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dc.contributor.advisorRees, Boben
dc.contributor.advisorWilliams, Mathewen
dc.contributor.advisorTopp, Kairstyen
dc.contributor.authorMyrgiotis, Vasileiosen
dc.date.accessioned2018-07-16T10:55:46Z
dc.date.available2018-07-16T10:55:46Z
dc.date.issued2018-07-03
dc.identifier.urihttp://hdl.handle.net/1842/31325
dc.description.abstractNitrous oxide (N2O) is a powerful greenhouse gas and a major contributor to ozone layer depletion. The application of nitrogenous fertilisers to agricultural soils is a major source of N2O on a global scale. Arable soils receive significant rates of synthetic nitrogen (N) and thus have a considerable N2O footprint. The reduction of the N2O footprint of agricultural systems is a key target for those countries that seek to reduce their contribution to climate change and achieve a more sustainable agriculture. These twin targets are part of Scotland's agro-environmental policy. Because soil N2O emissions vary significantly both temporally and spatially, measuring N2O emissions across wide agricultural areas is impractical. However, the quantification of the N2O footprint of important agricultural regions is very valuable to scientists, farmers and policymakers alike. In this context, agro-ecosystem biogeochemistry models are scientific tools, which are developed using in-depth knowledge on the underlying processes, and are used to quantify N2O emissions across spatial and temporal scales. In Scotland, arable agriculture is concentrated at the Eastern part of the country where wheat, barley and oilseed rape are the most widely cultivated crops. The main aim of this study was to quantify the amount of N2O that is emitted from arable soils due to the cultivation of these three crops in Eastern Scotland by using the Landscape-DNDC model. Landscape-DNDC is a mechanistic biogeochemistry model that describes the flows of energy, water and nutrients in agricultural ecosystems. As part of the study, the parametric sensitivities of key model outputs have been quantified using well-established sensitivity analysis methods, which were tailored in order to consider the particularities of N cycling in arable soils. Driven by the fact that the existence of spatiotemporal uncertainties around field-measured soil N2O data complicates the evaluation of model performance, a novel model evaluation algorithm has been developed and was used to assess the model's predictive accuracy. By combining the knowledge of the model's parametric sensitivity with the abilities of the evaluation algorithm, nine key parameters of Landscape-DNDC were calibrated to UK edaphoclimatic conditions (using the Metropolis-Hastings Bayesian calibration algorithm). Model calibration led to improved prediction of field-measured soil N2O emissions at a set of sites. The model was then coupled to geographically explicit data on climate, soil N2O and crop management and used to simulate N2O emissions from the arable soils of Eastern Scotland. The results show that, on average, 0.59 % of the applied fertiliser N (kg N ha-1) was lost to the atmosphere as N2O. This factor is much lower than the generic N2O emission factor (EF) of 1% and closer to the UK cropland-specific N2O EF (i.e. 0.79%). The predicted annual N2O was the combined result of different drivers (i.e. fertiliser rate, soil and climate variables) but the geographic distribution of the estimated N2O EFs revealed some hotspots of high N2O EF (larger than 1%). Interestingly, these hotspots were caused by the cultivation of winter oilseed rape on soils with high bulk density and clay content. The comparison of the simulated yields per hectare with respective measured data and of the simulated nitrate (NO-3 ) leaching and crop N uptake factors with respective literature-based values showed that the prediction of soil N2O was not made at the expense of realistic prediction of other important aspects of agro-ecosystem biogeochemistry. Also, the study found that the simulated N2O is almost twice as sensitive to soil input uncertainty as the simulated NO-3 is, while, crop N uptake is rather insensitive to this source of uncertainty. Finally, the study shows that the uncertainty around the nine calibrated model parameters affects the prediction of NO-3 leaching strongly but its role in regards to the simulation of N2O emissions is small.en
dc.contributor.sponsorNatural Environment Research Council (NERC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.haspartThe University of Edinburgh. College of Science and Engineeringen
dc.relation.hasversionV. Myrgiotis, M. Williams, R.M. Rees, K.E. Smith, R.E. Thorman, and C.F.E. Topp. Model evaluation in relation to soil N2O emissions: An algorithmic method which accounts for variability in measurements and possible time lags. Environmental Modelling & Software, 84(C):251-262, 2016en
dc.relation.hasversionV. Myrgiotis, M. Williams, C.F.E. Topp, and R.M. Rees. Improving model prediction of soil N2O emissions through Bayesian calibration. Science of The Total Environment, 624:1467-1477, 2018en
dc.relation.hasversionV. Myrgiotis, R.M. Rees, C.F.E. Topp, and M. Williams. A systematic approach to identifying key parameters and processes in agroecosystem models. Ecological Modelling, 368:344-356, 2018en
dc.relation.hasversionR. Sandor, F. Ehrhardt, B. Basso, G. Bellocchi, A. Bhatia, L. Brilli, M. D. A. Migliorati, J. Doltra, C. Dorich, L. Doro, N. Fitton, S. J. Giacomini, P. Grace, B. Grant, M. T. Harrison, S. Jones, M. U. F. Kirschbaum, K. Klumpp, P. Laville, J. Leonard, M. Liebig, M. Lieffering, R. Martin, R. McAuliffe, E. Meier, L. Merbold, A. Moore, V. Myrgiotis, P. Newton, E. Pattey, s. recous, S. Rolinski, J. Sharp, R. S. Massad, P. Smith, W. Smith, V. Snow, L. Wu, Q. Zhang, and J. F. Soussana. C and N models Intercomparison - benchmark and ensemble model estimates for grassland production. Advances in Animal Biosciences, 7(03):245-247, Oct. 2016.en
dc.subjectnitrous oxideen
dc.subjectsoilen
dc.subjectScotlanden
dc.subjectagricultureen
dc.subjectClimate Changeen
dc.subjectmodellingen
dc.titleSimulating soil N2O emissions in arable Eastern Scotlanden
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dc.rights.embargodate2100-12-31
dcterms.accessRightsRestricted Accessen


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