Evaluation of the ECOSSE model for simulating soil carbon under short rotation forestry energy crops in Britain

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.

Evaluation of the ECOSSE model for simulating soil organic carbon under Miscanthus and short rotation coppice‐willow crops in Britain

“…The performance of the ECOSSE model in simulating SOC and SOC changes was recently evaluated for SRF plantations in the United Kingdom (Dondini et al ., ). The same approach has been used in the present study to test its application for transitions to Miscanthus and SRC‐willow in the United Kingdom.…”

“…The application of the rescaling factors has been necessary as the Miami model has been developed to estimate NPP at a global scale and based on environmental variables only, while land‐cover type is a key aspect in the ECOSSE model. In the present study, this approach has provided good plant input predictions, and consequently SOC figures, at 17 Miscanthus and 16 SRC‐willow sites in the United Kingdom; it has also been previously applied with good results on the prediction of SOC in 29 transitions to SRF (Dondini et al ., ). However, localized weather conditions at some sites may cause divergent yields compared to that predicted by the Miami model.…”

“…The statistical analysis of the results presented here is in accordance with the results presented by Dondini et al . () for SRF, revealing no significant error between modelled and measured SOC and SOC changes, as well as no significant model bias. The latter is a promising result, given that this work is an independent evaluation of ECOSSE, and therefore, the model had not been further improved or parameterized to produce the outputs under Miscanthus and SRC‐willow plantations.…”

“…The ECOSSE model has already been validated and applied spatially to simulate land‐use change impacts on SOC and GHG emissions over different soil types, to simulate SOC change under energy crops and to simulate soil N and nitrous oxide (N 2 O) emissions in cropland sites in Europe (Smith et al ., ; Bell et al ., ). It has also been previously evaluated against a range of soils under short rotation forestry (SRF) plantations across the United Kingdom (Dondini et al ., ).…”

Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate

“…ECOSSE can be used to carry out site-specific simulations with detailed input data (e.g. Bell et al 2012;Dondini et al 2015), or spatial simulations using the limited data typically available at larger scales (e.g. Smith et al 2010b).…”