Sam Ottoy

Assessing top- and subsoil organic carbon stocks of Low-Input High-Diversity systems using soil and vegetation characteristics

The soil organic carbon (SOC) stock is an important indicator in ecosystem service assessments. Even though a considerable fraction of the total stock is stored in the subsoil, current assessments often consider the topsoil only. Furthermore, mapping efforts are hampered by the limited spatial density of these topsoil measurements. The aim of this study was to assess the SOC stock in the upper 100cm of soil in 30,556ha of Low-Input High-Diversity systems, such as nature reserves, in Flanders (Belgium) and compare this estimate with the stock found in the topsoil (upper 15cm). To this end, we combined depth extrapolation of 139 measurements limited to the topsoil with four digital soil mapping techniques: multiple linear regression, boosted regression trees, artificial neural networks and least-squares support vector machines. Particular attention was given to vegetation characteristics as predictors. For both the stock in the upper 15cm and 100cm, a boosted regression trees approach was most informative as it resulted in the lowest cross-validation errors and provided insights in the relative importance of predictors. The predictors of the stock in the upper 100cm were soil type, groundwater level, clay fraction and community weighted mean (CWM) and variance (CWV) of plant height. These predictors, together with the CWM of specific leaf area, aboveground biomass production, CWV and CWM of rooting depth, terrain slope, CWM of mycorrhizal associations and species diversity also explained the topsoil stock. Our total stock estimates show that focusing on the topsoil (1.63Tg OC) only considers 36% of the stock in the upper 100cm (4.53Tg OC). Given the magnitude of subsoil OC and its dependency on typical ecosystem characteristics, it should not be neglected in regional ecosystem service assessments.

Impacts of selected Ecological Focus Area options in European farmed landscapes on climate regulation and pollination services: a systematic map protocol

BackgroundThis systematic map protocol responds to an urgent policy need to evaluate key environmental benefits of new compulsory greening measures in the European Union’s Common Agricultural Policy (CAP), with the aim of building a policy better linked to environmental performance. The systematic map will focus on Ecological Focus Areas (EFAs), in which larger arable farmers must dedicate 5% of their arable land to ecologically beneficial habitats, landscape features and land uses. The European Commission’s Joint Research Centre has used a software tool called the ‘EFA calculator’ to inform the European Commission about environmental benefits of EFA implementation. However, there are gaps in the EFA calculator’s coverage of ecosystem services, especially ‘global climate regulation’, and an opportunity to use systematic mapping methods to enhance its capture of evidence, in advance of forthcoming CAP reforms. We describe a method for assembling a database of relevant, peer-reviewed research conducted in all agricultural landscapes in Europe and neighbouring countries with similar biogeography, addressing the primary question: what are the impacts of selected EFA features in agricultural land on two policy-relevant ecosystem service outcomes—global climate regulation and pollination? The method is streamlined to allow results in good time for the current, time-limited opportunity to influence reforms of the CAP greening measures at European and Member State level.MethodsWe will search four bibliographic databases in English, using a predefined and tested search string that focuses on a subset of EFA options and ecosystem service outcomes. The options and outcomes are selected as those with particular policy relevance and traction. Only articles in English will be included. We will screen search results at title, abstract and full text levels, recording the number of studies deemed non-relevant (with reasons at full text). A systematic map database that displays the meta-data (i.e. descriptive summary information about settings and methods) of relevant studies will be produced following full text assessment. The systematic map database will be published as a MS-Excel database. The nature and extent of the evidence base will be discussed, and the applicability of methods to convert the available evidence into EFA calculator scores will be assessed.