Eurostat

The impacts of agricultural, environmental and climate change policies in Europe are investigated using the CAPRI (Common Agricultural Policy Regionalised Impact) model. Although the model runs at a regional level, primarily NUTS2, the underlying data have been spatially disaggregated to finer scale units called 'Farm Structure Units'. In this study we present these spatially disaggregated layers of agri-environmental variables for a time series from 2000 to 2018. This harmonized and spatially consistent data set includes crop types (area, yield), livestock types (totals and densities) and various parameters associated with nitrogen application and losses. These data can be used to calculate nitrogen budgets as well as providing inputs to biogeochemical or land management models.

The paper tackles some of the major challenges associated with coastal risk reduction, based on restored river-delta-coast continuums. The presented approach builds on the Ebro River-Delta system, located in the Spanish Mediterranean coast, where available data and results from hands-on restoration actions provide quantitative evidence on the synergies and trade-offs when implementing restoration. The work addresses the evolution of climatic- and human-induced risks driven by recent deltaic evolution, starting in the 1960s, when socioeconomic development increased and river regulation by large dams in the lower river course started. Such development has driven up freshwater demand, consequently diminishing both liquid and solid discharges in the river system, which has affected the natural deltaic evolution, compounded by sea-level-rise and changes in storminess due to global warming. Evolving erosion and flooding risks (problem symptoms) consider the decrease in sediment input to the delta and the increase in mean sea level (problem origin). To address the origin of the problem, we address sediment availability and transport in the lower river coast and reservoirs, followed by an assessment of sediment transport and deficit at the coastal fringe. The proposed solutions consider reservoir by-pass techniques, sediment mobilisation and downstream transport, as well as downstream transport capacity under present and future climates. Regarding the problem symptoms, we analyse the alternatives to enhance coastal resilience by means of restored coastal roughness resulting from embryonic dunes, lagoons and ridge and runnel topography. The analysis also considers coastal sediment recycling, based on a combination of wind and wave induced transports that provide a natural conveyor belt in dynamic equilibrium. Finally, the paper systematises the main synergies and trade-offs that must be considered in any coastal restoration plan, targeting a low carbon reduction of coastal risks while maintaining the important socio-economic assets in the river-delta-coast system. The conclusion section briefly explores the extrapolation of the proposed approach to other vulnerable coastal regions in Europe.

Purpose This study explores the features and consequences of heterogeneity among clients of the largest Islamic microfinance institution in Pakistan, identifying differences in business and household outcomes between sub-groups of borrowers. Study Design Our research is based on a longitudinal survey of 500 new clients of the institution conducted between 2015 and 2017, which we use to construct a unique dataset of entrepreneurs applying for interest-free microcredit loans. Evidence of significant heterogeneity was found among entrepreneurs at the time of the baseline survey. Findings We find differences in business and household outcomes between different types of entrepreneurs based on their level of need. In particular, ‘necessity’ entrepreneurs, who were poorer at the time of the baseline survey, tended to experience greater reductions in poverty levels over the study period, while female clients were found to have decreased their savings frequency more than male clients over the sample period. Contributions Our results challenge the conventional approach in microcredit programmes to treat the poor as a homogenous group to whom a rigid loan product is offered. We investigate this issue in the specific context of religious communities and institutions, which may further compound issues of client heterogeneity given a range of additional operational objectives. We also make a theoretical contribution in terms of applying agency theory and transaction cost economics in this novel context. Implications Our findings have several theoretical and managerial implications with respect to the application of the Shari'ah principles of Islamic Finance among a heterogenous group of entrepreneurs.

The last European agricultural census, organized by Eurostat, took place in 2020, collecting more than 300 variables on agriculture and farm structure from 9.03 million farmers in the EU (and EFTA countries Iceland, Switzerland and Norway). Data are aggregated to NUTS 2, NUTS 1 or national levels before they are publicly released on the Eurostat website due to confidentiality regulations that do not allow individual data to be disclosed.With a newly developed methodology, it is now possible to create multi-resolution grids of these variables, where the size of the grid cells depend on the spatial density of the data. All grid cells respect a series of confidentiality rules, such as a minimum number of farms per grid cell (at least 10) a dominance rule (the largest contributes cannot have more than 85% of the total production in a grid cell) and a quality rule (estimates must have an estimated coefficient of variation less than 35%).We will here present a range of thematic maps based on the 2020 European agricultural census to highlight regional and country differences, giving a new insight in the geographical patterns of agriculture in Europe, some expected, some more unexpected. All of them correspond to contextual indicators for the Common Agricultural Policy (CAP) and are divided into three broad categories: structural components (i.e., agricultural holdings, land use, livestock patterns, and labor input); the demographics of farmers (i.e., age, gender, and skills); and agricultural production methods (i.e., irrigation and organic farming).For example, our analysis shows that high farm densities occur in plains and fertile valleys, while organic farming is concentrated in areas with high grassland proportions. Young farmers' holdings are located in a belt from France through Switzerland, Austria, Czechia, Slovakia and Poland. These data sets allow for more local policy evaluation and offer researchers opportunities to draw causal spatial inferences. The data are available through the Gisco viewer:https://ec.europa.eu/eurostat/web/experimental-statistics/geospatial-data-agricultural-census 

The uptake of organic farming is heterogeneous across the European Union (EU) and across farming systems. According to the 2020 agricultural census, at 14.8 million ha, the area under organic farming accounted for 9.1% of the total EU agricultural land, and close to 20% of the area under organic farming in the world. The EU’s Farm to Fork strategy, aims to have 25% of the EU’s agricultural land under organic farming by 2030.While the agricultural census from 2020 collected data on organic farming uptake by farming type, geospatial data was not available, so far. Disclosing census data at a more granular level is subject to confidentiality treatment as specified in the Implementing Regulation (EU) 2018/1091 on Integrated Farm Statistics (IFS). Confidentiality requirements on the frequency and dominance of farm holdings must be applied to map the data. Following the recent implementation of a methodology that accounts for these requirements, spatial mapping of the 2020 census became possible, providing an unprecedented view on EU agriculture. In addition, this also increases the number of data points available for analysis by up to three magnitudes.Using this data, we spatially map the shares of organic farming associated to permanent grasslands, green fodder, arable crops including cereals, and permanent crops such as fruit, olives, and vineyards. We quantify current levels and local uptake gaps, investigate which farming types are characterized by the largest share of organic farming, and identify the most important drivers of organic farming across the EU. To identify these drivers we distinguish natural factors such as physiography (soils), climate, but also socio-economic characteristics related to farm structure (e.g. farm physical and economic size, demography, labour), as derived from the new datasets. This is then compared to the commitment EU Member States have quantified up to 2028 in terms of the share of utilised agricultural area (UAA) supported by the Common Agricultural Policy for organic farming as part of the Performance Monitoring and Evaluation Framework.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

The European Statistics Awards Programme is a multiannual program for engaging with, and tapping into the potential of, the research and innovation community at large. This is achieved through competitions with pecuniary prizes for the best performers. One of the strands within that program offers awards for the accurate nowcasting of monthly time series of interest to policymakers. This paper presents the design of the nowcasting competition as well as the performance of the best entries of the first annual round. In terms of accuracy, the competition has generated promising results—which do however need further fine-tuning before being possible to deploy in statistical production. The competitions feature a particular prize for submissions having a potential for scaling up to regular statistical production—and in response to this, teams have submitted ample documentation (including code). The awards program should also generate experience that the official statistics community can learn from. This paper demonstrates how the multi-annual nature of the program has allowed various design changes to be implemented for the last nowcasting round. The main work now lies ahead: the paper outlines the plans for further developing the most promising competition contributions, potentially leading to new Eurostat experimental statistics products.

A growing volume of evidence marks the potential of Artificial Intelligence (AI) in medicine, in improving diagnostic accuracy, clinical decision support, risk/event prediction, drug discovery, and patient management. However, the continuous integration of AI into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates these guidelines quality and summarizes ethical frameworks, best practices, and recommendations. The Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool was used to assess the quality of guidelines based on six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The protocol of this review including the eligibility criteria, the search strategy data extraction sheet and methods, was published prior to the actual review with International Registered Report Identifier (IRRID) of (DERR1-10.2196/47105). The initial search resulted in 4,975 studies from two databases and five studies from manual search. Nine articles were selected for data extraction based on the eligibility criteria. We found that while guidelines generally excel in scope, purpose, and editorial independence, there is significant variability in applicability and the rigour of guideline development. Well-established initiatives such as DECIDE-AI, SPIRIT-AI, and CONSORT-AI have shown high quality, particularly in terms of stakeholder involvement. However, applicability remains a prominent challenge among the guidelines. We conclude that the reproducibility, ethical and environmental aspects of AI in medicine still need attention from both medical and AI communities. This review emphasizes the crucial need for high-quality guidelines and opens a new avenue in evaluating guidelines themselves. Our work highlights the need for working toward the development of integrated and comprehensive reporting guidelines that adhere to the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). This alignment is essential for fostering a cultural shift towards transparency and open science, which are pivotal milestone for sustainable digital health research.