Announcements

ISMC at the World Congress of Soil Science in Nanjing China

ISMC organized a session on “Modelling Soil Processes from Pedon to Global Scale” at the 23rd World Congress of Soil Science in Nanjing China. The session had 18 oral and 12 poster presentations covering a wide range of modelling applications from the pore to the global scale. Additionally, ISMC as a IUSS working group presented their activities since the last World Congress of Soil Sciences. 

ISMC is breaking new ground in promoting the ISMC Conference at Rio

To advertise the 5th International Soil Modelling Consortium Conference in Rio de Janeiro, Brazil from 15th to 19th September 2026 ISMC is breaking new ground in promoting the Conference by printing T-shirts advertising the conference.

Late abstract submission and  registration for the conference can be still done via the ISMC 2026 homepage.

Application for ISMC Publication Awards 2026 still open

The application for the ISMC publication 2026 is still open. Canditates can apply directly for the ISMC Publication Award by sending an email to the coordination office ismc.coord@gmail.com along with the publication.

Featured Paper

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Please share your recent paper if you want to be featured in the ISMC newsletter. With your contributions, we will select one paper to be featured in every newsletter. Submission can be done here. 

How to integrate biology, physics and chemistry for a better description of soil water dynamics?

Numerous and diverse edaphic organisms have the capacity to modify several physical and chemical soil characteristics that influence water transfers. Considering these modifications in modeling approaches would make for more accurate descriptions and modeling of water fluxes in soils. Some impacts of biological activity on soil physical aspects (e.g. modification of the pore space) have been described for 5–10 years now, and are being increasingly accounted for in water transfer models. However, the situation is not the same for biologically-driven chemical modifications linked to the secretion of organic molecules by soil organisms: modeling their consequences on pore space chemical properties and water transfers has just started. We here shortly survey prominent effects of biological activity on water-transfer related soil properties, and describe their coupling with existing water transfer models. We then propose possible ways for a better integration of biological soil modifications into such models. Among these, we point out that an energy-based theoretical framework would not only be consistent with the basic principles of thermodynamics, but would also foster synergies between ecologists, physicists and chemists, to better describe and predict water dynamics in soils and interactions with the soil biota. This would pave the way to model the evolution, on the scale of a few decades, of the water flow regulation services provided by soils. More information can be found here.

Field-Scale Soil Moisture Predictions in Real Time Using In Situ Sensor Measurements in an Inverse Modeling Framework: SWIM2

Affordable autonomous soil sensors and IoT technology enable real-time soil moisture monitoring, which offers opportunities for real-time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates continuous sensor data and unbiased, periodic soil samples with an FAO-based soil water balance model using a Bayesian inverse modeling algorithm, DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis). SWIM2 estimates 12 soil and crop parameters and their associated probability distributions and correlations, providing soil moisture predictions with uncertainty estimates. The SWIM2 framework is illustrated and validated in a real-time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with in situ precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM2 achieves robust soil moisture predictions for a 7-day horizon, with accuracies comparable to sensor measurements. Predictions improve substantially in precision within the first 20 calibration days and maintain high predictive power throughout the growing season. The impact of in situ measurements and temporal covariance of the observational errors (“error covariance”) was assessed, indicating that good knowledge of the error covariance and independent soil moisture samples are essential to correct for sensor bias and ensure accurate model calibration, while continuous sensor data ensure accurate and precise estimates of the dynamics. This study demonstrates the use of soil moisture sensor data in a Bayesian inverse modeling framework, offering practical solutions for real-time soil moisture prediction and irrigation decision-making, enhancing water management across agricultural fields. More information can be found here.