Summary Assessment of soil organic matter content using laboratory analysis can be costly and time consuming, so limiting how often land managers assess this important property. This work demonstrates an ability to estimate topsoil organic matter content from field observations alone and provides a method by which rapid and cost‐effective assessments of soil organic matter status may be made. Models using environmental factors from the National Soil Inventory of Scotland ( NSIS ) dataset as inputs to a neural network model were used to predict loss on ignition ( LOI ). Two models, one for all soils and one for soils with small organic matter contents ( LOI < 20%), were developed. It was found that the model developed for all soils produced reasonable predictive results across the entire LOI range ( R 2 = 0.877), although it was not as effective at predicting small LOI values ( R 2 = 0.354) as the small organic matter content model ( R 2 = 0.674). Both models were tested with imagery and data from samples outwith the NSIS dataset to validate the approach. Predictive results were less accurate than when using NSIS data. A discussion of possible improvements to make the model useful for field observations of soils is given.
Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale), soil texture (sand, silt, clay), bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils.
Water quality remains a main reason for the failure of waterbodies to reach Good Ecological Status (GES) under the European Union Water Framework Directive (WFD), with phosphorus (P) pollution being a major cause of water quality failures. Reducing P pollution risk in agricultural catchments is challenging due to the complexity of biophysical drivers along the source-mobilisation-delivery-impact continuum. While there is a need for place-specific interventions, the evidence supporting the likely effectiveness of mitigation measures and their spatial targeting is uncertain. We developed a decision-support tool using a Bayesian Belief Network that facilitates system-level thinking about P pollution and brings together academic and stakeholder communities to co-construct a model appropriate to the region of interest. The expert-based causal model simulates the probability of soluble reactive phosphorus (SRP) concentration falling into the WFD high/good or moderate/poor status classifications along with the effectiveness of three mitigation measures including buffer strips, fertiliser input reduction and septic tank management. In addition, critical source areas of pollution are simulated on 100 × 100 m raster grids for seven catchments (12–134 km 2 ) representative of the hydroclimatic and land use intensity gradients in Scotland. Sensitivity analysis revealed the importance of fertiliser inputs, soil Morgan P, eroded SRP delivery rate, presence/absence of artificial drainage and soil erosion for SRP losses from diffuse sources, while the presence/absence of septic tanks, farmyards and the design size of sewage treatment works were influential variables related to point sources. Model validation confirmed plausible model performance as a “fit for purpose” decision support tool. When compared to observed water quality data, the expert-based causal model simulated a plausible probability of GES, with some differences between study catchments. Reducing fertiliser inputs below optimal agronomic levels increased the probability of GES by 5%, while management of septic tanks increased the probability of GES by 8%. Conversely, implementation of riparian buffers did not have an observable effect on the probability of GES at the catchment outlet. The main benefit of the approach was the ability to integrate diverse, and often sparse, information; account for uncertainty and easily integrate new data and knowledge.
Construction and demolition fines (C&D-fines) and green waste compost (GWC) are two commonly generated urban waste materials that represent repositories of geochemical value. Here technosols were produced from volumetric mixtures of these materials ranging from 0–100% C&D-fines, with the remaining proportion comprised of GWC. Agronomic assessment was carried out by way of pot and rhizobox plant growth experiments with ryegrass, barley and pea to determine germination, plant mass and rooting behaviours. Geochemical and mineralogical evaluation was achieved by soil pore water solution measurements combined with X-ray powder diffraction analyses respectively, to characterise the technosols and their distinct deviations from a reference agricultural geogenic soil (soil). The results demonstrated that germination, growth and root mass/surface area of vegetation were up to 80-fold greater after 30-days in the technosol composed of equal volumes of the two materials (50% C&D-fines: 50% GWC) compared to the soil. High concentrations of Ca and Mg in pore waters (550–800 mg·L−1) were dominant features of the technosols, in contrast to the soil (<50 mg·L−1), resulting from gypsum and calcite enrichment of the C&D-fines. In contrast, the GWC represented a source of soluble K (450–1000·mg·L−1). Highly elevated Ca concentrations in extended leaching tests of the C&D-fines reflected ongoing gypsum dissolution, whereas soluble Mg and K were rapidly depleted from the GWC. In summary, short-term performance of the technosols as plant growth substrates was strong despite their geochemical and mineralogical distinction from soil. Gleaning additional geochemical value from combining urban wastes in this way is potentially suited to myriad scenarios where geogenic soils are contaminated, sealed or otherwise absent. Further assessment will now be needed to determine the geochemical longevity of the technosols before wider scale applications can be recommended.
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