BackgroundMonitoring the properties of dissolved organic carbon (DOC) in soil water is frequently used to evaluate changes in soil quality and to explain shifts in freshwater ecosystem functioning.MethodsUsing >700 individual soils (0–15 cm) collected from a 209,331 km2 area we evaluated the relationship between soil classification (7 major soil types) or vegetation cover (8 dominant classes, e.g. cropland, grassland, forest) and the absorbance properties (254 and 400 nm), DOC quantity and quality (SUVA, total soluble phenolics) of soil water.ResultsOverall, a good correlation (r 2 = 0.58) was apparent between soil water absorbance and DOC concentration across the diverse range of soil types tested. In contrast, both DOC and the absorbance properties of soil water provided a poor predictor of SUVA or soluble phenolics which we used as a measure of humic substance concentration. Significant overlap in the measured ranges for UV absorbance, DOC, phenolic content and especially SUVA of soil water were apparent between the 8 vegetation and 7 soil classes. A number of significant differences, however, were apparent within these populations with total soluble phenolics giving the greatest statistical separation between both soil and vegetation groups.ConclusionsWe conclude that the quality of DOC rather than its quantity provides a more useful measure of soil quality in large scale surveys.

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The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be identified in soils of Great Britain, (2) whether conventional soil classification or aggregate vegetation classes (AVCs) could predict SQIs and (3) to what extent do soil types and/ or AVCs act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQI which were named as; soil organic matter (SOM), dissolved organic matter (DOM), soluble N, reduced N, microbial biomass, DOM humification (DOMH). SOM was identified as the most important SQI in the discrimination of both soil types and AVCs. Soil attributes constituting highly to the SOM factor were, microbial quotient and bulk density. The SOM indicator discriminated three soil type groupings and four aggregate vegetation class groupings. Among the soil types, only the peat soils were discriminated from other groups while among the AVCs only the heath and bog classes were isolated from others. However, the peat soil and heath and bog AVC were the only groups that were distinctly discriminated from other groups. All other groups heavily overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types. We conclude that conventionally classified soil types cannot predict the SQIs defined from large areas with differing climatic and edaphic factors. Localised areas with similar climatic and topoedaphic factors may hold promise for the definition of SQI that may predict the soil types or AVCs.

Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter , Organic matter humification , Soluble nitrogen , Microbial biomass , Reduced nitrogen and Soil humification index . Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.