| Main authors: | Luis Garrote, David Santillán, Ana Iglesias |
| iSQAPERiS Editor: | Jane Brandt |
| Source document: | Garrote L., Santillán D., Iglesias A. (2019) Report on the evaluation of scenarios of changed soil environmental footprint for a range of policy scenarios. iSQAPER Project Deliverable 7.4 64 pp |
The evaluation of the changed environmental footprint of soil under the scenarios analysed is based on the enhancement of the soil environmental functions associated to the three ecosystem services studied in the previous section. The results of the analysis are presented in this section. The methodology for evaluating the soil environmental footprint is presented first. Then, the global results are presented, showing maps of the projected change in soil environmental footprint under the corresponding scenario. Secondly, the analysis is focused on the differential effect on agroclimatic regions of Europe and China. Thirdly, the variability of soil environmental footprint response to agricultural management practices is presented through box plots for the different agroclimatic regions. Finally, some conclusions are drawn.
1. Methodology for the evaluation of the soil environmental footprint
The iSQAPER upscaling model provides the projected increase in soil ecosystem services as a response to increased implementation of beneficial agricultural management practices. This increase leads to improvement of soil ecosystem functions, like food provision or carbon storage, and thus improves the environmental footprint of the soil.
A simple geometric interpretation of soil environmental footprint has been devised in order to obtain a global picture of how the combined effect of the three ecosystem services improves the environmental footprint of the soil. The interpretation is based on the schematic view of Figure 36. The projected increases of soil ecosystem services are represented in a radar chart showing the increased values for the three ecosystem services under consideration. The environmental footprint is considered to be proportional to the area of the resulting triangle.
The area of the triangle can be easily calculated. If the improvements of soil ecosystem functions are x1, x2 and x3, the area of the triangle is given by the expression:
Figure 36
This simple geometric interpretation accounts for the combined effect of all three pairs of ecosystem services. As a reference, an increase of 1% in all three ecosystem services would lead to an improvement of soil environmental footprint of 1.3 and an increase of 2% would lead to an improvement of 5.2. It should be noted that, under this interpretation, soil environmental footprint has a positive connotation because it is linked to soil ecosystem services: the larger the contribution of soil to ecosystem services, the larger its environmental footprint. In other interpretations, environmental footprint has a negative connotation because it is linked to the consumption of environmental resources.
2. Evaluation of the improved soil environmental footprint
The methodology to quantify soil environmental footprint was applied to the results of the iSQAPER upscaling model for the three scenarios considered. The results are presented in this section.
2.1 Spatial distribution of the soil environmental footprint
The following figures present the spatial distribution of the effects of the scenarios on soil environmental footprint. The effects of the Expected scenario are presented in Figure 37, the effects of the Towards 2050 scenario are presented in Figure 38 and the effects of the Regional Targets scenario are presented in Figure 39. The figures show the projected improvement of the soil environmental footprint with the methodology described above.
Figure 37 
Figure 38 
Figure 39
The results show a significant improvement of soil environmental footprint, with average values between 0.10 for the Expected scenario in China and 1.41 average value for the Regional Targets scenario in Europe. The improvement in the Expected scenario ranges from 0.02 to 0.25 in Europe, with an average value of 0.13. The improvement in the Expected scenario in China ranges from 0.02 to 0.15, with average of 0.10. The Towards 2050 scenario provides a much better improvement. Average values are 1.07 in Europe and 0.92 in China. Values in Europe range from 0.14 to 1.94 and from 0.15 to 1.41 in China. The Regional Targets scenario shows the best improvement of soil environmental footprint, ranging from a value of 0.24 to 2.44 in Europe and 0.18 to 1.91 in China. Average values are 1.41 in Europe and 1.13 in China.
2.2 Effect on soil environmental footprint by agroclimatic regions
The compared values of average results of the improved soil environmental footprint in the agroclimatic regions of Europe and China are shown in Figure 40. Figure 40 shows that the Towards 2050 and Regional Targets provide a much better improvement of soil environmental footprint than Baseline, with the Regional Targets scenarios showing the best results.
The region that shows the greatest improvement of soil environmental footprint in Europe is Mediterranean-South, with an average value of 0.97 for the three scenarios. The European region that shows the least improvement is the Alpine region, with an average value of 0.67. In China, the largest improvement corresponds to the Continental-Cold region, with an average value of 0.88. The least improvement is shown by the Subtropical-Wet region, with an average value of 0.53.
Figure 40
The largest improvement in Europe for an individual region corresponds to Mediterranean-South for the Regional Targets scenario, with mean value of 1.72. The region that shows the smallest improvement is the Alpine region in the Expected scenario, with mean improvement of 0.10. In China, the largest improvement corresponds to the Continental-Cold region, with mean value of 1.38 for the Regional Targets scenario. The least improvement is shown by the Subtropical-Wet region, with a mean value of 0.07 for the Expected scenario.
2.3 Variability of the effect on soil environmental footprint
The variability of the results of the evaluation of soil environmental footprint for different scenarios is shown in Figure 41 and Figure 42. Both figures show box and whisker plots of the values of soil quality indicators in agroclimatic regions of Europe (Figure 41) and China (Figure 42). Boxes show the values of the mean plus and minus one standard deviation and whiskers show the maximum and minimum values obtained for the region. The scenario that shows larger standard deviation in Europe is Regional Targets, with 0.29, which implies a coefficient of variation of 0.20, but the scenario with largest variability is Expected, with a standard deviation of 0.03 and a coefficient of variation of 0.30. In China, the largest standard deviation corresponds also to the Regional Targets scenario, with a value of 0.34, and a coefficient of variation of 0.30. The Expected scenario shows the least standard deviations in Europe and China with average values of 0.03, which correspond to coefficients of variation of 0.24. The Regional Targets scenario shows the largest dispersion, particularly regarding maximum values.
Figure 41
Results for Europe, presented in Figure 41, show that the region with largest standard deviation is Mediterranean-South, with an average value of 0.24 for the three scenarios. However, the largest variability is shown by the Alpine region, with an average standard deviation of 0.22 and a coefficient of variation of 0.32. Boreal is the region with least variability with average standard deviation of 0.09 and a coefficient of variation of 0.11. The largest individual variability corresponds to the Alpine Region for the Regional Targets scenario, with standard deviation of 0.37 and coefficient of variation of 0.33. The largest standard deviation is shown by Mediterranean-South, with a value of 0.37 for the Regional Targets scenario. The region that shows least variability is Atlantic North for the Regional Targets scenario, with standard deviation of 0.01 and a coefficient of variation of 0.08.
Figure 42
The region where average standard deviations is largest in China is Desertic, with average value of 0.26 for the three scenarios. The largest average variability is also shown by Desertic, with an average coefficient of variation of 0.39. Continental-Cold is the region with least variability with average standard deviation of 0.07 and coefficient of variation of 0.08. The region with the largest individual variability is Desertic for Regional Targets scenario, with standard deviation of 0.42 and coefficient of variation of 0.39. Continental-Cold is the region that shows least variability with standard deviation of 0.08 and coefficient of variation of 0.07 for the Regional Targets scenario. Overall, there is more variability in China than in Europe, with coefficients of variation of 0.21 in Europe and 0.29 in China.
3. Qualitative values of improved soil environmental footprint
The results of improved soil environmental footprint obtained for the three scenarios are presented in this section using a qualitative scale. The scale in qualitative categories is shown in Table 6. The numerical values of soil environmental footprint have been divided in five qualitative categories. The column on the right shows the average value of the increase of soil ecosystem services, in percentage, which corresponds to the boundaries of the range.
Table 6. Qualitative values used in the classification of soil environmental footprint
The following figures present the spatial distribution of the results of the classification of soil environmental footprint. The results for the Expected scenario are presented in Figure 43, the results for the Regional Targets scenario are presented in Figure 44 and the results for the Towards 2050 scenario are presented in Figure 45. The figures show the qualitative categories of the projected improvement of the soil environmental footprint for Europe and China.
Figure 43 
Figure 44 
Figure 45
The figures allow the comparison of the improvement of soil environmental footprint in the three scenarios. For the Expected scenario the improvement of the soil environmental footprint is very low. The Towards 2050 scenario shows a much better improvement of soil environmental footprint. In Europe, high values are located in the Iberian Peninsula. The regions of low values are centred in Eastern France, the Netherlands, Belarus and the Baltic countries, and Romania. In China, the low values are located in the Southeast. For the Regional Targets scenario, there are no low values in Europe. High values are located in the Iberian Peninsula and in a band going from Poland to the former Yugoslavian countries. There are a few patches of very high values in the Iberian Peninsula. In China, the dominant value is moderate, although there is a large region to the Southeast with low values and some patches of high values distributed over the Continental-Temperate region.
4. Summary and conclusions
The results of improved soil environmental footprint obtained for the three scenarios are compared in this section. Table 7 presents the global overview, showing the average results obtained for the three scenarios in Europe and China. The same results are visualized in Figure 46.
Table 7. Average results for improved soil environmental footprint in the three scenarios for Europe and China
| Europe | China | |
| Expected | 0.13 | 0.10 |
| Towards 2050 | 1.07 | 0.92 |
| Regional Targets | 1.41 | 1.13 |
Figure 46
The average response in Europe is an improvement of soil environmental footprint of 0.13 for the Expected scenario. This corresponds to an average increase of 0.36% of each ecosystem service. The average improvement for the Towards 2050 scenario is 1.07, which corresponds to an average improvement of ecosystem services of 1.03%. In the case of the Regional Targets scenario, the improvement of soil environmental footprint is 1.41, which corresponds to an average improvement of soil ecosystem services of 1.19%. In China, the average improvement of soil environmental footprint for the Expected scenario is 0.10, with an average improvement of 0.32% of soil ecosystem services. For the Towards 2050 scenario, the improvement is 0.92, corresponding to an average increase of 0.96% of soil ecosystem services. In the Regional Targets scenario, the improvement of soil ecosystem services is 1.13, which corresponds to an average increase of 1.06% in soil ecosystem services. Overall, the improvement of soil environmental footprint in China is around 9% less than in Europe for the scenarios under consideration. The comparative results are visualized in Figure 47, where the triangles corresponding to the average values of soil ecosystem services are shown for the three scenarios in Europe and China. In both cases the triangles corresponding to the Towards 2050 and Regional Targets scenarios are much larger than the Baseline, with Regional Targets clearly larger than Towards 2050.
Figure 47
Figure 48 compares the results of improved environmental footprint obtained in the Towards 2050 and Regional Targets scenarios to those obtained in the Expected scenario. The Towards 2050 scenario implies around three times the implementation levels of those in the Expected scenario. However, the increase of soil environmental footprint is close to ten times. The average improvement is 829% better in Europe and 959% better in China. This due to the combined effect of the three ecosystem services, which reinforce each other under the proposed methodology for evaluation. The Regional Targets implies the same level of implementation as the Towards 2050, but the intervention is focused on the areas where soil quality is poor. This produces a significantly better response, with an average improvement of soil environmental footprint of 1090% in Europe and 1187% in China.
Figure 48
Figure 49 presents the comparison of the results for improved soil environmental footprint obtained in the Towards 2050 and Regional Targets scenarios to those obtained in the Expected scenario for individual agroclimatic regions in Europe and China. The Towards 2050 scenario corresponds to three times the implementation level of the Expected scenario. This leads to a response that is close to the 1000% line. In fact, the slopes of the fitted regression lines in the Towards 2050 scenario are 7.08 for Europe and 8.95 for China. The regional effect of focussing the intervention on the less quality soils is apparent for all regions, because the points corresponding to the Regional Targets scenarios are all above the line corresponding to 1000% of the Expected scenario. The slopes of the fitted regression lines are 8.35 for Europe and 10.87 for China
Figure 49
5. Gaps in knowledge
Limitations of analysis are derived from the modelling tools and datasets used (detailed in »Effect of farming on soil quality and »Effect of management on soil quality). The upscaling model is a generalization of results obtained in long-term experiment sites. These scientific experiments concentrated on specific management practices, crops and soil quality variables under local conditions. The results were generalized to the four classes of management practices, seven farming systems and three soil quality indicators adopted in model conceptualization. Although this process was validated by case study sites, it is a significant extrapolation and results should be viewed with caution. The model is also based on a set of assumptions regarding the differential effect of management practices under local conditions. Although these assumptions are based on evidence, there is no specific information regarding the quantitative values of the differential effect.
There are also limitations derived from the definition of regional scenarios in the multi-actor framework (detailed in »Soil management scenarios). The information on the rate of implementation of agricultural management practices is fragmentary and the desirable rate of implementation is the result of subjective judgement by participating actors. Several assumptions were made on the quantification of the policy scenarios in terms of implemented agricultural management practices. These assumptions influence the results obtained.
Finally, a particular method was chosen to aggregate information on the three soil quality indicators in one single value for soil environmental footprint. This method is based on a geometric construction that does not account for the relative weight of each indicator. It also produces a value that scales as the square of individual indicator values.
Note: For full references to papers quoted in this article see