Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

https://doi.org/10.1016/j.jenvman.2010.10.051Get rights and content

Abstract

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

Introduction

When the Common Agricultural Policy (CAP) was established in Article 39 of the Treaty of Rome (1957) food security across Europe was paramount and as a consequence, policies focused primarily on the ‘optimum utilisation of the factors of production’. However, since the 1980s, CAP measures have increasingly supported methods of agricultural production that protect the environment and maintain the countryside. In 1992 this led to Council Regulation (European Economic Community (EEC)) No 2078/92. The Regulation directs that countries should implement schemes for the protection of the European countryside; in particular, agricultural production methods should be compatible with the requirements of the protection of the environment and the maintenance of the countryside (Commission of the European Communities, 1992). Further environmental support was provided by the Agenda 2000 programme, which included a priority area to enhance and extend the adoption of agri-environmental measures within the European Union (EU) under the Rural Development Regulation (Council Regulation (EC) 1257/1999). Agri-environmental measures became the only compulsory component of the Member States’ rural development programmes submitted to the Commission and by 2009, some 18% of the EU-27’s utilisable agriculture area was managed under agri-environment schemes (AESs) (European Commission, 2010).

To determine the extent to which such policy objectives are being fulfilled and to identify changes necessary to bridge the gap between policy aims and outcomes, EU Member States are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, very few evaluations attempt to measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. Kleijn and Sutherland (2003) identified 62 studies that assessed the impact of European AESs on biodiversity, and for the majority of studies the methodologies used were inadequate to reliably assess the efficacy of AESs in terms of environmental performance. Measures of farmer participation i.e. the number of participating farmers or area of land under agreement, have been widely used to document the extent of progress made towards the achievement of particular policy objectives. However, although this approach is frequently adopted owing to the ease of recording such information, it is evident that participation per se does not guarantee delivery of environmental protection or improvement (Kleijn and Sutherland, 2003).

In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable approach that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques (Mortimer et al., 2009, Mortimer et al., 2010, Purvis et al., 2009). It is a step-wise process based on the adaptation of the basic stages of MCA (see DCLG, 2009) allowing the scoring of farms against weighted indicators. The AFI methodology is constructed so that high values represent good environmental performance. A similar approach has been used previously as a structure for evaluating landscape and habitat enhancement mechanisms (Park et al., 2004). The method was developed specifically to allow assessment of differences in the environmental performance of farms according to AES participation. However, it also has the potential to be used for the wider surveillance of differences in environmental performance with respect to agricultural system and underlying factors such as farm size, farmer age, level of education and region.

The AFI methodology has been successfully applied with primary data to investigate the influence of agri-environmental scheme participation on environmental performance (Knickel and Kasperczyk, 2009). An aim of the current paper was therefore to test whether its use could be extended for the routine surveillance of environmental performance of agri-environment schemes using established data sources. This paper explores the use of the AFI methodology in combination with data from the Farm Accountancy Data Network (FADN). FADN was launched in 1965 following the introduction of Council Regulation 79/65, with the overall objective of determining the income of agricultural holdings and the impacts of the CAP for all Member States of the European Union. As such, it is the only source of micro-economic data that is harmonised across the EU. The survey only covers agricultural holdings in the Union that due to their size could be considered commercial. On average, 2000 farm holdings are surveyed annually by each Member State and in the UK the Department for the Environment, Food and Rural Affairs (Defra) assumes responsibility, via the Farm Business Survey (FBS). Overall, the aim of the paper was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. Responses were also considered in relation to farm size, farmer age, level of education and Government Office Region because of their potential to influence environmental performance.

Section snippets

Method

Data from the FBS (England) were obtained from Defra for three broad farm types derived from FADN farm descriptors: arable, lowland livestock, and upland livestock (Table 1).

Individual holdings within each of these three broad types were included in the analyses if consecutively surveyed in 1995, 2000 and 2005, enabling temporal changes to be analysed across a ten-year time series. A farm holding was excluded from analyses if the broad farm type changed between years. Holdings were also omitted

Agri-environment Scheme participation

Agri-environment scheme participation as a factor in the mixed model analyses was included in five of the 11 interactions specified for each farm type in addition to being included separately. It was determined that for lowland and upland livestock holdings agri-environment scheme participation had no significant influence on AFI values. However, a significant interaction between agri-environment scheme participation and region was found for arable holdings (see Section 3.2.).

Region

AFI scores for

Discussion

The application of the AFI methodology to FBS data revealed that, of the explanatory variables explored, the key factors determining the environmental performance of lowland livestock holdings were farm size, farmer age, region and level of education. In contrast, AFI values for arable holdings were strongly influenced by region and agri-environment scheme participation, whilst upland livestock holdings were influenced by region only. The fact that year had no significant influence indicates

Conclusion

The primary objective of the paper was to use the Multi-Criteria Analysis methodology of the AFI to measure changes in the environmental performance of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify any difference in AFI score due to participation in agri-environment schemes. The AFI methodology revealed that agri-environment scheme participation was an important factor only when considered with region for arable holdings. In

Acknowledgements

This paper is an output from the EU-funded project ‘AE-Footprint’ (Development of a common generic methodology for evaluating the effectiveness of European Agri-environmental Schemes, SSPE-CT-2005-006491). We would like to thank the Department for the Environment, Food and Rural Affairs (Defra) for granting permission to use the Farm Business Survey data.

References (32)

  • B.A. Woodcock et al.

    Effects of seed mixture and management on beetle assemblages of arable field margins

    Agric. Ecosyst. Environ.

    (2008)
  • Commission of the European Communities

    Council Regulation (EEC) No 2078/92/EEC of 30 June 1992

    Off. J. Eur. Communities

    (1992)
  • J. Davis et al.

    Economics of farmer early retirement policy

    Appl. Econ.

    (2009)
  • DCLG

    Multi-criteria Analysis Manual

    (2009)
  • European Commission

    Agriculture in the European Union - Statistical and Economic Information 2009

    (2010)
  • V.L. Kirner et al.

    Effect of off farm income, farm size, natural disadvantage and farming system on the sustainability of dairy farming in Austria - an empirical approach on the basis of farm accountancy data

    Ber. Landwirtsch

    (2007)
  • Cited by (43)

    • The farm-by-farm relationship among carbon productivity and economic performance of agriculture

      2022, Science of the Total Environment
      Citation Excerpt :

      Although a physical indicator (i.e., the intensity of use) might have been more suitable, in this study an expenditure indicator has been preferred because it was not compulsory to collect data on quantities of fertilisers and pesticides in all periods analysed, and it is thus not available for all farms and for all years. For this reason, for the vast majority of studies using FADN data to reconstruct the intensity of fertiliser use and protection input, the intensity of expenditure is used instead (see, e.g., Bartolini et al., 2021; Dabkienė et al., 2020; Westbury et al., 2011). Even the European Commission uses the indicator “c.33.

    • Development of agri-environmental footprint indicator using the FADN data: Tracking development of sustainable agricultural development in Eastern Europe

      2021, Sustainable Production and Consumption
      Citation Excerpt :

      In line with that, it is essential to identify the current environmental state and to track the changes and achievements on farms. The environmental impacts of the agricultural sector have been analyzed in numerous studies (Purvis et al., 2009; Westbury et al., 2011; Mauchline et al., 2012; Nowak et al., 2019; Kasztelan and Nowak, 2021). Some environmental pressure variables like GHG emissions, biodiversity measured by the bird population index, phosphorus management and others cover a broad spectrum of the environmental impacts and, therefore, have commonly adopted for the quantification of the environmental pressures (Vlontzos and Pardalos, 2017; Svanbäck et al., 2019).

    • A life cycle assessment study of dairy farms in northern Germany: The influence of performance parameters on environmental efficiency

      2020, Journal of Environmental Management
      Citation Excerpt :

      An additional reduction in environmental impacts associated with agricultural production is necessary and possible in the coming decades, also in the context of Agri-environment schemes that have been implemented across EU member states. Further, the Agri-Environmental Footprint Index was developed to assess the environmental impact of European Agri-environment schemes (Westbury et al., 2011). In this context, case studies like the current study provide important information on crucial factors for environmental efficiency.

    View all citing articles on Scopus
    View full text