Recent results in spatiotemporal modelling of soil organic carbon changes in Hungary

The ability of soil to store a large amount of organic carbon (SOC) is one of its most important characteristics, making it an active and indispensable participant in the global carbon cycle. SOC influences various soil related functions and services, such as agricultural productivity, water retention and management, buffering capacity against toxic elements and compounds, which are essential to provide healthy food and clean drinking water. Furthermore, SOC is widely recognized as playing a crucial role in mitigating and addressing various environmental crises and challenges, such as climate change, land degradation, declining biodiversity, water and food security. Consequently, not only soil scientists but also researchers from other disciplines, practitioners, stakeholders, and even policymakers have shown growing interest in information on the spatial and temporal variability of SOC at various scales.

In the past few years, significant efforts have been made in Hungary to predict the spatial, and more recently, the spatiotemporal variability of SOC using various digital soil mapping techniques. Recently, a space-time model of SOC was developed using a combination of machine learning and space-time geostatistics to predict SOC change at point support and various aggregation levels (i.e., 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary (Szatmári et al., 2024). This work is based on soil data derived from the Hungarian Soil Information and Monitoring System between 1992 and 2016, as well as spatially and temporally exhaustive environmental covariates. Notably, geostatistics plays a central role by accounting for the spatiotemporal correlation of errors, which is essential for reliably quantifying the uncertainty associated with the aggregated SOC change predictions. The performance of the developed model was assessed using five times repeated 10-fold cross-validation, yielding acceptable results. A series of SOC maps were compiled for the period between 1992 and 2016 for each support, along with the quantified uncertainty, representing a significant advancement in Hungary. Furthermore, the presented methodology can overcome the limitations of recent approaches in spatiotemporal SOC modelling, allowing the prediction of SOC and SOC change, with quantified uncertainty, for any year, time period and spatial scale. This capability addresses current and anticipated demands for dynamic SOC information at both national and international levels.

The aim of this presentation is to outline the methodology developed, to highlight some methodological challenges, to present the resulting maps, and finally, but importantly, to discuss these findings in a broader context.

Acknowledgements: This research was funded by the National Research, Development and Innovation Office (NKFIH; grant number: FK-146391) and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

References:

Szatmári, G., Pásztor, L., Takács, K., Mészáros, J., Benő, A., Laborczi, A., 2024: Space-time modelling of soil organic carbon stock change at multiple scales: Case study from Hungary. Geoderma 451, 117067.