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Archive:Agri-environmental indicator - risk of land abandonment

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This article provides a fact sheet of the European Union (EU) agri-environmental indicator risk of land abandonment. It consists of an overview of recent data, complemented by all information on definitions, measurement methods and context needed to interpret them correctly. The risk of land abandonment article is part of a set of similar fact sheets providing a complete picture of the state of the agri-environmental indicators in the EU.

Farmland abandonment is a cessation of management which leads to undesirable changes in biodiversity and ecosystem services.

Main indicator: 

  • The risk of farmland abandonment is estimated through statistical analysis of key drivers (see supporting indicators) combined into a composite index indicator. 

Supporting indicators:

  • Weak land market
  • Low farm income
  • Lack of investment in the farm
  • High share of farm holders over the age of 65 years
  • High share of farm holders with low qualification
  • Remoteness and low population density
  • Low farm size
  • Low share of farms committed to specific schemes linked to continue farming

Main statistical findings

Key messages

  • Farmland abandonment due to economic, structural, social or difficult regional factors is estimated to have a higher risk in southern Member States (Portugal, Spain, Italy, Greece, Romania). In northern Europe, the risk is higher in the Baltic States, northern Finland and Sweden and in north-western Ireland.
  • NUTS2 regions with a higher risk of farmland abandonment also have a higher share (around 30%) of holdings with farm-type ‘grazing livestock’.
  • Difficulties in the indicator compilation arise from data availability and resolution. Farmland abandonment appraisal requires to access data at very fine scale in order to assess the distribution of situations within a NUTS2 region (avoiding regional averaging which masks specific situations). This was not possible with the Farm Structure Survey (FSS) dataset available to the European Commission Joint research Centre. Moreover, it was found that official European datasets (FSS, Farm Accountancy Data Network) do not hold the same level of geographic reporting amongst all Member States; thus creating time consuming processing and preventing building comparable outputs for all MS.

Assessment

In the analysis that follows, results are presented by NUTS2 region as (i) this is the reporting unit required by the COM(2006) 508 on Agri-Environmental Indicators and (ii) because of the resolution available of inputs data (FADN and FSS). However, it is acknowledged that NUTS2 regions often hold diverse agro-economic conditions and that farmland abandonment is a local phenomenon, not impacting the whole NUTS2 area in a similar manner.
Driver D1 – Weak land market
High land sales and rental prices are generally linked to a high demand for agricultural land and hence a lower risk of land abandonment.
The FADN parameters ‘Rent paid, including rent for building, quotas’ and ‘Rented UAA’ are used in a ratio to provide proxy information on the rental price of agricultural land. The average is calculated for the 3 years (2006-2008) for each holding in the database.
Central European countries such as Poland, Slovakia, part of the Czech Republic, part of Hungary, Slovenia and part of Bulgaria have very low renting prices. A similar situation is encountered in the Baltic States and in northern Sweden.
This can be seen as a sign of a low demand for land, hence possibly leading to land abandonment.

Farmland is at higher risk of abandonment as an economic resource when it ceases to generate a sufficient income.
FADN variable ‘Farm Net Value Added’ expressed per Agricultural Working Unit (FNVA/AWU) is used.
As the farming economical context is still very heterogeneous between Member States, a unique European threshold value for the farm income does not make sense (different economic and structural situations are present in Member States, in particular incomes are still very disparate – e.g. a low agricultural income in the Netherlands could still be a high value for Bulgaria. There is a ratio of 1 to 15 in EU27 between the minimum and maximum regional agricultural income). Therefore, the methodology for Driver D2 on ‘Farm income’ compares the farm income to the national general income (all sectors) in order to identify differences; assuming that when differences are large, agriculture may not be economically sustainable anymore, leading to people leaving the farming sector for possible opportunities in other sectors.
The weighted average farm income per annual working unit is calculated and compared to the Gross Domestic Product (GDP) per capita at market prices - Euro per inhabitant” (Source: Eurostat http://epp.eurostat.ec.europa.eu/portal/page/portal/national_accounts/data/database - GDP and main components - Current prices (nama_gdp_c). The national GDP is a proxy for the national income. In the analysis, the GDP per capita for the period 2006-2008 is used.

The higher risk appears on the first quintile (ratio < 0.58), identifying the whole of Ireland, most of Portugal, southern France (Languedoc-Roussillon in particular), central and southern Italy, the whole of Slovenia, mountain areas in western Austria, central and southern Greece, the whole of Cyprus, western Bulgaria, eastern Romania, central Slovakia, central / eastern and southern Poland, and some areas in northern Sweden and eastern Finland.
Caution should be taken in interpreting these results as different situations in MS may have different underpinning explanations. Indeed, the results are based on farm income only, while the total household income may change the picture. Ability to pull income from diversification activities (tourism, external income for part-time work, external income of the partner …) may matter to ensure the survival of rural families; however these data are not available in the dataset.
Driver D3 – Low investment in the farm
Investment behaviour reflects farm dynamism, its adaptation capacity and expectations about the future. New investments are a signal of a medium/long term strategy and can be a proxy of the willingness to continue farming activity.
In view of removing the bias as small farms have often lower investments (in absolute terms) than large farms, the amount of investment per holding was normalized by its physical size. FADN variables ‘Total investments before deduction of subsidies’ is divided by the holding size.
Regions with the lower investment ratio are found in Spain (except north-east), in central and southern Italy, in most of Greece, the whole of Romania, in several Czechs regions and in western Poland.
The investment parameter in FADN database can have reliability weakness for some (Mediterranean) countries. Explanations provided by these MS refer to investment made with ‘family loans’. Many farmers considering those as private and do not report them in the farm accounts; consequently debts and investments are missing for these farms. This is a known issue in FADN database.
Moreover, some Italian experts have mentioned some changes of definition for this parameter during the period 2006-2008 in Italy, acknowledging some possible data deficiencies.
Driver D4 – Age of farm holder
Farmland abandonment is more likely to occur when farmer population is old / close to retirement.
The ratio between farm holders above 65 years and the total number of farm holders has been calculated (Figure 10) in order to have a proxy for the distribution of the farmers’ age population.
There is an unfavourable age ratio in Portugal, most of Italy, southern Greece, Bulgaria, Romania and Lithuania with 40% or more of the farm holders’ population above 65 years old.
Driver D7 – Remoteness / Low population density
Farmland abandonment is likely to occur in remote areas with insufficient access to basic services (healthcare, school, and other services) and fewer marketing opportunities.
Low population density: A geographic layer containing population density grid was used to classify the EU-27 LAU2 based on the OECD methodology to build LAU2 typology (urban or rural) (Reference: http://www.eea.europa.eu/data-and-maps/figures/population-density-2). The population density information had been broken down into several classes; very low densely populated areas (< 50 inhabitants/km2) were identified.
Remoteness: The travel time by road network to urban centres was selected as an indicator of remoteness. Travel time was computed for each LAU2 to reach the closest urban centre (at least 50.000 inhabitants). Threshold of more than 1 hour travelling time was applied to identify remote LAU2.
Agricultural area in remote and scarcely populated areas was estimated by overlaying Corine Land Cover (CLC) 2006 dataset (for Greece CLC 2006 is missing, CLC 2000 was used instead) to the geographic layer of remoteness/ low population density. The share of this area compared to the total agricultural areas at NUTS2 level (NUTS1 for DE, UK; NUTS0 for CY, EE, LT, LU, LV, MT, SI) is shown in Figure 34. Regions with the higher share of agricultural land (more than 19%) in remote and scarcely populated areas occur in Portugal, Spain, south-west and Corsica in France, Tuscany / Molise and Sardinia in Italy, most of Greece, the Nordic Baltic States, Scotland and Wales, and Ireland.
Composite index
The combination of single drivers into a composite index of risk of Farmland Abandonment is done through an empirical framework for building composite indicator, following a methodology proposed by the OECD (2008).
The framework will be tested at European and at national levels based on their normalised values.
(a) normalisation of the drivers at EU27 level
Figure 35 shows a ranking of NUTS2 regions from the lower to the higher risk using 20% quintile interval, combining the drivers with the highest data robustness and analytical soundness. These regions are found in Portugal, central Spain, Tuscany, Molise and Sardinia in Italy, part of Peloponnese / part of Macedonia in Greece, Latvia, Estonia, northern Sweden, and in Connacht and Donegal in Ireland.
Surprisingly, Tuscany in Italy is identified under this scenario as a region with a higher risk of farmland abandonment. Data screening on the components of the composite index shows the three economic drivers (D1, D2, D3) with low values, comparable to southern Italian regions; but ‘farmers’ age’ and ‘remoteness’ are relatively high, identifying consequently Tuscany with a higher risk of land abandonment. The unexpected low value for D2 ‘farm income’ might be due to the presence of others source of income outside agriculture (e.g. diversification activities such as farm tourism) not included in the farm income. Information on the household income would be relevant for this point. Moreover, Italian experts have confirmed FADN data consistency issues in 2006-2007. It may also indicate the need to set different weighting factors on each driver in order to calibrate the model to better fit national conditions, as it looks like drivers 4 and 7 may need a lower weight, in Italy at least. However, this last point would require extensive experts’ consultation to reach a robust weighting system. A downscaling procedure from NUTS2 to NUTS3 level has also shown that it is only NUTS3 ‘Grosseto’ with a high risk, demonstrating the usefulness of having detailed scale data for assessing the risk of farmland abandonment.
b) normalisation of the drivers at MS level
The normalisation procedure was also done at MS level as farm land abandonment is a phenomenon very much linked to national economic, structural and political conditions. Maps are presented for two MS (Spain and Slovakia).


Data sources and availability 

Indicator definition

Farmland abandonment is a cessation of management which leads to undesirable changes in biodiversity and ecosystem services. 

Measurements

Main indicator:

  • The risk of farmland abandonment is estimated through statistical analysis of key drivers (see supporting indicators) combined into a composite index indicator.

Supporting indicators:

  • Weak land market
  • Low farm income
  • Lack of investment in the farm
  • High share of farm holders over the age of 65 years
  • High share of farm holders with low qualification
  • Remoteness and low population density
  • Low farm size
  • Low share of farms committed to specific schemes linked to continue farming 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Data sources and availability

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Further Eurostat information

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Database

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