About
EOAgriTwin
In the context of a rapidly growing global population, increasing crop production is essential to meet the rising food demand. However, abiotic stressors, such as droughts and heat waves, and biotic threats, including pests and diseases, which are becoming more frequent due to climate change, threaten crop productivity and necessitate the proactive adaptation of agricultural management. The Earth Observation based Digital Twin for Resilient Agriculture under Multiple Stressors EOAgriTwin project which is an innovative component of the European Space Agency’s Digital Twin Earth (ESA DTE) initiatives aims to create a virtual replica of agricultural systems to monitor crop conditions and production under various stressors. The objectives of the project are to enhance the understanding of cropping systems under biotic and abiotic stresses and to derive actionable insights and information to support sustainable food production and global food security.
AIM
To create a comprehensive virtual replica of agricultural systems, at multiple scales, with a focus on agriculture under multiple stressors, and to deliver functional Digital Twin to support monitoring of crop condition, simulation of growth dynamics and production under different conditions and stress factors.
Use Cases
Crop specific drought and heat risk assessment in Germany
Basemap: Open Street Map, National Borders (simplified): United Nations Geospatial
CRS: EPSG:3857, WGS 84, Pseudo-Mercator
Germany, Italy, and Kenya were selected as use case areas due to their diverse agricultural systems and exposure to various stressors. Germany has faced increasing drought and heat stress in recent decades, affecting major crops such as wheat, barley, and maize. The use case focuses on assessing drought-related yield risks and monitoring crop water use using Earth Observation (EO) and process-based models. Italy, particularly the Po Valley, experiences frequent droughts and crop diseases, impacting staple crops like maize and tomatoes. The focus is on integrating EO data and different data driven and mathematical weather based models to monitor drought effects, crop condition and disease risks. Kenya, where smallholder farming dominates, presents a unique challenge due to biotic and abiotic stressors, including pests and unpredictable weather patterns. The use case evaluates the push-pull cropping system, leveraging EO and modeling approaches to support climate-smart and eco-friendly pest control strategies. These diverse conditions allow the Digital Twin to be tested across multiple agro-climatic zones, ensuring its adaptability and scalability.