Joanne Ursula Smith

Quantitative methods to evaluate and compare Soil Organic Matter (SOM) Models

Evaluation and comparison of the performance of soil organic matter models is often based upon visual/graphical comparison of the simulated values produced by the model with actual values from field experiments. Such methods provide an immediate qualitative description of the differences, highlighting trends, different types of errors and distribution patterns of simulated and measured values. However, model evaluations or comparisons should ideally incorporate both a qualitative visual/graphical assessment and a quantitative statistical appraisal. Statistical methods have been selected that are suitable for quantitative evaluation and comparison of soil organic matter models. The methods included each provide information on some distinct aspect of the accuracy of the simulation.

Regional estimates of carbon sequestration potential: linking the Rothamsted carbon model to GIS databases

Abstract Soil organic matter (SOM) represents a major pool of carbon within the biosphere. It is estimated at about 1400 Pg globally, which is roughly twice that in atmospheric CO2. The soil can act as both a source and a sink for carbon and nutrients. Changes in agricultural land use and climate can lead to changes in the amount of carbon held in soils, thus, affecting the fluxes of CO2 to and from the atmosphere. Some agricultural management practices will lead to a net sequestration of carbon in the soil. Regional estimates of the carbon sequestration potential of these practices are crucial if policy makers are to plan future land uses to reduce national CO2 emissions. In Europe, carbon sequestration potential has previously been estimated using data from the Global Change and Terrestrial Ecosystems Soil Organic Matter Network (GCTE SOMNET). Linear relationships between management practices and yearly changes in soil organic carbon were developed and used to estimate changes in the total carbon stock of European soils. To refine these semi-quantitative estimates, the local soil type, meteorological conditions and land use must also be taken into account. To this end, we have modified the Rothamsted Carbon Model, so that it can be used in a predictive manner, with SOMNET data. The data is then adjusted for local conditions using Geographical Information Systems databases. In this paper, we describe how these developments can be used to estimate carbon sequestration at the regional level using a dynamic simulation model linked to spatially explicit data. Some calculations of the potential effects of afforestation on soil carbon stocks in Central Hungary provide a simple example of the system in use.

Simulation of greenhouse gases following land-use change to bioenergy crops using the ECOSSE model. A comparison between site measurements and model predictions

This article evaluates the suitability of the ECOSSE model to estimate soil greenhouse gas (GHG) fluxes from short rotation coppice willow (SRC‐Willow), short rotation forestry (SRF‐Scots Pine) and Miscanthus after land‐use change from conventional systems (grassland and arable). We simulate heterotrophic respiration (Rh), nitrous oxide (N2O) and methane (CH4) fluxes at four paired sites in the UK and compare them to estimates of Rh derived from the ecosystem respiration estimated from eddy covariance (EC) and Rh estimated from chamber (IRGA) measurements, as well as direct measurements of N2O and CH4 fluxes. Significant association between modelled and EC‐derived Rh was found under Miscanthus, with correlation coefficient (r) ranging between 0.54 and 0.70. Association between IRGA‐derived Rh and modelled outputs was statistically significant at the Aberystwyth site (r = 0.64), but not significant at the Lincolnshire site (r = 0.29). At all SRC‐Willow sites, significant association was found between modelled and measurement‐derived Rh (0.44 ≤ r ≤ 0.77); significant error was found only for the EC‐derived Rh at the Lincolnshire site. Significant association and no significant error were also found for SRF‐Scots Pine and perennial grass. For the arable fields, the modelled CO2 correlated well just with the IRGA‐derived Rh at one site (r = 0.75). No bias in the model was found at any site, regardless of the measurement type used for the model evaluation. Across all land uses, fluxes of CH4 and N2O were shown to represent a small proportion of the total GHG balance; these fluxes have been modelled adequately on a monthly time‐step. This study provides confidence in using ECOSSE for predicting the impacts of future land use on GHG balance, at site level as well as at national level.

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

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

In this paper, we focus on the impact on soil organic carbon (SOC) of two dedicated energy crops: perennial grass Miscanthus x Giganteus (Miscanthus) and short rotation coppice (SRC)‐willow. The amount of SOC sequestered in the soil is a function of site‐specific factors including soil texture, management practices, initial SOC levels and climate; for these reasons, both losses and gains in SOC were observed in previous Miscanthus and SRC‐willow studies. The ECOSSE model was developed to simulate soil C dynamics and greenhouse gas emissions in mineral and organic soils. The performance of ECOSSE has already been tested at site level to simulate the impacts of land‐use change to short rotation forestry (SRF) on SOC. However, it has not been extensively evaluated under other bioenergy plantations, such as Miscanthus and SRC‐willow. Twenty‐nine locations in the United Kingdom, comprising 19 paired transitions to SRC‐willow and 20 paired transitions to Miscanthus, were selected to evaluate the performance of ECOSSE in predicting SOC and SOC change from conventional systems (arable and grassland) to these selected bioenergy crops. The results of the present work revealed a strong correlation between modelled and measured SOC and SOC change after transition to Miscanthus and SRC‐willow plantations, at two soil depths (0–30 and 0–100 cm), as well as the absence of significant bias in the model. Moreover, model error was within (i.e. not significantly larger than) the measurement error. The high degrees of association and coincidence with measured SOC under Miscanthus and SRC‐willow plantations in the United Kingdom, provide confidence in using this process‐based model for quantitatively predicting the impacts of future land use on SOC, at site level as well as at national level.

Projecting Soil C Under Future Climate and Land-Use Scenarios (Modeling)

Abstract Soil carbon sequestration can be estimated from field to global scale using numerical soil/ecosystem models. In this chapter, we describe the structure and development of models that have been widely used at international level, from simple models that include carbon only to models that include descriptions of the dynamics of a range of nutrients. We also present examples of the application from field to global scale of different models to answer a range of different questions on the impact of land use and climate changes on soil carbon sequestration. A full discussion of the impact of soil carbon modeling on political and socioeconomical aspects is included to emphasize the need of a close interaction between model developers, researchers, land owners/users and policy makers to ensure the development of robust approaches to climate change, food security and soil protection. Whatever type of models are used to meet future challenges, it is important that they continue to be tested using appropriate data, and that they are used in regions and for land uses where they have been developed and validated.