Journal Paper Digests 2026 #8
- Field-Scale Soil Moisture Predictions in Real Time Using In Situ Sensor Measurements in an Inverse Modeling Framework: SWIM2
- Soil Organic Carbon Changes in Agricultural Areas of Europe—Synthesis of Repeated Regional Soil Surveys
- A RothC-based spatiotemporal analysis of soil organic carbon stocks in agricultural soils of the Netherlands (1986–2022)
Drivers of organic carbon dynamics in surface and subsurface agricultural soils of New South Wales, Australia Open Access
Understanding of organic carbon (OC) stock in surface (0–30 cm) and subsurface (30–60 cm) soils and its determinants are crucial for managing OC in agricultural lands.
Aim Our aim was to examine how land use, soil type, and environmental drivers influence OC stocks in the 0–60 cm soil layers of agricultural regions in New South Wales (NSW), Australia.
Method Soil OC (SOC) and nitrogen (N) stocks were measured across diverse soil types from 49 farms representing pastures and cropping in NSW. The dominant drivers of SOC stocks were identified using random forest and structural equation modelling frameworks.
Key results Overall, cropping and pasture soils carried similar SOC stocks in the 0–60 cm soil layer (80 ± 7 and 91 ± 7 Mg C ha−1, respectively). However, pasture soils had a significantly greater SOC stock in the 0–10 cm soil layer (28 ± 1 Mg C ha−1) than soils from the same layer under cropping (21 ± 2 Mg C ha−1). Ferrosols contained more than twice the SOC stock (158 ± 19 Mg C ha−1) in the 0–60 cm soil layer than Vertosols (73 ± 5 Mg C ha−1) and Chromosols (67 ± 6 Mg C ha−1). The SOC stocks within different layers decreased with increasing depth at variable rates in different soils.
Conclusions The increased SOC stock in surface soils was mainly driven by climate factors (i.e. precipitation and evapotranspiration), while subsurface SOC stocks depended more on soil properties (i.e., pH and total iron and manganese contents).
Implications Pasture cultivation in iron/aluminium mineral-rich soils may favour SOC build-up in surface soil but not in subsurface soil.
A RothC-based spatiotemporal analysis of soil organic carbon stocks in agricultural soils of the Netherlands (1986–2022)
Soils are the largest terrestrial carbon reservoir, with soil organic carbon (SOC) playing a critical role in maintaining soil quality and associated ecosystem services. Accurately estimating SOC stocks at high spatial and temporal resolution over large scales remains challenging, particularly in agricultural systems where carbon inputs are often uncertain or unavailable. In this study, we used the RothC model to simulate SOC stocks in Dutch agricultural mineral soils from 1986 to 2022, at 25 m × 25 m resolution. We examined the temporal and spatial variation of the total SOC stock and its distribution over RothC carbon pools and unravelled how livestock manure inputs and land use affect the observed trends. Averaged SOC stocks in the topsoil (0 – 30 cm) increased by 13.2% under grassland, decreased by 10.4% under cropland, and decreased by 3.9% in areas with changing land use. Carbon gains in grassland were linked to systematically higher manure inputs and accumulation in stable pools, whereas lower manure inputs and more intensive management led to declining labile SOC pools. Independent validation on three spatial datasets showed the highest model performance for point-based field data (model efficiency coefficient MEC = 0.32 in 1986 and 0.37 in 2022). Observed changes in SOC over time could be less well reproduced (MEC ≈ 0) across all datasets, but simulated spatiotemporal patterns were consistent with previous observational studies. The study illustrates the potential of RothC for national-scale SOC stock assessment and monitoring, while highlighting the need for improved input data and temporal validation data. Importantly, this modelling approach effectively captures SOC stock dynamics, which remains challenging for purely empirical, statistical models. Future work could benefit from hybrid modelling approaches that integrate RothC with machine learning, enhancing the ability to capture currently unexplained variability and improve simulation performance.
Soil Organic Carbon Changes in Agricultural Areas of Europe—Synthesis of Repeated Regional Soil Surveys
Across Europe, increasingly more soil-related data is being collected. Soil organic carbon (SOC) is one of the most frequently collected parameters from soil monitoring networks due to the connections between SOC and many soil health indicators and ecosystem functions. Furthermore, SOC changes are also related to CO2 emissions and sinks, thus influencing climate change. SOC-related data is therefore also fundamental for greenhouse gas emission reporting in the sector land use, land use change and forestry. Much of the SOC data at continent-, country-, and regional-level scale in Europe come from soil monitoring networks (SMNs) that are highly diverse and scattered. In this review, we gather results from European SMNs covering agricultural land with more than one completed sampling campaign in order to compare changes in SOC content and stock from SMNs across Europe. Sixteen countries and regions are represented in the review, representing 24% of the agricultural land (cropland and grassland) of the European Union, United Kingdom and Switzerland. The results and data included in this review were collected between 1955 and 2024. While both gains and losses in SOC are found from European croplands and grasslands, a loss of SOC was found for 56% of the agricultural area covered by the included studies. In cropland areas and general agricultural land, SOC loss and gain were found equally frequently, while SOC loss was found for the majority of the grassland areas surveyed. Given the prevalence of SOC loss, soil health appears under pressure, and improved and harmonized soil monitoring data are needed to quantify SOC changes and their consequences for soil health at the continental scale.
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.