Dondini, M, Jones, EO, Richards, M, Pogson, MA, Rowe, RL, Perks, M, McNamara, NP, Smith, JU and Smith, P (2015) Evaluation of the ECOSSE model for simulating soilcarbon under short rotation forestry energy crops in Britain. GCB Bioenergy, 7 (3). pp. 527-540. ISSN 1757-1707

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Abstract

Understanding and predicting the effects of land-use change to short rotation forestry (SRF) on soil carbon (C) is an important requirement for fully assessing the C mitigation potential of SRF as a bioenergy crop. There is little current knowledge of SRF in the UK and in particular a lack of consistent measured data sets on the direct impacts of land use change on soil C stocks. The ECOSSE model was developed to simulate soil C dynamics and greenhouse gas (GHG) emissions in mineral and organic soils. The ECOSSE model has already been applied spatially to simulate land-use change impacts on soil C and GHG emissions. However, it has not been extensively evaluated under SRF. Eleven sites comprising 29 transitions in Britain, representing land-use change from nonwoodland land uses to SRF, were selected to evaluate the performance of ECOSSE in predicting soil C and soil C change in SRF plantations. The modelled C under SRF showed a strong correlation with the soil C measurements at both 0–30 cm (R = 0.93) and 0–100 cm soil depth (R = 0.82). As for the SRF plots, the soil C at the reference sites have been accurately simulated by the model. The extremely high correlation for the reference fields (R ≥ 0.99) shows a good performance of the model spin-up. The statistical analysis of the model performance to simulate soil C and soil C changes after land-use change to SRF highlighted the absence of significant error between modelled and measured values as well as the absence of significant bias in the model. Overall, this evaluation reinforces previous studies on the ability of ECOSSE to simulate soil C and emphasize its accuracy to simulate soil C under SRF plantations.

Item Type:	Article
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics S Agriculture > S Agriculture (General)
Divisions:	Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher:	Wiley Open Access
Date Deposited:	19 Oct 2016 10:48
Last Modified:	18 May 2022 10:30
DOI or ID number:	10.1111/gcbb.12154
URI:	https://researchonline.ljmu.ac.uk/id/eprint/4646

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