This part of the documentation describes how to write scrapers for the Farmsubsidy project.

How to contribute

There is a separate GitHub repository collecting the different scrapers:

In the repository there is a separate folder for each scraper, named by the country code of the scraper, e.g. at for Austria:

There is also a very detailed Data Overview document on Google Docs. Public data urls are not up-to-date any more, but make sure to notice the Data Documentation URLs at the end of the detail view sheet:

For each scraper there is a corresponding issue on GitHub. If you want to help out with a scraper have a look at the open issues, see if there is already somebody responsible and drop a note to avoid that there are several people working on the same scraper in parallel.


Before you write your scraper:

  • Check, if there is a download button on the data website (I actually didn’t when I wrote the example scraper! :-))
  • Do some creative googling if someone else already has written a scraper for the site! If so: try to get in contact and ask if the scraper can be used under an open licence.

Data Sources

Data is provided on a country-by-country basis. Mostly you will find a web form where you can filter the data by things like year, amount or the region and get back an HTML table with the single payments. Sometimes data is also provided in a downloadable format.

Here are some examples:

  • Belgium (just hit search button)
  • UK (get data by searching for amount > 1.000.000)
  • Germany (get data by searching for EGFL > 1.000.000)
  • Slovenia (get data by selecting a sum and search)

You can find the relevant data source on the issue page of a country in the GitHub repo.

Format of the existing data files

To get an idea of how data is structured in the DB have a look at the Data model chapter in the Website documentation section.

You can find the data scraped by the old proprietary scrapers in the following folder:

Please download one of the compressed country data files and unpack it, e.g.:

A data package consists of the following files:


Due to its size you probably can’t open recipient.txt or payment.txt with a visual editor but need an editor like vi and use it from the command line.

Each file contains the data for the corresponding data model in CSV format, here are some extracts.

Start of a recipient.txt file:

1;1;"AT1";"AT1";"Adrigan Barbara";;;;;"AT";"AT";"Burgenland";"Lutzmannsburg";;;;;;;;
2;2;"AT2";"AT2";"Aibler Maria";;;;;"AT";"AT";"Burgenland";"Zillingtal";;;;;;;;
3;3;"AT3";"AT3";"Allacher Ilse und Matthias";;;;;"AT";"AT";"Burgenland";"Gols";;;;;;;;

Complete scheme.txt file:

"AT7";"Öffentliche Lagerhaltung (Intervention)";"Intervention";;"AT"
"AT1";"Direct";"Direct payments under European Agricultural Guarantee Fund";;"AT"

Start of a payment.txt file:


Scraper Data Format

CSV Format

The new GitHub scrapers will be used to scrape farmsubsidy data for the year 2013 and newer and only have to output a payment file with a reduced data format and no recipient and scheme files. Please write your scraper so that it will take the year as an input parameter and writes files like this:


The reduced data format looks like the following:

"Nordmilch AG";;;;;"D1";15239.34;
"Emsland-Stärke GmbH";Am Bahnhof 4B;;15938;Golßen;"D2";32305.45;

The scraped data will be loaded into the database with a (yet to be written) Django management command. Recipient names will be matched against existing recipient names.

The following table describe the single attribute formats.

Attribute Description Mandatory Data Type
rName Name of recipient YES String
rAdress1 Adress field 1 for recipient (Street) NO String
rAdress2 Adress field 2 for recipient (other) NO String
rZipcode Zipcode of recipient town NO String
rTown Town of recipient NO String
globalSchemeID Scheme ID from existing scheme.txt YES String
amountEuro Amount in Euro (1) YES(or 2) Float
amountNationalCurrency Amount in national currency (2) YES(or 1) Float


Since the names you scrape will be later matched against the names already existing in the database please make some searches on the Farmsubsidy website and see, how names are formatted there. Try to keep names written as they are on the website so matching will be easier and double entries will be prevented.


For the scheme ID please take an existing scheme ID from the scheme.txt file of the country (see Format of the existing data files). If you can’t find a fitting scheme ID ask on the GitHub issue page and use a temporary schemeID like AT-TMP1.


Please provide either the amount in Euro or in the national currency (e.g. for UK). Don’t make any implicit conversions, leave field not provided blank!

UTF-8 Encoding

Please make sure that you use UTF-8 as an encoding for your output file format and keep recipient data in the original language and characters.

Here are some examples:

  • Bólyi Mezőgazdasági Termelő és Kereskedelmi Zrt. (Hungary)
  • GREENGROW spółka z ograniczonš odpowiedzialnoœciš (Poland)
  • Südzucker GmbH (Germany)
  • Alcoholes Gcía de la Cruz Vega (Spain)


At the moment, the following technologies/programming languages for scrapers are supported:



Scrapy is a python scraping framework with a lot of built in scraping functionality, for introductory information see the Scrapy website:


For running a Scrapy spider, please install the Scrapy version from the requirements file:

You can find a Scrapy project deployment in the GitHub repository in the scrapy_fs folder. In this deployment, there is already the data structure defined in the file.

Writing a spider

There is a reference implementation for a scrapy spider for the GB website. The spider can be found at (Link:


If you want to write a spider with Scrapy, please add/name your spider in an analog way and write a note in the root gb (COUNTRY_CODE) directory that the spider is being realized with Scrapy.

A Scrapy spider can be executed like that from the scrapy_fs directory:

scrapy crawl GB -a year=YEAR

A CSV output can be generated like this:

scrapy crawl GB -a year=2012 -o payment_2012.txt -t csv


Scrapy won’t maintain the order of the attributes of the csv file. That’s ok.


If you have your own preferred way of writing scrapers with Python, you can do that as well. Then please write your scraper in a form, that it can be executed from the command line. Add the requirements you need to the global python requirements file:


If you’ve written a Python scraper you think can serve as a good starting point for other scrapers and can be entered here as a reference implementation, please drop a note!


You can also write a Ruby scraper, please also create the scraper in a command line-executable form.

Add your requirements to the global Ruby Gemfile:


If you’ve written a Ruby scraper you think can serve as a good starting point for other scrapers and can be entered here as a reference implementation, please drop a note!


If you have another technology you want to use, please ask the person currently responsible for maintaining the Scrapers (try on GitHub). The reason for limiting the technologies a bit is that all scrapers for the different countries have to be maintained and an executable environment have to be kept up to be able to run the scraper from a central location independently from the creators.

Changelog (Scraper)

This changelog deals mainly with the data format definition for the scrapers (see: Scraper Data Format) and the technology supported in the scraper repository (see: Technology).

Changes in version DRAFT1 (2014-03-15)