Energy-consumption in four large European countries

Normally these posts start with an intro on the subject and at the end I provide some information about our product and company. This time I will do it the other way round:

For information about the generator of 3D-graphs, please have a look at website and download  the free demo-package (zip). It  should be unpacked in a folder and the .jar file (the 3D-graph generator) can be started immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com and you can follow us on Twitter: @AnRep3D

communication

Photo by geralt on Pixabay

Now it’s time for this fortnight’s subject: the energy-consumption in Germany, United Kingdom, France and Italy. A long time ago a 3D-graph holding these countries was presented, but this time we take a different approach for the dimensions of the buildings. The sourse data are from the IEA with information about the population from sources like Wikipedia and Worldometers. The population is relevant because this time only the “per capita” consumption will be presented.

brexit

Previously all of the EU28 was compared to other countries. This time we take a couple of EU28 member-states.At this moment the United Kingdom is still a part of the EU28.

Photo by stux on Pixabay

 

Again the jumps in time are rather large, to have really impressive changes visible in the graph. Of course more subtle differences can be observed in a 3D-graph, but it’s less fun to write about. Dimensions defined in the graph (all presented as units of energy, like ktoe (kilotonne of oil equivalent) or PJ (PetaJoule) are this time:

  • Height: non-fossil sources (like nuclear, hydro, geothermal, solar wind and biological
  • Width: coal and oil combined
  • Depth: natural gas

The yellow part of the height represents sources without direct CO2 emissions (nuclear, hydro, geothermal, solar and wind) and the green part – although non fossil – is the section which is a part of the CO2 cycle and therefore more disputed than the regular renewables like solar and wind. The reason why natural gas is presented separately from coal and oil is because coal & oil are seen as the “old economy” now. Natural gas is fossil as well, but more of a transition source. Its related CO2-emissions per unit of energy are lower, but certainly not negligible.  Be aware that natural gas includes LNG (Liquefied Natural Gas). Apart from this, all four countries will have values in every dimension.

As always, the screenshot of the 3D-graph show below can be double clicked, to open the graph in a web-browser. The instructions about how to zoom, translate, rotate and tilt the the graph are given in small print below. Remember that all values are “per capita”!

3D-graph about energy consumption

 

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.

What is remarkable in this 3D-graph? Although the total energy-consumption per inhabitant (GJ per captita: sum of height, width and depth) is not extremely different (in 2015 Italy’s comsumption was about 2/3rd of Germany’s), France has a very different shape, because the non-fossil part (height) is 2.5 – 4.5 times higher than for the other countries. The green part is not very different, but the yellow part is (4 – 8.5 times higher) – mainly because France has a lot of nuclear energy.Germany is still heavily relying on coal and oil, although the consumption went down in twenty years. The UK however, did much better. Then other differences are also interesting. E.g. all countries using less energy from coal and oil in 2015, compared to 1995. Of course the crisis helped to reduce the energy consumption. This explains why natural gas also went down. Otherwise a shift towards this source would have been more likely. Between 1995 and 2015 the green part (bio-fuel and waste) increased for all four countries. It would be interesting to investigate what the composition of this section is. The usage of potential food should be avoided and the same applies to the usage of agricultural land for biofuels instead of food. On the other hand, collecting waste from garbage can be useful as long as the recycling of valuable materials – including plastics – are at a high level. Toxic exhausts should also be avoided. Although this can all be visualised in a 3D-graph, we leave it like this for now. In the end the purpose of this blog is to show the power of the AnRep3D- (or is it EnRep3D-?) generator.

biological waste

Photo by Ben_Kerckx on Pixabay

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Visualising the energy-transition III – What about the USA?

Until now we have looked at the energy-mix of the five countries at a higher level. One dimension (depth) represented “fossil fuels” but fossil is the sum of (mainly) coal, oil and gas. When thinking about the CO2 equivalents emitted, we all know coal is roughly twice as bad as natural gas and oil is in between (respectively a magnitude of 100, 50 and 75 kg CO2/GJOil Rig

Photo by ambquinn on Pixabay

This means the composition of the fossil part is very important, as an amount of energy in  coal could be replaced by the same amount of energy in natural gas, but with half the CO2 being emitted. It’s still fossil, but for the transition it is an intermediate step. Let’s have a look at the data from the IEA again but this time with coal, oil and natural gas as dimensions. No nuclear and renewables as we already saw them in the previous graphs. To give a fair overview, both approaches are shown below: total use and use per capita, because the former tells us something about the global impact, but the latter is a more honest measure. Below two screenshots are shown. Both can be clicked, to open the real 3D-graph in your web-browser.

Instruction for manipulation of a graph: 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.

3D-graph on fossil fuel consumptionThis first graph won’t be a surprise. Everybody knows China uses a lot of coal – far more than the USA – and not a lot of oil and natural gas. Be aware that the past in front and the future to the rear. This is just a choice as the numbers can be fed to the generator in any order, creating different arrangements in the 3D-graph. The graph also shows that the consumption of coal rose quickly in China between 1995 and 2015. For India it’s far less impressive and the other countries are quite stable.

3D-graph on fossil fuels per capita

For the second graph, the time-axis is reversed: the present (well, 2015 I mean) is to the front and 1995 is to the rear. Here we see the average coal-consumption (height of building) per inhabitant and this time China is still high. Here, the USA is surprising. The population increased by about 20% between 1995 and 2015 but the coal-consumption went down (after an intermediate increase). The EU and Japan show more or less the same pattern and none of the replaces the coal with oil (depth of building) or natural gas (width of building). All managed to keep their energy-usage more or less stable or even reduce it. On the per capita level, all of them reduced their energy-consumption.

India and China and their energy-consumption doubled or tripled over two decades (the in total increase is stronger than for the value per capita). We cannot judge them for that, as they are simply developing later than the others, but we can ask them politely to use either less coal or get rid of the CO2 – and in the end they will!

For more information about the generator of 3D-graphs, please have a look at website and download  the free demo-package (zip). It  can be unpacked in a folder and the .jar file (the 3D-graph generator) can be started immediately. A manual and some examples of in- and output-files are also enclosed in the zip-file.

For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com and you can follow is on Twitter: @AnRep3D

Shaft in coal mine

Photo by hangela on Pixabay

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Visualising the energy-transition II – What about China?

In the previous post we saw a 3D-visualisation of the energy-consumption in five large countries. The shape of the buildings showed the composition of the energy-mix and the different dimensions could be compared within a country, but also between countries.

Now something was unfair about this representation as not all countries have the same number of inhabitants. This time we will show the consumption “per capita”. On average, that is, and at the same time we have to admit that it’s still not completely fair. Why? I will explain below.

Power Station

Photo by stevepb at Pixabay

The graph has the same structure as it had the last time, only this time it shows toe/person. A toe – 1 toe is about 42 GigaJoule or nearly 12 MWh, but the toe (tonne of oil equivalent) is trivial as GJ (GigaJoule) or MWh (MegaWatt-hours) would have provided the same graph anyway, using a slightly different scaling factor in the parameter-line of the input-file.

Again the height of the buildings represents renewables like solar, wind and hydro plus biofuels & waste, but the latter are shown in green.

The width shows nuclear energy and

the depth visualises the total fossil fuel consumption (mainly coal, oil and gas) by an inhabitant (again: on average).

This information is also available in the 3D-legend (3D-key), hovering in front of the graph. Energy carriers, like electricity, steam and hydrogen are not shown as they are not primary sources. Trade is also ignored. Again the data have been obtained from the International Energy Agency (IEA). Below a clickable screenshot-collage is shown. It shows the front, top and side-view of the actual 3D-graph behind the link. Comparing the energy-consumption per individual, the picture is completely different. In 2015 the average Chinese individual consumes more or less the same amount of fossil fuels as the EU, but the average American still consumes twice as much.

screeshots from 3D-graph

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.

We can complain China as a country is using such a large amount of fossil fuel, but the average Chinese is close to the consumption-level in the EU and far below the USA.

Then there is something more. We all know China has a lot of industry and a lot of their products are actually used by us, Western consumers in EU and US. This means it would only be fair to count the energy-consumption related to the exported goods as a part of the consumption of the country receiving those goods. From this point of view, the real average consumption of energy by an inhabitant of the People’s Republic of China would even be lower and for the EU and US it would increase! This poses the question: “are we treating China unfairly when it comes down to energy-consumption?”

DragonPhoto by ractapopulous on Pixabay

For more information about the generator of 3D-graphs, please have a look at our website and download  the free demo-package (zip). It  can be unpacked in a folder and the .jar file (the 3D-graph generator) can be started immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel.

Our email-address is info@anrep3d.com and you can follow is on Twitter: @AnRep3D

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Visualising the energy-transition in 3D

Everybody knows about our global energy-transition, but are we doing well? Fossil fuels will come to an end, starting with coal. Renewables (e.g. solar, wind, hydropower and geothermal energy and biomass) are thought to be the solution, but at the same time biomass is disputed as it competes with food production and can destroy woods. Then we have nuclear energy. No carbon dioxide, but disputed as well for different reasons. The question is: which direction are we going, when looking at the facts? AnRep3D is able to visualise these complex numbers. Sometimes I wonder if we should offer a package called EnRep3D (Energy Representation in 3D), with a different manual. We’ll see!

Photo by SatyaPrem on Pixabay

During the last weeks I applied AnRep3D to different, non-financial areas. The advantage of finance is that all axes represent a value in money. Energy can do the same, as it has its own units. In finance we have US-dollars, Euros, Yuan, Yen and many other currencies representing the same value. In energy it’s more or less the same: TWh, GJ, MMBtu and ktoe are different measures to express an amount of energy. When we are thinking about the mix of different types of energy sources, it doesn’t matter which measure we will take as long as it’s the same for all values.

Photo by JACLOU-DL on Pixabay

The International Energy Agency provides a lot of data and I am happy to use them. The units are kilotonne of oil equivalents (ktoe – 1 ktoe is about 42 TeraJoule or nearly 12 GWh). I selected five large countries or actually four as Japan is small compared to the People’s Republic of China, India, the EU and the USA, but still interesting (as I will explain below).

Photo by luctheo on Pixabay

This time I won’t tell you about the process of collecting data, but just present the 3D-graph. Be aware that the population of the five countries is very different, partially explaining different amounts of energy. China and India have over 1 bln. inhabitants, Japan, USA and EU are over one, three and five hundred thousand people. Yet it’s not about the size of the buildings, but their shape (the ratios of their dimensions, that is). Regardless the size, we can compare the height (renewables) to the width (nuclear) and the depth (fossil). By the way: energy carriers, like electricity, steam and hydrogen are not shown as those are not primary sources. Trade is also ignored. Let’s have a look at the 3D-graph! Like always the screenshots can be clicked to open the real 3D-graph in your browser (JavaScript and WebGL enabled).

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.

From this point of view we can see that the EU had more nuclear power than the US, but less renewables. Even if we ignore the green part, representing biofuels and waste, the energy-total of other renewables (represented by the yellow part (solar, wind, hydropower and geothermal energy) is still higher in the US. Traveling to the rear of the 3D-graph, we can see that in 2015 this is still the same.

Then Japan! It is a dwarf between the other powers, but nevertheless interesting. If we look from above at the last three buildings (2005, 2010 and 2015), the third one is suddenly very narrow (see top of the image to the left). Between 2010 and 2015 nearly all nuclear power disappeared. This can be explained by the Fukushima-disaster in 2011.

 

 

 

Finally we can observe China and India from aside. Their buildings are really tall, meaning a lot of renewables are in their energy-mix (have a look at the legend in front). Yet India is more or less green, meaning biofuels  and waste are the main types of renewables, wheras China shows an increasing part of yellow. This yellow part represents the other renewables, like solar and hydropower.

If we look at the depth of their buildings, we see every next building is more elongated, meaning the supply of fossil-based energy increased. From 2000 to 2015 it doubled for India and nearly tripled for China. Their buildings remain narrow, indicating that nuclear energy is still not very important, although a lot of nuclear power stations are under construction in these countries.

For more information about the generator of 3D-graphs, please have a look at website and download  the free demo-package (zip). It  can be unpacked in a folder and the .jar file (the 3D-graph generator) can be started immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel . Our email-address is info@anrep3d.com and you can follow us on Twitter: @AnRep3D

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Intermezzo: storytelling

Yes, storytelling is hot. Does this mean that I have to tell you everything about all the hard labour, my setbacks and successes? I don’t think so, since you are a business reader and hopefully the best way to get your attention is to demonstrate the potential business value of AnRep3D. Yet, while reading a book about “storytelling” I realised that this blog doesn’t tell a lot about AnRep3D and its history. That’s why I will tell my story in a concise way. Just for now.

picture by Wikiimages on Pixabay

 

 

 

 

 

 

 

 

 

 

 

 

How it started

I remember visiting the “reality cube” of the University of Groningen in the year 2004 and being really impressed by what I saw. Then I asked people with the reality cube if it would be possible to visualise abstract data e.g. from measurements. Actually, this question was inspired by Michael Ridpath’s novel “Trading Reality” They told me they sometimes did visualise data, but it took the team a couple of weeks to prepare new software showing those data. This meant that such a 3D-graph would cost EUR 100 * 40 * 2 * 2 (tariff per hour * hours per week * number of team-members) = over EUR 15000

When I got back at the incubator where I was working back then, I told everybody about the potential of an automated conversion of data into 3D-graphs instead of this lengthy process they told me about. No human labour, just an input-file and an output-file within seconds.

Photo by pixel2013 on Pixabay

Nobody understood why 3D-graphs would add business value (think about the first comments on mobile phones: useless, no added value, no-one needed such a device) and actually they even didn’t understand what was meant by a 3D-graph (and until now, luckily even Excel still doesn’t understand).

Prototyping and crisis

The only way to educate my market was to come up with a prototype but I’m not a software engineer. Seeing no alternative, I learnt VRML (Virtual Reality Markup Language – later on called Virtual Reality Modelling Language).

After a while I was able to create a generator in QuickBasic that converted numbers into a 3D-graph in VRML. It worked, but then I discovered al kinds of flaws and started improving. At the same time I investigated all kinds of markets and in the end the financial markets seemed to be the most interesting ones (probably because I had limited knowledge of some other areas). VRBI (Virtual Reality Business Intelligence) was born!

My dream was to create a real VR environment, where one could walk literally through a graph. A more modest first step was to use stereoscopy. This time I failed completely as I really don’t understand hardware at all. I asked my oldest son for help – and actually hired him – but he didn’t like the subject although he is really good at hardware.

Recently I discovered my old blog-posts from over ten years ago about this period. It was very frustrating and I spent a lot of money buying devices, but things didn’t work out because of the crappy technology. Then the crisis hit us hard and for a couple of years I had to work as a consultant and travel a lot, just to let my family survive.

Entering the 3rd millennium

After a couple of years I decided to start again, taking smaller steps, just showing a 3D-graph on the screen just in the way the popular games did. That was what I was able to realise with some effort. While I was struggling with the technology, several browser plug-ins for VRML were terminated. Most standalone viewers were incomplete and VRML’s successor (X3D) never became popular, so I ended up in a vacuum. There was one good VRML stand-alone viewer left, but (at least back then) it was hard to work with and rather unstable. I didn’t expect my audience (primarily business people) to work with this kind of solution, so eventually I moved to HTML5, the modern Internet language. As a matter of fact, this new version uses JavaScript – something I don’t understand very well.

Long story short: fortunately a kind of cloud-service by Fraunhofer came up (called X3DOM), translating my X3D-like scripts. Finally I was able to jump into the future! My youngest son helped me by creating a Java-framework for input/output. I leant a bit of Java – just enough to re-write all the calculations within, needed for the X3D-like output and added the “engine” part of the generator.

 

A step aside

Meanwhile, my middle son decided to join our university of applied business sciences. I told him he could try and sell some spin-off products which were left unused after the focus on annual reports. About the same time I had a clash with the tax-department because my revenues were too low and I wasn’t allowed to go on as an entrepreneur. In the end, after 25 years, I handed over my company (SCIENTASSIST) to my son. For a couple of years I kept improving those spin-off products. Using them myself (and blogging about them – this old blog is partially in English) I was able to think like a user instead of being a visionary. In the end, VRBI was a nice product and I went back to the original idea, still inspired by Michael Ridpath.

 

AnRep3D was born (again)

After this experience, I went back to my original idea: visualising data from Annual Reports for a group of companies through several years. After modernising the old attempts, I knew I had to work with the AnRep3D generator myself to be able to come up with a mature product. Working on my blog-posts I discovered a lot of potential improvements. Finally I had a good, basic product and started my campaigns and here we are – two years and three months after the first post of this blog. After all it has been quite a journey!

Do you want to try the AnRep3D-generator yourself? Download a free demo-package, without registering!

For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com  and you can follow is on Twitter: @AnRep3D

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Agriculture in 3D – minerals, livestock and farmland – part II

It’s been a long time since part I of this post! Partially, because of other obligations but also because of an issue with our 3D-graphs not being accessible. Now they are available again and it’s time for the second part of the post on the issue of Nitrogen and Phosphorus in European agriculture.

manure spread over farmland

photo by Hans on Pixabay

For decades agriculture became more intensive in several European countries and as a result the amount of manure went up. Manure is a fertiliser, but when the amount of minerals exceeds the need of the farmland, it will be more of a poison. As more and more manure was deposited on the farmland, all kinds of side-effects came up as a result of this dumping. To understand the negative impact of the surplus this article can offer some insight.

Currently we have a climate crisis, but in the past we have dealt with acid rain and the ozone depletion. Acid rain was and is actually connected to the Nitrogen issue.

Dying forest

photo by alegria2014 on Pixabay

In the end it all comes down to the area of farmland related to the number of livestock held in this area. We don’t want to limit the number of animals – on the contrary with e.g. “megastables” coming up – but at the same time the surface of a country won’t increase (apart from legal tricks perhaps). How to get rid of the dung, holding these minerals? The easiest way is to dump it on the farmland, but this is not a sustainable solution. A long time ago, the EU came up with laws to reduce the surplus and here we are! To see whether these laws are successful, we put a couple of values in a table and generated a 3D-graph with the help of the AnRep3D generator. The data were mainly obtained from Eurostat.

The N- and P surplus-values were converted to gram per square meter of surplus (g/km2). The surface of farmland is in km2. LiveStock Units (LSU) are a way to translate different kinds of animals (geese, sheep, cattle, horses of even mooses) to a uniform value which can be used in calculations. Here we use thousands of them (kLSU = kilo-Livestock Units)

Lady with horsesPhoto by langll on Pixabay

The 3D-graph (showing the N and P surplus in relationship to the area of agricultural land and the number of LiveStock Units) is available. Below we present a couple of screenshots from different angles. Double-click a screenshot to see the live 3D-graph in your browser. Below the screenshots is an explanation of how to manipulate the 3D-graph. (Be aware: WebGL and Javascript have to be enabled, but that’s the common default setting).

3D-graph agriculture from different angles

 

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.

What do we learn from the 3D-graph behind the screenshots? We can see that larger countries seem to have a smaller surplus than the smaller ones. (As the order is from large surface to small from left to right, it’s easy to recognise.) One of the reasons is that they also have a lower Livestock to Area ratio (kLSU/km2) However, it’s not a linear relationship. E.g. the UK has an issue with high suplus-value, despite the large surface of agricultural land. Spain, with a higher number of (k)LSU and a comparable surface has a lower surplus. Greece, have about the same surface for agriculture as Ireland, has a larger surplus despite the much lower LSU-count. For Spain the surplus went up from 2013 (front) to 2014 and 2015 (mid and rear), but Poland managed to reduce the surplus in 2014, but it went up again in 2015. If we can concentrate on the kLSU/km2 ratio (the shape of the “buildings” as seen from the top), we can see Denmark, the Netherlands and Belgium have a high ratio and their surplus is (very) high as well. Countries like Sweden and Austria have lower ratios (lower kLSU, larger area) and their surplus-values are much lower indeed. Yet, manure is not the only cause of the surplus and sources outside of agriculture are attributing as well.

GeesePhoto by Skitterphoto on Pixabay

Finally the green part is interesting. The surplus-value for Nitrogen (mainly ammonia and nitrate) is represented bu the yellow part of the building. The green part represents the P part (almost solely phosphate). Phosphate can endanger water quality by causing algae bloom. We can clearly see that Spain has a higher P-surplus than France with the N-ratio being the opposite. The same applies to Denmark in comparison with the Netherlands. Of course the surplus is also related to the amount of mineral needed in the soil. With a low level of certain minerals, the surplus could be lower – but not necessarily for both N and P. Then, the composition of the manure (coming from different types of animals, that is) can also cause differences between the surplus-values for N and P.

Well, that’s it for now. Next time we will revisit Energy once more, before turning back to Finance. Hopefully it is understood that AnRep3D has a lot of potential in different areas! For more information about the generator of 3D-graphs, please have a look at our website (https://anrep3d.com ) There a  free demo-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 info@anrep3d.com and you can follow us on Twitter: @AnRep3D

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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.

Livestock

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 (https://anrep3d.com) There a  free demo-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 info@anrep3d.com  and you can follow is on Twitter: @AnRep3D

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