CYBELE generated innovation and created value in the domain of agri-food, and its verticals in the sub-domains of Precision Agriculture (PA) and Precision Livestock Farming (PLF) in specific, as demonstrated by the real-life industrial cases supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspired at demonstrating how the convergence of High-Performance Computing (HPC), Big Data, Cloud Computing and the IoT could revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intended to safeguard that stakeholders had integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and were capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE developed large scale HPC-enabled test beds and delivered a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.
Competitive conflicts for land use between the energy and food sectors have appeared, which could be mitigated by the vertical integration of RES in farms through new circular business models. By this approach, farms will become climate neutral, optimising their production and reducing their impact on natural resources and biodiversity, on top of providing energy services to communities and diversifying their economic income. However, there is a need to identify, understand and overcome major existing barriers perceived by agricultural communities. Moreover, current initiatives do not to effectively consider and address the complex interactions and factors from the farming and RES context, thus missing to support decision making based on accurate projections, estimations and forecasts. HarvRESt will work on these needs by improving the existing knowledge and its fragmented status, which will be feed to an Agricultural Virtual Power Plant able to run different scenarios and farm configurations to determine the best operation procedures for a given RES solution. This data will be then provided to a decision support system able to weight trade-offs and key indicators to provide ad-hoc recommendations to farmers and policy makers, thus enabling the consecution of improved production rates on renewable energy, food & feed within agro communities. For the successful execution of HarvRESt and implementation of recommendations, a multi-actor approach fostering co-creation sessions together with the provision of training materials for farmers empowerment will be implemented. The full approach of HarvRESt will be supported and executed at 4 use cases representing different topologies of farms, a diversity of stakeholders and organizational structures, distinct geographical conditions and a wide variety of RES technologies. Together with HarvRESt community and mapped initiatives, the project will act as a hub for knowledge and best-practices on RES integration at farm level.