Through the commodification of nature, the framing of the environment as a ‘natural resource’ or ‘ecosystem service’ has become increasingly prominent in international environmental governance. The economic capture approach is promoted by international organizations such as the United Nations Environmental Program (UNEP) through Reducing Emissions from Deforestation and Forest Degradation (REDD), Payments for Ecosystem Services (PES) and The Economics of Ecosystems and Biodiversity (TEEB). This paper will inquire as to how forest protection is related to issues of social and ecological justice, exploring whether forest exploitation based on the top-down managerial model fosters an unequitable distribution of resources. Both top-down and community-based approaches to forest protection will be critically examined and a more inclusive ethical framework to forest protection will be offered. The findings of this examination indicate the need for a renewed focus on existing examples of good practice in addressing both social and ecological need, as well as the necessity to address the less comfortable problem of where compromise appears less possible. The conclusion argues for the need to consider ecological justice as an important aspect of more socially orientated environmental justice for forest protection. https://doi.org/10.1017/S0376892916000436 https://www.linkedin.com/in/helenkopnina/
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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Objectives: Current study explores the potential of the safety rating scale in order to determine the surplus value for evidence based practise. This study wants to contribute to this knowledge gape by exploring the safety scale by analysing the change between two safety ratings. First, the absolute change in safety is investigated. Secondly the study explores to what extent family background characteristics and case management characteristics determine the extent of change in perceived safety. Materials and Methods: The study analysed 105 Dutch child protection cases who had registration files with filled out LIRIK checklist, Action Plan and additional baseline safety and end safety measure as perceived by case managers. Results: On average perceived safety increased from an insufficient level to sufficient level. Significant regression coefficients with larger changes for primary school children (6 - 12 years) and lower changes for children within the ‘socio economic problems cluster’. The results reveal significant vulnerability for preschool children and families attending the socio-economic cluster due to limited improvement. Conclusion: According to this study the safety measure can be of value to outcome monitoring. The safety measure is a practical measure that reflects on the current state of safety within a family according to professionals and can be used on several occasions during case management. In addition, on aggregated level pre and post measures can be analysed for quality management purpose. Further exploration of this measure is needed. Publishers article: https://www.ecronicon.com/ecpe/ECPE-10-00873.php
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
Circular agriculture is an excellent principle, but much work needs to be done before it can become common practice in the equine sector. In the Netherlands, diversification in this sector is growing, and the professional equine field is facing increasing pressure to demonstrate environmentally sound horse feeding management practices and horse owners are becoming more aware of the need to manage their horses and the land on which they live in a sustainable manner. Horses should be provided with a predominantly fibre-based diet in order to mimic their natural feeding pattern, however grazing impacts pasture differently, with a risk of overgrazing and soil erosion in equine pastures. Additionally, most horses receive supplements not only with concentrates and oils, but also with minerals. Though the excess minerals are excreted in the manure of horses, these minerals can accumulate in the soil or leach to nearby waterways and pollute water resources. Therefore, the postdoc research aims to answer the main question, “What horse feeding practices and measurements are needed to reduce and prevent environmental pollution in the Netherlands?” The postdoc research is composed of two components; a broad survey-based study which will generate quantitative data on horse feeding management and will also obtain qualitative data on the owners’ engagement or willingness of horse owners to act sustainably. Secondly, a field study will involve the collection of detailed data via visits to horse stables in order to gather data for nutritional analysis and to collect fecal samples for mineral analysis. Students, lecturers and partners will actively participate in all phases of the planned research. This postdoc research facilitates learning and intends to develop a footprint calculator for sustainable horse feeding to encompass the complexity of the equine sector, and to improve the Equine Sports and Business curriculum.
