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

Indicator Assessmentexpired Created 08 Jan 2007 Published 08 Jun 2007 Last modified 12 Nov 2013, 09:26 AM
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Key messages

 

In the European region agricultural activity leads to expanding agricultural areas over the 2000-2050 period, while in Russian Federation and North Asia region  the amount of arable land is decreasing, as land is taken out of production. This land is available for restoration of natural biomes, mainly boreal and temperate forests, steppe and grasslands. (Assessment was created in 2007)

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?

Land cover distribution (percent) - Russia and North Asia

Note: Distribution of land-cover types (in % of total regional area) in the baseline scenario and for the options in 2050

Data source:

The Netherlands Environmental Assessment Agency

Downloads and more info

Land cover distribution (percent) - Europe

Note: Distribution of land-cover types (in % of total regional area) in the baseline scenario and for the options in 2050

Data source:

The Netherlands Environmental Assessment Agency

Downloads and more info

For European region

Baseline development

  • The European agriculture maintains its position in expanding world markets under continued agricultural policy and trade rules and regulations. This region already has an intensive agriculture, and increased production leads to expanding agricultural areas.

Effects of options

  • Liberalisation. Hence, the upward trend in agricultural land use of the baseline is reversed as agricultural production declines by 24%. The abandoned land is slowly returning to a more natural state, with a higher biodiversity value; however this process is still not be completed by 2050. Mediterranean forests, woodland, and shrub and temperate forest areas, show the biggest improvement.
  • Relatively modest volumes of bio-fuel production, relative to the energy consumption, emerge in the climate mitigation case. Suitable land is scarce and the net loss of habitat remains limited in size, affecting primarily temperate forest area.
  • The forestry option. Establishing plantations therefore leads to additional habitat loss that is not yet counteracted in 2050 by biodiversity gains in slowly restoring forests.

For Russia and North Asia region

Baseline development

  • The amount of arable land is decreasing, as land is taken out of production. This land is available for restoration of natural biomes, mainly boreal and temperate forests, steppe and grasslands.
  • The wood production in this region has dropped sharply between 1990 and 2000, and only recovers at the former production levels after 2040. Not much additional semi-natural forest area is therefore lost to forest exploitation in the baseline. Nevertheless, model calculations underestimate the total demand for this region, as Russia also produces for Europe and China. This increased trading will put additional pressure on the remaining vast boreal forest biome.

Effects of options

  • The option with the largest effect for Russia and North Asia is reduction of climate change. Developments in the baseline have led to large areas of abandoned agricultural land that can be exploited. The increased land use more than counteracts the positive effect of climate measures.
  • Liberalisation of agricultural markets leads to a small increase in the area of arable land, at the expense of natural biomes (forest, grassland and steppe).
  • The other options all have a very small effect. The effect of the forestry option is underestimated if the region will become an important production area for other regions.
  • The main reasons for the described trends in land cover distribution structure are considered to be climate change, and possible measures following by the alternative scenario such as liberalization of market, changes in forestry activities and etc.

Indicator specification and metadata

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

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

No methodology references available.

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.

Data sources

Generic metadata

Topics:

Environmental scenarios Environmental scenarios (Primary topic)

Tags:
land cover | forward looking indicators
DPSIR: Pressure
Typology: Performance indicator (Type B - Does it matter?)
Indicator codes
  • Outlook 046
Geographic coverage:
Armenia, Austria, Azerbaijan, Belarus, Belgium, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Iceland, Ireland, Italy, Kazakhstan, Kyrgyzstan, Latvia, Liechtenstein, Lithuania, Luxembourg, Moldova, Netherlands, Norway, Portugal, Russia, Spain, Sweden, Switzerland, Tajikistan, Turkmenistan, Ukraine, United Kingdom

Contacts and ownership

EEA Contact Info

Anita Pirc Velkavrh

Ownership

EEA Management Plan

2010 (note: EEA internal system)

Dates

European Environment Agency (EEA)
Kongens Nytorv 6
1050 Copenhagen K
Denmark
Phone: +45 3336 7100