|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 conceptual framework for the evaluation of soil environmental footprint is presented in this section. The policy scenarios defined in »Soil management scenarios are presented first. Then, the implementation of these scenarios in the iSQAPER upscaling model is discussed. Finally, some information is provided regarding the analysis of results.
|1. Definition of policy scenarios|
|2. Implementation of policy scenarios on iSQAPER upscaling model|
|3. Agroclimatic regions|
Here we evaluate the impact of policy scenarios defined in »Soil management scenarios on soil environmental footprint in Europe and China. Policy scenarios are defined as a certain level of additional implementation of agricultural management practices. Scenarios are defined locally on the case study sites through consultation with stakeholders and taking into account the output from »Integrating promotion of soil quality and sustainable land management into policy. There are three characteristic scenarios:
- Expected: The Expected scenario maintains the observed tendency in the implementation of beneficial agricultural management practices. It represents a policy scenario where no particular emphasis is placed on soil health protection. The rates of implementation were estimated from previous projects, like SmartSoils, that studied the level of implementation of management practices.
- Towards 2050: This scenario assumes an intensification on the rate of implementation of agricultural management practices as a result of public policies. Experts in each case study site were asked to give their expectation on the desirable rate of implementation of each group of agricultural management practice at their sites. This target can be considered as a reference, in order to obtain projected values of the effect of this policy.
- Regional Targets: This scenario assumes the same rate of implementation of agricultural management practices, but considers that policy efforts are focused on areas where soil threats are more active and soil quality indicators are poorer. The emphasis, therefore, is place on targeting the regions where the practices would be more beneficial.
The levels of implementation of agricultural management practices are presented in the following tables. Table 2 corresponds to the Expected scenario and Table 3 corresponds to the Towards 2050 and Regional Targets scenarios. The tables show the projected increase (in percentage) of the implementation of the agricultural management practices in the time horizon of the analysis.
Table 2. Level of implementation of agricultural management practices for the Expected scenario in Europe and China
|Study site||Organic matter||Reduced Tillage||Crop rotation||Organic farming|
|1 De Peel (NL)||1.1||0.8||1.1||1.1|
|2 Argentré du Plessis (FR)||1.3||0.4||1.3||0.8|
|3 Cértima (PT)||1.3||0.8||1.3||1.1|
|4 SE Spain (ES)||1.3||0.8||0.8||1.6|
|5 Crete (GR)||1.1||1.1||0.5||1.3|
|6 Ljubljana (SL)||1.3||0.8||1.1||1.1|
|7 Zala (HU)||1.3||0.5||0.5||0.5|
|8 Braila (RO)||0.3||0.8||1.3||0.3|
|9 Trzebieszów (PL)||1.3||0.2||0.8||1.1|
|10 Tartuuma (EE)||0.8||1.3||0.4||0.5|
|11 Qijang (CN)||1.3||0.5||0.5||0.5|
|12 Suining (CN)||0.8||1.3||0.5||0.5|
|13 Zhifanggou (CN)||0.8||1.1||0.8||0.5|
|14 Gongzhuling (CN)||0.5||1.3||1.1||0.5|
Table 3. Level of implementation of agricultural management practices for the Towards 2050 and Regional Targets scenarios in Europe and China
|Study site||Organic matter||Reduced Tillage||Crop rotation||Organic farming|
|1 De Peel (NL)||3.3||2.3||3.3||3.3|
|2 Argentré du Plessis (FR)||4.0||1.3||4.0||2.3|
|3 Cértima (PT)||4.0||2.3||4.0||3.3|
|4 SE Spain (ES)||4.0||2.3||2.3||5.0|
|5 Crete (GR)||3.3||3.3||1.7||4.0|
|6 Ljubljana (SL)||4.0||2.3||3.3||3.3|
|7 Zala (HU)||4.0||1.7||1.7||1.7|
|8 Braila (RO)||1.0||2.7||4.0||1.0|
|9 Trzebieszów (PL)||4.0||0.7||2.7||3.3|
|10 Tartuuma (EE)||2.7||4.0||1.3||1.7|
|11 Qijang (CN)||4.0||1.7||1.7||1.7|
|12 Suining (CN)||2.7||4.0||1.7||1.7|
|13 Zhifanggou (CN)||2.7||3.3||2.7||1.7|
|14 Gongzhuling (CN)||1.7||4.0||3.3||1.7|
The implementation of the policy scenarios in the iSQAPER upscaling model implies a number of steps.
- First, the local values at the case study sites need to be upscaled to the entire region under analysis. A simple spatial interpolation procedure has been adopted for this task. This produces a smooth map of implementation across Europe and China which accounts for regional variations.
- Secondly, the implementation level has to be applied at each cell in the domain. In the iSQAPER upscaling model, the implementation of the management practices is carried out by selecting a random number of cells such that the practice is implemented in the prescribed percentage of the cultivated area for the cropping pattern under study. In the cells where the measure is implemented, we compute the values of the soil quality indicators by multiplying the current value by the response ratio, determined from local conditions as described in »Conceptual approach of the upscaling model. The soil quality indicators of cells where the practice is not implemented remain unchanged, i.e., the response ratio is null. To account for the effect of the randomly chosen cells for implementation, we conduct 100.000 realizations of the raffle, and compute the mean value and standard deviation of the response ratio in every cell.
- In the Regional Targets scenario, this procedure has been modified to concentrate the policy efforts on the cells that show lower values of the standardized soil quality indicator. We assume that the implementation level will be higher in areas where the value of the soil quality indicator is low, since policy will be more focused on increasing the implementation level in the regions where the action is most needed.
- Finally, we need to account for the possibility of several agricultural practices applied in the same cell. The analysis at LTE sites were carried out by applying one singe agricultural management practice. If two or more practices are applied on the same plot, it can be expected that combine effect would be less than the sum of individual effects. We have accounted for this possibility in the model by computing the probability of having more than one agricultural management practice applied to a single cell under ransom selection and defining an efficiency coefficient.
The results of the implementation of the policy scenarios in the iSQAPER model are presented below.
Spatial distribution of implementation levels in the Expected scenario
The spatial distribution of the implementation of agricultural management practices in the Expected scenario is presented in the following figures. The implementation of organic matter is presented in Figure 5, the implementation of reduced tillage is presented in Figure 6, the implementation of crop rotation is presented in Figure 7 and the implementation of organic farming is presented in Figure 8. The figures show the projected percentage increase in application of the management practices contemplated in the Expected scenario.
Spatial distribution of implementation levels in the Towards 2050 and Regional Targets scenarios
The spatial distribution of the implementation of agricultural management practices in the Towards 2050 and Regional Targets scenarios is presented in the following figures. The implementation of organic matter is presented in Figure 9, the implementation of reduced tillage is presented in Figure 10, the implementation of crop rotation is presented in Figure 11 and the implementation of organic farming is presented in Figure 12. The figures show the projected percentage increase in application of the management practices contemplated in the scenarios Towards 2050 and Regional Targets.
The results are analysed in agro-climatic regions relevant for policy making. These regions were defined by combining the information on physical factors, such as climate classes, soil types or biomes and socio-economic factors, such as administrative organization.
The adopted agro-climatic regions for policy analysis in Europe and China are shown in Figure 13.
The codes used to identify farming systems and agro-climatic regions are shown in Table 4.
Table 4. Codes used in the visualization of results
|Region EU||Code||Region CN||Code|
Note: For full references to papers quoted in this article see