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You are here: Home / Data and maps / Indicators / Land cover distribution and change - outlook from MNP

Land cover distribution and change - outlook from MNP

This content has been archived on 12 Nov 2013, reason: Content not regularly updated
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Contents
 

Assessment versions

Published (reviewed and quality assured)
  • No published assessments

Justification for indicator selection

The purpose of this indicator is to highlight changes in the productive or protective uses of the land resource to facilitate sustainable land use planning and policy development. Information on land use change is critical for integrated and sustainable land use planning. Such information is useful in identifying opportunities to protect land uses or promote future allocation aimed at providing the greatest sustainable benefits for people.

Changes in arable and permanent crop land and wooded areas give important information about a country's/region's endowment in agricultural and forest resources, both from an economic and an environmental perspective. Economically, changes in land use will, for example, result in changes in the volume of produce available and influence employment opportunities. From an environmental point of view, unsustainable land use is an important factor in erosion and desertification may pose a threat to ecosystems, and lead to natural habitat loss and landscape changes. Changes in land use by agricultural operations, in particular, arable land use, provide signals regarding environmental impacts of agricultural sector.

The outlook presents plausible future of agriculture developments in European region and can be used for estimation of changes of environmental presures (particularly when it comes to use of land, water and soil pollution, and loss of biodiversity). It helps to assess achievability of targets and identify appropriate policy response options for making agriculture more sustainable.

Scientific references:

Indicator definition

Definition:
Land cover distribution and change presents information on distribution of land-cover types across the total world terrestrial area: agricultural and natural (tropical rain forest; tropical dry forest; tropical grassland and savannah; desert; Mediterranean forest, woodland and shrub; temperate broadleaf and mixed forest; temperate coniferous forest; temp grassland and steppe; boreal forest; tundra; polar; extensive grassland).

Model used:
IMAGE

Ownership:
Netherlands Environmental Assessment Agency (MNP)

Temporal coverage:
2000 - 2050

Geographical coverage:
Russia and North Asia: Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan; Europe: Austria, Belgium, Denmark, Cyprus, Czech republic, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Lichtenshtain, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Swetzerland, Slovakia, Slovenia, United Kingdom

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 6th 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

Key policy question

How much and in what proportions is agricultural, forest and other semi-natural and natural land being taken for urban and other artificial land development?

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.



Table 1: Most important assumptions and uncertainties for the different options and qualitative expert judgment of the consequences on biodiversity losses. Consequences are the effects of correcting for the aforementioned uncertain or ignored factors (assumptions); + means less biodiversity loss; - means more biodiversity loss.

 Option 
 Assumptions and uncertainties 
 Consequences 
 for baseline 
 biodiversity 
 Consequences 
 for option 
 biodiversity 
Liberalisation of
agricultural market
Slower implementation of
trade reform, leading to
less dramatic shifts in
land-use
0 #Developed -
Developing ++
Poverty reduction Investment targeting on
off farm income
No change +

Emphasis on extra
infrastructural investment
-No change -

Reduced population
growth through removal
of reduction
No change In the long term
+/++
Limiting climate
change
Climate sensitivity
+ / -- + / --

Costs of alternative
measures
+ / -- + / --

Biodiversity response to
change
?? ??
Sustainable meat
production
Costs of sustainable
production overestimated
No change -

Elasticity of meat prices
and consumption
No change -/+

Environmental impacts of
sustainable production
No change ??
Forestry option Yields in baseline too
high
-- ++

Conversion wood
neglected
+ +

Shifts in global trade
relations to areas with
more virgin forests
--/0 +

Plantation establishment
on available land
no effect
+/++
Protected areas Land-use classes with
more extraction than
presumed
- -

More detailed maps +? +?

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

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.

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Anita Pirc Velkavrh

Ownership

No owners.

Identification

Indicator code
Outlook 046
Specification
Version id: 1

Permalinks

Permalink to this version
0af7002e8f732886b8a4dc0bda9e9036
Permalink to latest version
U20NJ7VP9K

Classification

DPSIR: Pressure
Typology: Performance indicator (Type B - Does it matter?)

Geographical coverage

[+] Show Map

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