We are what we Eat

Discover food impact on health and environment in Europe

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FOOData: food in data

This project aims to show connections between food production, environment and health conditions in European countries from 2009 to 2019, focusing in particular on different countries' production of food, their health, and pollution status.

We hypothesize that areas with more agricultural environmental damage will have worse health outcomes and that different types of pollution and food production affect health differently.

To do so we collected data from EUROSTAT, the European Environmental Agency and FAO.

Food Production

What do European country produce and how much?

Pollution and environment

What are the environmental issue european countries have to face?

Health

How are european countries doing in term of health?

Citizens

Countries

Years

Datasets

Data Visualization

Visualize FOOData

Life Expectancy by sex and NUTS regions





Analysis

Foodata's data analysis

We started by wondering whether areas with more agricultural pollution will have worse health outcomes and how different types of pollution and food production are correlated with life expectancy. The outcomes of our search made it clear that there are correlations between these three dimensions, in a time span of ten years and considering the European Countries as spatial area. In particular, we noticed some interesting correlations in our mashup visualizations.

Food Production and Life Expectancy by Country

Considering the production of meat (cattle, chicken and pig), the three major productors in Europe are Germany first, followed by Spain and France. If we take into consideration the life expectancy in these three countries, we will notice the subsequent trends (in a time span of 10 years):

  • Life expectancy for males in Germany is the lowest of the three countries at 78 while France is next with 79.
  • Germany also has the lowest life expectancy for females, 83 years old, while Spain and France are at 85.
Germany is the the greatest producer of meat (pig meat in particular) and also has the lowest life expectancy of the three major producers. However, we must underline that food production is not equivalent to food consumption. Many factors influence life expectancy thus further analysis should be done.

Farm Industrialization Level and Life Expectancy

In NUTS 2 regions, farms of all input levels (high, medium, and low) occur together: regions with many farms tend to contain a mix of all three times.

The data is overall inconclusive and does not support our original hypothesis.

Germany, an area with relatively low levels of farms of all inputs, has a lower life expectancy than Southern Europe, especially Spain and Italy (the number one and two countries for life expectancy, respectively). Spain and France have the largest numbers of high-input farms but also long life expectancies.

Air Pollution and Life Expectancy by Country

The data on air pollution and life expectancy overall did not support our original hypothesis that more pollution lead to lower life expectancies.

  • France has the highest levels of air pollution but also high life expectancy.
  • Spain and Italy have moderately high levels of air pollution but also are the top two countries for longest life expectancy.
  • The data for Poland and Romania support the original hypothesis; they have higher levels of pollution and shorter life expectancies.

Air pollution overall has decreased from 2009-2019 while life expectancy has increased over the same period, however this may be only correlation since many countries with the highest levels of pollution also have longest life expectancies.

Greenhouse gas emissions and food production by Country

For this visualization we opted for a graph that takes into account greenhouse pollutants (carbon dioxide, hydrofluorocarbones, methane, nitrogen trifluoride, nitrous oxide, perfluorocarbones, sulphur hexafluoride), agriculture products (rice paddy, potatoes and wheat) and livestock products (meat: cattle, pig, chicken; eggs; cheese; milk) in 10 years, considering the media of the emissions and food production of the mentioned categories in this period. It turned out that:

  • the major producers in the agriculture sector are: 1. France, 2. Germany, 3. Poland, 4. Romania, 5. Italy
  • the major producers in the livestock products sector: 1. Germany, 2. France, 3. Poland 4. Netherlands, 5. Italy, 6. Denmark, 7. Ireland
It is important to notice that Ireland is only at seventh position as livestock producer and it covers an even lower position for what concearns agricultural sector. The fact that in ten years this Country produced the majority of pollutants in Europe (490 tonnes circa) is relevant in our graphs. Probably the pollution there is not strictly related to the food production, a question that goes beyond our study.

Also Portugal is one of the most polluted countries in these 10 years, but the production values are not relevant.

If we consider the most productive States, Germany, France and Italy recorded quite high greenhouse pollution values. A bright spot is Cyprus with a negative value on emissions (-66 tonnes), but also one of the less productive countries in Europe.

Agricultural area and food production

This visualization represents the different amount of food prodution and land use per year for each european country. Having to deal with measures relative to different time periods, we noticed that not every conutry showed both values for every year, but nonetheless many conforntations can still be appreciated.
We noticed some important things:

  • France, Germany, Poland, Netherlands, Italy and Spain are the biggest food productors in Europe
  • France, Spain and Germany have also the most farm-devoted amount of land
  • France and Germany produced the most meat and dairy products
  • France also produces the most non-meat and non-dairy products

It can be noted that even if Spain is in second place for average land devoted to agriculture, it is the fifth for production, with a focus on meat and dairy products and that while Italy and Netherland show a great difference in average land dedicated to farming, they seem to have similar food productions.

We can then assume that the differences in land devoted to farming will influence not so much the amount of food produced but instead the kind of production: this semms logical considering the largest amount of land required to produce smaller amounts of meat and dairy products with respects to cereals and potatoes.

Food production and water pollution

In the case of this visualization we opted for plotting water pollution (a value that we only have for year 2015) along with production values, giving the chance to observe variations in these latter values in relation with the previous, stable one.
Here what is possible to notice is that:

  • France and Germany show the biggest amounts of both food production and water pollution
  • Spain and Belgium follow in terms of food production but present a percentage of water pollution visibly inferior
  • The following four countries in terms of water pollution are Poland, Sweden, Greece and Italy
  • Of this four Greece and Sweden are less productive in terms of food than Italy and Greece but present a higher level of water pollution

Also it can be noted that, even if they present lower production levels, countries such as Belgium, Greece, Sweden and Spain seem to have more polluted surface waters,
On the other hand France and Germany, which are the main producers of products both meat/dairy and not seem to show the highest values of pollution.

From this visualization we could infer that, while it cannot be stated a straight correlation betweeen the kind of food produced by a country and the pollution of its water, it could be said that this is probably one of the influencing factors, along with others such as for instance urbanization dynamics and waste management.



Documentation

Foodata's Documentation

FOOData was created for the Open Access and Digital Ethics exam by Vanessa Bonanno, Giorgia Sampò, and Sarah Tew.FOOData explores food production, pollution, and health markers within Europe both individually and together to get a clearer picture of their interlation. FOOData uses geospatial visualizations to reconnect users with the geographic reality of food production and pollution.

FOODATA relies on 8 different datasets:



Crops and livestock production in European countries

The production section is based on data found at FAOSTAT, the Food and Agriculture Organization Corporate Statistical Database maintained by the statistics division of the Food and Agriculture Organization of the United Nations.

FAOSTAT presents a huge dataset on food and agriculture with data from over 245 countries and territories with a time span which goes from 1961 to 2020. Data are divided into 13 diverse domains, such as trade, prices, population and employment, but also forestry, land, climate change and food production. In turn, food production is divided in three main categories: Crops and livestock products, Production indices, Value of agricultural production.


Original dataset

Crop statistics are recorded for 173 products, covering a huge variety of categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Fruit, and more. Data are expressed in terms of area harvested, production quantity and yield. The objective is to cover the production of all primary crops for all countries and regions in the world.

About Livestock products, the user can select data between three main domains: Live animals, Livestock primary and Livestock processed. Due to the great amount of categories and the fact that certain types of production are not common in Europe, or even non-existent (e.g., camel meat), we opted for the most common and most produced foods in Europe.

The time span which FAOSTAT proposes goes from 1961 to 2020. In this case we narrowed it to a period of 10 years and did not include 2020, as explained below.


Foodata production dataset

The final information in Foodata production csv file includes:

Between the vast variety of foods, the chosen items are: cow milk; whole cow milk cheese; eggs, hen, in shell; cattle, chicken and pig meat; potatoes; rice; wheat. As anticipated, the period of time in which we investigate food production, pollution and life expectancy across Europe goes from 2009 until 2019: nonetheless, the availability of data along this period is not always possible, as one can notice in the visualisation section.

Legal analysis

FAOSTAT datasets are are licensed under CC BY-NC-SA 3.0 IGO, which states the user is free to share — copy and redistribute the material in any medium or format — and adapt — remix, transform, and build upon the material.

Ethical analysis

Due to the nature of Crops and Livestock products dataset, data about people are not included, so it is not possible to examine the graphs from an ethical point of view.

Gender

As stated above, there are no gender-related data.


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Pollution and environmental damage in European countries

This datasets is devoted to pollution issues and environmental damages in European countries and was obtained from the mesh-up of 5 datasets coming from Eurostat and one form the European environmental Agency.

The dataset published by Eurostat were also collected from other sources such as JRC, FADN and EEA itself.

Another important aspect of these datasets was the fact that they did not adopt all the same NUTS classification for geographical locations nor they had the same chronological organization.

The dataset taken under consideration were:

They all converged in a final dataset:

At the end we got a dataset where all the info about environment and pollution for european countries were stored, even if with different levels of granularity, to be later employed in our visualization.


Original Datasets

Eurostat presented a variety of datasets, from which the ones that we found were most related to environmental issues were selected. The datasets cover both air, water and soil environmental damage, and fields were selected that regarded only Agriculture, Farming and forestry values.

Statistics are collected for all geographically european countries, concerning not only member states but also Norway, England and Turkey. Also the NUTS categorization adopted from the datsets concerned encompassed Nuts 0, Nuts 2 and Nuts 3.


Foodata production datasets

The final information in Foodata pollution and environment CSV includes:

  • Year
  • Geographical area
  • NUTS Codes
  • Irrigable/irrigated crops
  • River basin district NUTS code
  • River basin district name
  • River basin district surface water pollution percentage
  • High/Medium/low input farms hectares
  • Soil erosion levels and sectors
  • Soil erosion tonnes per hectare
  • Greenhouse gas pollutant and sectors
  • Greenhouse gas pollutant tonnes
  • Air pollutant
  • Air tonnes

We filtered the original datasets, using the download interface offered from Eurostat, to select only values regardin farming, agriculture and forestry.


Data availability

The datasets under consideration were published by Eurostat but collected from other autorities such as EEA (European Environmental Agency), JRC (Joint Researche Center) and FADN (Farm Accountancy Data Network). Data was overall of a high quality, but of very different granularyty, due to the different NUTS categorizations and time periods applied. Data was also overall consistently available for regions outside the EU but included in the dataset, including Iceland, Liechtenstein, Norway, Switzerland, the UK, Montenegro, North Macedonia, Albania, Serbia, and Turkey. The only issue encountered was the categorization of some regions relative to past NUTS regions (with relative indication), that was solved confronting the geographical difference and, when possible, incorporating the regions under the current representing label by hand.

Although most of the datasets resulted as last updated in 2021 or 2020, it is unspecified why data after 2019 is unavailable. Because COVID-19 had a significant and anonymous impact on health, pollution, and food production in Europe data from after 2019 has been excluded from FOOData.

The edited pollution dataset used in FOOData will not be updated in the future.


Data quality

The measures collected from Eurostat datasets are all indicators frequently used to represent various kinds of environmental damages: we tried to convey information regarding soil, water and air, since agricultural and farming activities affect differently these fields.

Eurostat's datasets in particular are of very high quality since EU member states share collection, measurement, and reporting standards. They are also exhaustive and seek to represent member states and their regions equally, although, as mentioned previously, there are some granularitty differencies, and some gaps especially before 2017.


Legal analysis

The original and edited datasets are licensed under CC-BY 4.0 except for the EEA's one which is under the "EEA standard re-use policy" , and are all open for distribution and reuse. Furthermore Eurostat itself re-publishes material coming from EEA, licensing it on a CC-BY 4.0, so there must be compliance between the two licenses. The data was originally collected with the express intent to publish and share. There is no legal impediment to reproducing or remixing this data.


Ethical analysis

Datasets regarding environmental damage and pollution are free of sensitive data since, even when using NUTS 3 granularity, they tend to represent very large geographical areas and personal informationn is not included by default. As averages the data gives information about the region as a whole and do not pose individual privacy risk.

However, for what regards air pollution, greenhouse gas emissions and surface water pollution there are chances of people acting on that information to look for better living standards outside countries boundaries, but that does not look like an ethical issue as much as a normal inferencial process granted to common citizens and perfectly sustainable.


Gender

This dataset is largely free of gender bias and gender-related data.


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LIFE EXPECTANCY ACROSS EUROPE

FOOData uses life-expectancy to indicate overall population health. This data comes from Eurostat: Life expectancy by age, sex and NUTS 2 region. The original dataset contained life expectancies by age, sex, NUTS 2, NUTS 1, and NUTS 0 regions from 1990 to 2019. The FOOData edited dataset limits the Eurostat data to life expectancy at birth from 2010-2019 in current EU member countries.

Data availability

The dataset was overall of high quality however there were gaps for some regions, including in all of Germany and the Netherlands above the NUTS 0 level until 2002, Denmark until 2007, Ireland until 2013, some but not all regions of Poland until 2013. Data was also overall less consistently available for regions outside the EU but included in the dataset, including Iceland, Liechtenstein, Norway, Switzerland, the UK, Montenegro, North Macedonia, Albania, Serbia, and Turkey. To mitigate this issue the final dataset was limited to current, full EU member states from 2010-2019. For more specific information on Eurostat's use of the NUTS regions with demographic data please see Eurostat's metadata.

The original dataset also included life expectancies by age measured annually from age 0 to 84 and then in one group of 85+. The final dataset used in FOOData only looks at life expectancy in the first group. This is for clarity since FOOData is concerned with general health markers and the environment; shifts in life-expectancies over the human lifetime are less useful when understanding the impact of the physical environment on health without significant other demographic and environmental data.

The data was equally available for men and women and included the average via "total".

Although last updated in 2021, it is unspecified why data after 2019 is unavailable. Because COVID-19 had a significant and anomalous impact on health, pollution, and food production in Europe data from after 2019 has been excluded from FOOData.

The edited life expectancy dataset used in FOOData will not be updated in the future.


Data Quality

Life expectancy is frequently used as an overall population health indicator. Informed by many environmental, socio-economic, educational and other factors, life expectancy is relatively easy to calculate and collect accurately.

Eurostat's dataset in particular is of very high quality since EU member states share collection, measurement, and reporting standards. It is also exhaustive and seeks to represent member states and their regions equally, although, as mentioned previously, there are some gaps, especially before 2010.


Legal analysis

The original and edited datasets are licensed under CC-BY 4.0 and open for distribution and reuse. The data was originally collected with the express intent to publish and share. There is no legal impediment to reproducing or remixing this data.


Ethical analysis

Life expectancy is an oft-used measure of health partly because it is much less sensitive and private than other health data. As an average it gives information about the population as a whole rather than individual members and does not pose individual privacy risks.

Gender

This dataset is largely free of gender bias, however recently binary gender classification into male and female has come under re-evaluation. That being said, the EU member states are bound by the same statistics reporting standards so the dataset is at least consistent in its use of two and only two genders.


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Metadata

Foodata's Metadata

Legal Checklist

Original Datasets Metadata

Final Datasets Metadata

Legal Checklist

A B C D E F G H I
1
 To check: D1
D12 (Life expectancy)
 D3 D4 D5 D6 D7 D8
2
1. Privacy issues
3
 1.1 Is the dataset free of any personal data as defined in the Regulation (EU) 2016/679? Yes Yes Yes Yes Yes Yes Yes Yes
4
 1.2 Is the dataset free of any indirect personal data that could be used for identifying the natural person? Yes Yes Yes Yes Yes Yes Yes Yes
5
 If so, is there a law that authorize the PA to release them? Or any other legal basis? Identify the legal basis. N/A N/A N/A N/A N/A N/A N/A N/A
6
 1.3 Is the dataset free of any particular personal data (art. 9 GDPR)? Yes Yes Yes Yes Yes Yes Yes Yes
7
 If so is there a law that authorize the PA to release them? N/A N/A N/A N/A N/A N/A N/A N/A
8
 1.4 Is the dataset free of any information that combined with common data available in the web, could identify the person? Yes Yes Yes Yes Yes Yes Yes Yes
9
 If so, is there a law that authorize the PA to release them? N/A N/A N/A N/A N/A N/A N/A N/A
10
 1.5 Is the dataset free of any information related to human rights (e.g. refugees, witness protection, etc.)? Yes Yes Yes Yes Yes Yes Yes Yes
11
 1.6 Do you use a tool for calculating the range of the risk of de-anonymization? No No No No No No No No
12
 Do you anonymize the dataset? With which technique? No No No No No No No No
13
 Did you check the three mandatory parameters: singling out, linking out, inference out? N/A N/A N/A N/A N/A N/A N/A N/A
14
 1.7 Are you using geolocalization capabilities ? Yes No Yes Yes Yes Yes Yes Yes
15
 Do you check that the geolocalization process can’t identify single individuals in some circumstances? Yes No Yes Yes Yes Yes Yes Yes
16
 1.8 Did you check that the open data platform respect all the privacy regulations (registration of the end-user, profiling, cookies, analytics, etc.)? Yes Yes Yes Yes Yes Yes Yes Yes
17
 1.9 Do you know who are in your open data platform the Controller and Processor of the privacy data of the system? AO Staff Coop s.c.a r.l. No European Evnvironmental Agency European Evnvironmental Agency Joint Research Centre (JRC) European Environment Agency DG Agriculture
and Rural
Development		 Eurostat
18
 1.10 Where are the datasets physically stored (country and jurisdiction)? Viale delle Terme di Caracalla
00153 Rome, Italy		 Contact Point: Joseph Bech building, 5 Rue Alphonse Weicker, L-2721 Luxembourg 2920
Luxembourg
Luxembourg
 2920
Luxembourg
Luxembourg
 2920
Luxembourg
Luxembourg
 Kongens Nytorv 6,
1050, Copenhagen,
Denmark 		2920
Luxembourg
Luxembourg
 Contact Point: Joseph Bech building, 5 Rue Alphonse Weicker, L-2721 Luxembourg
19
 Do you have a cloud computing platform? No No No No No No No No
20
 Have you checked the privacy regulation of the country where the dataset is physically stored? (territoriality) Yes Yes Yes Yes Yes Yes Yes Yes
21
 1.11 Do you have non-personal data? Yes Yes Yes Yes Yes Yes Yes Yes
22
 Are you sure that are not “mixed data”? Yes Yes Yes Yes Yes Yes Yes Yes
23
 2. IPR of the dataset
24
 2.1 Have you created and generated the dataset ? No No No No No No No No
25
 2.2 Are you the owner of the dataset? Who is the owner? No, the owner is Food and Agriculture Organization of the United Nations. Otherwise there is a flag "FAO estimate", "unofficial figure", etc. No, Eurostat No, Eurostat No, Eurostat No, Eurostat No, European Environmental Agency No, Eurostat No, Eurostat
26
 2.3 Are you sure to not use third party data without the proper authorization and license ? Yes Yes Yes Yes Yes Yes Yes Yes
27
 Are the dataset free from third party licenses or patents? Yes Yes Yes Yes Yes Yes Yes Yes
28
 2.4 Have you checked if there are some limitations in your national legal system for releasing some kind of datasets with open license? Yes Yes Yes Yes Yes Yes Yes Yes
29
 3. Licenses
30
 3.1 Do you release the dataset with an open data license ? Yes Yes Yes Yes Yes Yes Yes Yes
31
 In case of the use of CC0 do you check that you have all the right necessary for this particular kind of license (e.g., jurisdiction)? Yes N/A N/A N/A N/A N/A N/A N/A
32
 3.2 Do you include the clause: "In any case the dataset can’t be used for re-identifying the person" ? Yes Yes Yes Yes Yes Yes Yes Yes
33
 3.3 Do you release the API (in case you have) with an open source license ? Yes Yes Yes Yes Yes Yes Yes Yes
34
 3.4 Do you check that the open data/API platform license regime is compliance with your IPR policy ? Yes Yes (Using Tableau student it needs to be non-commerical which it is) Yes Yes Yes Yes Yes Yes
35
 Do you have all the licences related to the open data platform/API software? Yes Yes Yes Yes Yes Yes Yes Yes
36
 4. Limitations on public access
37
 4.1 Do you check that the dataset concerns your institutional competences, scope and finality? Yes ? Yes Yes Yes Yes Yes Yes
38
 Do you check if the dataset concerns other public administration competences? Yes Yes Yes Yes Yes Yes Yes Yes
39
 4.2 Do you check the limitations for the publication stated by your national legislation or by the EU directives ? Yes Yes Yes Yes Yes Yes Yes Yes
40
 4.3 Do you check if there are some limitations connected to the international relations, public security or national defence ? Yes Yes Yes Yes Yes Yes Yes Yes
41
 4.4 Do you check if there are some limitations concerning the public interest ? Yes Yes Yes Yes Yes Yes Yes Yes
42
 4.5 Do you check the international law limitations ? Yes Yes Yes Yes Yes Yes Yes Yes
43
 4.6 Do you check the INSPIRE law limitations for the spatial data? Yes Yes Yes Yes Yes Yes Yes Yes
44
 5. Economical Conditions
45
 5.1 Do you check that the dataset could be released for free ? Yes Yes Yes Yes Yes Yes Yes Yes
46
 5.2 Do you check if there are some agreements with some other partners in order to release the dataset with a reasonable price ? Yes, FAOSTAT data is free and available to everyone Free and public Free and Public Free and Public Free and Public Free and Public Free and Public Free and Public
47
 5.3 Do you check if the open data platform terms of service include a clause of “non liability agreement” regarding the dataset and API provided ? Yes Yes Yes Yes Yes Yes Yes Yes
48
 5.4 In case you decide to release the dataset to a reasonable price do you check if the limitation imposed by the new directive 2019/1024/EU are respected ? N/A N/A N/A N/A N/A N/A N/A N/A
49
 Are you able to calculate the “marginal cost”? N/A N/A N/A N/A N/A N/A N/A N/A
50
 Are you able to justify the “reasonable return on investment” N/A N/A N/A N/A N/A N/A N/A N/A
51
 limited to cover the costs of collection, production, reproduction, dissemination, preservation and rights clearance? N/A N/A N/A N/A N/A N/A N/A N/A
52
 There is a national law that justify your public administration to apply the “reasonable return of investment”? N/A N/A N/A N/A N/A N/A N/A N/A
53
 5.5 In case you decide to release the dataset to a reasonable price do you check the e-Commerce directive1 and regulation? N/A N/A N/A N/A N/A N/A N/A N/A
54
 6. Temporary aspects
55
 6.1 Do you have a temporary policy for updating the dataset ? No No No No No No No No
56
 6.2 Do you have some mechanism for informing the end-user that the dataset is updated at a given time to avoid mis-usage and so potential risk of damage ? Yes Yes Yes Yes Yes Yes Yes Yes
57
 6.3 Did you check if the dataset for some reason can’t be indexed by the research engines (e.g. Google, Yahoo, etc.) ? Yes Yes Yes Yes Yes Yes Yes Yes
58
 6.4 In case of personal data, do you have a reasonable technical mechanism for collecting request of deletion (e.g. right to be forgotten)? N/A N/A N/A N/A N/A N/A N/A N/A

Original Datases Metadata

A B C D E F G H I
1
Dataset FAOSTAT Eurostat Life Expectancy Eurostat
Greenhouse gas emissions by source sector 		Eurostat
Air pollution		Eurostat
Estimated soil erosion by water, by erosion level, land cover and NUTS 3 regions		 European environmental Agency
Percentage of water bodies not in good ecological status or potential, per river basic district		 Eurostat
Utilised agricultural area (UAA) managed by low-, medium- and high-input farms		 Eurostat

Share of irrigable and irrigated areas in utilised agricultural area (UAA) by NUTS 2 regions
2
 TitleCrops and livestock products FAOSTAT Life expectancy by age, sex, and NUTS 2 region Greenhouse gas emissions by NUTS 0 regions Air pollutants by source sector and NUTS 0 regions Estimated soil erosion by water, by erosion level, land cover and NUTS 3 regions Percentage of water bodies not in good ecological status or potential, per river basic district Utilised agricultural area (UAA) managed by low-, medium- and high-input farms by NUTS 2
Share of irrigable and irrigated areas in utilised agricultural area (UAA) by NUTS 2 regions
3
Identifier demo_r_mlifexpenv_air_gge env_air_emisaei_pr_soiler WISEaei_ps_inp aei_ef_ir
4
 Description Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus... (read more) The dataset contains demographic information about the average lifespan at birth based on sex and geographic location within the EU from 2010-2019. The dataset uses 27 NUTS 0 countries (EU member states) and 302 NUTS 1 and 2 regions. This dataset includes data on greenhouse gas emissions inventory, as reported to the European Environment Agency (EEA). Note that Eurostat is not the producer of these data, only re-publishes them.

The European Union (EU) as a party to the United Nations Framework Convention on Climate Change (UNFCCC) reports annually its greenhouse gas inventory for the year t-2 and within the area covered by its Member States. The inventory contains data on carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). The EU inventory is fully consistent with national greenhouse gas inventories compiled by the EU Member States.		 The European Union (EU) as a party to the Convention on Long-range Transboundary Air Pollution (LRTAP Convention) reports annually its air pollution inventory for the year t-2 and within the area covered by its Member States. Under the Convention, parties are obliged to report emissions data for numerous air pollutants.

This dataset includes data on air pollutants: sulphur oxides (SOx), ammonia (NH3), nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOCs), particulate matters (PM10, PM2.5), Lead (Pb), Cadmium (Cd), Mercury (Hg), Arsenic (As), Chromium (Cr) Copper (Cu), Nickel (Ni), Selenium (Se) and Zinc (Zn), as reported to the European Environment Agency (EEA). The EU inventory is fully consistent with national air pollution inventories compiled by the EU Member States.

Note that Eurostat is not the producer of these data, only re-publishes them. The producer of the data is the European Environment Agency.

Note that in the European Union emission inventory report 1990-2019 under the UNECE Convention on Long-range Transboundary Air Pollution, EU-28 totals do not include data from the United Kingdom. Therefore, Eurostat labels the same totals as EU-27.		 Data on soil erosion are published under agreement with data provider - Joint Research Centre of the European Commission (JRC – Ispra), one of the partners of the Memorandum of Understanding for cooperation on the development of Agri-environmental indicators.

Soil erosion by water is one of the most widespread forms of soil degradation in Europe. Since it is difficult to measure at large scales, soil erosion models are a crucial estimation tool at regional, national and European levels.

This dataset represents the soil erosion rates by water estimated on the basis of the Revised Universal Soil Loss Equation (RUSLE) empirical computer model in tonnes per ha of EU territory per year (t ha-1 yr-1), in EU-28 Member States for NUTS 3 level administrative areas. Note that Eurostat is not the producer of these data, only re-publishes them. For more information please consult the Eurostat Statistics Explained article Agri-environmental indicator – Soil erosion.		 The report presents only the key results, while more detailed WFD results are presented in the WISE-Freshwater WFD visualisation tool. Results are presented as interactive tables, graphs and maps (“Dashboards”), and as overviews at EU, Member State and river basin districts (RBDs) levels.

More than 150 “dashboards” (Web-visualisations), presenting status and pressure information, are now available online. The dashboards present the data Member States have reported via the first (in 2010-2012) and the second River Basin Management Plans (RBMPs, in 2012-2016).		 This data set shows the hectares and the percentage of utilised agricultural area (UAA) managed by low-, medium- and high-input farms in the EU Member States and the United Kingdom for the time period 2005-2017. Each farm is classified according to the level of input use per hectare, which is calculated on the basis of the spending (in constant euros) on purchased inputs per hectare of UAA. The inputs considered here are purchased fertilisers and soil improvers, pesticides (plant protection products), other means for protection such as traps and baits, bird scarers, anti-hail shells, frost protection and purchased feed. This approach allows covering both crop and livestock productions.

The volume of inputs used (per hectare) is estimated by dividing input expenditures (per hectare) by the input price index for the year and country in question. This results in input expenditures per hectare in constant national input prices. Fertiliser expenditure (purchased fertilisers and soil improvers) is divided by the fertiliser price index in the country of the same year in order to estimate the volume used. Similarly, crop protection expenditure (plant protection products, traps and baits, bird scares, anti-hail shells, frost protection) is divided by the pesticide price index in the country of the same year. Purchased feed cost is also divided by the feed price index in the country of the same year. The result is thus expressed in constant inputs prices (Euro per ha). The method allows not only to deduct inflation, but also the fluctuation of input prices. Thus it estimates the trend in the volume of inputs used per hectare. However, it does not capture differences in input prices between countries and the differences in prices within each category of inputs (for example between a pesticide A and a pesticide B). Therefore it does not give the exact volume of inputs used for a specific country and year.

In a second step, the distribution of the UAA is considered by the ranked input intensity (bivariate approach) in each geographical level (EU, MS, NUTS) for the specific year of reference (2010 for all MSs, 2013 for HR). Three classes of intensity (low, medium, high) are then defined, by deriving the associated level of input corresponding to the 33rd (q33) and the 66th (q66) UAA quantiles:

A farm is classified under the class “low intensity” if its input level is below or equal to the intensity value associated to the Q33 of UAA.
A farm is classified under the class “high intensity” if its input level is greater than the intensity value associated to the Q66 of UAA.
A farm is classified under the class “medium intensity” if its input level is greater than the intensity value associated to the Q33 of UAA and it is below the intensity value associated to the Q66 of UAA.
For EU27_2020, as well as for EU28, those thresholds are represented by 88 euros/ha for low intensity farms, by 560 euros/ha for high intensity farms, and by between 88 and 560 euros/ha for medium intensity farms.

These levels should not be considered as strict cut-off values, but rather as reference in time in order to be able to study the evolution of farm intensification through the years.

Once the farms are classified in one of the three intensity classes (low, med, high), a global ratio indicator is computed as the weighted sum of input over the weighted sum of UAA for each intensity class.

Intensification in a country with very low intensity does not mean the same for the environment than intensification in a country with high intensity. That is the purpose of the variable, "average input expenditures per hectare in constant input prices". It is not the ideal measurement of intensity; however, it is the best estimate that we can obtain until now from the available data.		 Share of irrigable and irrigated areas in the utilised agricultural area (UAA); the irrigable area is the area which is equipped for irrigation, while the irrigated area measures the actual amount of land irrigated; based on Farm Structure Survey data.
5
 ModifiedUpdated 2021-02-22 Updated 03.06.2021 Updated 04.06.2021 Updated 05.10.2021 Updated 18.12.2019 Updated 03.07.2018 Updated 06.11.2020 Updated 11.03.2019
6
 Theme Crops and livestock products, food production Life expectancy, demograhic data Greenhouse gas emissions Air pollution Soil erosion Surface water pollution Utilised agricultural area Share or irrigable and irrigated water
7
 Rights Holder The Food and Agriculture Organization (FAO) of the United Nations Eurostat EEA EEA JRC EEA FADN Eurostat
8
Accrual Periodicity AnnualAnnualAnnualAnnual2000/2010/20162015Annual2005/2007 and then every three years
9
 Subject Crops and livestock products Life expectancy Greenhouse gas emissions Air pollution Soil erosion Water pollution Agriculture Irrigation
10
Publisher FAOSTAT Eurostat Eurostat Eurostat Eurostat European Environmental Agency Eurostat Eurostat
11
Issued 06/02/2022 06/02/2022 06/02/2022 06/02/2022 06/02/2022 06/02/2022 06/02/2022 06/02/2022
12
 LanguageEnglish, Français, Español EnglishEnglish EnglishEnglish EnglishEnglish English
13
 KeywordsFood production, crops production, livestock production Life expectancy, EUGreenhouse gases, environment, EU Air pollution, Environment, EUSoil erosion, EU, land cover, water soil erosion Water pollution, EU, surface waterAgriculture, Utilised gricultural area, Farming, EU Irrigation, EU
14
Source https://www.fao.org/faostat/en/#data https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_mlifexp https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_gge&lang=en https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_emis&lang=en https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_pr_soiler&lang=en https://www.eea.europa.eu/data-and-maps/figures/proportion-of-classified-surface-water-7 https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_ps_inp&lang=en https://data.europa.eu/data/datasets/arinrajid0ph6h0uozi8w?locale=en
15
 Temporal Coverage1961/20201990-20192010-20192010-20192000-201620152010-20192005-2016
16
Spatial Coverage Countries (all over the world), Regions (e.g., continents), Special Groups (e.g., Europe) Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia Europe: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The United Kingdom, Turkey, Norway, Liechtenstein, Albania, Iceland, Montenegro, North Macedonia, Serbia
17
 Data formatXLS, CSV TSV, SDMX-ML XLS, CSV, HTML, PC-AXIS, SPSS, TSV, PDF XLS, CSV, HTML, PC-AXIS, SPSS, TSV, PDF XLS, CSV, HTML, PC-AXIS, SPSS, TSV, PDF CSV, XLS XLS, CSV, HTML, PC-AXIS, SPSS, TSV, PDF TSV, SDMX-ML
18
 URL https://www.fao.org/faostat/en/#data/QCL https://data.europa.eu/data/datasets/hkmclok7z575xxdc9masw https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_gge&lang=en https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_emis&lang=en https://appsso.eurostat.ec.europa.eu/nui/show.do https://www.eea.europa.eu/data-and-maps/figures/proportion-of-classified-surface-water-7 https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_ps_inp&lang=en https://data.europa.eu/data/datasets/arinrajid0ph6h0uozi8w?locale=en
19
 License CC BY-NC-SA 3.0 IGO Creative Commons Attribution International 4.0 Creative Commons Attribution International 4.0 Creative Commons Attribution International 4.0 Creative Commons Attribution International 4.0 EEA basic re-use policy Creative Commons Attribution International 4.0 Creative Commons Attribution International 4.0
20
Download https://www.fao.org/faostat/en/#data/QCL https://data.europa.eu/data/datasets/hkmclok7z575xxdc9masw?locale=en https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.do https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.do https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.do https://www.eea.europa.eu/data-and-maps/figures/proportion-of-classified-surface-water-7/percentage-of-number-of-water/at_download/file https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.dohttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?file=data/aei_ef_ir.tsv.gz

Final Datasets Metadata

A B C D
1
 Dataset FAOSTAT Eurostat Life Expectancy Foodata - pollution
2
Identifier https://github.com/giorgiasampo/Foodata/blob/main/datasets/food_production.csv https://github.com/giorgiasampo/Foodata/blob/main/datasets/life_expectancy.csv https://raw.githubusercontent.com/giorgiasampo/Foodata/main/datasets/pollution_dataset.csv
3
 TitleFOOData - Crops and livestock products FAOSTAT FOOData - Life expectancy at birth by sex and NUTS 2 region FOOData - Agricultural area, soil erosion, irrigable and irrigated land, greenhouse gas emissions, air pollution and water pollution
4
Description The dataset contains the production different kinds of food in Europe (27 Countries). List of food selected: cheese, whole cow milk; eggs, hen, in shell; meat, cattle; meat, chicken; meat, pig; milk, whole fresh cow; potatoes; rice, paddy; wheat. The dataset contains demographic information about the average lifespan at birth based on sex and geographic location within the EU from 2010-2019. The dataset uses 27 UTS 0 countries (EU member states) and 302 NUTS 1 and 2 regions. The datasets contains data relative to agricultural are employed for low, medium and high input farms, soil erosion due to agriculture and forestry, irrigable and irrigated land, greenhouse gas emissions due to agriculture and farming, air pollution and water pollution per country.
5
 ModifiedUpdated 2021-02-22 Updated 2021-01-31 Updated 2021-01-31
6
Theme Crops and livestock products, food production Life expectancy, demograhic data Resource usage and pollution
7
 Rights HolderThe Food and Agriculture Organization (FAO) of the United Nations EurostatEurostat and European Environmental Agency
8
Subject 9 types of food, 27 CountriesLife expectancy Pollution
9
 Publisher Giorgia Sampò, Sarah Tew, Vanessa Bonanno Giorgia Sampò, Sarah Tew, Vanessa Bonanno Giorgia Sampò, Sarah Tew, Vanessa Bonanno
10
Issued 06/02/2022 06/02/2022 06/02/2022
11
Language English, Français, EspañolEnglish English
12
 Keywords Food production, crops production, livestock production Life expectancy, EU Pollution, environment, water, air, soil, EU
13
Source https://www.fao.org/faostat/en/#data/QCL https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_mlifexp https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_gge&lang=en
https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_emis&lang=en
https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_pr_soiler&lang=en
https://www.eea.europa.eu/ims/ecological-status-of-surface-waters
https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_ps_inp&lang=en
https://data.europa.eu/data/datasets/arinrajid0ph6h0uozi8w?locale=en
14
 Temporal Coverage2009/2019 2009-20192010-2019
15
 Spatial CoverageEurope, 27 countries (Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden) EU member states: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden EUROPE: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Turkey, United Kingdom
16
Data formatXML, CSVCSVCSV
17
URLhttps://github.com/giorgiasampo/Foodata/tree/main/datasetshttps://github.com/giorgiasampo/Foodata/blob/main/datasets/life_expectancy.csv
18
LicenseCC BY-NC-SA 3.0 IGOCreative Commons Attribution International 4.0Creative Commons Attribution International 4.0
19
Downloadhttps://github.com/giorgiasampo/Foodata/tree/main/datasetshttps://github.com/giorgiasampo/Foodata/blob/main/datasets/life_expectancy.csvhttps://github.com/giorgiasampo/Foodata/blob/main/datasets/pollution_dataset.csv
20

License

Foodata's License

The website, datasets, and Github repository are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO).

The original pollution and health datasets are licensed under CC BY-SA 4.0.

The two algorithms developed for this project are licensed under the GNU General Public License version 3.

The mashup pollution and health datasets are licensed under CC BY-NC-SA 3.0 IGO.

The original and mashup food production dataset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO).

The dataset may not be used for re-identifying individuals in any case for any purpose.

FOOData and its creators are not liable for the use and reuse of this site or data by external parties.

The website design is copyrighted by Bootslander and designed by BootstrapMade.

The visualizations are licensed under CC BY-NC-SA 3.0 IGO. They were made with Tableau under student licensing for non-commercial purposes. Tableau's Terms of Service apply to FOOData visualizations. These terms are not in contradiction with CC BY-NC-SA 3.0 IGO licensing. For more information see "Your Use of the Service" in Tableau's Terms of Service.

Disclaimer

FOOData, including its FOOData website, repository, and all associated files and datasets will not be updated after February 7, 2022. While there are no current plans to discontinue the project, the creators reserve the right to discontinue access to the FOOData website, repository, and all associated files at any time.

Team

The FOOData Team

Sarah Tew

MA student, Digital Humanities and Digital Knowledge BA in Classics and Art History, New College of Florida

Vanessa Bonanno

MA student, Digital Humanities and Digital Knowledge Bachelor Degree in Multimedia Science and Technology

Giorgia Sampó

MA student, Digital Humanities and Digital Knowledge Bachelor Degree in Communication Science

Contact

Contact Us

Location:

Alma Mater Studiorum, Via Zamboni 34

Call:

+39 3270830124

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