36 - Spatial data science and AI for the analysis of agroecosystems impacted by climate and land-use changes

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Objective

This research programme aims to find sustainable and resilient solutions for managing our agroecosystem at landscape level and to augment on-site monitoring programmes. In addition, trends in the Swiss agroecosystem will be spatially analysed using historical and current data by combining existing datasets with advanced environmental monitoring and AI methods. This will improve our understanding of the impacts of farming practices at regional level and contribute to the development of cost-effective agri-environmental monitoring programmes.

• Specific objectives

  • Analyse spatially explicit historical, current and future trends due to climate change, management activities, land-use changes and policy measures by combining existing datasets with advanced environmental monitoring and AI methods
  • Scale up process-based/mechanistic/statistical models to the entire agricultural landscape through the development and use of AI
  • Develop data acquisition and analysis algorithms to capture the state of the agroecosystem at scale (e.g. remote sensing, monitoring networks such as MAUS, NABO, flows, in-situ data)
  • Create a digital twin of Agroscope research sites (including NaBO and ALL-EMA monitoring sites, long-term experiments) for the development of models
  • Use AERO hyperspectral drones to develop models of trial fields for monitoring agroecosystems
  • Improve GeoWP data acquisition
  • Develop and apply specialist knowledge of spatial data science at Agroscope for internal use and to support the state and the cantons
  • Establish an agricultural geoinformation platform as part of the federal geodata infrastructure

Expected outcomes

Geoinformation- and AI-assisted monitoring methods help increase our understanding of processes at landscape level and make recommendations for the sustainable management of agroecosystems. The findings increase the efficiency and cost/benefit ratio of monitoring programmes (e.g. MAUS, NABO, ALL-EMA) and assist in exploiting the potential of digitalisation for simplifying the direct payments. We can make better use of existing data and bolster the competitiveness of Agroscope by acquiring expertise in this key technical/analytical field.

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