Soil Respiration (‘Rs’) is the second biggest global carbon flux, the sum total of the ‘breathing’ of soil and all the organisms within it. Rs is also difficult to predict because of its complexity and the difficulty in making belowground measurements. In SoilSymphony, we aim to resolve uncertainty partitioning soil respiration by pairing Rs measurements with automated minirhizotrons running at extremely high time frequency.
• Are root biomass, root growth rate, or other root traits key determinants of RS? • How can we overcome physiological constraints on image methods (e.g. turgor-driven root size) for full dynamic subdaily timeseries of roots? • Can RS be partitioned dynamically using measurements of root and leaf dynamics soil moisture and water? How does dynamic partitioning compare against standard methods? • Using root traits, do process-based models (representing real processes, but not all) or data-driven models (incorporating all processes, but not explicitly), predict RS more accurately?
SoilSymphony is funded from 2025-2029, and we are currently in the early phase of the project, setting up sites, building new instruments and beginning to develop models.
The core SoiLSymphony team is Ian, Jason, Ryan, and Richard. We are collaborating with the rest of the PSB group, with Matt Saunders (TCD) and Ken Byrne (UL)