Assessing the Geographic Representativity of Farm Accountancy Data: Opportunities for new FADN Changes

This is the abstract section. One paragraph only (Maximum 200 words). The environment both affects agricultural production, via soils, weather, water availability etc and agriculture affects the environment via its impact locally on landscape, water, soil nutrition and biodiversity and more widely via its impact on climate change. Locating agriculture within its spatial environment is thus very important in making decisions by farmers, policy makers and other stakeholders. Within the EU, countries collect detailed farm data to understand the technical and financial performance of farms as part of the Farm Accountancy Data Network. However knowledge of the spatial-environmental context of these farms is very limited as the spatial location of farms within these surveys is very limited. In this paper we develop a methodology to geo-reference farms in this data. We chose Ireland as a case study as the dominant farm systems are pasture based mainly animal systems. Thus the local environment is particularly relevant to output. Agriculture in Ireland is also amongst the largest as a proportion of the size of the economy and thus the environmental impact is likely to be more important. Applying this methodology has a number of challenges because Ireland does not have a system of post codes. In addition there are complications in relation to place names which may be in English or Irish or indeed a combination, often with non harmonised spellings and often with non-unique place names. The methodology we develop in this paper overcomes these difficulties allowing us to link, using resulting GIS coordinates, localised environmental to the individual farm data. The primary objective of the survey is to provide a nationally representative picture of farm outputs and outcomes. As a result the survey may not necessarily be representative spatially or the pattern of environment x farm system. Within the paper we assess the relative spatial representativity.


Issue Date:
2015
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/212678




 Record created 2017-04-01, last modified 2017-04-26

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