1. Geo-referencing FADN Farms Date: October 22/23 rd 2015 Venue: EAAE Seminar, Edinburgh 1. Brian Moran; 2. Stuart Green; 3. Prof. Cathal O'Donoghue (Teagasc);

2. Farm Accountancy Data Network (FADN)  Tool for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy.  Started in 1965 - It consists of an annual survey carried out by each Member State. Legal Requirement.  The survey does not cover all the agricultural holdings in the Union but only those which due to their size could be considered commercial.  The aim is to provide representative data along three dimensions: region, economic size and type of farming.  The annual sample covers approximately 80.000 holdings. They represent a population of about 5.000.000 farms in the EU, which covers approximately 90% of the total utilised agricultural area (UAA) and account for about 90% of the total agricultural production.  The FADN is the only source of microeconomic data that is harmonised, i.e. the bookkeeping principles are the same in all countries.

3. Irish FADN – Who’s Involved?  DAFM – Department of Agriculture, Food and the Marine  Oversight  CSO - Central Statistics Office  Population Data  Teagasc – Agriculture and Food Development Authority  Liaison Agency FADN in Ireland DAFMCSOTeagasc Farming Population

4. Teagasc – Liaison Agency  Then Ireland entered the EEC in 1973 – obligation to deliver data to FADN  Whilst DAFM had responsibility they requested that AFT would collect analyse and deliver data to FADN  Unit established in AFT in 1972  Has run consecutively for over 40 years as the Teagasc National Farm Survey  Main Objectives  Provide Statutory Irish Data to EU Commission (FADN)  Determine Output, Costs, Incomes By Farm System(6), Size(7) & Regions  Provide Database of Micro Data on Irish Agriculture for Research, Policy analysis & Stakeholders********  Measure Variation in Technical and Financial performance for Farm Management/Advisory Purposes  Currently operated in the Agricultural Economics and Farm Surveys Department

5. Points to Note  The farmers who participate are chosen at random  About 10% of the sample changes each year  Farmer participation is voluntary  The data collectors are employees of Teagasc  Farms are visited 2-3 times per year  An accounts book is completed – customised MS Excel Program is used  Submitted for validation and processing by internal staff  Farm Management Reports are generated  Populations from CSO  Produce the NFS Final Report  Finalise FADN data

6. Data Collected 1.On average there's about 1000 variables per farm. 2.Economic and financial data, sales and purchases, production costs, assets, liabilities, production quotas and subsidies 3.Physical and structural data, crop areas, livestock numbers, labour and recently location. 4.All at the farm level – Cost allocation is not done on an enterprise level 5.Data collection is the responsibility of a liaison agency in each Member State

7. FADN – Current State of Play  Due to its unique position there are often request to collect new pieces of data  FADN unit have recently completed a process of reviewing and revising the farm return, in use for the first time for 2014 data which will be submitted in 2015/2016.  Very tedious and laborious process to undertake changing the farm return, took 2-3 years to complete.  The end version is a very much ‘watered down’ from the original proposals.  Different data collection methods in each member states.

8. New Developments & Expansion of the FADN Dataset accepta ble difficult very difficult Geo-reference Irrigation system Agricultural training of the manager Share of OGA labour in total work Asset Valuations at historical value. Recording quantities of N, P and K in fertilisers. Crops: Information on the area of GMO- and energy crops Livestock - Recording the destination of sales Average weight of animals slaughtered - External File OGA directly related to the holding Distinguish sources of financing (EU, co-financed, national) Distinguish between basic units (heads, ha, ton/farm/other) XML becomes the only accepted format for data transmission:

9. FADN 2014-New Farm Return  Includes a geo – coordinate for each holding  The agricultural holding is located where main part or all agricultural production takes place. It can be an agricultural building (i.e. largest administrative building/construction used to house livestock or other buildings or constructions used for agricultural production e.g. a greenhouse) or another identified part of the holding such as the most important parcel of the holding.  In case there is no agricultural building to which a location of the holding could be attributed, the most important parcel will be chosen as the reference point. The same is valid for the agricultural holdings having the land area in different regions.  The holder’s residence can be considered as the reference place only when it lies within 5 kilometres (in a straight line) of the place where the main part or all the holding’s agricultural production takes place.

10. Confidentiality and rounding possibilities  The usual FADN rules as regards confidentiality will apply. In the Commission, farm location data will only be used for grouping farm data according to geographic criteria other than those in the farm return.  (i) precise coordinates are not required. It will be necessary to provide the location only to the nearest 5 minutes  (ii) There are also country specific rules, for UK, Sweden & Finland. How the data is to be collected  Reference to administrative records such as cadastre databases (LPIS):  Conversion of the address to the latitude and longitude coordinates by appropriate software;  Using maps to derive the geo-coordinates;  Using a GPS device providing exact coordinates of the location of the holding

11. Our Experience in Spatially enabling the National Farm Survey  The aim was to try and associate a geographic X-Y point (in Irish National Grid coordinates) for each participant in the NFS for 2007 in order to attribute new environmental, geographical or meteorological data to each farm.  The only geographic information collected is the address of the correspondent  The history of Irish toponymy is a complicated history of local place-names surviving against imposition of standards by different authorities.  The official allocation or recognition of place names (vested in An Coimisiún Logainmneacha) is based upon the historical development of administrative units

12. Example Addresses, all “correct”  Teagasc Research Centre, Malahide Rd, Kinsealy, Co. Dublin Examples of common alternate address forms:  Teagasc, Kinsealy, Malahide, Co. Dublin  Teagasc, Kinsaley, Malahide, Co. Dublin  Teagasc, Malahide, Co. Dublin  Teagasc, Mullach Ide, Baile Atha Cliath All of these addresses are “official” and correct. On top of these official variations there are accidental misspellings, colloquial alternative spellings and reversals:  Tegasc, Kinsealy, Malahaide, Co. Dublin  Teagasc, Malahide, Kinsealy, Co Dublin

13. GEO-Directory  This product is created based on the OSI cadastral database of building locations against the An Post database of delivery addresses  The Geodirectory is supplied as database with tables and fields allocating every address to a building and every building to a geographic 6-figure position in Irish National Grid (ING) coordinates

14. Methodology There are three parts to the problem of spatially enabling the national farm survey for allocation of environmental attributes: 1. Matching addresses in the NFS to possible addresses in the GDD 2. Allocating a geographic point that represents the matched GDD addresses that deals with the one-to-many matching possibilities and retains an element of confidentiality in the data. 3. Ascribing a representative sample of the environmental attribute to the point The first was achieved within MS ACCESS and the second two using ArcGIS.

15. Address in Ireland does not uniquely identify a rural address

16.  As the majority of NFS addresses match to multiple building points we have to decide how turn this into one point for the correspondent.  Because of the inherent precision in the environmental datasets wherein the digital soil map has a 75m limit to precision, the Teagasc Indicative habitat map has a minimum mapping unit of 1ha (and the climate models have a 1km cell size – there is no need for precision > 100m.  Another point to bear in mind is that the GDD point is allocated to the farmhouse not the farm, this is relevant for large farms or fragmented farms.

17. Rural Economy Research Centre 17 The same matched clusters but with the geographic centre marked as a black star and 1 standard deviation of the cluster from that centre marked with a grey circle. An example of the geo-coding process. Each cluster of points of the same colour have been matched to a single NFS address. One point of a given colour means that the NFS matched to a single address in the GDD.

18. Project Outcome  This initial project successfully assigned a geographic coordinate label to farms in the 2007 national farm survey database. The process was then extended to cover all farms that participated in NFS since 1995.  With a coordinate, spatially referenced environmental, economic and geographic data can now be assigned to each farm thus expanding the usefulness of the NFS dataset.  The backdating of the process means its is now possible to retrospectively include historical data into analysis using the NFS

19. Matching Method & Confidentiality  Researcher matches their spatial data to the Geo_directory building IDs  They send that data to the NFS team  We hold a reference file that contains the FADN Farm details and the building ID  We match the file and then remove the building Id and return the farm data and the spatial variables.  Therefore we don’t release the location of the holding

20. New Developments & Expansion of the Irish NFS Dataset  Inclusion of new environmental to enable us calculate the carbon footprint of milk and beef – LCA analysis.  Turn out dates  Slurry Storage and Spreading data  Monthly data – Milk, Animals Numbers, Feed Usage  Data Collection – Extend the scope and reach of the survey by linking up with external databases  Other new variables  Cereals – Moisture content & Basis of sale – eg if crop forward sold  Dairying – Forward sale

21. Opportunities  To enrich the data without burdening the farmer  Farm level Data collection is very time consuming  To Date - Link has enabled use of  weather data  soils information  distance data  Habitat maps  Potential now through FADN to extend this to all EU member states  Link with LIPIS Challenges  Maintaining confidentiality  FADN sample is not picked to be spatially representative  Multiple Parcels

22. What we can do and can’t do  Can  Introduce spatially referenced data  Soils  Weather  Altitude  Local Market Information  Local Crop Growth Curves  Land Parcel  Water Quality  Can’t  However spatial resolution of data is not necessarily at farm or parcel scale  Regression Grass growth = f(Agronomic Variables)  Farm R2 – 28%  District R2 – 66%

23. Where are we going with All this?  Spatial Context within Analysis  Productivity Analysis – better handle on farm efficiency conditioning on  Spatial Policy – impact of move to agronomic based Areas of Natural Constraints  Innovation pathways – spatial correlation  Like environmental risks with Agri-environmental decisions  Transport Costs  Farm Level Bio-Economic Models  Localised Advice

24. Localised Advice  Events happen locally  Fodder Crisis  Agronomic Conditions  Localised Financial Management  Low take-up of decision support tools and financial planning  Build upon real-time administrative, meteorological and satellite data  Link Farm Spatially related production and cost models as a function of real time spatially based data  To simulate top middle and bottom third financial benchmarks  Better to have predicted financial data than no data  Developing interpretative tools for farmers to understand what they are being given  Training to assist understanding