Obtaining soil and land quality indicators using research chains and geostatistical methods

Abstract

Soil and land quality indicators play an important role in the assessment and evaluation of soil and land quality. In contrast with the general definitions of soil and land quality, working with indicators demands a better awareness of at which scale level measurements were made, at which scale calculations and models were developed and validated, and at which scale answers are needed. We propose that soil and land quality indicators may be classified by three characteristics: 1) scale level, 2) complexity, and 3) transferability. Each characteristic is represented by an axis in the Soil and Land Quality Indicator Diagram.

Indicators with a high complexity can not be measured directly, but need to be calculated with one or more models, eg. pedotransfer functions and hydrological simulation models. For the application of the indicator it is then important to know how the indicator value was obtained, i.e. which models were used. A specific sequence of models used for obtaining an indicator value is called a ‘research chain’ and is indicated in the Scale Hierarchy and Knowledge Type Diagram. The use of research chains allows the user to consider and evaluate alternative options for the assessment of a specific indicator.

In this study values for three soil quality indicators were obtained through two alternative research chains. The research chains differed by the choice of used pedotransfer functions and soil hydrological models. The two research chains yielded for each of the three indicators two sets of thirty year averages for 166 locations in the study area. Per location the obtained indicator values were compared with a t-test. The research chains were found to yield significantly different values for all three indicators.

The spatial and temporal variability of the data was analyzed for each step, i.e. per model, along both research chains. Alternative models yielded different spatial and temporal variability structures. Therefore, the choice of research chain not only affects the mean value of an indicator, but also the associated spatial and temporal variability structure. Knowledge of the spatial and temporal variability is important for upscaling purposes.

Based on these results we conclude that the successful application of soil and land quality indicators depends on:

• 1) the definition of suitable indicators based on scale level, complexity, and transferability;

• 2) the careful selection and definition of research chains; and

• 3) the combined presentation of indicator values and used research chains.