs will be conservative tracer breakthrough characteristics, nitrate dispersion characteristics, and rate of N2O production. We aim to produce outputs from the model which match the trends in the experimental samples in a meaningful way, leading not only to an improved understanding of N2O production, but also a capability of predicting N2O production for different conditions.


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