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Indicator Selection

The set of indicators used to determine a soil's quality is also called a minimum data set. To select a minimum data set, two main methods have been established: expert opinion and statistical data reduction.

Expert opinion, by definition, requires expert knowledge of the system. Using a hierarchical framework for choosing the indicators may help make selection more systematic. As shown in the figure below, management goals dictate the soil functions of interest, which in turn, suggest related indicators. For instance, if animal waste disposal is a goal for a particular field, filtering and buffering is an important soil function. Under filtering and buffering, organic matter content and pH are potential indicators.

The indicator set must be further refined according to climate, soil, and plant community or other factors. This is the method used by the Soil Management Assessment Framework.

minimum data set

Statistical data reduction has been demonstration to effectively choose indicators in a number of soil systems (Brejda et al., 2000a,b; Andrews et al., 2001; Andrews and Carroll, 2001). This method can eliminate disciplinary bias that could be a problem with expert selection of indicators but it does assume that appropriate candidate indicators are in the original data set (so a minimum level of knowledge is required). The major weakness of this method is the need for a large existing dataset. It is unlikely that managers will have access to data sets that are suitable in size (either number of indicators measured or number observations made) to make this method feasible for individuals use. If you feel you have a sufficiently large data set and are interested in using this selection method, contact us.


Andrews, S.S. and C.R. Carroll, 2001. Designing a decision tool for sustainable agroecosystem management: Soil quality assessment of a poultry litter management case study. Ecol. Applic. 11 (6): 1573-1585.

Andrews, S.S., D.L. Karlen, and J.P. Mitchell. 2001. A comparison of soil quality indexing methods for vegetable production systems in Northern California. Agriculture, Ecosystems and Environment 1760: 1-21.

Brejda, J.J., T.B. Moorman, D.L. Karlen, and T.H. Dao. 2000. Identification of regional soil quality factors and indicators: I. Central and southern high plains. Soil Sci. Soc. Am. J. 64: 2115–2124.

Brejda, J.J., T.B. Moorman, J.L. Smith, D.L. Karlen, D.L. Allan, and T.H. Dao. 2000. Distribution and variability of surface soil properties at a regional scale. Soil Sci. Soc. Am. J. 64:974–982.