SmartSOIL Tool

Aim of the tool

The SmartSOIL toolbox is an interactive platform with tools showing the impacts of field management practices on soil organic matter and soil organic carbon (SOC) content.

The SmartSOIL DST Learning Module guides the user through a series of steps to learn about the regional SOC and yield impacts of relevant management practices. The module provides a reference field for your region that you can compare and analyze with alternative experimental fields for more informed decision making.

Overview of management scenarios and regions

Region and Country

Scenarios included in the SmartSOIL tool

Denmark, Zealand Select and see scenarios
Poland Select and see scenarios
Hungary North Select and see scenarios
Hungary South Select and see scenarios
Italy Select and see scenarios
Spain Select and see scenarios

Underlying data and assumptions

The SmartSOIL tool has been developed in an iterative process involving several rounds of feedback and testing with the key stakeholders in the case study regions. Building on the SmartSOIL Simple Model (a combination of a carbon model, C-Tool, and a crop yield model) as well as EU-wide and region-specific datasets, this tool was developed to allow farmers and advisers across the EU to explore the effects of changing management practices on soil carbon, crop productivity and economic outcomes. By selecting a specific EU region the user will be able to choose from different management and cropping scenarios which are relevant to that region. Potential changes in yield and soil organic carbon content which can result from implementing the different practices are shown in the SmartSOIL Tool as detailed graphs annotated with explanations. In addition, information on benefits and costs for the different management practices is provided, helping the user make informed decisions about more sustainable and cost-efficient soil management practices. We introduced predefined scenarios, some based on data from real farmers in the regions and some defined by SmartSOIL partners aiming at comparison of different management practices. Not all combinations were selected, and the tool is mainly designed as a learning module for farmers and advisers. There are links from the SmartSOil tool to comprehensive additional information from the SmartSOIL toolbox (factsheets, videos etc.) and this this combined information will be a valuable resource for all stakeholders interested in Soil carbon management

The C-tool model

C-tool model calculates the carbon stock development for the top soil (0-25 cm) and sub soil for the next 30 years based on your settings. C-Tool is linked with a yield model and C-Tool provide input data to this model.

The Yield model

The Yield Model will calculate the Nitrogen input / Yield relationship curve based on your settings. You can change management practices to see how this will influence the curve. Your output will be compared with a reference for the farm/Climate/region type

Costs and benefits of management options

Gross margin calculations

The crop production data is derived either from SmartSOIL case study regions (in Denmark, Hungary, Italy, Poland, Spain and UK)[1] or from FADN data on cereal production[2].  Data from the FADN report is averaged over the available time period (either 2004 to 2012 or all available years for new member states), this is to allow for variations in commodity prices and input costs. For each crop the FADN data relates to farms specialising in that crop, i.e. where it accounts for more than 40% of the standard output. The gross margin calculations based on the FADN data do not include the impacts of coupled or other crop-specific subsidies, these varied across member states and crops, although these were phased out by 2010. The FADN data does not differentiate between winter and spring sown crops; however we have included data on winter and/or spring sown crops where we have relevant data from the case study regions.  We have also included data on rainfed and irrigated crops and specific regions where these are available. The data for the gross margin calculations are summarised in terms of output (revenue), this includes primary crops (grain) and secondary crops (e.g. straw), and costs[3].

Impacts of measure implementation

The measures can impact on farm systems through both changing crop output (i.e. changes in yields) and through either increasing or reducing costs. Data on impacts were collected from a variety of sources that reflected either general impacts as reported in the scientific and grey literatures or where available specifically for the SmartSOIL case study regions. Impacts on yields were entered into our calculations as ‘high’, ‘middle’ and ‘low’. These reflect the range of yield impacts identified in the literature with ‘high’ representing the largest increase in yields, ‘low’ the largest decrease in yields and ‘middle’ the average yield impact. We use these to reflect the uncertainty over impacts and the often context specific (climate, soils, crops) nature of the studies reporting them.

Several cost categories have been identified: investment costs (e.g. seed costs for cover crops), operational (e.g. fuel or plant protection), displacement (e.g. loss of revenue from straw or displacing spring for winter crops to accommodate cover crops), avoided costs (e.g. reduced fertiliser inputs or lower fuel costs for cultivation). These data were collected for SmartSOIL regions and also identified from other sources[4]. As with yield impacts, the cost data are likely to be context specific, where estimates are applied across different crops and countries we have tried to reflect similarities, however the data should be taken as indicative.

Revised gross margin and cost-effectiveness estimates

The yield and cost impacts are used to provide two sets of estimates of the impact of measures. First, the gross margins are recalculated using the implementation cost estimates together with the ‘high’, ‘middle’ and ‘low’ yield impacts to estimate the change in gross margin following implementation in both money and percentage terms. The change in gross margin is then combined with estimates of the impact of each measure on soil organic carbon (SOC) and total greenhouse gas (GHG) emissions (including on-farm fuel use, fertiliser manufacture and application emissions). This allows the cost-effectiveness of the measures to be estimated in terms of the change in gross margin per tonne of CO2e reduction that can be achieved by the measures.

The cost-effectiveness tables included in the SmartSOIL Tool for the recommended practices use positive (+) and negative (-) symbols to show whether it is cost effective to gain SOC and reduce GHG emissions using each practice. The symbols correspond to the following ranges:

Values between 0 and 100: + or -

Values between 100 and 1000: ++ or --

Values over 1000: +++ or ---

Comparison of cost-effectiveness estimates across measures is useful from a policy perspective as it identifies measures that can be implemented with either zero cost (i.e. win-win) or within the bounds of policy related carbon prices. By estimating the area over which measures can be implemented the total CO2e abatement potential of measures could then be estimated.


As noted above, much of the data used in producing these tables is context specific and the estimated impacts have been applied across a range of countries and crops. Therefore the estimates can only be considered as being indicative. For this reason we have presented the estimates for a wide range of countries, thereby offering the potential to identify examples with comparable gross margin and cost impacts to the situation faced by users. 


[1] Denmark - Zealand; Hungary – central region; Poland - Mazovian Voivideship; Italy – Tuscany; Spain – Aragon; UK – Eastern Scotland

[2] European Commission – EU FADN (2014) EU Cereal Farms Report 2013, based on FADN data, European Commission DG Agriculture and Rural Development. This report provides data for specialist cereal farms for selected commodities between 2004 and 2012

[3] Seeds,  Fertilizers, Crop protection, Water,   Motor fuels and lubricants, Machines & buildings upkeep, Contract work, Energy, Other

[4] For example, similar measures were considered in relation to natural water retention measures, see