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European agriculture is undergoing a rapid evolution that challenges agronomic research to scale from field to landscape. In particular, the undergoing processes (e.g. urbanization or land abandonment) and the multiple ecosystems services provided by agricultural areas are requiring to broaden the research at the regional level. Since some decades, the European Union is promoting the collection of agricultural data to evaluate the farmers' eligibility for subsidies and to assess the Common Agricultural Policy performances. Part of these datasets is being increasingly used beyond their administrative functions, as for the Farm Accountancy Data Network (FADN) and the Land Parcel Identification System (LPIS). Starting from a bibliometric analysis of the scientific literature using these datasets, we will discuss two examples of their application for characterizing cropland and cropping systems. Our aim is to discuss the relevance of these datasets as tools to improve the monitoring and management of agroecosystems at the regional level.

Creative Commons. Attribution 3.0 Unported (CC BY 3.0) ; [EN] Parcel concentration is implemented in smallholding areas with the aim of decreasing costs of production and increasing profitability of farms. In Spain, its origins date back to 1953 and it is currently an instrument for territorial regulation in rural areas. All previous reports for parcel concentration include a prediction of costs for the necessary works, in order to estimate, approximately, expenditures for Public Administrations and to plan the execution of actions. In all of these, there is a reference about the importance of orography in the cost of works, fundamentally for the road network, which is the primary work. The incidence of orography on the costs of parcel concentration works is quantified based on information obtained from works for parcel concentration already done in the province of Teruel, together with data about orography in those areas, obtained from a digital model for terrain lifting. In addition, a model is obtained through the ordinary minimum squares regression method, which explains the costs of works and which uses as an explaining variable the average slope of the terrain. ; [ES] La concentración parcelaria se aplica en zonas de minifundio con el objetivo de disminuir los costes de producción y aumentar la rentabilidad de las explotaciones. En España sus orígenes datan del año 1953 y en la actualidad es un instrumento de ordenación territorial en el medio rural. Todos los informes previos de concentración parcelaria contienen una previsión del coste de las obras necesarias para conocer, de forma aproximada, los gastos para la Administración Pública y planificar la ejecución de las actuaciones. En todos ellos se hace referencia a la importancia de la orografía en el coste de las obras, fundamentalmente la de la red de caminos, que es la obra principal. A partir de la información obtenida de los trabajos de concentración parcelaria ya realizados en la provincia de Teruel, junto con datos sobre la orografía en dichas ...

A renewed call for replications has emerged in social science research. An important form of replication involves exploring the extent to which findings from a given study hold in other contexts. This study draws on opinion polling data to replicate key findings across time and space based on an original study in one location analyzing attitudes toward public school assignment policies. The replication finds that many of the original findings hold, though one important exception reflects the changing context. We note that the increasing availability of relatively inexpensive methods of quantitative data production facilitates replication and comment on how the temporal interval between the original study and the replication may influence the extent to which findings replicate. We argue that largely successful replications help to clarify the conditions under which findings replicate, and that sociologists are in the early stages of determining which strategies work best for replicating which findings.

National audience ; European agriculture is undergoing a rapid evolution that challenges agronomic research to scale from field to landscape. In particular, the undergoing processes (e.g. urbanization or land abandonment) and the multiple ecosystems services provided by agricultural areas are requiring to broaden the research at the regional level. Since some decades, the European Union is promoting the collection of agricultural data to evaluate the farmers' eligibility for subsidies and to assess the Common Agricultural Policy performances. Part of these datasets are being increasingly used beyond their administrative functions, as for the Farm Accountancy Data Network (FADN) and the Land Parcel Identification System (LPIS). Starting from a bibliometric analysis of the scientific literature using these datasets, we will discuss two examples of their application for characterizing cropland and cropping systems. Our aim is to discuss the relevance of these datasets as tools to improve the monitoring and management of agroecosystems at the regional level.

National audience ; European agriculture is undergoing a rapid evolution that challenges agronomic research to scale from field to landscape. In particular, the undergoing processes (e.g. urbanization or land abandonment) and the multiple ecosystems services provided by agricultural areas are requiring to broaden the research at the regional level. Since some decades, the European Union is promoting the collection of agricultural data to evaluate the farmers' eligibility for subsidies and to assess the Common Agricultural Policy performances. Part of these datasets are being increasingly used beyond their administrative functions, as for the Farm Accountancy Data Network (FADN) and the Land Parcel Identification System (LPIS). Starting from a bibliometric analysis of the scientific literature using these datasets, we will discuss two examples of their application for characterizing cropland and cropping systems. Our aim is to discuss the relevance of these datasets as tools to improve the monitoring and management of agroecosystems at the regional level.

National audience ; European agriculture is undergoing a rapid evolution that challenges agronomic research to scale from field to landscape. In particular, the undergoing processes (e.g. urbanization or land abandonment) and the multiple ecosystems services provided by agricultural areas are requiring to broaden the research at the regional level. Since some decades, the European Union is promoting the collection of agricultural data to evaluate the farmers' eligibility for subsidies and to assess the Common Agricultural Policy performances. Part of these datasets are being increasingly used beyond their administrative functions, as for the Farm Accountancy Data Network (FADN) and the Land Parcel Identification System (LPIS). Starting from a bibliometric analysis of the scientific literature using these datasets, we will discuss two examples of their application for characterizing cropland and cropping systems. Our aim is to discuss the relevance of these datasets as tools to improve the monitoring and management of agroecosystems at the regional level.

The European Commission fosters the opening and reusing of the Integrated Administration and Control Systems (IACS) administrative data from the European paying agencies as useful tools for monitoring the Common Agricultural Policy (CAP) performances. In this scenario, the Open IACS project goes in the same direction by demonstrating the reuse of the IACS data for computing CAP indicators and for agri-environmental analysis. This work reports the methods adopted in Italy to process and harmonize IACS geospatial data required to compute two Context indicators for CAP: Utilized Agricultural Area (UAA) and share of UAA by land use types. These are defined as context indicators within the EU framework for assessing CAP performances and are generally computed with statistical surveys from Eurostat. The indicators are computed for a study area located in South Italy and statistical summaries are reported.

In recent years, data have become part and parcel of contemporary capitalism. This has created tensions between the growing demand for personal data and the fundamental right to data protection. Against this background, the EU's adoption of the General Data Protection Regulation (GDPR) in 2016 is puzzling. Why did the EU adopt a regulation that strengthens data protection despite intensive lobbying by powerful business groups and the EU's supposed neoliberal bias? We make two arguments to explain this outcome. First, we use process tracing to show how issue‐specific institutions played a crucial role during the agenda‐setting stage (1990s–2009) and policy formulation stage (2009–2012). They triggered and structured the drafting process by strengthening the position of data protection advocates within the European Commission. Second, we use discourse network analysis to show that the Snowden revelations of 2013 fundamentally changed the discursive and coalitional dynamics during the decision‐making stage (2012–2016). The salience shock it produced "saved" the GDPR from being watered down, by incentivizing policymakers to distance themselves from business interests and by exposing the geo‐economic dimension of data protection. This article thus offers a comprehensive explanation of the GDPR, while contributing to the literature on the political economy of data protection.

One of the aims of the Common Agricultural Policy (CAP) is ensuring that farmers within the European Union (EU) make a reasonable living through the provision of subsidies. This serves as a great incentive to the farmers as they ensure that consumers have a stable supply of food. In order to ensure that these subsidies are judiciously expended, at the various country levels, computer-aided interpretation of very high resolution imagery and field campaigns are used to check the aerial claims of farmers before the subsidies are given out. These validated claims are consolidated into the Land Parcel Identification System (LPIS). Even though the LPIS is becoming freely available in a number of EU countries, there is still restricted access to this data in many regions of Germany. Even where they are available, the dataset is published annually and sometimes outdated. This makes it difficult to identify crop types at any point in time before the release of the dataset. This research seeks to tackle these issues by making use of the freely available Sentinel-1 and Sentinel-2 data and existing LPIS dataset of Germany for 2016 and 2017.This research adopts a two-step OBIA workflow that consists of parcel-identification and crop type mapping. In the first step, various features including vegetation indexes from Sentinel-2 and radar polarization ratios from Sentinel-1 are calculated. The optimal shape, scale and compactness parameters for delineating agricultural parcels using the Multiresolution Segmentation (MRS) algorithm for every region of Germany is identified based on the extracted features. The parameter combinations whose segmentation result has the highest geometric match to the LPIS parcels are considered the optimal ones. In the process, the optimal features are also identified. In the second step, using the LPIS parcels of 2017 as the reference layer, the crop type of newly segmented agricultural parcels, regardless of which point in time and geolocation, would be identified using a Transfer Learning (TL) scheme, which is still being developed. Initial results from the first step show that Sentinel-1 is not suited for identifying agricultural parcels largely due to the speckled and non-homogeneous nature of the images even after applying a multi-temporal speckle filter. With respect to the mono-temporal Sentinel-2 datasets, the scenes acquired during the summer season have the highest geometric match to the LPIS parcels. However, the highest geometric match was only realized by using a multi-temporal dataset that covered five different dates of the 2017 growing season.

County and municipal governments are primary creators of foundational geospatial data, including essential layers such as parcels, road centerlines, address points, land use, and elevation. This data is aggregated into regionalized layers that make up state and national frameworks, and it is sought after by researchers for analysis and by cartographers to serve as base map layers. Despite the importance of these layers, policies about whether this data is free and open to the public varies from place to place. In most areas of the United States, counties and municipalities are not required to comply with federal rules or state initiatives for open data. As a result, some regions offer hundreds of open data layers to the public, while their neighbors may have zero, preferring to restrict the data due to privacy, economic, or accuracy concerns. The state of Wisconsin recently passed legislation requiring that certain foundational geospatial data created by counties must be made available to the public. By contrast, its neighboring state of Minnesota uses a voluntary approach that allows counties to choose for themselves if their geospatial data will be free and open. This paper compares the implications and outcomes of these diverging data cultures. We also advocate for the role that academic libraries can play to support the culture of practices around collection, documentation, and discoverability of local data, pointing to the Big Ten Academic Alliance Geoportal and GeoData@Wisconsin as examples.

In the worldwide within the context of the agricultural development policies some agricultural crops are qualified as Special Agricultural Crops (SAC) having important leading effects on food security, agricultural employment and rural development. Especially the Modality Paper, for agricultural issues released by World Trade Organization (WTO) in 2006, covers some criteria are characterized for classifying the agricultural crops as special. In this context, based on both international criteria and the principles mentioned above some agricultural crops such as especially tea and hazelnut and tobacco, cotton, olive, wheat etc. having important effects on national and regional development in Turkey would be specified as SAC. So, in Turkey, beside the European Union (EU) Common Agricultural Policy (CAP) reform, it is required that the development of Spatial Data Model (SDM) for administration SAC lands as well. In this thesis, the tea crop having strategic position on agricultural development and growing under state guarantee in Turkey assumed to specify as SAC and development of SDM for tea farming as a case study. And also it is emphasized that development of Tea Agricultural Land Administration Model (TALAM) and the requirements on Spatial Data Infrastructure (SDI) for spatial data management of tea agricultural lands. In addition to this, as a result of this study, it is aimed to development of the Land Administration Domain Model - Tea Agricultural Model (LADM-TAM) for sustainable tea agriculture and tea growing and integration this model to Turkish National Geographical Information System (TUCBS).

This article illustrates how a parcel-based geographic information system can be used to identify and quantify land-use changes within subareas of individual planning jurisdictions as the basis for evaluating the implementation of local land-use policies. We describe a method for using property-parcel polygons and property-appraiser tax-roll data to analyze the effects of changes in land use on the exposure of people and property to hurricane flooding in coastal communities in Florida. This method allows us to test the conformity of local government growth-management practices to a state mandate which calls for the limiting of development in hurricane hazard zones. We apply this method to analyze hurricane hazard exposure in Okaloosa County, which is a coastal county located in the Panhandle of Florida.

New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowdsourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: (1) what is the spatial availability of these images across the European Union (EU), and (2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary—the largest open-access platform for such images—against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337,031 points. For 37.78% of the LUCAS points a crowdsourced image is available within a 2 km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowdsourced image within 300 m from a LUCAS point, illustrating the huge potential of crowdsourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785,710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowdsourced imagery looking at parcels was assessed for eight different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowdsourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

Following Coleman's analysis of social capital, the norms that discourage adolescent substance use should be more successfully transmitted to young people who enjoy greater stores of social capital. We hypothesize that youth derive social capital from their families and from their schools, and test whether higher levels of capital from each context are influential in resisting substance use and abuse. Using data from the National Educational Longitudinal Study of 1988, we show that social capital in the family is helpful in protecting adolescents from using alcohol and marijuana, whereas social capital built at school has essentially no effect on the same outcomes. We discuss the implications of these findings for future research on social capital as well as for policy interventions using schools as sites to discourage adolescent drug use.