Knowledge Management and Decision Support for Sustainable Land Management

Much research has focused on desertification and land degradation assessments without putting sufficient emphasis on prevention and mitigation, although the concept of sustainable land management (SLM) is increasingly being acknowledged. A variety of SLM measures have already been applied at the local level, but they are rarely adequately recognised, evaluated, shared or used for decision support. WOCAT (World Overview of Technologies and Approaches) has developed an internationally recognised, standardised methodology to document and evaluate SLM technologies and approaches, including spatial distribution, allowing the sharing of SLM knowledge worldwide. The recent methodological integration into a participatory process allows now analysing and using this knowledge for decision support at the local and national level. The use of the WOCAT tools stimulates evaluation (self-evaluation as well as learning from comparing experiences) within SLM initiatives where all too often there is not only insufficient monitoring but also a lack of critical analysis. The comprehensive questionnaires and database system facilitate to document, evaluate and disseminate local experiences of SLM technologies and their implementation approaches. This evaluation process - in a team of experts and together with land users - greatly enhances understanding of the reasons behind successful (or failed) local practices. It has now been integrated into a new methodology for appraising and selecting SLM options. The methodology combines a local collective learning and decision approach with the use of the evaluated global best practices from WOCAT in a concise three step process: i) identifying land degradation and locally applied solutions in a stakeholder learning workshop; ii) assessing local solutions with the standardised WOCAT tool; iii) jointly selecting promising strategies for implementation with the help of a decision support tool. The methodology has been implemented in various countries and study sites around the world mainly within the FAO LADA (Land Degradation Assessment Project) and the EU-funded DESIRE project. Investments in SLM must be carefully assessed and planned on the basis of properly documented experiences and evaluated impacts and benefits: concerted efforts are needed and sufficient resources must be mobilised to tap the wealth of knowledge and learn from SLM successes

Ethyl Glucuronide in Scalp and Non-head Hair: An Intra-individual Comparison

Aims: Analysis of ethyl glucuronide (EtG), a minor metabolite of ethanol, is a valid tool for the assessment of social and chronic excessive alcohol consumption. Standardized analysis of EtG is usually done in head hair. As head hair cannot always be provided, alternative hair matrices become more and more interesting. Therefore, a study was performed that compared the intra-individual EtG concentrations in scalp hair and non-head hair (chest, arm, leg and axillary hair). Methods: Hair samples were collected from 68 subjects undergoing an expert assessment for fitness to drive. Aqueous extracts of the hair matrix were cleaned by solid-phase extraction, using an Oasis MAX column. EtG was first derivatized with perfluoropentanoic anhydride and then quantified by GC-MS/MS in negative chemical ionization mode, using EtG-d5 as internal standard. Results: For categorizing drinking behaviour, the two EtG cut-off values recommended by the Society of Hair Testing were applied for all different hair types. For chest, arm and leg hair, correct classification ratios were >83%. This corresponds to sensitivity values >78% and specificities >75%. Such values indicate together with φ coefficients (rφ) > 0.7 a high correlation of the categorization of the drinking behaviour based on these body hair EtG concentrations compared with the indexing based on scalp hair EtG-values. However, it must be taken into consideration that the time frame represented by non-head hair may extend way back. Conclusions: These results indicate that chest, arm and leg hair can be a valid alternative to assess the drinking behaviour of a subject if head hair is not available; whereas axillary hair is not suitable as alternative matri

Comparison of long-term field-measured and RUSLE-based modelled soil loss in Switzerland

Long-term field measurements to asses model-based soil erosion predictions by water are rare. We have compared field measurements based on erosion assessment surveys from a 10-year monitoring process with spatial-explicit model predictions with the Revised Universal Soil Loss Equation (RUSLE). Robust input data were available for both the mapped and the modelled parameters for 203 arable fields covering an area of 258 ha in the Swiss Midlands. The 1639 mapped erosion forms were digitized and converted to raster format with a 2 m resolution. A digital terrain model using 2 m resolution and a multiple flow direction algorithm for the calculation of the topographic factors and the support practice factor was available for modelling with the RUSLE. The other input data for the RUSLE were determined for each field. The comparison of mapped and modelled soil loss values revealed a substantially higher estimation of soil loss values from modelling by a factor of 8, with a mean mapped soil loss of 0.77 t/ha/yr vs. modelled soil loss of 6.20 t/ha/yr. However, high mapped soil losses of >4 t/ha/yr were reproduced quite reliably by the model, while the model predicted drastically higher erosion values for mapped losses of <4 t/ha/yr. Our study shows the value of long-term field data based on erosion assessment surveys for model evaluation. RUSLE-type model results should be compared with erosion assessment surveys at the field to landscape scale in order to improve the calibration of the model. Further factors related to land management like headlands, traffic lanes and potato furrows need to be included before they may be used for policy advice

Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data

Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm’s capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today’s era of digitalization