A brief summary of the conceptual approach of iSQAPER upscaling model is presented in this section. The details are fully described in »Effect of management on soil quality. The model is based on a geospatial database of soil quality indicators (SQI) and agricultural management practices (AMP) and on the relationships between AMP and SQI established on Deliverables »Management practices and soil quality and »Soil quality indicators from long-term experiments.

1. Basic framework

The upscaling model intends to provide results of the scientific knowledge at the local level to a wider geographical context, to understand how agricultural management practices that mitigate soil threats also affect ecosystem services. In order to perform this task, the model accounts for the basic processes that influence agricultural management of the soil. The basic approach is illustrated on Figure 2, where the relevant processes are represented.

Figure 2

The central actor in the process is the farmer, who is managing a plot of land where a certain crop is grown under a typical farming system. This plot of land is subject to a physical context, determined by biome, soil type, climate and other factors that control biophysical processes. The farmer is also immersed in a socio-economic context that influences agricultural activity: Common Agricultural Policy, environmental policy, financial instruments, market conditions and socio-economic development determine managing decision regarding crop selection and management practices. The choice of management practices is also influenced by existing soil threats, like soil erosion, desertification, loss of organic matter and many others. The farmer intends to control local soil threats by applying suitable management practices.

Science developed in iSQAPER project determines that certain agricultural management practices may have a beneficial effect on agricultural soil conditions. These conditions are described through a set of suitable indicators, chosen because they represent the status of the soil. The analysis of Long Term Experiment (LTE) sites proves that these effects can be objectively quantified in terms of such indicators. Under the upscaling approach, policy is considered to be a driver of change, motivating farmers to adopt beneficial management practices. The upscaling model intends to quantify the global effect of policies promoting beneficial agricultural practices. In order to do so, functional relations are established between the agricultural management practices and the soil quality indicators for different farming systems. The improved values of soil quality indicators can then be used to evaluate the soil environmental footprint by accounting for soil functions that support ecosystem services. Through the upscaling model a spatial representation of soil environmental footprint may be generated under a set of policy scenarios. These upscaled maps can be used as a decision support tool for policy identification and implementation.

2. Linking farming systems, management practices and soil quality indicators

The dynamic models developed here aim to determine the effect of the evolving physical and socioeconomic context (climate, population, economic development, policies) on the implementation of dominant management practices that have an impact on soil quality. The complex interplay between physical, chemical and biological factors that affect soil quality needs to be simplified in order to produce global results at the continental scale. For this reason, the analysis is focused on a limited number of essential components that are introduced in this section. The components of the upscaling model are summarized in Figure 3.

Figure 3

3. Deriving functional relations for ecosystem services

Functions that relate agricultural management practices and soil quality indicators are defined from the results compiled for the LTE sites. We start from the reference values obtained in »Management practices and soil quality and published in Bai et al., 2018 and adapt them to different farming systems accounting for the variability of local conditions.

Table 1. Relevant results (response ratios) of Long Term Experiment sites, derived from Table 1 of Bai et al. (2018), shows the median values of the response ratios, together with the standard deviation of the results obtained for the relevant combinations of soil quality indicators and agricultural management practices.

Table 1. Relevant results (response ratios) of Long Term Experiment sites

These mean values are adapted to local conditions through interaction with local stakeholders from case study sites. Experts were asked to fill a questionnaire about the impact of management practices on soil quality for the farming systems available at their case study site. Based on their responses and on the analyses carried out in »Soil quality: assessment, indicators & management, the effect of the management practice for every farming system was classified into qualitative categories that modified the average response ratios obtained from LTE sites.

4. Spatial analysis

The objective of the upscaling model is to produce maps of improvement of soil environmental footprint under different policy scenarios. Therefore, the model needs to account for spatially-explicit representation of soil processes. The foundation of the spatial representation is the data catalogue introduced in »Effect of farming on soil quality. The unit of computation is the model cell, which corresponds to a spatial resolution of 0.5 minutes (approximately 9 km at the Equator). Information about the grid cell includes the climate zone, the soil type, the cropping patterns within the cell (there may be several), the soil status described by the available soil quality indicators and the current degree of implementation of each category of agricultural management practice in the region. The scenario determines the additional degree of implementation of each agricultural management practice to be achieved in the time frame of the analysis. Upscaling functional relations are applied to appropriate grid cells where each agricultural management practice is considered to be implemented. This leads to a modification of the soil quality indicators, which is the initial output of the upscaling model.

In order to estimate the effect of management practices on soil quality indicators, it is essential to account for values of each point in the coarse-scale geographical analysis. The basic rationale of the upscaling model is that the influence of soil management practices will be larger on areas with relatively lower values of soil quality indicators. Assuming that the rest of conditions are equal, the fact that a local point shows a low value of the soil quality indicators may be explained by poorer soil management practices.

Local conditions were established based on the variable considered most relevant for each soil quality indicator. Yield was linked to climate zone, soil biomass was linked to biome and soil organic carbon was linked to soil type. The local variable selected for Yield is climate zone, taken from the Köppen-Geiger climate classification system. The basic variable for zonation is the World Map of Köppen-Geiger Climate Classification distributed by the University of Vienna (Rubel and Kottek, 2010). Local yield for a certain farming system is compared to the distribution of yields for the same farming system obtained from all cells in the same climatic zone. The local variable selected for Soil Organic Carbon is soil type. The basic variable for zonation is the Digital Soil Map of the World distributed by FAO (Version 3.6, completed January 2003). Local Soil Organic Carbon for a certain model cell is compared to the distribution of SOC obtained from all cells of the same soil type.

In order to account for local conditions, soil quality indices are re-scaled to standardized variables that compare local values to conditions for the same local group. The “Standardized Soil Quality Index” is defined applying the following equation:

Where SSQI is the standardized soil quality index for a certain local group (for instance, cereal yield in Arid (B) climate); μis the average value of the soil quality index in all cells in the same local group and σ is the standard deviation of the soil quality index values of all cells in the same local group.

The response of soil quality indicators to the susained application of the management practice is based on the conclusions of the analysis of the LTE sites. The main value is the response ratio, RR, defined as:

Where SQI₀ is the value of the soil quality indicator in the reference condition and SQI_MP is the value of the soil quality indicator after the application of the management practice. The results of the long term experiments show that there is a significat uncertainty in the response ratios observed in different locations. The distributions of the response ratios were characterized in Table 1 through their median values and their standard deviation. These two values are taken as input for the local influence models. Local conditions are accounted through the standardized soil quality indicator.

The local influence model determines the response ratio for the individual cell as a function of the standardized soil quality indicator. The effect of the measure is considered to be larger or smaller values of the standardized soil quality indicator, according to the function definition shown in Figure 4.

Figure 4

5. Results of upscaling model

The upscaling model described above was applied in »Effect of management on soil quality to obtain results of an additional implementation of 10% of the four agricultural management practices. Results were obtained for the three soil quality indicators in all seven cropping patterns. A summary of those results is presented on Appendix 1 of the »full report, where results obtained for the soil quality indicators in the different cropping patterns have been averaged using as weights the area of each cropping pattern in the cell.

Notes:

For full references to papers quoted in this article see » References

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