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The Soil and Water Assessment Tool (SWAT) enables the simulation of watershed and river basin quantity and quality of surface and ground water under the influence of land use, management, and climate change. It can be used to monitor and control soil erosion, non-point source pollution and basin management. The recent version (SWAT+) was implemented by a dedicated QGIS plugin (QSWAT) widening the userbase and the potential modelling application worldwide. QSWAT, along with additional software for preparing the input dataset and for performing the calibration/validation phase, further extends the watershed modelling capabilities. Such tools and the growing diffusion of public open geospatial datasets are expected to increase the range of applications especially with the availability climate projections datasets. The latter will enable users to simulate all the watersoil phenomena at watershed level under future conditions to better understand and plan suitable action for preserving the natural resources. [ABSTRACT FROM AUTHOR]

Open LOD platform based on HPC capabilities for Integrated Administration and Control System of Common Agrarian Policy (Open IACS) is an EU funded project (4.5 MEur) fostering the administrative data reuse in the context of the Common Agricultural Policy (CAP) and granting open access to farmers, policy makers and third parties. Data from European paying agencies for agriculture will be harmonised, and a network of Linked Open Data will be developed. An interoperable system of EU High Performance Computing Centres will provide a platform for demonstrating different use case scenarios (e.g. applications for computing CAP performance indicators, climate change indicators and integration between environmental and agricultural data). [ABSTRACT FROM AUTHOR]

The ever-growing availability of Earth Observation (EO) data is demonstrating a wide range of potential applications in the realm of land management. On the other hand, large volumes of data need to be handled and analysed to extract meaningful information and Geomatics coupled with new approaches such as Artificial Intelligence (AI) and Machine Learning (AI) will play a pivotal role in the years to come. Training datasets need to be developed to use these new models and Volunteered Geographic Information can be one of the promising sources for EO processing. Among the various applications, agriculture may benefit from the large dataset availability and AI processing. However, several issues remain unsolved and further steps should be taken in the near future by researchers and policy makers. [ABSTRACT FROM AUTHOR]

This contribution reports the results of the application of the JRC guidelines to identify Italian agricultural areas, other than mountain areas, facing significant natural constraints (Areas with Natural Constraints -- ANCs). Spatial analysis has been carried out to integrate climatic, soil, terrain and agricultural data. The latter were integrated with administrative boundaries to classify each municipality as ANC if at least one biophysical criterion was relevant. We report maps, tables and graphs of the Italian ANCs defined by biophysical criteria depicting the new geography where agricultural activities might face significant natural constraints. [ABSTRACT FROM AUTHOR]

A big leap in the agricultural sector is expected thanks to the operational deployment of Copernicus data and services, the new Earth Observation program of the European Commission. The huge quantity of data delivered freely along with the good level of resolution (spatial, temporal and spectral) will open up new opportunities for achieving the well-known sustainability of agriculture. At the same time, the new trends of crowdsourcing and citizen science are delivering a great deal of geographical data collected by ordinary users with a bottom-up process. The two type of data and their integration will be the base for the future application in the agricultural sector, albeit management of big geospatial dataset and quality of user generated data are issues to be addressed. [ABSTRACT FROM AUTHOR]

Availability of up-to-date geographic datasets, including the information voluntarily created by ordinary people, is crucial for disaster management in areas affected by catastrophic events and humanitarian crises. OpenStreetMap (OSM) is the most popular Volunteered Geographic Information (VGI) project that has been extensively used for collaborative mapping in areas affected by natural disasters. This article provides an overview on how OSM data are exploited in disaster response scenarios and illustrates their specific application during the recent Italian earthquakes [ABSTRACT FROM AUTHOR]

Agricultural production plays an important role in the global economy, contributing greatly to the supply of food resources and human well-being. Mapping the areas of agricultural coverage and calculating their respective average annual production values is useful for orienting food security strategies, in a context of climate change and a growing global population. Despite their importance, today no complete and updated maps exist for all types of crops. To provide this critical data lack, we have harmonized statistical datasets at a regional level of detail across Europe (EU-27) to develop a unified dataset consisting of the best available data in the context of agricultural monitoring activities carried out by the CREA. This document illustrates the methodologies adopted to create an harmonized database and an open and easy-to-use cartographic atlas of agricultural area and production, based on accessible, interoperable and reusable data available from Eurostat. The availability of detailed maps of area and crop production at a regional level can help optimize a wide range of agricultural monitoring, ranging from early warning on phytopathological conditions, to assessment of crop conditions, to production forecasts and the assessment of damage caused by extreme weather events, agricultural statistics, agricultural insurance and climate mitigation and adaptation actions. [ABSTRACT FROM AUTHOR]