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Welcome and general introduction by Project Coordinator Jose Alfonso Gomez Calero, IAS-CSIC: -Actual potential of tree deficit irrigation, sensorization and differentiated spatial management for optimizing water use under droughts. Speaker: Juan Jose Alarcón. CEBAS-CSIC -Actual potential of conservation agriculture and green cover crops in the rotation for optimizing soil water retention in annual crops. Speaker: Tomas Dostal. CVTU -Cost Benefit Analysis and carbon/water footprint for specific agricultural systems across countries and farm typologies. Speaker: Gianni Quaranta. UNIBAS, MEDES. -Regional crop modelling for evaluating water use in agriculture. Speaker: Gabrielle de Lannoy. KU Leuven -Training and cooperation in large EU China projects, lessons learned. Speaker: Ian Dodd. ULANC -Key policy recommendations from SHui. Speaker: Rossana Salvia. UNIBAS, MEDES Round Table: Future of optimization of water use in agriculture. Drivers and identification of gaps in knowledge and implementation. Moderator: Jose Alfonso Gomez Calero. IAS-CSIC Participants: Miguel Barnuevo. Union de Pequeños Agricultores; Tim Hess. Cranfield University; Dirk Raes. KU Leuven and Mª Ferrer. FENACORE. SHui (Soil Hydrology research platform underpinning innovation) ran from September 2018 to August 2022 to address best use of soil and water in European and Chinese cropping systems via transdisciplinary research from plot to regional scales. Combining long-term experiments and modelling analysis at different scales evaluated the impact of Best Management Practices (BMPs) on water-limited crop productivity and soil retention, including socio-economic issues. The project also developed tools to facilitate implementation of soil and water saving technologies in specific farming situations. The objective of this in-person meeting in Brussels is to present the main project findings to stakeholders and policy makers, as well as to discuss in a round table the issues related to use of scarce water resources in agriculture. This project is co-funded by the European Commission within H2020 Framework Programme (Project: 773903). This project is co-funded by the Chinese Ministry of Science & Technology under CFM (China-EU Co-Funding Mechanism) Peer reviewed

We acknowledge financial support from the Spanish Science Ministry (MINECO) through projects EB-SLAM (DPI2017-89564-P) and María de Maeztu Seal of Excellence (MDM-2016-0656); and from the European GNSS Agency Grant LOGIMATIC (H2020-Galileo-2015-1-687534). Advanced autonomous navigation solution based on the integration of Global Navigation Satellite Systems (GNSS) and sensors onboard straddle carrier vehicles for port automation. No

A supervisor system for monitoring the operation of automated agricultural vehicles has been developed. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure such as pausing and resuming the vehicles while working. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system performs well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weeds treatments. The system is able to detect failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system is able to prevent collisions between vehicles by pausing/resuming the vehicles while working to avoid intersecting trajectories. The first video shows a tractor that selectively sprays the weed patches. The tractor trajectory, shown in the left bottom corner, was designed to cover the entire field and try the weed patches, represented in green, with a sprayer bar of 6 m long (12 nozzles). The second video shows two tractors that simultaneously and coordinately cover the same field by following parallel trajectories. The potential collisions during the turns on the headlands (see left bottom corner) are detected and solved by the Supervision System. Sponsorship: - RHEA: Robot fleets for highly effective agriculture and forestry management. Tipo proyecto: 7º Programa Marco; FP7-NMP-2009-LARGE-3, Grant Agreement: 245986; Duración: 2010-2014. - GroW: Sistema de inspección terrestre en vehículos autónomos y su aplicación efectiva a la detección de malas hierbas y su control localizado (GroW). Entidad Financiadora: MINECO, PLAN NACIONAL; AGL2011-30442-C02-02 Duración: 2012-2014. Peer reviewed