Data-driven optimization of timing and efficiency in key agronomic operations (such as soil preparation, seeding, fertilization, and pest control)

Current decision-making is often based on fixed schedules, experience, or limited field data, which can result in suboptimal timing, inefficient use of inputs, and variability in crop performance. We are looking for innovative solutions, particularly Decision Support Systems (DSS), capable of integrating multiple data sources such as soil characteristics, weather and climate data, and crop-specific information. The objective is to improve decision-making by providing reliable, field-specific recommendations on when and how to perform agronomic operations. A key need is the ability to account for spatial and temporal variability, enabling more precise interventions and better alignment with actual field conditions. Solutions should support farmers and agronomists in making informed decisions that can increase efficiency, reduce input use, and improve sustainability outcomes. We are particularly interested in approaches based on advanced analytics, predictive modeling, and AI, as well as solutions that can be integrated into existing farm management practices and tools. Through this collaboration, we aim to identify and validate technologies that can effectively address these challenges and be scalable across different crops and farming systems.As agronomists, we have access to a strong network of farms where these solutions could be tested and validated. These farms share our approach and values, focusing on agroecology and low-impact agricultural practices, providing an ideal environment for real-world implementation and experimentation.