Agriculture in 3D – minerals, livestock and farmland

To be honest, it was quite a struggle. In Europe we have very nice and publicly available statistics, so I thought it would be easy to show how AnRep3D is able to visualise agricultural data. Indeed it was quite easy to get the data for the Nitrogen (N) and Phosphate (P) surplus per hectare (is about 2.5 acres) of farmland. But then I noticed gaps, negative surplus-values (that’s possible of course – it means there is a lack of the mineral). And of course Europe is quite large, so I had to determine which countries to show and also which years to report. For clarity: nitrate and phosphate are useful compounds in agriculture, but a surplus will pollute land and water, causing damage to the environment. That’s why Europe collects those data.

Fertilising with manurePhoto by pascvii on Pixabay

It made sense to use the height of the AnRep3D “buildings” for nitrate (N – rather large values) with a green roof for phosphate (P – more modest values). To avoid confusion: N is the chemical symbol for Nitrogen and P for Phosphorus, but we use them to identify the derived compounds NO3 (-) and PO4 (3-).

We know the real issue with minerals on farmland is the number of animals in relation to the available amount of farmland. The manure has to go somewhere and putting it on the land is the traditional solution. With this in mind the width of the buildings could represent the number of animals and the depth the surface (square kilometers – km2 is about 247 acres) of farmland. So far so good, but then other issues arose. The agricultural land numbers includes wasteland and woods, but we all know the manure won’t get there. In the end it turned out the surface reported by the European statistics covers for permanent grassland, permanent crops and arable land. The others seem to be subcategories.

Farmland bird's view

Photo by Tom Fisk on Pexels

The years didn’t match quite well with those of the minerals, so in the end I took 2013, 2014 and 2015 applying interpolation for the minerals.

The next issue was about the animals. It’s nice to count them, but if one country has mainly cattle, the next one goats and sheep and another one mainly chickens? It’s not a fair comparison. Fortunately there is a standardised unit, correcting for the impact of the animal and this is the LiveStock Unit (LSU). Yet the numbers made no sense and didn’t match with my reference-values either. In the end I disovered the LSU-tables offer two units: numbers (just a headcount and not LSU at all) and real LSU. After this the input-file was ready in a couple of minutes.


Photo by sasint on Pixabay

The only choice I had to make was about the size of the values. The N- and P-values were converted to gram of surplus per square kilometer (g/km2). The surface of farmland (arable, permanent crops and permanent grassland) was already converted from Hectare to km2. Only the LSU could either be in kLSU (kilo-LSU = thousands) or in hLSU (hecto-LSU = hundreds). The former offers a compact graph, the latter provides a better overview for smaller countries, but is very wide. I decided to generate two different 3D-graphs this time. The one with kLSU is compact enough to show all the countries in it in a glance (the other one is for part II in the next post). A couple of countries have very high values for the nitrate-surplus, with the Netherlands being the number one. Be aware that because of the parallax the names of the countries are not in front of their “buildings” in the screenshot.

3D-graph agriculture

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

For more detail a zoomed screenshot is shown below (also clickable).

Detailed 3D-graph agriculture

For now, this will do. In the next post we will discuss the LSU and surface of farmland – and show the other graph.

For more information about the generator of 3D-graphs, please have a look at our website. Therethe free 3D-graph package (zip) can be downloaded, unpacked in a folder and the .jar file can be used immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is  and you can follow is on Twitter: @AnRep3D

About AnRep3D

AnRep3D is the new company, founded after the handover of Scientassist (together with VRBI) to one of my sons. From now I will focus on three-dimensional graphs for the financial markets, showing the main figures from annual reports in comparison. As per 2021 a second product is available: EnRep3D. It is meant to visualise energy. Although the engine is the same, the texts, manual, website and examples (including blogposts) are focused at energy.
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