Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.
|Pages (from-to)||491 - 503|
|Journal||Soil Use and Management|
|Publication status||First published - 2015|
- Bacterial community structure
- Field scale soil properties
- Land management
- Neural network analysis