|Main authors:||Luis Garrote, David Santillán, Ana Iglesias|
|iSQAPERiS Editor:||Jane Brandt|
|Source document:||Garrote L., Santillán D., Iglesias A. (2018) Report on key management practices affecting soil quality and their applicability in various farming systems. iSQAPER Project Deliverable 7.2 140 pp|
|1. Definition of policy implementation and agro-climatic regions|
|2. Proposed aggregated indicators for »Policies and environmental footprint|
|3. Validation of the approach|
1. Definition of policy implementation and agro-climatic regions
The basic idea is to perform a sensitivity analysis of soil quality indicators to agricultural management practices for the different farming systems. We assume a nominal increase of 10% in the application of the management practice under analysis and estimate the impact in terms of expected change of standardized soil quality indicators. The implementation of the management practice is carried out by selecting a random number of cells such that the practice is implemented in 10% of the cultivated area for the farming system under study The selection of cells is conditioned by the current degree of implementation of the practice in the different regions, if these data are available. We assume that the implementation level will be higher in areas where the current level of implementation is low, since policy will be more focused on increasing the implementation level in the regions where the practice is not fully adopted. This aspect is illustrated in Figure 13.
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 the previous chapter. 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.
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 Figures 14 and 15.
2. Proposed aggregated indicators for »Policies and environmental footprint
The projected changes of the soil quality indicators are used to evaluate the impact of each policy scenario on the soil environmental footprint (see »Policies and environmental footprint).
Here we suggest a possible approach, which should be validated in the workshop reported in »Soil management scenarios and fully implemented in »Policies and environmental footprint. The favourable or unfavourable effect of agricultural management practices on soil environmental footprint will be evaluated by analysing the expected evolution of main soil quality indicators. A positive change of several soil quality indicators implies a strong favourable impact on soil environmental footprint. Conversely, a negative change of soil quality indicators implies a negative impact on soil environmental footprint. A scale will be co-defined with the aid of the participants in the workshop to establish the relationship between the evolution of soil quality indicators and the impact on soil environmental footprint. A tentative preliminary scale for classification is shown in Figure 16.
3. Validation of the approach
The upscaling model was validated and co-designed in a workshop reported in »Soil management scenarios. In the workshop, project partners, invited scientists and stakeholders discussed the strengths and weaknesses of the proposed approach and will contribute to improve the model.
Note: For full references to papers quoted in this article see