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Podhora, A.; Helming, K.; Adenäuer, L.; Heckelei, T.; Kautto, P.; Reidsma, P.; Rennings, K.; Turnpenny, J.; Jansen, J. |
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Title |
The policy-relevancy of impact assessment tools: Evaluating nine years of European research funding |
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Journal Article |
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Year |
2013 |
Publication |
Environmental Science & Policy |
Abbreviated Journal |
Environmental Science & Policy |
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31 |
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85-95 |
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impact assessment tools; policy appraisal; science policy interface; sustainable development; european commission; affecting land-use; of-the-art; integrated assessment; sustainable development; agricultural systems; analytical framework; union; part |
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Since 2002, the European Commission has employed the instrument of ex-ante impact assessments (IA) to help focus its policy-making process on implementing sustainable development. Scientific tools should play an essential role of providing the evidence base to assess the impacts of alternative policy options. To identify the contribution of research funding for IA tool development, this paper analysed the variety of IA tools designed in projects funded by European Framework Programmes (FPs) 6 and 7. The paper is based on project information available on the European Cordis website, individual project websites and a verification of the results by the project coordinators. We analysed the projects from the interests of IA practitioners as tool users (European policy and impact areas addressed by the tools, jurisdictional application levels and tool categories). Out of the 7.781 projects funded in FP6 and FP7, 203 could be identified that designed tools for the IA process. Nearly half of them applied to environmental, agricultural and transport policy areas. Within these areas, the tools primarily addressed environmental impact areas, less economic and least social impact areas. The IA tools focused on European policies. Models represented the largest tool category, whereas approximately half of the tools could not be clearly categorized. Concerning our analysis criteria, the tool descriptions available on the internet were often unclear and thus may limit the application potential of the tools because of a mismatch of technical terms and categorisation criteria between tool providers and tool users. Future IA tools require a joint political and scientific typology and a narrowing of the gaps, e.g., with view to multi-jurisdictional application and a clear reference to the steps of the IA process. (C) 2013 Elsevier Ltd. All rights reserved. |
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1462-9011 |
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TradeM |
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MA @ admin @ |
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4500 |
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Mansouri, M.; Destain, M.-F. |
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Title |
Predicting biomass and grain protein content using Bayesian methods |
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Journal Article |
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Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
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29 |
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4 |
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1167-1177 |
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crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
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This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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1436-3240 1436-3259 |
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CropM |
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MA @ admin @ |
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4664 |
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Abdelrahman, H.M.; Olk, D.C.; Dinnes, D.; Ventrella, D.; Miano, T.; Cocozza, C. |
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Occurrence and abundance of carbohydrates and amino compounds in sequentially extracted labile soil organic matter fractions |
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Journal Article |
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2016 |
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Journal of Soils and Sediments |
Abbreviated Journal |
Journal of Soils and Sediments |
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16 |
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10 |
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2375-2384 |
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Light fraction; Mobile humic acid; Organic farming; Particulate organic matter; SOM sequential extraction |
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Purpose The study aimed to describe the carbohydrates and amino compounds content in soil, the light fraction (LF), the >53 μm particulate organic matter (POM), and the mobile humic acid (MHA) fraction and to find out whether the carbohydrates and amino compounds can be used to explain the origin of SOM fractions. Materials and methods Soil samples were collected from two agricultural fields managed under organic farming in southern Italy. The LF, the POM, and the MHA were sequentially extracted from each soil sample then characterized. Seven neutral sugars and 19 amino compounds (amino acids and amino sugars) were determined in each soil sample and its correspondent fractions. Results and discussion The MHA contained less carbohydrate than the LF or the POM but its carbohydrates, although dominated by arabinose, were relatively with larger microbial contribution as revealed by the mannose/xylose ratio. The amino compounds were generally less in the LF or the POM than in the MHA, while the fungal (aspartic and serine) and bacterial (alanine and glycine) amino acids were larger in the MHA than in the LF or the POM, underlining the microbial contribution to the MHA. Results from both sites indicated that total carbohydrates content decreased moving from the LF (younger fraction) to the MHA (older fraction), which seems to follow a decomposition continuum of organic matter in the soil-plant system. Conclusions The study showed that the MHA is a labile humified fraction of soil C due to its content of carbohydrates and concluded that the content of carbohydrates and amino compounds in the LF, the POM and the MHA can depict the nature of these fractions and their cycling pattern and response to land management. |
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1439-0108 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4992 |
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Mansouri, M.; Dumont, B.; Leemans, V.; Destain, M.-F. |
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Bayesian methods for predicting LAI and soil water content |
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Journal Article |
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2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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15 |
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2 |
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184-201 |
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crop model; bayes; data assimilation; extended kalman filtering; particle filtering; variational filtering; leaf-area index; parameter-estimation; crop models; moisture; instruments; management; sensors; state |
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LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. |
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1385-2256 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4629 |
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Allan, C.; Nguyen, T.P.L.; Seddaiu, G.; Wilson, B.; Roggero, P.P. |
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Integrating local knowledge with experimental research: case studies on managing cropping systems in Italy and Australia |
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Journal Article |
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2013 |
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Italian Journal of Agronomy |
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Ital. J. Agron. |
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8 |
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2 |
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15 |
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participatory action research; agronomic research; local knowledge; knowledge integration |
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The sustainable development of agricultural systems is currently challenged by many complex agro-environmental issues. These are characterized by an incomplete understanding of the situation and the problems that arise, and the conflicting opinions that result, issues over boundaries that are often difficult to define, and controversy over the multiple goals and uncertain outcomes. Added to these characteristics, we also have the slow and often inadequate uptake and implementation of research outcomes in this complex, real world. In order to improve sustainability of agro-ecosystems, agronomic research must move away from the linear research approaches and extension practices adopted so far that have focused purely on biophysical agro-ecosystems. The theoretical operational space of agronomic research must be transformed by considering agronomic issues as part of a broader social-agro-ecosystem. One aspect of this transformation is the inclusion of knowledge collected on a local level with the participation of farmers on the ground. The integration of local experiential knowledge with traditional agronomic research is by necessity based on the participation of many different stakeholders and there can be no single blueprint for how best to develop and use the input received. However, agronomists and policy advisors require general guidelines drawn up from actual experience in order to accelerate positive agronomic change. We address this need through a comparative analysis of two case studies; one involves multi-stakeholder research in a cropping system in the dairy district of Arborea, Sardinia, Italy. The central question was: How can high crop production be maintained while also achieving the EU target water quality and minimizing the production costs? The second case is a multi-stakeholder soil health project from south-eastern Australia. Here the central question was: How can soil decline be prevented and reversed in this district, and soils made more resilient to future challenges? The Social Learning for the Integrated Management and sustainable use of water (SLIM) framework, a useful heuristic tool for exploring the dynamics of transformational change, guided the analysis of the case studies. Within this framework, a key indicator of success is the emergence of new knowledge from the creation of new spaces for learning between researchers and local stakeholders. The Italian case study appears to have been the most successful in this sense, as opportunities for joint exploration of research data allowed new potential farming responses to the central question to emerge. The multi-stakeholder processes in the Australian case focused more on providing public openings for individual learning, and missed the opportunity for new knowledge to emerge through joint exploration. We conclude that participatory approaches may enable transformative practice through knowledge integration, but that this process is not an automatic outcome of increased community participation. |
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2039-6805 1125-4718 |
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MA @ admin @ |
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4482 |
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