Many studies on soil quality have searched for a way to aggregate the information obtained for each soil quality indicator into a single soil quality index, even though this was deemed impossible by Sojka and Upchurch (1999). For example, Velasquez et al. (2007) summed the contributions of each of five sub-indicators (hydraulic properties, chemical fertility, aggregation, organic matter and biodiversity) to derive the general indicator of soil quality (GISQ). In the SMAF, an additive index yields a number between 1 and 10 (Andrews et al., 2004). However, if assessed soil functions or ecosystem services rank very differently in importance, then some kind of weighting is mandatory.

For example, in the recent Canadian monitoring of soil quality within the agri-environmental indicator assessment, a soil quality compound index is calculated as the weighted average of the performance indices for erosion, soil organic carbon content, trace elements and soil salinization (Clearwater et al., 2016). Another example is the multi-objective approach based on principles of systems engineering proposed by Karlen and Stott (1994). The main soil functions are weighted according to their importance for the overall goal in soil quality management at a given site, and an overall rating of soil quality with respect to the predefined goal is obtained by summing the weighted soil functions. An exemplary application of this approach can be found in Lima et al. (2013), who used SIMOQS (Sistema de Monitoramento da Qualidade do Solo) software developed in Brazil to calculate a soil quality index (Table 5).

Table 5: Example of weighting of soil functions and associated indicators (Lima et al., 2013)

Visual soil assessments are also often summarized in an overall soil quality rating (McGarry, 2006; Mueller et al., 2014; Shepherd et al., 2008). Typically, the scores for the different indicators are summed up, with some weighting applied. In the Muencheberg Soil Quality Rating, the weighted sum of the basic indicators is multiplied with values for hazard indicators such as contamination, acidification and flooding (Mueller et al., 2014).

Instead of deriving an overall soil quality index, colour coding for different indicators alone or aggregated according to soil functions is more meaningful. For example, in the outputs the Cornell soil health test, in Sindi, and in the Australian soil quality monitoring framework a traffic light system of 3-5 colours indicates low, adequate or excessive values for a given indicator. Other graphical presentations such as amoeba diagrams (or spider diagrams) can likewise convey more information on trade-offs and synergies than a single number or index (Rutgers et al., 2009; Rutgers et al., 2012).

The ultimate purpose of a soil quality index is to inform farmers and other land managers about the effect of soil management on soil functionality. An aggregated presentation of the outcome of soil quality assessments, especially by graphical means, can indeed be useful also for educational purposes and for communicating to society as a whole the consequences that human decisions can have on soil-based ecosystem services.

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