Direct link to our Datastory : https://alexandrehutter.github.io/
With 7.7 billion people around the world in 2019, food demand outbreak has raised serious concerns regarding productive agricultural land availability. As for now cultivable surface expansion has been the only realistic solution but let's be honest, with the raising awarness on climate change and environment, very few people believe that intensive agriculture would work on the long run. With this "guide" on how agriculture is currently shaping the surface of the world, we would like to provide a line of thoughts on the destructive foodstuffs and inversly the products that would deserve our attention to reshape the future of our alimentation.
This study relies on FAOSTAT data set from the United Nation, helping us in the process.
We will first focus on a general idea of the global tendancies. Then, the correlation between export and biomes area loss is investigated in order to see if agriculture directly impinged on natural biomes. Moving on to the “destructive” impact of certain foodstuffs, if the correlation is high, we consider that it is not a good choice to cultivate. Finally, we established an indice to advise countries on which food to grow based on how rentable they are in term of space usage and how locally consumed they are. We added the economical factor in a lesser extend to rank the best ones.
- What is the global evolution of the production quantity per country from 1995 to 2007?
- What is the area lost of savanna, shrubland, grassland and forest per country over a year?
- Do we see a correlation between the area lost by ecosystems and exportation?
- Can we find an indice to rank foodstuffs and advice countries on how to extend agriculture in a more sustainable way?
We used data sets taken from FAOSTAT published by the United Nation and related to food and agriculture. It gives us a really broad set of informations to answer to our problematic. We selected 4 data sets.
First, we want an insight on where food is produced. “Fao_data_crops_data.csv” will give us the surface of new plantations for every kind of products through time in the different countries of the world by looking at “Area Harvested” for every product. We can also investigate data for a particular type of product. For example, we could find out about rentable products when comparing the “Area Harvested” to the “Production Quantity”.
As we also would like to have an idea about what kind of ecosystem is lost to benefit to agriculture, we will investigate the files “Emissions_Agriculture_Burning_Savanna_E_All_Data_(Norm).csv” and “Emissions_Land_Use_Forest_Land_E_All_Data_(Norm)”. The first one lists diverse information on burnt savannas such as the quantity of burnt biomass or the different gas emissions. We can look at “Area Burnt” to analyze the surface of savanna replaced for agriculture. Similarly for the second one, the major part of the data set consists of informations about gas emissions. For our study, the interesting value is the superficie of burnt forests, “Area”, that, as for savannas, we can use to answer our initial question.
Finally, as we also would like to develop on a more social aspect of the problem, we would like to investigate the quantities of imports and exports using the data set “Trade_Crops_Livestock_E_All_Data_(Normalized).csv”. This data set consists of a lot of different commercialized products, not all of them coming from agriculture so we would have to select maybe 5 to 10 food products we find interesting to study (maybe after investigating on the first data set). Using the informations “Export Quantity” and “Import Quantity”, we can find out which countries are the biggest “providers” and which ones are depending on others regarding the selected products. This data set is completed by another one coming from FAO listing commodities and corrresponding codes. This will allow us to choose our crop depending on commodity group. The data sets are CSV files, smaller than 0.1 GB, which is easily manageable.
List of the used data sets:
- Emissions_Agriculture_Burning_Savanna_E_All_Data_(Norm).csv (less savana )
- Fao_data_crops_data.csv \where every products are planted
- Emissions_Land_Use_Forest_Land_E_All_Data_(Norm) (less forests )
- Trade_Crops_Livestock_E_All_Data_(Normalized).csv (imports and exports)
- export_items_1574515779.csv (Fao commodity)
We will do those following steps until Milestone 4:
- Synthetize our findings
- Find nice "static" illustrations for the poster, in contrast to the interactive graphs that we have in the data story
- Assemble text and image in a Power Point slide
- Print the final poster
- Final meeting to homogenize what we will tell during the poster presentation
- Kamil : Computed the the correlations and the destructivity of the crops, provided the heatmaps and worked on crop suggestion
- Célia : Provided help on most parts and worked on the research questions and the heatmaps.
- David : Worked on most graphs, on the crop suggestion and helped to establish the problematic and research questions.
- Alexandre : Did the webscraping for the areas, created the datastory and also provided help here and there.
Everybody will work on the final presentation (but some might not be there, having examinations the same day)