The resulting dataset is large and complex and can be used to answer a number of questions. Following the research questions outlined above, we group them into three different categories. The first part focusses on national-level trade and consumption patterns. The second part focuses on the results at a primary product level. In the third part, we used bilateral trade information to quantify “consumed” environmental impact estimated by changes in Species Habitat Index and corrected for a consumption perspective. All subsequent values of import, export, production, or consumption refer to the respective area needed for these components, not the actual trade volumes.
National trade profiles and their recent dynamics
The resulting database contains data on 223 countries, for which we quantified five headline values: (1) production, the amount of cropland in a country (as reported in the FAOSTAT database), (2) export, the amount of cropland in a country for export production, (3) local, the amount of domestic cropland for local consumption (calculated as difference between production and export), (4) import, the amount of cropland supplying a country’s consumption from imports, and (5) consumption, the amount of cropland required for a country’s (apparent) consumption, (import + local). Figure 2 shows how the five values relate to each other.
In Fig. 3, we show results for the 25 largest contributors to agricultural production and consumption (complete results can be found in the Supplementary Information). Together, these countries host more than 73% of the global cropland area and their consumption requires 68% of global cropland area. Using the five headline values and the ratios between them, we could characterize trade profiles of all these countries. First, the size of the bars indicates the total area these countries contribute to the agricultural production and consumption. We see that this is dominated by China and India, who together account for more than 25% of both total production and consumption.
Second, if the import (red bar in Fig. 3) is bigger than the export (green), countries can be characterized as net importers in terms of cropland area. In Fig. 3, this also means that the total bar of a net importer is right-heavy, which is for example the case for China, Mexico, Iran, Germany, and Japan. On the other hand, net exporters are characterized by a larger green bar and a left-heavy total (India, USA, Brazil, Argentina, Canada, Ukraine, Australia, and Kazakhstan). All other countries in this list have an export-to-import ratio close to 1.
Finally, we can distinguish countries based on the relative amount of local production that is not exported (“local”, brown bars in Fig. 3). This allows us to identify countries that exhibit balanced trade patterns but vary in their contribution to the global trade of agricultural products. Nigeria and Pakistan, for example, consume many of their produced goods locally and do not contribute a lot to international trade, whereas France relies more on imports, but also exports a lot, i.e., has a relatively smaller “local” component.
A different way of characterizing a country’s trade profile is by calculating the relation between a country’s relative export areas (share of export areas in the total production area) and relative import areas (areas for imports divided by total area for consumption). Placing the results along two axes shows the relevance of trade for the country and whether a country can be considered a net importer or a net exporter in terms of cropland area (Fig. 4).
Figure 4a shows a conceptual overview of this representation by indicating where some of the extreme cases would lie in such a plot. We plotted the respective bars from Fig. 3 to allow comparison. This visualization allows us to get a sense of the distribution in the entire dataset in the year 2012 (Fig. 4b). In this graph, countries that are located in the upper left triangle are considered net importers of cropland area, whereas those in the bottom-right triangle are net exporters of cropland area. Countries that are located close to the diagonal are more or less balanced in terms of cropland trade.
Generally, we found that there are more importing than exporting nations and that there are quite a few countries that rely heavily on imports, such as Japan, Korea, Saudi Arabia, and The Netherlands, with more than 75% of their consumed area being imported. Another group of major importing countries, including the United Kingdom, Germany, Italy, and Egypt, still import more than half of their consumption in terms of area. On the side of exporting countries, there are a few which export more than half of their production, including France, Malaysia, Côte d’Ivoire, and Paraguay. There are also quite a large number of countries in the bottom-right corner that mainly produce for their own domestic demand and do not import a lot.
The positions of countries in Fig. 4b are entirely dependent on relative values and do not reflect absolute trade volumes. This is why, for example, two of the biggest importing countries, China (CHN) and India (IND), are plotted next to countries like the Philippines (PHL) and Madagascar (MDG), respectively. Their total imports and exports are much higher (see Fig. 3 or Appendix), but so is their local production and consumption. Thus, their respective ratios of export overproduction and import overconsumption are similar.
For the highlighted countries, we also plotted the change of relative import and export through time (from 2000 to 2012). With only a few exceptions, all highlighted countries increased their relative import during that time (moving up on the y-axis). At the same time, most of the countries also increased their relative export (moving right on the x-axis). For the entire dataset, we found that 159 countries increased their relative imports, 127 increased their relative exports, and 108 showed increases in both values.
Product-level trade profiles
Looking at data at the level of individual products provides in-depth insights into patterns and dynamics in specific countries but is too complex to present for the entire dataset. We thus picked three countries, Paraguay, Malaysia, and Côte d’Ivoire as examples (Fig. 5). These are the countries with the highest species habitat loss, according to the SHI. All three use more than half of their cropland area for export production (see Fig. 4b). They also represent three different world regions and different production systems that can be considered representative for these regions.
We found that the exports of these three countries are all dominated by a single product group. Paraguay mainly reported exports of soybeans (66%), maize (16%), and wheat (11%), as well as some other cereals and oilseeds. Most of its exports (67%) were found to be used as animal feed in other countries. Malaysia’s exports in 2012 were dominated by palm oil (86%) and rubber (11%). Some of the palm oil and some other oilseed crops were found to be used as feed for animal products (totalling only about 4%). Finally, Côte d’Ivoire reported exports of cocoa (57%), coffee (11%), rubber (3%), and cashew nuts (20%), as well as some oilseeds (totalling about 7%). The share of products used as animal feed was negligible (less than 1%).
An additional analysis of the differences in production area between the years 2000 and 2012 showed that the most exported goods were also the ones responsible for most of the expansion of agricultural areas in the respective countries (see Supplementary Data, Sheet 5). For Paraguay, we found that soybeans were responsible for 66% of the total expansion in agricultural area. In Malaysia, oil palms had a share of 142% of the total expansion, meaning that their production replaced the existing production systems on top of the expansion into previously unused land. Finally, for Côte d`Ivoire, we could show that cashews and cocoa both contributed roughly a third to overall expansion (34% and 30%, respectively).
Bilateral trade data and consumption-based species habitat indices
Finally, the dataset also allowed us to look at the bilateral information, i.e., corrected exports from producer to consumer country. In Fig. 6, we display export flows from countries with a high loss in SH (more than 2% of species habitat lost from 2000 to 2013). The export flows from these 20 countries equalled about 19.6% of the total trade volume in 2012. We found that the picture is dominated by exports from Brazil, which is exporting to almost every other country, but with the vast majority of 32% of its exports going to China, followed by 3.9% to Japan, 3.5% each to Iran and Germany, and 3.2% each to South Korea and France. Interestingly, India, which is also among the largest importers (in terms of total area), only imports around 0.5% of Brazil’s exports. For the three countries we highlighted in Fig. 5, we found that they not only differ in the items they export, but also where the exports go to. For Paraguay, we found that 13% of the country’s exports go to Brazil, 9% to Russia, and 6.4% to Germany. Malaysia’s exports go mainly to China (21%), India (11%), Pakistan (6.4%, not shown in Fig. 6), and the US (5.8%). Finally, Côte d’Ivoire reports 14% of exports going to the US, 12% to India, and 7% to Germany.
This bilateral trade information can be used to assess “relocated” environmental impacts, such as the loss in species habitat quantified by the SHI. In the original dataset of SH loss (Fig. 7a), countries like Paraguay, Honduras, Brazil, Madagascar, Côte d’Ivoire, Malaysia, and Indonesia show high values, indicating that they lost a lot of species habitat in the years from 2001 to 2013 (between 4 and 9%). On the other hand, most countries in Europe, Africa, and the Middle East have small values of SH loss, meaning that they lost almost no additional habitat since 2000.
The SHI estimates habitat conditions in the producing country. To quantify the impact of consumption on habitat loss, we calculated new indices of consumed habitat loss based on a combination of the original SHI data and the trade data (see methods). These consumption-based indices reveal a strikingly different pattern. The measure for the average habitat loss per consumed hectare (cSH_av) shows that quite a number of countries have higher values for consumption than for production (Fig. 7b). These results highlight that in these countries, each hectare linked to consumption is, on average, associated with a higher value of SH loss than the country has in the original SHI data. This is particularly true for countries in Western Europe, North America, and the Arabic Peninsula which have relatively low rates of species’ habitat loss domestically but import substantial loss from countries with higher SH loss. Interestingly, some countries also show high values in cSH_av despite not being net importers. This happens because their imports are mainly stemming from countries with SH losses higher than the domestic values (Chile, Uruguay, Australia, and New Zealand).
Countries where the value of cSH_av is lower than the original SHI are those countries where the domestic SH loss is higher compared to the average SH loss of their trade partners. This means that the pressure on domestic species habitats is higher, on average, than the pressure of the countries where they source their imports. These countries include, for example, Guatemala, Malaysia, Côte d’Ivoire, Madagascar, Indonesia, Mexico, Japan, and Finland.
The second indicator, cSH_rel, calculates the consumption-based species habitat loss relative to a country’s production in terms of cropland area (Fig. 7c). This gives additional information on a country’s net trade patterns and suggests how much importing countries rely on production in other countries with a higher SH loss. By comparing the values to the original SHI data, we can show whether a country’s total production or consumption is the main driver of current global habitat loss. The general picture is similar to the comparison with cSH_av. However, there are some marked changes. Especially, the big net exporters of cropland areas in the Americas and those in Southeast Asia/Oceania have lower values. On the other hand, countries that consume a lot compared to their production, i.e., net importers of cropland, show now markedly higher values. Examples here include Iceland, Western Europe, Northern Africa, Southern Africa, The Middle East, China, and New Zealand.
Table 1 shows the original ranking of selected countries according to the SHI and the ranking of the same countries in the two new indices. The values for all countries, as well as the maps with the absolute values can be found in the Appendix.