The BAB was represented at the iCROPM 2026 conference (Crop Modeling for Agriculture and Food Security under Global Change – https://icropm2026.org/) with a talk and a poster. iCROPM regularly brings together the international research community in crop modeling to discuss advances, strengthen collaborations, and further develop the field. This year, the focus was on scientific innovations, methodological advancements, and interdisciplinary approaches to improve the accuracy and relevance of crop models in addressing global agricultural challenges.
In her presentation, “Integrating Process-Based and Data-Driven Models for Wheat Yield Prediction Under Variable Meteorological Conditions”, Yvonne Stickler addressed the challenges that Austria’s heterogeneous meteorological conditions pose to yield prediction models. She also presented approaches to improve prediction accuracy by combining process-based models with statistical methods and machine learning techniques.
Dieter Kömle presented a poster titled “Understanding Crop Yield Across Models and Scales: Explainable Machine Learning for Wheat and Maize in Austria”. This work investigated the factors affecting yields (weather, soil, topography, management) of winter wheat and maize using FADN and INVEKOS data across different machine learning methods.
The contributions generated keen interest at the conference and facilitated the establishment of new connections within the research community.”
