Environmental assessment tools for the evaluation and improvement of European livestock production systems

Different types of assessment tools have been developed in Europe with the purpose of determining the environmental impact of various livestock production systems at farm level. The assessment tools differ in terms of which environmental objectives are included and how indicators are constructed and interpreted. The paper compares typical tools for environmental assessment of livestock production systems, and recommends selected indicators suitable for benchmarking. The assessment tools used very different types of indicators ranging from descriptions of farm management and quantification of input to estimates of emissions of, e.g., nitrate and ammonia. The indicators should be useful in a benchmarking process where farmers may improve their practices through learning from farms with better agri-environmental performance. An example of this is given using data on P-surplus on pig farms. Some indicators used the area of the farm as the basis of the indicator — e.g. nitrogen surplus per hectare — while others were expressed per unit produced, e.g. emission of greenhouse gasses per kilogram milk. The paper demonstrates that a comparison of organic vs. conventional milk production and comparison of three pig production systems give different results, depending on the basis of the indicators (i.e. per hectare or per kilogram product). Indicators linked to environmental objectives with a local or regional geographical target should be area-based — while indicators with a global focus should be product-based. It is argued that the choice of indicators should be linked with the definition of the system boundaries, in the sense that area-based indicators should include emissions on the farm only, whereas product-based indicators should preferably include emissions from production of farm inputs, as well as the inputs on the actual farm. The paper ends with recommendations for choice of agri-environmental indicators taking into account the geographical scale, system boundary and method of interpretation

An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits

To enhance documentation and maintenance practices, developers conventionally establish links between related software artifacts manually. Empirical research has revealed that developers frequently overlook this practice, resulting in significant information loss. To address this issue, automatic link recovery techniques have been proposed. However, these approaches primarily focused on improving prediction accuracy on randomly-split datasets, with limited attention given to the impact of data leakage and the generalizability of the predictive models. LinkFormer seeks to address these limitations. Our approach not only preserves and improves the accuracy of existing predictions but also enhances their alignment with real-world settings and their generalizability. First, to better utilize contextual information for prediction, we employ the Transformer architecture and fine-tune multiple pre-trained models on both textual and metadata information of issues and commits. Next, to gauge the effect of time on model performance, we employ two splitting policies during both the training and testing phases; randomly- and temporally-split datasets. Finally, in pursuit of a generic model that can demonstrate high performance across a range of projects, we undertake additional fine-tuning of LinkFormer within two distinct transfer-learning settings. Our findings support that to simulate real-world scenarios effectively, researchers must maintain the temporal flow of data when training models. Furthermore, the results demonstrate that LinkFormer outperforms existing methodologies by a significant margin, achieving a 48% improvement in F1-measure within a project-based setting. Finally, the performance of LinkFormer in the cross-project setting is comparable to its average performance within the project-based scenario

Methods and tools supporting urban resilience planning: experiences from Cork, Ireland

To prevent flood disasters, policymakers call for resilient cities which are better able to cope with flood hazards. However, actual adoption of resilience measures in urban planning is still limited, partly because it is not sufficiently clear how and to what extent resilience should and can be enhanced. To develop resilience strategies, information on the current resilience and on the effects of measures should be available. Since cities are complex systems, an assessment of resilience requires the input of different actors. To obtain and combine this input, a comprehensive approach which brings together many actors is required. Furthermore, resilience must be integrated in planning frameworks in order to enhance adoption by city policy makers. Tools which support and structure the contribution of different disciplines and actors will help to obtain information on the current resilience and to develop a shared vision on measures to enhance urban resilience. We illustrate our view with an example on Cork, Ireland