Journal Paper Digests 2026 #13
- Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling
- A Conceptual Framework for Assessing Soil Structural Attributes Across Contrasting Land-Use Types
- From Soil Threats to Soil Health: Prevention or Remediation
- Continental-Scale Evidence of Farm Management Impacts on Soil Carbon
- Hardsetting in sandy soils: a review Open Access
Hardsetting in sandy soils: a review Open Access
Hardsetting soils dry with high strength, but soften again upon re-wetting. Affected soil layers can form a transient constraint to root growth, developing and diminishing with fluctuations in soil water. Several studies have explored physical and chemical mechanisms for hardsetting, including the role of certain soil particle arrangements and the presence of cementing agents between sand grains. However, the process is not fully understood and is likely to vary between soils. Traditionally, hardsetting has been aligned with Red-brown earths within the context of Australian agricultural soils; however, this review explores its increasing recognition as a constraint in sandy soils. High strength that restricts root elongation is a common constraint in sandy soils, but the contribution of hardsetting to this problem is largely unknown. Measuring and identifying hardsetting has proven challenging due to the lack of objective standards in quantifying it as a soil property. In-field measurements such as the use of cone penetrometers can provide an indicator of high strength, but only at the present moisture level. Measurements taken from intact or reassembled soils can provide greater value when used to determine how the strength of the soil changes across a range of moisture levels. The management of high-strength agricultural soils that have hardsetting properties may require different approaches to current deep tillage practices, to prevent the natural reconsolidation of hard layers.
Continental-Scale Evidence of Farm Management Impacts on Soil Carbon
There are high expectations that agricultural practices can mitigate climate change and improve soil health by increasing soil organic carbon (SOC) stocks. However, existing large scale SOC monitoring treats agricultural management as a black box, meaning that observed patterns and trends cannot inform on the option space of agricultural practices to improve or deteriorate SOC stocks. Here, we combine for the first time management data from large scale systematic farm surveys (n = 248,362 farms) and representative soil monitoring data (n = 8834 locations) to quantify the impact of agricultural practices on three SOC metrics across all pedoclimatic zones of Europe (EU + UK): stocks, stocks relative to pedoclimatic benchmarks, and yearly change in SOC concentration. Our findings show that in arable and tree crops, but not in grasslands, management intensity is a significant contributor to SOC loss, with impact varying by soil and climate region. However, we also observed that several practices (e.g., high share of manure, organic management, and a high proportion of leys in crop rotation) demonstrated potential for increasing SOC stocks. Under a scenario where all agricultural land in Europe would be managed as that of the 10% most optimally managed farms in terms of SOC benefit, SOC stocks would increase by 1.58 Pg C across Europe (95% CI: 1.27–1.89 Pg C). Whereas under a scenario where farms are managed as the 10% least optimally managed farms, SOC would decrease by −0.92 Pg C (−1.15 to −0.68 Pg C). However, it is important to note that these estimates reflect steady-state SOC stocks only (i.e., they do not represent the transient build-up or loss over time, or interactions with a changing climate). This paper thus quantifies how agricultural practices influence patterns in SOC stocks at the continental scale, identifying leverage points for site-specific policies to improve SOC stocks.
From Soil Threats to Soil Health: Prevention or Remediation
While soil threats and soil health are two interrelated, sometimes confused, concepts, we demonstrated here that a clear separation between these two concepts associated to a mapping of both soil threats and soil health is necessary. Soil threats are commonly defined as processes that may degrade the soil properties, functions or services, while soil health describes the state of the soil at a given moment in time. As a consequence, an unhealthy soil is a soil which is degraded compared to a reference. Mapping soil threats or soil health results then in different but complementary views of the situation. Mapping soil threats informs actions to prevent soil degradation, while mapping soil health indicates the capacity of soils to provide functions and places where remediation is needed. In this study, we demonstrated the differences between these concepts by comparing projection maps for 2050 of soil threats and soil health by considering soil compaction and loss of soil organic carbon (SOC) as soil threats and bulk density and SOC stock as basic soil properties to evaluate both soil threat and soil health in terms of the above-mentioned two soil descriptors. These maps were produced by digital soil mapping, taking into account changes in climate and land use in the European Union (EU). Soil threats were mapped using soil property change between 1980 and 2050 as indicators, that is, a decrease in SOC stocks for SOC loss and increase in soil bulk density for compaction. For soil health assessment, as references are needed, we defined soil areas that could be considered as homogeneous by combining soil, climate and land use information and defined for each area a threshold for soil health based on a quantiles approach. As a result, the obtained soil threat and health maps were very different, as healthy soils can be under threat but not have crossed the threshold yet, while unhealthy soils may not be under threat anymore if no more degradation occurs. These results demonstrate that reading a map requires a good prior understanding of the meaning of the indicators used in order to be able to interpret it in terms of threat or health and to be able to select appropriate metrics, which will not be the same in both cases. Indeed, while soil health maps identify degraded areas where the soil lost part or all its capacity to provide functions and that need remediation, soil threat maps offer vital information about potential vulnerabilities and areas requiring intervention or management strategies.
A Conceptual Framework for Assessing Soil Structural Attributes Across Contrasting Land-Use Types
Soil structure governs ecosystem functioning across scales, but its complexity requires integrative approaches that capture geometric and functional properties. This study proposes a methodological framework that integrates field-based visual evaluation of soil structure (VESS), X-ray computed tomography (CT) and soil hydraulic property (SHP) assessment to quantify structural attributes for contrasting land-use types (arable land, grassland, forest). This approach assesses soil structure from three perspectives: aggregate architecture, macropore connectivity and hydraulic function. As a conceptual framework to isolate structural and texture effects and quantify differences related to land use, we chose three sites in Switzerland with similar topsoil texture and close proximity (~1 km). Undisturbed topsoil samples were collected for CT and SHP measurements (250 mL, 5–10 cm depth) while VESS was performed in situ (5–10 cm and 0–30 cm depth). Assessment of SHP included measuring the soil water retention curve and the saturated and unsaturated hydraulic conductivity. CT imaging (91 μm pixel size) quantified macropore volume and connectivity metrics (Euler-Poincaré characteristic EPC and gamma indicator). Saturated hydraulic conductivity data aligned closely with CT metrics, especially macroporosity and the EPC, highlighting their utility in bridging structural observations with functional implications. Despite smaller total porosity, soils at the arable site showed a better VESS score and greater macroporosity and saturated hydraulic conductivity than soils at the grassland site, underscoring the importance of combining different metrics in structural interpretation. The combined methods capture complementary aspects of soil structure, ranging from aggregate-scale features to pore connectivity and hydraulic function, and improve structural interpretation for soil health assessment. Following upon this methodological framework with a small sample size (11 samples) and results related to site specific conditions, future research should validate whether relationships between field-based VESS scores and laboratory metrics hold across broader pedological conditions, to potentially make VESS a quantitative predictor of soil structural functionality for large-scale monitoring.
Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling
Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP-RF) trains to predict error of MEP-simulated ET. MEP-RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP-RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard-to-obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP-RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.