Land cover distribution and change - outlook from MNP (Outlook 046) - Assessment published Jun 2007

Indicator definition

Units

Distribution of land-cover types is measured in % of total world area.

Policy context and targets

Context description

Pan-European Level
There are no international conventions or other policy documents at the Pan-European level efficiency of implementations of which can be measured by this indicator. Chapter 10 of Agenda 21 emphasizes importance of Integrated Approach to the Planning and Management of Land Resources and stimulates countries to use land resources in amore sustainable way.

EU policy context
However, there no directly related policy documents which regulate size and use of arable land for environmental reasons, the EU 6^th Environmental Action Programme promotes the integration of biodiversity considerations in agricultural policies and encourages more environmentally responsible farming, including, where appropriate, extensive production methods, integrated farming practices, organic farming. Achievement of this objective can indirectly be measured by this indicator. If the indicators would include information about the organic farming by crops it can also reflect achievability goald related to Organic farming. Organic farming is an environmentally sustainable form of agricultural production.Its legal framework is defined by Council Regulation 2092/91 and amendments. The adoption of organic farming methods by individual farmers is supported through agri-environment scheme payments and other rural development measures at Member State level. In 2004 the EU Commission published a 'European Action Plan for Organic Food and Farming' (COM(2004) 415 final) to further promote this farming system.

EECCA policy

Not available

Targets

Pan-European Level
There are no international targets for this indicator does not exist.

EU level
There are no specific targets at the European and Pan-European level, although different documents reflect the need for better arable land planning. No specific EU target on the share of organic farming area in arable land. A number of EU Member States have already set targets for area under organic farming, often 10-20 % in 2010.

EECCA level
Not available

Related policy documents

• COM (1998) 42
  Communication of the European Commission to the Council and to the European Parliament on a European Community Biodiversity Strategy. COM (1998) 42
• Sixth Environment Action Programme
  DECISION No 1600/2002/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 22 July 2002 laying down the Sixth Community Environment Action Programme

Methodology

Methodology for indicator calculation

To create the indicator the GTAP-IMAGE-GLOBIO model was used.

To analyse the economic and environmental consequences of changes in global drivers and policies, a global economic-biophysical framework by combining the extended GTAP model was developed (Van Meijl et al., 2005) with the IMAGE model (Alcamo et al., 1998; IMAGE Team, 2001).

The standard GTAP model (Hertel, 1997) is characterised by an input-output structure, based on regional and national input-output tables. The model explicitly links industries in a valueadded chain from primary goods, over continuously higher stages of intermediate processing, to the final assembling of goods and services for consumption (Hertel, 1997). For this study an extended version of the standard GTAP model was developed that improved the treatment of agricultural production and land-use (Van Meijl et al., 2005). Since it was assumed that the various types of land-use are imperfectly substitutable, the land-use allocation structure was extended by taking into account the degree of substitutability between agricultural types (Huang et al., 2004). For this reason, OECD's more detailed Policy Evaluation Model (OECD, 2003) structure was used. Moreover, in this extended version of the GTAP model the total agricultural land supply was modelled using a land-supply curve, specifying the relation between land supply and a rental rate (Van Meijl et al., 2005). Through this landsupply curve, an increase in demand for agricultural products will lead to land conversion to agricultural land and a modest increase in rental rates when enough land is available. If almost all agricultural land is in use, increase in demand will lead to increase in rental rates. The exogenous trend of the yield was taken from the FAO study "Agriculture towards 2030"(Bruinsma, 2003).

The economic consequences for the agricultural system are calculated by GTAP. The outputs of GTAP include sectoral production growth rates, land-use, and an adjusted management factor describing the degree of land intensification. This information is used as input for the IMAGE simulations, together with the same global drivers as used by GTAP. Since the IMAGE model performs its calculations on a grid scale (of 0.5 by 0.5 degrees) the heterogeneity of the land is taken into consideration on a grid level (Leemans et al., 2002). Protected areas cannot be used for agricultural use in the IMAGE land-use model. Therefore, a fixed map of protected areas (taken from UNEP-WCMC) is also used as input of the IMAGE model. IMAGE simulations deliver an amount of land needed per world region and the coinciding changes in yields resulting from changes in the extent of used land and climate change. Next, these additional changes in crop productivity are given back to GTAP, therefore correcting the exogenous (technology, science, knowledge transfer) trend component of the crop yield. A general feature is that yields decline if large land expansion occurs, since marginal lands are taken into production. In the near term, these factors are more important than the effects of climate change. Through this iteration, GTAP simulates crop yields and production levels on the basis of economic drivers and changes in environmental conditions. This combined result is once more used as input in IMAGE to consistently calculate the environmental consequences in terms of land use.

 Option 

 Assumptions and uncertainties 

 Consequences 

 for baseline 

 biodiversity 

 Consequences 

 for option 

 biodiversity 

More detailed descriptions about the methodology and the model can be found here.

Methodology for gap filling

No methodology for gap filling has been specified. Probably this info has been added together with indicator calculation.

Methodology references

• Cross-roads of Planet Earth's Life. Exploring means to meet the 2010 biodiversity Targets, pp. 62-72 The annexes of the document provide information about the models, main assumptions and  uncertainties.

Uncertainties

Methodology uncertainty

IMAGE

As a global Integrated Assessment Model, the focus of IMAGE is on large-scale, mostly first order drivers of global environmental change. Most of the relations in IMAGE can be characterised as “established but incomplete knowledge”. A large number of uncertain relationships and model drivers that depend on human decisions can be varied.

For the energy sub-model (TIMER; de Vries et al., 2001), an elaborate uncertainty assessment pointed out that assumptions for technological improvement in the energy system and translation of human activities (such as human lifestyles, economic sector change, and energy efficiency) into energy demand were highly relevant for the model outcomes. Central to climate change modelling are the responses to increased greenhouse gas concentrations. In the IMAGE model this concerns the responses in global temperature increase and local climate shifts. Another model element relevant to the biodiversity issue is the implementation of specific land-use allocation rules determining conversion of natural biomes (see preference rules in Alcamo et al., 1998). These rules are most relevant for the calculated biodiversity value. Only a limited set of land-use change is implemented, that is obviously a simplification of actual land-use changes. This limits the assessment of careful land-use planning, for instance, bio-energy production and forest plantations on available, already impacted, areas instead of natural biomes.

GLOBIO

The unavoidable differences in the quality of datasets used create uncertainty in the estimated dose response relationships.

Especially low impact pressures, like grazing in grassland ecosystems, selective logging or nitrogen deposition close to critical load values have high uncertainty. For secondary vegetation a mean value is used, but a time dependent component (reflecting natural recovery) needs to be incorporated. Still, the order of the pressure effects on biodiversity to be far more certain than the exact values.

The climate dose-response relationship cannot be based on data that measure the climate effects directly, as most effects will show up in future. Therefore, the relationships are based on model exercises that estimate climate envelopes for species (Bakkenes et al., 2002) or vegetation types (Leemans & Eickhout, 2003). Meta analyses (Parmesan & Yohe, 2003; Walther et al.2002) and other model studies (Thomas et al., 2004) confirm the main tendencies of the IMAGE-GLOBIO3 exercises, but the modelled effects are relatively low.

Thus the effect of climate change might be underestimated in this study.

For fragmentation, five review studies on minimum area requirement (MAR) of animal species (data on 156 mammal and 76 bird species) were used. This study is biased towards the European region. Establishing dose-response relationship suffers from the different definitions of individual MAR, but the overall picture comparing the different studies is remarkably consistent.

GTAP

The agricultural production and land-use outcomes of the Computable General Equilibrium model (GTAP) are dependent on the demographic and macro-economic growth assumptions, which are both surrounded with considerable uncertainty. Land-use is dependent on the position and elasticity of the land-supply curve, and trade flows are very dependent on the values of the Armington elasticities, which are difficult to estimate.

Most importantly, macro-economic growth is surrounded with more uncertainty than demographic growth.

Data sets uncertainty

No uncertainty has been specified

Rationale uncertainty

The indicator by itself does not identify the causes or pressures leading to the change in land use.