Developing above and below ground carbon stock models and tools for farm and landscape managment.

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dc.contributor.advisorBurgess, Paul J.
dc.contributor.advisorCorstanje, Ronald
dc.contributor.authorBeka, Styliani
dc.date.accessioned2023-10-18T14:06:11Z
dc.date.available2023-10-18T14:06:11Z
dc.date.issued2022-11
dc.description.abstractAgriculture and land use are responsible for about 11% of the UK’s territorial greenhouse gas emissions. Therefore, a policy measure to mitigate climate change is to incentivise additional soil organic carbon and biomass carbon storage on farms. However, physical measurements of soil organic carbon and biomass carbon can be difficult due to the high cost and labour requirements. Hence, this PhD aimed to review, develop, apply and evaluate scalable and robust methods for creating soil organic carbon and biomass carbon maps and models, to enable more informed farm and landscape management. Current methods for developing farm-scale carbon inventories are reviewed and it is demonstrated that few models provide spatial estimates with a level of uncertainty. Additionally, three spatial soil organic carbon models with different scales of input and output data, for the top 10 cm of the soil for nine grassland sites are developed and evaluated. Across the evaluation dataset, the fine-scale models were able to better predict the soil organic carbon (0-10 cm) variability found in the measured values. This difference has important implications if soil organic carbon values derived from models are used to provide a baseline from which carbon payments are derived. An integrated spatial approach using LiDAR data and two Bayesian Belief Network models is developed to quantify the total biomass carbon stock of different land covers and landscape features across five case studies. The two Bayesian Belief Network models successfully allocated the total biomass carbon values to one of four classes with an error rate of 6.7% and 4.3% for the land cover and landscape features respectively. An advantage of the approach is that the predicted values can be determined remotely using historic land cover and LiDAR height data. A novel tool is then established that combines the empirical SOC model with the probabilistic biomass carbon model for baseline farm carbon stock estimation. The derived results include itemised values and related uncertainty for each land cover parcel and landscape feature. Lastly, an investigation of the opportunities and obstacles for spatial farm level C accountancy is conducted.en_UK
dc.description.coursenamePhD in Environment and Agrifooden_UK
dc.description.sponsorshipNatural Environmental Research (NERC)en_UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20398
dc.language.isoenen_UK
dc.publisherCranfield Universityen_UK
dc.publisher.departmentSWEEen_UK
dc.rights© Cranfield University, 2022. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.en_UK
dc.subjectField-scale carbon accountingen_UK
dc.subjectspatial carbon inventory toolen_UK
dc.subjectsoil organic carbon storageen_UK
dc.subjectspatial modellingen_UK
dc.subjectnet-zeroen_UK
dc.subjectcarbon paymentsen_UK
dc.subjectbiomass carbonen_UK
dc.subjectspatial carbon variationen_UK
dc.subjectintegrated methoden_UK
dc.subjectcarbon storageen_UK
dc.subjectlandscape featuresen_UK
dc.titleDeveloping above and below ground carbon stock models and tools for farm and landscape managment.en_UK
dc.typeThesis or dissertationen_UK
dc.type.qualificationlevelDoctoralen_UK
dc.type.qualificationnamePhDen_UK

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