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Project Number: 16196
Project Acronym: Climaca

General information
Project title: Blending Earth observations and agricultural knowledge through Artificial Intelligence towards a scalable monitoring and assessment scheme for agricultural ecosystems
Starting date: 01/02/2024
Duration in months: 23

Budget NOA: 309.000 €

Type of action: Basic Research Financing Action
Call (part) identifier: National Recovery and Resilience Plan (Greece 2.0)
Topic: Sub-action II, Funding Projects in Leading-Edge Sectors
Keywords: AI, EO, Machine Learning, Causal discovery, Remote sensing, CAP, Green Deal, Agricultural Systems, Ecosystems
Website: -

climaca attribution

The European Union Green Deal has set targets for the conservation of species and habitats, land and ecosystem restoration, climate change adaptation and mitigation, and fostering sustainability in European farming and rural areas. Although, since its launching, the Common Agricultural Policy (CAP) has aimed at the sustainable management of natural resources while supporting farmers and improving productivity, it has not sustained success in mitigating environmental degradation. The reason was that CAP measures were horizontally implemented across regions with different environmental, climatic, and ecological characteristics. Yet, it represents one of the largest shares of expenditure from the EU budget. With the new CAP, EU member states must showcase how management instruments can contribute toward the efficient management of natural resources, support viable farm income, enhance long-term food security, and enhance ecosystem services, taking into account local specificities. Climaca, by taking advantage of environmental observations, will ensure accessible, interoperable, or deployable information necessary for shaping the development of sustainable policies. It will develop a roadmap, through establishing data-driven and domain-aware developments, towards implementing locally adapted, resource-efficient, and systemic interventions for achieving environment-friendly and climate-resilient farming systems. To do so, advanced Earth Observation, Artificial Intelligence and Machine Learning tools combined with agricultural domain knowledge will create a personalized recommendation system to provide actionable advice for the optimal management options to support well-functioning agroecosystems. In the end, CAP-related policymakers will be empowered with integrated knowledge for designing a strategic plan and promoting the balance between human-nature interactions enhancing the overall sustainability of agricultural systems and maintaining ecosystem services.