Poor Data Availability Research Articles

Comparing Life Cycle Assessment (LCA) of Salmonid Aquaculture Production Systems: Status and Perspectives

Aquaculture is the fastest growing food sector worldwide, mostly driven by a steadily increasing protein demand. In response to growing ecological concerns, life cycle assessment (LCA) emerged as a key environmental tool to measure the impacts of various production systems, including aquaculture. In this review, we focused on farmed salmonids to perform an in-depth analysis, investigating methodologies and comparing results of LCA studies of this finfish family in relation to species and production technologies. Identifying the environmental strengths and weaknesses of salmonid production technologies is central to ensure that industrial actors and policymakers make informed choices to take the production of this important marine livestock to a more sustainable path. Three critical aspects of salmonid LCAs were studied based on 24 articles and reports: (1) Methodological application, (2) construction of inventories, and (3) comparison of production technologies across studies. Our first assessment provides an overview and compares important methodological choices. The second analysis maps the main foreground and background data sources, as well as the state of process inclusion and exclusion. In the third section, a first attempt to compare life cycle impact assessment (LCIA) and feed conversion ratio (FCR) data across production technologies was conducted using a single factor statistical protocol. Overall, findings suggested a lack of methodological completeness and reporting in the literature and demonstrated that inventories suffered from incomplete description and partial disclosure. Our attempt to compare LCA results across studies was challenging due to confounding factors and poor data availability, but useful as a first step in highlighting the importance of production technology for salmonids. In groups where the data was robust enough for statistical comparison, both differences and mean equalities were identified, allowing ranking of technology clusters based on their average scores. We statistically demonstrated that sea-based systems outperform land-based technology in terms of energy demand and that sea-based systems have a generally higher FCR than land-based ones. Cross-study analytics also strongly suggest that open systems generate on average more eutrophying emissions than closed designs. We further discuss how to overcome bottlenecks currently hampering such LCA meta-analysis. Arguments are made in favor of further developing cross-study LCA analysis, particularly by increasing the number of salmonid LCA available (to improve sample sizes) and by reforming in-depth LCA practices to enable full reproducibility and greater access to inventory data.

CO2 emissions accounting for the chemical industry: an empirical analysis for China

The chemical industry is one of the most important industry sectors in terms of energy consumption and CO2 emissions in China. However, few studies have undertaken accounting of the CO2 emissions in the chemical industry. In addition, there are some shortcomings in the traditional accounting method as a result of poor data availability, such as the incomplete consideration of emission sources and overestimation of actual emissions. Based on the traditional accounting method and the actual situation of the chemical industry, this study proposes a method called the Emission Accounting Model in the Chemical Industry, which covers fossil energy-related emission, indirect emission generated by electricity and heat, carbonate-related process emission and the reuse of CO2. In particular, fossil energy used as feedstock is included. By applying the Emission Accounting Model in the Chemical Industry in China, the calculated CO2 emissions would be 19–30% less than the result from the traditional method. In addition, it is found that the indirect CO2 emissions generated by electricity and heat account for 67% of the total amount, the fossil energy-related emissions account for approximately 37%, the process-related emissions accounted for 2%, and reuse of CO2 accounts for − 6% in 2016. The production of ammonia, ethylene and calcium carbide generated approximately half of the total CO2 emissions in 2016. In addition, in view of emission sources and carbon source flow, two other bottom-up accounting methods are proposed that can take effect when the chemical plant-level data are available.

The need for comparative data in spondyloarthritis

Spondyloarthritis comprises a group of inflammatory diseases, characterised by inflammation within axial joints and/or peripheral arthritis, enthesitis and dactylitis. An increasing number of biologic treatments, including biosimilars, are available for the treatment of spondyloarthritis. Although there are a growing number of randomised controlled trials assessing treatments in spondyloarthritis, there is a paucity of data from head-to-head studies. Comparative data are required so that clinicians and payers have the level of evidence required to inform clinical decision-making and health economic assessments. In the absence of head-to-head studies, statistical methods such as network meta-analyses and matching-adjusted indirect comparisons (MAICs) are used for assessing comparative effectiveness.Network meta-analysis can be used to compare treatments for trials using a common comparator (e.g. placebo); however, for those without a common comparator or where considerable heterogeneity exists between the study populations, a MAIC that controls for differences in study design and baseline patient characteristics may be used. MAICs, unlike network meta-analyses, are of value for longer-term comparisons beyond the placebo-controlled phase of clinical trials, which is important for chronic diseases requiring long-term treatment, like spondyloarthritis. At present, there are a number of limitations that restrict the effectiveness of MAIC, such as the poor availability of individual patient-level data from trials, which results in patient-level data from one trial being compared with published whole-population data from another. Despite these limitations, drug reimbursement agencies are increasingly accepting MAIC as a means of comparative effectiveness and greater methodological guidance is needed.This report highlights a number of challenges that are specific to conducting comparative studies like MAIC in spondyloarthritis, including disease heterogeneity, the paucity of biomarkers and the duration of studies required for radiographic endpoints in this slow-progressing disease.

Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability

Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability

Qualitative analysis of the use of building performance simulation for retrofitting lower quality office buildings in Australia

Building Performance Simulation (BPS) has become an important tool in the upgrading of the existing commercial building stock. It allows a user to predict energy savings from upgrades, and thereby optimise the energy, financial or other benefits of an energy efficiency upgrade. The current paper presents the findings from a qualitative investigation into the use of BPS in the Australian commercial building retrofitting industry. The interviews had a particular focus on challenges for lower quality (known as secondary grade or mid-tier) office buildings. Twelve semi-structured interviews were undertaken with stakeholders in the building energy retrofitting industry, including consultants, government administrators and building managers. Thematic analysis of the interview data revealed numerous challenges for BPS users, primarily related to the lack of reliable, accessible data regarding building operations and energy use in Australia. The poor data availability encouraged the use of simulation to inform decisions but required the modeller to use assumptions and heuristics to develop the model. This, in turn, necessitated a reliance on the expert knowledge of the simulation user, and made the interrogation of a building model by a third party problematic.

Estimating Forest Fire Losses Using Stochastic Approach: Case Study of the Kroumiria Mountains (Northwestern Tunisia)

ABSTRACT Kroumiria Mountains (northwestern Tunisia) have experienced major fires, making them the main loss reason of Tunisian forested areas. The ability of accurately forecasting or modeling forest fire areas may significantly aid optimizing fire-fighting strategies. However, there are still limitations in the empirical study of forest fire loss estimation because the poor availability and low quality of fire data. In this study, a stochastic approach based on Markov process was developed for the prediction of burned areas, using available meteorological data sets and GIS layers related to the forest under analysis. The Self-organizing map (SOM) was initially used to classify spatiotemporal factors influencing the fire behavior. Subsequently, the SOM clusters were incorporated into a Hidden Markov Model (HMM) framework to model their corresponding burned areas. Results achieved using a database of 829 forest fires records between 1985 and 2016, showed the appropriateness of the HMM approach for the prediction of burned areas compared with a state-of-the art machine learning methods. The transition probability matrix (TPM) and the emission probability matrix (EPM) were also analyzed to further understand the spatiotemporal patterns of fire losses.

Gestão municipal de resíduos sólidos urbanos à luz da Política Nacional de Resíduos: estudo de caso no município de Japeri, RJ, Brasil

Este artigo visa analisar a gestão de resíduos urbanos (RSU) no município de Japeri, Região Metropolitana do estado do Rio de Janeiro, Brasil, à luz da Política Nacional de Resíduos Sólidos, criada em 2010, identificando os entraves na sua implantação. Trata-se de pesquisa de abordagem qualitativa realizada entre 2015 e 2016. Os procedimentos de coleta de dados utilizados foram: revisão da literatura; análise documental; entrevistas com gestores; observação direta e uso da técnica de grupo focal com representantes da comunidade. O estudo analisou a gestão de RSU de Japeri com uma perspectiva inovadora e colaborativa, ao usar a técnica do grupo focal, a qual evidenciou a importância em se envolver a comunidade na compreensão dos seus problemas e se mostrou adequada para fomentar esse debate. Entre os desafios a serem enfrentados por Japeri, tem-se: implantar o Plano Municipal de Gestão Integrada de Resíduos Sólidos (PMGIRS); reduzir a dependência dos órgãos estaduais e federais na gestão; universalizar a coleta regular de resíduos; ampliar a cobertura da coleta seletiva; envolver a sociedade na gestão, entre outros. Uma das limitações do estudo foi a pouca qualidade e disponibilidade de dados atuais sobre a gestão municipal de resíduos. A complexidade do tema também não permitiu que todas as questões fossem contempladas no estudo. A maior contribuição da pesquisa foi a sua abordagem, ao dar voz às demandas e sugestões de representantes da comunidade. Para o sucesso do PMGIRS, a participação social é imprescindível, a fim de impedir a sua descontinuidade ou que ele se transforme em mero instrumento político. Espera-se que a técnica de grupo focal empregada possa ser adaptada a outros contextos. Devido à escassez de estudos mais aprofundados sobre o tema, este artigo poderá vir a contribuir com o preenchimento dessa lacuna.

Graphene-based drug delivery systems in tissue engineering and nanomedicine.

The time and dosage form of graphene derivatives have been found to determine therapeutic and toxic windows in several cell lines and preclinical models. The enhanced biological action of graphene derivatives is made possible by altering the chemistry of native materials via surface conjugation, or by changing the oxidation state. The high level of chemical reactivity vested in the planar structure of graphene can be used to load various drugs and biomolecules with maximum radical scavenging effect. The integration of graphene and polymers brings electrical conductivity to scaffolds, making them ideal for cardiac or neuronal tissue engineering. Drawbacks associated with graphene-based materials for biomedical applications include defect-free graphene formation and heteroatom contamination during synthesis process; reduced availability of sp2 hybridized carbon centers due to serum proteins masking; and poor availability of data pertaining to in vivo clearance of graphene-based formulations. Personalized medicine is an emerging area of alternative treatments, which in combination with graphene-based nanobiomaterials, has revolutionary potential for the development of individualized nanocarriers to treat highly challenging diseases.

Analysis of Mapping Techniques for Mountain Precipitation: A Case Study of Alpine Region, Austria

Truly representative precipitation map generation of mountain regions is a difficult task. Due to poor gauge representativity, complex topography and uneven density factors make the generation of representative precipitation maps a very difficult task. To generate representative precipitation maps, this study focused on analyzing four different mapping techniques: ordinary kriging, spline technique (SP), inverse distance weighting (IDW) and regression kriging (RK). The generated maps are assessed through cross-validation statistics, spatial cross-consistency test and by water balance approach. The largest prediction error is produced by techniques missing information on co-variables. The ME and RMSE values show that IDW and SP are the most biased techniques. The RK technique produced the best model results with 1.38mm and 72.36mm ME and RMSE values respectively. The comparative analysis proves that RK model can produce reasonably accurate values at poorly gauged areas, where geographical information compensated the poor availability of local data.

Inter-Comparison of Gauge-Corrected Global Satellite Rainfall Estimates and Their Applicability for Effective Water Resource Management in a Transboundary River Basin: The Case of the Meghna River Basin

The Meghna River basin is a transboundary basin that lies in Bangladesh (~40%) and India (~60%). Due to its terrain structure, the Bangladesh portion of the basin experiences frequent floods that cause severe human and economic losses. Bangladesh, as the downstream nation in the basin, faces challenges in receiving hydro-meteorological and water use data from India for effective water resource management. To address such issue, satellite rainfall products are recognized as an alternative. However, they are affected by biases and, thus, must be calibrated and verified using ground observations. This research compares the performance of four widely available gauge-adjusted satellite rainfall products (GSRPs) against ground rainfall observations in the Meghna basin within Bangladesh. Further biases in the GSRPs are then identified. The GSRPs have both similarities and differences in terms of producing biases. To maximize the usage of the GSRPs and to further improve their accuracy, several bias correction and merging techniques are applied to correct them. Correction factors and merging weights are calculated at the local gauge stations and are spatially distributed by adopting an interpolation method to improve the GSRPs, both inside and outside Bangladesh. Of the four bias correction methods, modified linear correction (MLC) has performed better, and partially removed the GSRPs’ systematic biases. In addition, of the three merging techniques, inverse error-variance weighting (IEVW) has provided better results than the individual GSRPs and removed significantly more biases than the MLC correction method for three of the five validation stations, whereas the two other stations that experienced heavy rainfall events, showed better results for the MLC method. Hence, the combined use of IEVW merging and MLC correction is explored. The combined method has provided the best results, thus creating an improved dataset. The applicability of this dataset is then investigated using a hydrological model to simulated streamflows at two critical locations. The results show that the dataset reproduces the hydrological responses of the basin well, as compared with the observed streamflows. Together, these results indicate that the improved dataset can overcome the limitations of poor data availability in the basin and can serve as a reference rainfall dataset for wide range of applications (e.g., flood modelling and forecasting, irrigation planning, damage and risk assessment, and climate change adaptation planning). In addition, the proposed methodology of creating a reference rainfall dataset based on the GSRPs could also be applicable to other poorly-gauged and inaccessible transboundary river basins, thus providing reliable rainfall information and effective water resource management for sustainable development.

Exploring equity focus of the SHAWN WASH programme in Nigeria

Abstract This study assesses the extent to which equity was mainstreamed in the UNICEF Sanitation, Hygiene and Water in Nigeria (SHAWN) programme. Mixed methods consisting of desk review, survey of 2,105 households, individual survey of physically disabled people, focus group discussions with community members and in-depth interviews with key informants were used. The WASH project displayed equity considerations in selection of local government areas for its interventions – prioritizing underserved and unserved rural areas, females and places with the greatest needs – leading to increased access to water and sanitation for the poor. However, access was less for people with physical disabilities. Institutional and financial sustainability considerations challenged equity. Gender equality gaps driven by cultural and religious barriers existed in the composition of government WASH departments and community WASH Committees (WASHCOMS) despite the prominence accorded to gender issues. Operationalization of equity was hindered by poor data availability for assessment of poverty and needs; and cultural, environmental and financial barriers. WASHCOMS require specific training to operationalize equity and inclusion at community and household levels. The potential positive effects of a rights-based approach and equity on social and behavioural sustainability should be considered in future programming.

Availability and feasibility of structured, routine collection of comorbidity data in a colorectal cancer multi-disciplinary team (MDT) setting

PurposeAvailability of comorbidity assessment at multi-disciplinary team (MDT) discussions is cornerstone in making the MDT process more robust and decisive in optimising treatment and improving quality of survivorship. Comorbidity assessments using tools, such as the ACE-27 questionnaire would aid in optimising the decision-making process at MDTs so that treatment decisions can be made without delay. This study determined the availability of comorbidity data in a CRC MDT and the feasibility of routine comorbidity data collection using the validated ACE-27 questionnaire. Secondary aims determined the optimal time and method of collecting comorbidity data.MethodsA retrospective mapping exercise (phase I; 6-months) examined the availability of comorbidity data within the MDT. Phase II prospectively collected comorbidity data using ACE-27 for a 3-month period following a short pilot.ResultsIn phase I, 73/135 (54%) patients had comorbidity data readily available informing the MDT discussion; 62 patients lacked this information. After a review of the patient records, it was clear that 41 of these 62 also had comorbidities and 21 out of the 135 had ≥ 2 major system disorders. Common referral sources to the MDT were surgical outpatient clinics (42%) and the endoscopy unit (13%). The average lead-time from referral to MDT discussion was 14 days. In phase II, an ACE-27 questionnaire was prospectively administered in 50 patients, mean age 54 years (range 20–84). Male: female ratio 26:24. Average time to administer ACE-27 was 4.8 min (range 1–15).ConclusionsThe phase I study confirmed the widely acknowledged view of poor comorbidity data availability within a CRC MDT. Phase II demonstrated the feasibility of routinely collecting comorbidity data using ACE-27.

Habitat conservation in Italy: the state of the art in the light of the first European Red List of Terrestrial and Freshwater Habitats

The importance of taking into account ecosystems, plant communities and habitats for the development of biodiversity conservation strategies is increasingly acknowledged. Recently, the first ever European Red List of Habitats was produced, which provided an evaluation of the extinction risk of EUNIS-based natural and semi-natural habitats in Europe. As assessment unit, it used the habitat intended as a plant community, thus representing a landmark for the role of vegetation science in nature conservation. In the present paper, the results of the European Red List of Habitats are analyzed at the national scale with specific reference to the terrestrial and freshwater habitat types occurring in Italy. More than three-quarters of the assessed European habitat types were recognized for the Italian territory. The distribution of the threat categories reflects approximately the situation at the EU28 level. About 35% of the assessed habitat types are referred to a threat category; no critically endangered habitat is present in Italy. The most frequently used criteria are those related to a reduction in quantity. Some critical issues arising from the analyses are discussed. In particular, the presence of knowledge gaps is pointed out, with remarkable reference to the poor availability of spatial and quantitative data, severely affecting the application of the criteria adopted for the assessment. Descriptions of habitat types from Italy are reported, some of which are representative, emblematic or even exclusive to the Italian territory. The outcomes of the analysis represent the starting point for the future development of a national-scale Red List of Habitats. Results also emphasized how habitat types with a too broad definition pose a limit to a proper evaluation of the regional biogeographic variability, often very high in Italy, with local floristic and phytocoenotic peculiarities which do not find room in the adopted European typology. This is the reason why the development of national subtypes stands as a necessary step for the development of a realistic and effective assessment at the national scale.

Hydrological model using ground- and satellite-based data for river flow simulation towards supporting water resource management in the Red River Basin, Vietnam

The Red River basin (RRB) exhibits substantial variation of water resource seasonally and annually. Sustainable water resource management in the RRB has been challenging due to the lack of in situ hydrological measurement data over the basin-wide scale. To address this issue, this study aimed to perform the setting up, calibration, and validation of the variable infiltration capacity (VIC) hydrological model forced with ground- and satellite-based datasets at a high spatial resolution of 0.1° for simulating the daily river flow of the Red River system in the RRB during the period of 2005–2014. By using the finely resolved land cover characterization with 15 types of land cover and leaf area index - the most important feature of vegetation that significantly influences the simulation of hydrological variables provided by the spatially distributed satellite remote sensing data, this study would not only address the poor data availability over the RRB but also enhance the accuracy of model simulation. The simulation results generally indicated that the calibrated VIC model could satisfactorily capture the river flow dynamics of the Red River system in the RRB. The VIC model's underestimated river flow compared to the observed data during the dry season for the downstream stations was likely due to the operation of the large man-made reservoirs and dams in the upstream catchments of the RRB that not represented by the VIC model. The findings also suggested that for further improving the VIC model performance, the use of more spatially representative meteorological data provided by satellite remote sensing should be considered in future studies.

Comparative Analysis of Spatial Interpolation Techniques for Mapping Annual Mean Rainfall Estimation within a Mountainous Region

The complex topography, poor gauge representativity and uneven density make it an uphill task to accurately map precipitation in mountainous regions. This challenge was confronted with the evaluation of four different mapping techniques: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Spline and Regression Kriging (RK). An evaluation of the resulting georasters using 1) cross-validation statistics, 2) a spatial cross-consistency test and 3) a water balance analysis reveals that the techniques ignoring the information on co-variables yield the largest prediction errors. Mean error and root-mean-square error values suggested that the most biased methods were IDW and spline, with a bias almost 2 to 5 times higher than ordinary kriging. The best model accounted for mean precipitation analysis is regression Kriging, with a mean error and root mean square error values of 1.38 mm and 72.36 mm respectively, which represents 42 % less bias and 16 % higher accuracy than OK results. Comparative performances show that the regression analysis made it possible to judiciously evaluate the variable patterns and get fairly accurate values at un-gauged locations where geographical information compensated the poor availability of local data.

Priorities and Future Actions for an Effective Use of Phytotherapy in Livestock-Outputs from an Expert Workshop.

This study reflects on the recognised need for more joined-up, high-quality research on phytotherapy that addresses the current societal challenges in finding alternatives to the use of antibiotics. The study applied a multidisciplinary participatory approach in an expert workshop exercise within the FP7 EU IMPRO project. Prior to this study, a literature review was elaborated on research in the field of phytotherapy as applied to farm animals, cooperation between research bodies and initiatives to reduce the use of antibiotics by using phytotherapeutic remedies. The review was delivered to the participants of the workshop so as to receive feedback on it and enrich the discussion. Different expertise, background in research or veterinary practice, and varying positions regarding phytotherapy were the criteria in targeting participants. A structured workshop was subsequently organised, with questions to experts addressing scientific validation of phytotherapy, effective treatment under farm conditions and necessary developments for the future. Challenges identified by the experts were as follows: poor study designs, lack of reproducibility of studies, poor standardisation of products, cost–benefit concerns, lack of veterinarian training and poor data availability. To overcome obstacles, the need for improved study designs for clinical trials was given priority in order to prove the efficacy of remedies and to implement a monitoring system which enables the assessment of the effectiveness of treatments in farm practice. Reflections in this report are intended to be a resource for scientists, policy makers and end users for an effective use of phytotherapy at farm level.

Mapping the Spatial Distribution of Rice Fields in Southern Coast of Caspian Sea Using Landsat 8 Time-series Images

Since rice is one of the important crops not only in Iran but also in the world, it is vital to determine the paddy rice field as accurately as possible using fast and economical methods such as remote sensing and GIS. Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases missions, food and water security, and human health. In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. The unique physical features of rice paddy fields during the flooding/open-canopy period were captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. In order to prepare information about spatial distribution of rice fields, Multi-Spectral and Multi-Temporal data can be helpful because in addition to the rice, all fields could be covered by mixture of water and soil regarding the time of crop calendar. The Landsat 8 data with 11 bands has visible, near infrared, shortwave infrared as well as thermal bands; and therefore, different vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI2105) which is sensitive to leaf water and soil moisture. In this study, special mapping algorithm that uses NDVI and LSWI2105 time series data derived from LANDSAT8 16-days 30-meter vegetation indices product of LANDSAT imagery developed to identify paddy rice fields. This algorithm works based on the sensitivity of LSWI2105 to the surface moisture and NDVI to the vegetation chlorophyll content. In this research method has been developed to define the relationship between the NDVI and LSWI2105 to detect the location of paddy rice fields in North part of Iran in 2014-15. The results, a validated with ground field works data at 56 well-distributed sample points. The overall accuracy of the method was 69.0909%.

Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month Tair mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical Tair modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum Tair, but also for maximum Tair. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum Tair (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.

Using synchrophasors in distribution network for synchronised switching

Sibelga is the distribution system operator for the area of Brussels, the capital of Belgium. In Sibelgas network, during reconfiguration of the medium-voltage rings between different substations, problems occurred. The reason were angle differences between the substations, caused by increasing renewable infeed and poor data availability of measurements. The idea is to monitor the voltage phase angles and amplitudes in the distribution system by phasor measurement units to have the necessary observability. These phasor data can be used for a manual check for synchronisation or the calculation of the phase angle difference to simulate the exchange loads and to avoid difficulties in the reconfiguration process or during parallel couplings in the network.

Future Developments in German Fish Market – Integration of Market Expert Knowledge into a Modelling System

Globally, fish has become more important in human nutrition, thus global consumption is expected to highly increase in the future years. Business-as-usual projections for fish market are limited by availability of reliable data at fish category level that hinders the differentiation in more detail on supply and demand as well as across EU member states. The new fish module in AGMEMOD provides midterm projections for the fish category markets in EU member states. For this, a status-quo simulation to the year 2030 is developed in AGMEMOD. In order to deal with the poor data availability, the model employs expert opinions about the German market, obtained through an elaborate questionnaire. Thus, expertise knowledge provides more detailed and accurate information on the German fish sector that helps to improve the insights in future German market developments. Based on this, AGMEMOD shows somewhat higher production development up to 2030, than in the original projections.