Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields

Nutrient loss from agricultural land following organic fertilizer spreading can lead to eutrophication and poor water quality. The risk of pollution is partly related to the soil water status during and after spreading. In response to these issues, a decision support system (DSS) for nutrient management has been developed to predict when soil and weather conditions are suitable for slurry spreading. At the core of the DSS, the Hybrid Soil Moisture Deficit (HSMD) model estimates soil water status relative to field capacity (FC) for three soil classes (well, moderately and poorly drained) and has potential to predict the occurrence of a transport vector when the soil is wetter than FC. Three years of field observation of volumetric water content was used to validate HSMD model predictions of water status and to ensure correct use and interpretation of the drainage classes. Point HSMD model predictions were validated with respect to the temporal and spatial variations in volumetric water content and soil strength properties. It was found that the HSMD model predictions were well related to topsoil water content through time, but a new class intermediate between poor and moderate, perhaps ‘imperfectly drained’, was needed. With correct allocations of a field into a drainage class, the HSMD model predictions reflect field scale trends in water status and therefore the model is suitable for use at the core of a DSS.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Bioavailability of 2,4-dichlorophenol associated with soil water-soluble humic material

Interaction of organic xenobiotics with soil water-soluble humic material (WSHM) may influence their environmental fate and bioavailability. We utilized bacterial assays (lux-based toxicity and mineralization by Burkholderia sp. RASC) to assess temporal changes in the bioavailability of [¹⁴C]-2,4-dichlorophenol (2,4-DCP) in soil water extracts (29.5 μg mL^-1 2,4-DCP; 840.2 μg mL^-1 organic carbon). HPLC determined and bioavailable concentrations were compared. Gel permeation chromatography (GPC) was used to confirm the association of a fraction (>50%) of [¹⁴C]-2,4-DCP with WSHM. Subtle differences in parameters describing 2,4-DCP mineralization curves were recorded for different soil-2,4-DCP contact times. Problems regarding the interpretation of mineralization data when assessing the bioavailability of toxic compounds are discussed. The lux-bioassay revealed a time-dependent reduction in 2,4-DCP bioavailability: after 7 d, less than 20% was bioavailable. However, GPC showed no quantitative difference in the amount of WSHM-associated 2,4-DCP over this time. These data suggest qualitative changes in the nature of the 2,4-DCP-WSHM association and that associated 2,4-DCP may exert a toxic effect. Although GPC distinguished between free- and WSHM-associated 2,4-DCP, it did not resolve the temporal shift in bioavailability revealed by the lux biosensor. These results stress that assessment of risk posed by chemicals must be considered using appropriate biological assays.