Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo - Viivi Aakula; Istem Fer; Julius Vira (European Journal of Agronomy, 2025)

Impact of weather and management practices on greenhouse gas flux dynamics on an agricultural grassland in Southern Finland - Laura Heimsch; Julius Vira; Istem Fer; Henriikka Vekuri; Juha-Pekka Tuovinen; Annalea Lohila; Jari Liski; Liisa Kulmala (Agriculture, Ecosystems & Environment, 2024)

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration - Istem Fer; Anthony K. Gardella; Alexey N. Shiklomanov; Eleanor E. Campbell; Elizabeth M. Cowdery; Martin G. De Kauwe; Ankur Desai; Matthew J. Duveneck; Joshua B. Fisher; Katherine D. Haynes; Forrest M. Hoffman; Miriam R. Johnston; et al. (Global Change Biology, 2021)
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.