Module 7: Indicators

Icône de l'outil pédagogique Authors

C. Bockstaller, N. Turpin, L. Stapleton, M. van der Heijde, O. Therond, T. Pinto-Correia, V. Voltr, M. Raley, I. Bezlepkina, J.-P. Bousset, J. Alkan Olsson, F. Ewert, P. Reidsma


Icône de l'outil pédagogique Introduction

Ex ante integrated impact assessment of new policies is a prerequisite for them to efficiently support sustainable development (SD). Recently, SEAMLESS Integrated Framework has been developed to assess ex ante impacts of agricultural and agri-environmental policies and technologies on agricultural systems across a range of scales, from field–farm to region and the European Union (Van Ittersum et al., 2008). This text briefly presents the set of sustainability indicators developed within the SEAMLESS project.

Indicators are quantitative tools that synthesize or simplify relevant data relative to the state or evolution of certain phenomena. They are tools for communication, evaluation and decision making that can take a quantitative as well as a qualitative form depending on the purpose of the indicators (Gallopin, 1997). The available indicators can therefore always from a users perspective be seen as essential in any model based Impact Assessment.

 


Icône de l'outil pédagogique The SEAMLESS indicators

An indicator list was developed within the SEAMLESS project which is structured and presented through a new indicator framework, i.e. a goal-oriented indicator framework (GOF). This framework covers a broad range of themes linked to the three main dimensions (environmental, economic, social) of sustainability, and generic themes across the three dimensions (Alkan Olsson et al., in review), for two domains; the sustainability of agriculture itself and the impact of agriculture on the rest of the world, i.e. on SD. Three objectives underpinned the development of the SEAMLESS-IF indicator list across scales: i) to provide policy-makers and stakeholders with indicators which they usually use and/or which they would like to use; ii) to ensure scientific soundness of SEAMLESS-IF indicators, i.e. their relevance to represent impacts at stake; iii) to cover the various themes in each dimension of the GOF (see Table 1).

Different methods can be used for quantitative assessment (measurement, data census, model output, transformation of model outputs) and qualitative assessment (expert advice, decision makers, participation of populations). Within SEAMLESS-IF indicators are primarily assessed by models (and model chains) and thus their development has been constrained by the nature of the available model outputs. Outputs from three main models integrated in SEAMLESS-IF are used for the indicator calculation: the agricultural sector model SEAMCAP; the farming system model FSSIM; and the cropping system model APES. However, despite the range of scales covered by the SEAMLESS-IF model chains some key indicators can currently not be assessed directly from model outputs. However, despite the high range of scales covered by the SEAMLESS model chains some of key indicators cannot currently be assessed at certain scales using model outputs. To address this problem generic upscaling procedures have been developed and associated to each indicator that needs to be upscaled.

Examples of indicators are shown in Table 1. Across scales a total of 80 environmental, 140 economic and only 11 social indicators are or are about to be integrated into SEAMLESS-IF. This new structured set of indicators offered by SEAMLESS-IF enables a multi-scale integrated assessment of SD from the farming systems to the agri-environmental zones and the EU level.

Table 1. Example of environmental indicators within the goal-oriented indicator framework (GOF) at different scales (farm, normal font; Nuts 2 region, italic; member state or EU level, bold).

 

Domain 1

Domain 2

Impacts on the agricultural sector

Impacts on the rest of the world

 


Dimension of sustainable development

Dimension of sustainable development

Themes

Environmental

Economic

Social

Environmental

Economic

Social

Ultimate goals

Pesticide use

Net farm income

Equity

Nitrate leaching

 


Equity

 


Percent of subsidies in farm income

Equity

Pesticide leaching

 


Equity

 


Percent of subsidies in farm income

Monetary poverty rate

 

Crop diversity

 


 


 


Agricultural income

 


Percent of area
with high leaching

 


 


 


 


 


Nitrate surplus

 


 


Processes for achievement

Soil Org.Mat. change

Direct payments

Labour use

 

Volatization

First pillar CAP expenditure

Fairness

P balance

Direct payments

Total labour use

NH3 emissions

export subsidy outlays

 


N2O emissions

Productivity of farm inputs

 

Potential employment

P balance

profit of the agr. processing industry

 


 


Value of farm production

 

 


N2O emissions

Terms of trade

 


 


Soil erosion

Share of animal production

Labour use

Soil erosion

Land shadow prices

Labour use

 


Water use
by irrigation

Share of animal production

Labour use

Water use
by irrigation

Land value

Labour use

Means

Energy use by min. fertilizer

Share of animal production

Labour use

Energy use by min. fertilizer

 


Labour use

 


Use of mineral P

Total costs

 


Use of mineral P

 


 


In comparison with many former initiatives the broad spectrum covered and the type of the proposed indicators allows for a deeper analysis of environmental pressures and impacts, economic costs and benefits and socio-demographic dynamics. For example, through the integration of the APES model, indicators assessing emissions like nitrate leaching can be calculated considering key processes, which is not the case for simple indicators describing farmers’ practices like nitrogen use (Bockstaller et al., 2008). However, this requires a detailed description of fertilization and pesticides management for a given area. Another example is the assessment of economic indicators at NUTS2 level with two related model chains, which enables capturing complementary impacts of policy options, Social indicators in this list were derived from economic data, on labour and income distribution since no social model is, until now, integrated in SEAMLESS-IF.


Icône de l'outil pédagogique Conclusion

The SEAMLESS-IF multi-scale approach with its explicit upscaling procedures, as well as the integration of the indicators into a generic flexible software system linked to a large database mark an important progress with respect to the creation of an efficient set of indicators to assess the sustainability of future agri-environmental policies. However, some methodological issues remain unclear, such as the determination of reference values and the aggregation of indicators into composite indices. For the latter, methods have been explored (Bockstaller et al., in review). Furthermore, there are still themes not covered by the GOF, e.g. impacts on biodiversity, and only few indicators are available representing the social dimension. However, as SEAMLESS-IF is a flexible system further extension of the indicator list is possible through the integration of new models.


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