Smart Digital Agriculture

Journal Paper Digests

2026-09-11T00:00:00-00:00

Journal Paper Digests 2026 #21

An energy-based model of soil fragmentation by tillage
Stacking machine learning models to estimate soil moisture using gravimetric and in-field sensed data
Do data from the RISMA soil monitoring network support agricultural modeling requirements?
Combinatorial group testing for efficient scaling across biological applications
Putting the soil health principles to the test in Iowa
A Copula-Based Regression Method to Predict Sediment Concentration and Load in Rivers Using Streamflow Data

A Copula-Based Regression Method to Predict Sediment Concentration and Load in Rivers Using Streamflow Data

Pollution and deposition of sediments in rivers and streams are critical environmental, ecological, navigational, and recreational concerns. While several well-known watershed models such as SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program-FORTRAN) are applied to predict sediment concentrations and loads in rivers and streams, their application requires the acquisition of a large amount of climatic, GIS, land use, and watershed input data, which is very time-consuming and cost-prohibitive. This study developed a copula-based regression method to predict sediment concentrations and loads in rivers using streamflow data, which is difficult to achieve using traditional methods. Predicted sediment concentrations from the method were verified and validated by field measurements from three US Geological Survey (USGS) gauge stations across the US, as well as by statistical metrics. Prerequisites, advantages, uncertainties, and limitations of the method were presented and discussed. Results revealed that sediment loads were not proportional to watershed drainage area, indicating that land use and anthropogenic activities also play an important role in sediment load. Overall, no significant increasing or decreasing trends of annual sediment loads were found at study sites in New York and Florida over the 11-year period from 2011 to 2021. This study suggests that the copula-based regression approach, which is time-saving and cost-effective compared to the traditional watershed models, is a promising alternative for predicting sediment concentrations and loads using streamflow data when a good dependence (or correlation) exists between the sediment content and streamflow after copula transformation.

Putting the soil health principles to the test in Iowa

One of the most popular soil conservation campaigns is based on the USDA Natural Resource Conservation Service’s Soil Health Principles (NRCS-SHPs). The NRCS-SHP program identifies four principles—maximize presence of living roots, minimize disturbance, maximize soil cover, and maximize biodiversity—with the underlying assumption that the more principles one follows, the greater improvements in soil health. Despite the popularity of the NRCS-SHPs, this underlying assumption has not been rigorously tested. To do so, we used nine long-term experiments all located in central Iowa, but with varying degree of NRCS-SHP adoption, to determine if greater adoption increases three slow-changing (maximum water holding capacity, bulk density [BD], and soil organic carbon) and three dynamic (microbial biomass carbon [MBC], potentially mineralizable carbon [PMC], and permanganate oxidizable carbon [POXC]) soil health indicators. We regressed these indicators with a soil health principle score that can scale soil management based on adoption of the NRCS-SHPs. Of the slow-changing soil properties, increased adoption of NRCS-SHPs only decreased soil BD (R2 = 0.22, p = 0.024). On the other hand, increased adoption of NRCS-SHPs strongly predicted increases in both MBC and PMC and across two sampling dates (R2 > 0.23, p < 0.015); POXC, however, did not increase with greater adoption. The consistent increases in MBC and PMC with greater adoption of NRCS-SHPs supports their usefulness as sensitive indicators of positive soil health change. Our study provides scientific evidence to support the NRCS-SHPs concept, improving its usefulness as an extension campaign, and stands as a step toward evidence-based soil conservation

Combinatorial group testing for efficient scaling across biological applications

Combinatorial group testing can reduce experimental costs and turnaround time by strategically pooling samples to minimize the number of measurements needed for a given experiment. Despite broad potential utility, it remains underutilized due to its intrinsic complexity and the lack of implementation tools. Here we present PoolPy, a unified end-to-end framework and web platform to benchmark, automate, and decode combinatorial group testing strategies. PoolPy tailors pooling designs to application-specific constraints, such as time, cost, or signal dilution, across experiment types. By implementing ten different pooling algorithms, which we comprehensively benchmark in silico across >100,000 conditions, we identify key design trade-offs that define pooling applicability to specific use cases. We experimentally validate PoolPy across diverse applications, including protein-ligand interaction screening, RT-qPCR viral testing and genome-wide protein-DNA interaction profiling, achieving a 60 to 93% reduction in number of measurements needed. Overall, PoolPy provides a scalable, user-friendly ecosystem to increase throughput and reduce costs across biological applications. PoolPy is available at https://poolpy.trouillonlab.org for open use.

Do data from the RISMA soil monitoring network support agricultural modeling requirements?

The Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) network provides soil water and agri-environmental data for conceptualization, calibration, and validation of remote sensing and modeling products for Canadian agriculture at 32 stations in seven watersheds in Saskatchewan, Manitoba, and Ontario. The data support work in precision agriculture, flood and drought management, and greenhouse gas management. However, project-based development has led to inconsistent data collection. This work assesses the adequacy of data collected by the RISMA network to support agricultural modeling. The work provides expert modeler review of parameter requirements for Versatile Soil Moisture Budget (VSMB), Soil, Vegetation, and Snow (SVS), Environmental Policy Integrated Climate (EPIC), Agricultural Policy Environmental eXtender (APEX)/ArcAPEX, Hydrogeosphere, HYDRUS, Denitrification-Decomposition (DNDCv.CAN), and Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and qualitatively assesses the alignment between network observations and model input requirements. All RISMA stations collect soil volumetric water content, soil temperature, soil electrical conductivity, and liquid precipitation. Select sites also collect precipitation by weight, temperature, relative humidity, wind speed and direction, solar radiation, and crop-related data such as yields, tillage, and crop species. To better support modeling, the RISMA network should standardize collection of weather, soil, snowfall, and crop production data, including planting and harvest dates, crop type, tillage, and fertilizer application. Standardizing these observations will enhance the network’s value for supporting site-specific modeling, regional upscaling, and assessing variability where local data are unavailable.

Stacking machine learning models to estimate soil moisture using gravimetric and in-field sensed data

Even though nutrient and irrigation management could be improved by accurate soil moisture monitoring, a cost-effective approach is not available for obtaining this information. The objective of this study was to use soil- and weather-derived predictors to estimate volumetric water content at multiple depths. Two multi-site data sets were used in this study. One dataset was derived from preplant soil samples collected from 19 sites located in soils with an ustic soil moisture regime between 2019 and 2022, and the second dataset was collected at sites that had udic soil moisture regime. At the second dataset, the soil sensors collected volumetric soil information near-continuously between 2021 and 2023. In the study, the performance of multiple linear regression, support vector regression, random forest, extreme gradient boosting, and a stacked feed-forward neural network meta-learner model were compared. The machine learning models for the preplant gravimetric soil moisture collected from sites that had an ustic soil moisture regime had low predictability (R2 < 0.3) and high root mean square errors. These results suggest that the models did not adequately capture the nonlinear and spatio-temporal drivers governing soil moisture variability. Machine learning models that estimated in-season soil moisture at sites located in the udic soil moisture regime had higher predictability, and the best models had R2 values as high as 0.99. The high performance of these models was attributed to the use of temporal predictors, and in general stacking provided consistent gains (up to Δ 𝑅2 =+0.114 at 10 cm; mean ̅̅̅̅̅̅̅̅ 𝑅2 =0.618 across depths) but offered limited benefit when temporal features are included. Overall, the results show that incorporating temporal context and, when needed, stacking complementary learners can produce more reliable soil moisture estimates for field-scale management.

An energy-based model of soil fragmentation by tillage

Tillage greatly modifies soil structure, yet existing approaches to modelling tillage-induced soil structural change remain largely qualitative or over-simplified. Here, we present a quantitative, energy-based reformulation of the classical tillage equation that predicts soil fragmentation from the initial soil state and the applied energy. The new model partitions tillage energy into surface creation through fragmentation, displacement of existing soil fragments, and plastic deformation of the soil. Soil moisture and mechanical properties control the energy partitioning among these processes. As fragmentation progresses, an increasing proportion of tillage energy is dissipated through the displacement of existing fragments, whereas at elevated soil water contents energy is consumed by plastic deformation. Soil fragmentation resulting in the creation of new fragment surface area scales with the remaining energy. Literature-derived soil fragmentation data from drop-shatter tests and tillage experiments were used for model parameterisation. The model was subsequently evaluated against separate, independent literature datasets not used for parameterisation, covering tillage-induced soil fragmentation across a range of soil conditions. Illustrative applications demonstrate the model’s ability to capture (i) the texture-dependent soil workability range and (ii) the diminishing effectiveness of repeated tillage operations. We outline how the model-derived fragment surface area can be linked to fragment size distributions under simplifying assumptions. Assuming spherical fragment geometry and a Weibull distribution of fragment sizes allows an estimation of the tillage-induced pore size distribution. Altogether, our physically grounded model provides a basis for predicting tillage-induced soil structural change and can be incorporated into agroecosystem models. Further model refinement would benefit from datasets that jointly quantify tillage energy input, soil water status, as well as pre- and post-tillage soil structure and hydraulic properties, enabling a more mechanistic description of the transition from brittle fragmentation to plastic deformation.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on September 11, 2026.

Journal Paper Digests

2026-08-31T00:00:00-00:00

Journal Paper Digests 2026 #20

Propagation and preservation of AI-discovered problem-solving strategies in human culture
Salt effects on soil thermal conductivity: Database construction and impact quantification
Dominance of temperature over organic matter composition and mineral protection in priming effect regulation
High-resolution mapping of compressed vis-NIR spectral data of Danish topsoils
Impact over Origin: Rethinking management of introduced species in novel ecosystems
Integrating Landsat-based irrigation mapping and actual evapotranspiration estimates with hydrological modelling to assess regional irrigation dynamics in a large basin experiencing extreme climate variability: Towards a national operational irrigation water accounting system
Satellite embeddings for crop type classification: a comparative examination
Indicators of Soil Health and Degradation: Contrasting Farmer and Agricultural Advisors’ Mental Models
Land-use driven microbial community legacy shapes soil functionality

Land-use driven microbial community legacy shapes soil functionality

Microbial communities are central to soil ecosystem function. However, the extent to which functional diversity is conserved across communities, providing resilience to environmental change, remains uncertain. Here, we investigated how microbial legacy and soil properties shape community assembly and function, by cross-inoculating distinct microbial communities into sterilised soils from agricultural and semi-natural habitats. Over a 10-month incubation, the soil environment drove microbial community convergence at high taxonomic ranks, but fine-scale community composition and functional outcomes remained distinct. Microbial communities showed a ‘home-field advantage’ in soil carbon use that increased cumulative respiration by 16-26% in agricultural soils and by 26-84% in semi-natural soils, demonstrating limited redundancy of broad ecological function between soil communities. Distinct communities also caused significant shifts in soil pH associated with contrasting inorganic nitrogen transformations, exposing limited conservation of specialised metabolic functions. In summary, microbial community legacy had a lasting influence on carbon and nitrogen cycling, and thus, the effects of anthropogenic land use change on soil microbial functional diversity will likely have substantial impacts on these key ecosystem processes.

Indicators of Soil Health and Degradation: Contrasting Farmer and Agricultural Advisors’ Mental Models

Soil degradation threatens crop productivity and rural livelihoods worldwide. Efforts to reverse this trend are more likely to succeed when farmers and agricultural advisors share an understanding of what defines ‘healthy’ or ‘degraded’ soils, since both groups influence soil management decisions. In Nepal’s Terai (lowland plains), the country’s main grain-producing region, such knowledge remains poorly documented. This study investigates which indicators farmers and advisors use to assess soil health and how they perceive local soil fertility trends. Semi-structured interviews with 43 farmers and five advisors were analysed using a mental model approach and visualised as weighted semantic web diagrams. After applying frequency thresholds, 32 indicators were retained and grouped into five categories: sensory signs, agronomic outcomes, biological indicators, management activities and scientific testing. Despite broad agreement that local soils are degrading (79% of farmers; all advisors), each group relied on distinct measures. Farmers mainly used sensory, agronomic and biological indicators, whereas advisors emphasised quantifiable indicators and scientific testing. Only 11 of the 32 indicators overlapped, revealing gaps between advisory frameworks and farmers’ soil assessment practices. Gender also subtly influenced farmers’ soil assessments. These gaps reflect more than divergent vocabularies: each group assesses soil health through the lens of their own experience, shaped by how they manage and interact with the land. Any single-indicator framework, whether farmer-derived or scientific, is therefore inherently partial. Drawing on this diversity of perspectives is essential for understanding and responding to complex environmental challenges, and developing soil management guidance that integrates local experience, gendered knowledge and scientific approaches.

Satellite embeddings for crop type classification: a comparative examination

Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.

Integrating Landsat-based irrigation mapping and actual evapotranspiration estimates with hydrological modelling to assess regional irrigation dynamics in a large basin experiencing extreme climate variability: Towards a national operational irrigation water accounting system

Irrigated agriculture is essential for regional and global food security and poverty alleviation — but growing water scarcity and climate change challenge the balance between agricultural production, communities’ livelihoods and ecosystem sustainability. The Murray-Darling Basin (MDB, ∼1.06 million km2), Australia’s largest river system and primary agricultural region, exemplifies this challenge. This research presents the first multi-decadal (1987–2024), high-resolution (∼30 m) Landsat-based monthly irrigation water-use (IWU) dynamics across the MDB. The assessment is underpinned by a framework using advanced blending and spatiotemporal interpolation that produces ∼30 m monthly, gap-free vegetation indices and actual evapotranspiration (ETa) for the MDB. Spatiotemporal patterns of summer irrigation are identified and predicted by Artificial Neural Networks (ANNs) from the Normalised Difference Vegetation Index (NDVI) and ETa timeseries. Together with soil moisture accounting, they provide timeseries quantifying irrigated areas and estimates of both rainfall-derived and irrigation-derived IWU components. Validation against: (i) independent field ETa; (ii) multi-scale agricultural survey statistics; and (iii) water-use records demonstrated encouraging performance. The annual timeseries of irrigated area and IWU reveal nearly four decades of evolution in irrigated extent, broad irrigation composition, and water-use under shifting climatic and management conditions; offering unprecedented insights into how long-term irrigation dynamics respond to hydroclimate variability and policy reform. Underpinned by operational Earth Observation inputs and an established ETa product, the framework demonstrates how Landsat observations can support systematic irrigation accounting spanning ∼40 years over large regions. While several components remain under development, the framework establishes a scalable and transferable pathway towards a sustained irrigation information product, consistent with emerging principles for operational satellite-based environmental monitoring systems.

Impact over Origin: Rethinking management of introduced species in novel ecosystems

Debates in invasion biology often emphasize species’ places of origin, although humans have moved organisms for millennia and ecological baselines are historically contingent. Many ecosystems now exist in novel states where restoration to presettlement conditions is infeasible and introduced species are embedded in ecological function. Although a global phenomenon, this is especially evident in Florida, USA, where hundreds of introduced taxa coexist with natives amid rapid environmental change. We argue that long-established introduced species, particularly in human-dominated landscapes, should be managed according to feasibility and empirically evaluated ecological, economic, and sociocultural impacts rather than reflexive eradication based on origin alone. This approach does not discount severe harm from some introductions; instead, it advances a research-first framework centered on impact assessment, functional role evaluation, and feasibility-based triage to prioritize limited conservation resources. As ecological novelty becomes widespread, origin-based heuristics grow increasingly misaligned with ecological reality, and invasion governance must evolve accordingly.

High-resolution mapping of compressed vis-NIR spectral data of Danish topsoils

Visible and near-infrared (vis-NIR) spectroscopy enables rapid and cost-effective soil characterization, supporting the estimation of soil chemical, physical, and biological properties. Most applications, however, focus on using spectroscopy to predict individual soil properties and augment conventional laboratory analyses, while the potential of spectral information to study soil variation in space remains largely unexplored. Latent variables derived from the dimensionality reduction of spectral data offer a compact representation of soil variability, capturing multiple soil properties simultaneously. When spatially predicted, these latent variables can provide a means to investigate soil-landscape relationships in a spatially explicit context. This study aimed to (i) model the spatial variation of latent variables derived from compressed topsoil vis-NIR spectral data across Denmark at 10 m resolution using a digital soil mapping approach; (ii) identify the drivers, in relation to SCORPAN factors, controlling their spatial variation; and (iii) examine how the predicted latent variables represent the soil-landscape relationships across Denmark. An earlier study compressed the data using principal component analysis, kernel principal component analysis, shallow autoencoders, and convolutional autoencoders. To predict the latent variables in space, we used 32 predictors comprising harmonized environmental layers representing climate, relief, and parent material. A weighted machine learning algorithm was used to model each latent variable, with bootstrap resampling providing pixel-level uncertainty estimates. Overall, climate was the main driver across all methods, followed by topography and parent material, mainly the extent of clay till deposits. By integrating compressed spectral data with diverse spatial predictors, we captured complex, non-linear soil-landscape relationships. The resulting high-resolution maps offer a spatial description of soil variation in relation to soil-forming factors and spectral signature. Our maps can be used to improve point-based predictions of soil properties or provide enhanced covariates to support digital soil mapping of soil properties and classes.

Dominance of temperature over organic matter composition and mineral protection in priming effect regulation

Consensus is emerging that the turnover of native soil organic matter (SOM) through the priming effect (PE) can be controlled by plant, soil and climate variables. Here, we apply a systematic approach to the quantitative assessment of the relative importance of plant C input quality, SOM persistence and temperature in regulating PE. We conducted a unique laboratory microcosm experiment comparing OM fractions originating from the topsoil and subsoil of a Central European temperate mixed forest incubated at two different temperatures, simulating the effects of climate warming. We repeatedly added 13C-labelled deciduous or coniferous leaf leachates or root exudates and determined PE. Our study emphasizes the critical role of temperature in regulating PE, with warming generally leading to reduced SOM turnover via PE and thus enhancing C storage in soils. Further, our results indicate the importance of OM composition together with mineral protection for PE regulation. Moreover, our findings challenge contemporary concepts that see mineral-associated OM as a stable SOM pool, and provide proof that at least a part of this pool is available with increased temperature. Our results thus provide new perspectives for future modelling and management efforts, and these may help promote enhanced SOM accumulation under future climate scenarios.

Salt effects on soil thermal conductivity: Database construction and impact quantification

Soil thermal conductivity (STC) is a key thermal property that affects transport of heat and water. Although the influences of bulk density, water content, and temperature on STC has been widely reported in previous studies, the effect of various salt types at a wide range of concentrations has been understudied, which leads to unsatisfactory accuracy of many STC predictive models. To quantify the impact of salts on STC, the most comprehensive STC database (to the best of our knowledge) considering salt effects was established in this study. Meanwhile, three machine learning models (i.e., Extreme Gradient Boosting-XGBoost, Random Forest-RF, and Backpropagation Neural Network-BPNN), explainable artificial intelligence (e.g., feature importance ranking and partial dependence plots), and Spearman correlation analysis were employed to quantify the effects of salt types and concentrations on STC. The results show that XGBoost performs best within the full STC range (R2= 0.86, RMSE= 0.16 W m−1 K−1), while BPNN performs better at greater STC range (e.g., >2.0 W m⁻¹ K⁻¹). STC gradually transforms from significantly negative to significantly positive correlation as NaCl concentration increases from 0∼2 mol L−1 to 4∼6 mol L−1, but the opposite trend was observed for CaCl2. In addition, STC increases with increase of positive temperature, bulk density, water content, and decrease of sub-freezing temperatures.

Propagation and preservation of AI-discovered problem-solving strategies in human culture

Intelligent machines have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such as Go and chess, demonstrates that AI systems are evolving from mere tools to sources of cultural innovation adopted by humans. However, the conditions under which intelligent machines transition from tools to drivers of persistent cultural change remain unclear. We identify three key dimensions that modulate machine influence on human problem-solving: the discovered strategies must be non-trivial, learnable, and offer a clear advantage. Using a cultural transmission experiment, we demonstrate that when these conditions are met, machine-discovered strategies can be transmitted, understood, and preserved by human populations, leading to enduring cultural shifts. Conversely, using agent-based simulations, we show how machine influence is constrained in the absence of these conditions. These findings provide a framework for understanding how machines can persistently expand human cognitive skills and underscore the need to consider their broader implications for human cognition and cultural evolution.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on August 31, 2026.

Journal Paper Digests

2026-08-14T00:00:00-00:00

Journal Paper Digests 2026 #19

Microbial construction of soil structure: Modeling soil aggregation dynamics under periodic disturbances
Predicting Soil Nitrogen Mineralization Rate in Agricultural Soils Using Near Infrared Reflectance Spectroscopy (NIRS): A Laboratory Incubation Study
Soil spatial scaling: Local measurements to global policy
Spatiotemporal modelling of soil organic carbon: integrating process-based and machine learning approaches

Spatiotemporal modelling of soil organic carbon: integrating process-based and machine learning approaches

Soil organic carbon (SOC) underpins the global carbon cycle and represents a central lever for climate change mitigation and food security. Yet its accurate spatiotemporal quantification remains a challenge, owing to the complex interactions among biological, chemical and physical processes operating across scales. This review critically evaluates the current state of SOC spatiotemporal modelling frameworks and their limitations, and future directions. Process-based models provide mechanistic insight into carbon dynamics but are constrained by parameterisation, structural assumptions and computational demands. In contrast, machine-learning (ML) approaches excel at capturing spatial patterns from large datasets but often struggle to represent temporal kinetics, enforce physical consistency or generalise across scales and environmental contexts. We argue that the next generation of SOC modelling will emerge from the convergence of process-based understanding and data-driven inference through knowledge-guided ML and hybrid modelling strategies. We synthesise four principal integration pathways: (1) meta-modelling to accelerate computationally intensive simulations; (2) sequential hybridisation to embed mechanistic trends as dynamic covariates; (3) ensemble frameworks to reduce structural uncertainty; and (4) data assimilation to constrain model trajectories with observations. We further highlight emerging frontiers, including residual and parameter learning, physics-informed neural networks, foundation models for earth observations, and omics-informed frameworks that link microbial functional potential to carbon turnover processes. We conclude that progress toward causally interpretable, uncertainty-aware monitoring frameworks will require tighter integration of mechanistic theory, interpretable artificial intelligence and cross-scale data synthesis.

Soil spatial scaling: Local measurements to global policy

Soil spatial variability extends from nanometres to continental scales, yet a unifying theoretical framework to explain how variability emerges and propagates across these scales remains absent. This limitation constrains the ability of soil science to predict behaviour under global change, upscale local observations for policy, and safeguard soil-derived ecosystem services. This review synthesises the development of soil spatial scaling research, critically evaluating established statistical and geostatistical approaches that have provided insights into heterogeneity and scale-dependent variance. However, while these methods effectively describe spatial patterns, they often fail to explain the mechanisms driving variability or connect processes across scales. To address this challenge, we draw from statistical physics, percolation theory, network science, and ecological scaling to propose the Soil Scaling Tensor (SST) framework. The SST integrates four components—structural, functional, interaction, and property scaling—into a mathematical formulation that explicitly incorporates the physical, chemical, and biological processes generating observed scaling behaviours. Unlike conventional approaches, the SST can account for anisotropy, scale dependence, and cross-scale interactions, while providing transformation rules for both upscaling and downscaling. Implementation of the SST requires multiscale sampling strategies, systematic criteria for selecting theoretical approaches, and robust validation procedures. SST requires more work to make it operational. Overall, advancing soil spatial scaling is essential for soil security, providing the predictive capacity required to support sustainable land management, food security, climate change mitigation, and biodiversity protection in an era of global environmental change.

Predicting Soil Nitrogen Mineralization Rate in Agricultural Soils Using Near Infrared Reflectance Spectroscopy (NIRS): A Laboratory Incubation Study

For spring-sown crops such as maize (Zea mays L.), the extended growing season enables a substantial contribution of organically derived nitrogen (N), particularly from soil organic matter (SOM) mineralization. Consequently, the soil N mineralization rate (SMR) is a key determinant of maize N uptake and yield formation. Year-to-year and spatial variability in climate, soil properties, and management practices introduce considerable fluctuations in SMR, making it a major source of uncertainty in site-specific N management. Near-infrared reflectance spectroscopy (NIRS) offers a rapid, cost-efficient and scalable tool with strong potential for improving SMR estimation and, consequently, site-specific N management in maize production. In this study, 268 soil samples were collected across Germany with varying SOM content and incubated for 105 days to simulate the maize growing period, allowing laboratory quantification of SMR. Simultaneously, NIRS spectra (1100–2498 nm) were collected, preprocessed, and SMR was modelled using Partial Least Squares Regression (PLSR) and different machine learning (ML) algorithms. SMR was modelled more accurately by ML approaches, with non-linear models such as the stacked ensemble model (R2 = 0.72, RMSE = 0.21 kg N ha−1 day−1) or random forest (R2 = 0.70, RMSE = 0.20 kg N ha−1 day−1) outperforming PLSR (R2 = 0.55, RMSE = 0.33 kg N ha−1 day−1). Incorporating ancillary soil and management data did not improve performance. NIRS-ML modelling improved SMR prediction by up to 36%, with slight underestimation of laboratory-measured SMR values due to indirect spectral relationship with NIRS. Nevertheless, the approach shows great potential as a rapid and scalable tool for SMR estimation, warranting further model refinement and validation across diverse environments.

Microbial construction of soil structure: Modeling soil aggregation dynamics under periodic disturbances

Soil structure and microbial activity are interdependent components of terrestrial ecosystems, especially in agricultural contexts, where soil health is crucial. This study presents a mathematical model that explicitly represents habitat-mediated positive feedback between microbial biomass and soil aggregation, in which aggregate development increases the capacity of the soil to support microbial growth. Disturbance simulations representing pesticide application and tillage revealed marked asymmetry in the system response. Microbial biomass recovered rapidly after repeated biomass reductions, whereas repeated aggregate disruptions caused persistent degradation of both soil structure and microbial biomass. Sensitivity analyses showed that this asymmetry was robust across a broad range of parameters and was strongly constrained by the rate of macroaggregate formation. These findings align with long-term field observations, suggesting that preserving the physical structure of the soil is more critical for ecosystem resilience than preserving microbial abundance. This study provides a theoretical foundation for sustainable soil management by emphasizing the fundamental role of microbe–aggregate interactions.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on August 14, 2026.

Journal Paper Digests

2026-08-06T00:00:00-00:00

Journal Paper Digests 2026 #18

Lower carbon fractions and microbial carbon use efficiency in ant-mound surface soils than in adjacent biocrusted soils of a revegetated desert
Visualizing soil chemical heterogeneity in time and space using optical sensors: advances, challenges, and prospects
A robust rank aggregation based interpretable minimum predictor set for scalable soil organic carbon mapping
Defining Soil as a Surface-Coupled Planetary System: Separating Pedogenesis From Habitability
Global Pattern of Flash Drought and Impact-Relevant Early Warning
Soil carbon stocks: Overlooked contributions from coarse fragments and subsoil across a climatic gradient
Introducing a new methodology for applying field experiments to evaluate gully evolution processes

Introducing a new methodology for applying field experiments to evaluate gully evolution processes

Gullies found in various climatic regions are a dominant erosional factor. Gully activation, sediment yields and evolution are critical when assessing land management practices, geomorphological landscape conservation and modelling. Gully headcut migration and widening are the main proxies of gully activity, affected by run-off discharge and in situ characteristics such as lithology, slope, biocrusts and vegetation. Because of these variations and the difficulties that lie in examining gully headcut evolution under field conditions, experiments are often undertaken in flumes and from historic aerial photos and post-event surveys. Few studies report actual gully growth processes in nature. We have developed a simple experimental field methodology for generating in situ discharges to undertake field experiments on gully evolution in various environments and for different research applications. Our objective for the development of this apparatus was to monitor the hydrologic and erosional response of first-order headcuts in biocrusted sands located in the north-eastern Negev, Israel. For this goal, a device was required that could generate specific discharges and could be easily carried in the field. We developed a Marriott tube-based flow apparatus, constructed of a sealed water tank with a faucet and a vertical open tube with a sealed slip mechanism set on its top. This apparatus generates discharges in the range of 0.5–20 L/s, and continuous filming allows documentation of processes on the headcut face and of surface water velocity, while permitting the definition of run-off discharge steps. Thirteen gully headcuts, situated in a sandy valley dissected by upslope to midslope gullies, with an average drainage basin area of 81 m2, were chosen to test the new run-off-generating methodology. In each gully headcut, nine increasing water discharges were applied. We include an in-depth description of the developed apparatus and present preliminary results from our experiments, exemplifying the potential of this tool in various research conditions.

Soil carbon stocks: Overlooked contributions from coarse fragments and subsoil across a climatic gradient

Soil inorganic carbon (SIC) plays a crucial role in the long-term sequestration of atmospheric CO2 in arid and semi-arid regions. However, conventional assessments focusing solely on the fine earth fraction (<2 mm) and surface layers may substantially underestimate total SIC stocks. This study quantifies the contribution of carbonate coatings on coarse fragments (>2 mm) and subsoil layers to total carbon stocks across a climatic gradient. We determined SIC and STC stocks across 15 soil profiles, developed in hyper-arid, arid, and semi-arid regions (mean annual precipitation: 90–350 mm yr−1), considering both fine (<2 mm) and coarse (>2 mm) fractions, to assess the extent of underestimation resulting from the exclusion of the coarse fraction. Approximately 27% to 42% of the total SIC stocks, sum of fine and coarse fractions, were as carbonate coatings on gravels of 20–76 mm in diameter. With increasing mean annual precipitation (MAP) from 90 to 350 mm, SIC stocks (kg C m−2) increased from 16.5 to 34.9 (165 to 349 Mg C ha−1, 1 Mg C ha−1 = 10 kg C m−2), while soil organic carbon (SOC) stocks (kg C m−2) increased from 2.17 to 7.54. Generally, SIC was the dominant component of STC (82.2–90.1%). More than 75% of SIC stocks and about 48% of SOC stocks were stored below 25 cm depth, highlighting the critical role of subsurface soil layers. The primary source of calcium for SIC formation was silicate weathering, indicating net atmospheric CO₂ sequestration. In the hyper-arid region (MAP of 90 mm yr−1), gypsum dissolution provided an additional calcium source. Our findings demonstrate that ignoring the coarse fraction and subsurface soil layers leads to significant underestimation of carbon stocks, with implications for accurate carbon accounting in drylands.

Global Pattern of Flash Drought and Impact-Relevant Early Warning

Flash drought has devastating impacts on terrestrial ecosystems and challenges the existing drought prediction systems due to its rapid onset and intensification. A global early warning system tailored to the specific impacts of flash droughts is critically needed to prepare for and mitigate their adverse effects. Here, we identify regions prone to flash drought according to detections based on the soil water deficit index, quantify the severity and frequency of flash droughts and their past trends, and assess the performance of the solar-induced fluorescence rapid change index (SIF-RCI) as a flash drought early warning for different ecosystems across the globe. Multiple products, GOME-2 SIF, CSIF, and GOSIF, collectively referred to as xSIF, are used to account for data dependency and uncertainties. The results show that croplands and grasslands in subhumid and semi-arid regions are highly susceptible to flash drought, and these regions have experienced a significant increasing trend in both the frequency and severity of water stress. The xSIF-RCI from all three products are capable of forecasting the majority of flash droughts at their early stages, particularly in croplands and grasslands. Over forested regions, the CSIF-RCI predicts flash droughts more effectively, while the GOSIF-RCI and GOME-2 SIF-RCI missed more than half of the flash drought events. This study demonstrates that xSIF products offer valuable information on the development of agricultural flash droughts; the associated robustness and uncertainty will guide the further development and refinement of SIF-based metrics for potential incorporation into drought early warning and monitoring systems.

Defining Soil as a Surface-Coupled Planetary System: Separating Pedogenesis From Habitability

The concept of soil is increasingly being examined within a planetary context, prompting renewed attention to the physical conditions that enable surface materials to support life. In parallel, planetary habitability has emerged as a framework for defining the minimum resources and conditions required for biological activity, independent of whether life is currently present. Here, we apply habitability principles to soils not to assert the presence of life, but to distinguish clearly between soil formation, biological viability, and functional performance. Habitability formalises a binary set of physical and chemical requirements—including accessible energy, liquid water, essential elements, and viable environmental conditions—that define whether an environment can support life in principle. By treating habitability as a state that applies to already formed soils, rather than as a defining criterion for soil existence, we resolve long-standing ambiguity around the role of biology in soil definition. This separation leads to a tightened planetary definition of soil as ‘an organised planetary surface system, comprising generally loose mineral and/or organic material, formed through sustained genesis via water-mediated surface coupling that produces internal physical, chemical, and/or biological organisation and enables system evolution through time, irrespective of the presence of extant life.’ Within this framework, soil health, soil quality, and soil security operate as continuous descriptors of function only within habitable or potentially habitable soils, while degradation is conceptualised as a contraction of habitable state space. Habitability thus provides a physically grounded foundation for interpreting soil function, selecting indicators, and differentiating soils from sediments and regolith across Earth and other planetary environments.

A robust rank aggregation based interpretable minimum predictor set for scalable soil organic carbon mapping

Digital soil monitoring depends on a difficult balance: models must be simple enough to support operational monitoring but still grounded in the environmental processes that control soil variation. However, current soil health assessments and mapping frameworks often depend on large covariate libraries that increase computational costs, introduce redundancy, and limit reproducibility across agroecological domains. This problem is particularly important for soil organic carbon (SOC), whose spatial distribution reflects interacting controls from climate, terrain, soil type, texture, vegetation, and land use. Here, we identify a minimum set of environmental predictors that is stable across selection algorithms and transferable to contrasting landscapes. Using 28,738 georeferenced topsoil samples from Ethiopia and 73 SCORPAN covariates, we benchmarked seven feature selection methods spanning filter, embedded, and wrapper families and integrated their rankings through robust rank aggregation. Models were evaluated on spatially disjoint highland, midland, and lowland test domains. The consensus set retained eleven predictors, dominated by soil texture, WRB reference soil group, and mean annual temperature, and explained 75 % of SOC variance. Relative to the full 73-covariate baseline, XGBoost trained on the reduced set lowered test RMSE by 25 %. SHAP analysis showed that the top three predictors accounted for 78 % of model attribution, indicating that the reduced set preserved the main pedogenic controls on SOC stabilization rather than simply removing redundant variables. The framework provides a parsimonious and interpretable basis for SOC prediction across heterogeneous landscapes, with direct relevance for scalable soil health monitoring, national carbon assessment, and precision land management decisions.

Visualizing soil chemical heterogeneity in time and space using optical sensors: advances, challenges, and prospects

Understanding how soil biogeochemical processes develop from microscale heterogeneity remains a central challenge in soil science. Many key transformations, including microbial respiration, nutrient cycling, and greenhouse gas production, occur in spatially structured microenvironments that are difficult to capture with conventional bulk measurements. Planar optodes have emerged as a powerful tool to address this challenge by enabling two-dimensional imaging of chemical parameters such as oxygen (O2), pH, ammonia (NH3), and carbon dioxide (CO2) across soil profiles with high spatial and temporal resolution. In this Perspective, we critically examine what planar optodes can reveal about soil biogeochemistry and what they cannot. We discuss the sensing principles underlying optode measurements and review recent applications that use optode imaging to visualize redox heterogeneity, rhizosphere processes, and biogeochemical hotspots that remain invisible to conventional approaches. These observations provide a dynamic view of soil chemistry and open new opportunities to generate mechanistic hypotheses about the controls of microbial activity and nutrient transformations. Particular emphasis is placed on the strengths of optodes for identifying microsites and guiding targeted sampling strategies. At the same time, we highlight key limitations, including challenges related to calibration, optical artefacts, and the interpretation of two-dimensional concentration fields in inherently three-dimensional soil systems. We also discuss how optode imaging can be integrated with complementary techniques such as microsensors, molecular analyses, gas flux measurements, and structural imaging to better resolve links between soil structure and biogeochemical processes. Finally, we outline emerging directions that could expand the role of optodes in soil research, including their integration with data-driven and process-based models. Used thoughtfully and in combination with other approaches, planar optodes can become a central tool for investigating soil biogeochemistry at the microscale.

Lower carbon fractions and microbial carbon use efficiency in ant-mound surface soils than in adjacent biocrusted soils of a revegetated desert

Drylands are key components of the global carbon cycle, and biological soil crusts (biocrusts) are important secondary groups of primary producers. Ants are among the most abundant soil-dwelling animals in drylands; however, the relationship between ant nesting activity and the surface soil carbon cycle in biocrust systems remains unclear. In a revegetated area of the Tengger Desert, we compared five surface (0–5 cm) microhabitats (ant-mound soil, the biocrust and soil beneath mounds, and adjacent control biocrust and soil) in terms of physicochemical properties, 12 carbon fractions, 13C-glucose-based microbial carbon use efficiency (CUE), and greenhouse-gas fluxes under standardized laboratory incubation. Compared with the control surface soil, ant-mound soils exhibited significantly lower water content, pH, and nutrient content, as well as a coarser texture. Mound soils also contained significantly less soil organic carbon (−62%), microbial biomass carbon (−63%), and labile fractions (e.g., particulate organic carbon − 69% and easily oxidizable carbon − 66%), as well as a significantly lower microbial CUE (0.305 vs. 0.418; −27%). During incubations, intact biocrust plus its underlying soil acted as a net CH4 sink (−2.9 to −3.7 µg C kg−1 d−1), whereas mound and exposed surface soils were net CH4 sources (+3.2 to +4.8 µg C kg−1 d−1); N2O efflux was higher in mound soil than in control surface soil, although control biocrust showed comparably high N2O. Because ant nest sites are not chosen at random, we interpret these contrasts as associations that may arise from nest construction, pre-existing site differences, or both. Ant mounds occupy only approximately 0.12% of the surface; therefore, their contribution to landscape-scale fluxes is small; the principal finding is a consistent surface-soil contrast showing that ant nesting is associated with lower near-surface carbon storage and microbial carbon-use efficiency in biocrust-covered drylands. Explicitly accounting for such faunal surface disturbance would improve assessments of biocrust carbon accrual in dryland restoration.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on August 06, 2026.

Journal Paper Digests

2026-07-29T00:00:00-00:00

Journal Paper Digests 2026 #17

Assessing Precipitation Effectiveness in the Context of Drought
From Light Scan to Flow: Estimating Soil Hydraulic Properties Through VNIR-SWIR Spectroscopy and Calibration-Free PTFs
Trends in Soil Health in the European Union
Clay content, not microbial community composition, regulates carbon stabilisation along a soil carbon and texture gradient impacted by reduced precipitation

Clay content, not microbial community composition, regulates carbon stabilisation along a soil carbon and texture gradient impacted by reduced precipitation

Soils are the largest terrestrial carbon (C) reservoir and play a crucial role in climate regulation; yet, sustaining soil organic carbon (SOC) stocks in agricultural systems remains a challenge. Although advances have been made in understanding SOC dynamics, the mechanisms controlling C stabilisation and decomposition remain uncertain, especially regarding the interplay of SOC content, soil texture, microbial community composition and environmental stressors such as drought. In this study, we addressed the key question whether high-SOC, high-clay soils support greater C decomposition of added litter due to additional breakdown of native SOC, or whether these soils have smaller C decomposition due to more C stabilisation. Additionally, the legacy effect of 120-day reduced precipitation was studied. We conducted a six-month incubation study using soils collected in a barley field along a natural gradient of SOC, clay and pH, with precipitation reduced by rainout shelters. Soils were amended with 13C-enriched plant litter to trace fresh C inputs into different C pools. We found that litter-derived CO2 production was highest in coarse-textured, low-SOC soils, supporting our hypothesis that clay content controls C stabilisation and decomposition. Accordingly, high-SOC, high-clay soils supported increased formation of mineral-associated organic carbon (MAOC), indicating rapid stabilisation and protection of fresh C by minerals. MAOC formation efficiency correlated positively with clay content and native SOC, suggesting that increased C input could further enhance SOC storage. Reduced precipitation did not affect litter decomposition or C stabilisation, indicating that short-term precipitation reduction has little effect on soil C turnover in cool-humid upland soils. Metagenomic and amplicon analyses revealed that microbial community structure and functional potential were largely stable across the gradient and unaffected by reduced precipitation. Our findings suggest that clay content, rather than native SOC or microbial community composition, is the primary factor shaping litter-C turnover and stabilisation on a field scale.

Trends in Soil Health in the European Union

Healthy soils are essential for our environment and society, as they deliver crucial ecosystem services. However, soils across the European Union (EU) are increasingly affected by multiple soil degradation processes. As a result of these increasing degradation incidences, recent EU policy initiatives have been taken to protect and restore soils, such as the good agricultural and environmental conditions under the Common Agricultural Policy, as well as the Soil Monitoring and Resilience Directive, the Mission Soil and the EU Soil Strategy for 2030, aiming to reach healthy soils by 2050. Soil monitoring through time as well as studying changes in soil status is needed to verify whether EU soils are on track to reach a healthy state by 2050. In this study, trends of seven soil degradation processes were analysed, using EU-scale spatial layers and data points produced from harmonised repeated monitoring surveys. Results suggest that the range of unhealthy soils decreased in the past decades regarding three indicators, namely soil erosion by water, harvest erosion and phosphorus deficiency. Conversely, results suggest that the area with unhealthy soils increased regarding loss of soil organic carbon, phosphorus excess and soil sealing. No clear trends were observed for soil pH. The temporal coverage of the available datasets only marginally covers the period since the start of the Mission Soil and the EU Soil Strategy. Thus, evaluating the effectiveness of these policy initiatives in protecting soils was not possible in this study. Projections suggest that four studied soil degradation processes will further deteriorate in the upcoming decades due to climate change, continued unsustainable management practices, land use changes and urbanisation. This shows the importance of the Soil Monitoring and Resilience Directive, the EU Soil Strategy and the Mission Soil to continue the investment in soil monitoring and related research, to accelerate the implementation of sustainable soil management practices, and to bring all indicators on track to reach healthy soils by 2050.

From Light Scan to Flow: Estimating Soil Hydraulic Properties Through VNIR-SWIR Spectroscopy and Calibration-Free PTFs

This study presents a three-step approach for estimating soil-water retention (WRF) and hydraulic conductivity functions (HCF) by integrating VNIR-SWIR reflectance spectroscopy with semi-physical, calibration-free pedotransfer functions (PTFs). A total of 135 soil samples from the Alento Observatory (southern Italy) were analyzed. In the first step, diffuse reflectance spectroscopy was used to derive particle-size distribution (PSD) by combining five spectral pretreatment algorithms and three machine learning techniques. The best-performing model demonstrated excellent predictive accuracy, achieving a coefficient of determination (R2) of 0.945 and a Root Mean Square Error (RMSE) of 0.051 for PSD mass fractions. In the second step, two semi-physical PTFs—the Arya-Heitman (PTFWRF-AH) and Mohammadi-Vanclooster (PTFWRF-MV) models—were applied to estimate the WRF using spectrally-derived PSD together with measured soil bulk density and saturated water content. The PTFWRF-MV model outperformed the AH variant, yielding a lower RMSE (0.049 cm3 cm−3) and higher R2 (0.676). The third step involved estimating the HCF by applying the Arya and Heitman PTF alongside measured saturated hydraulic conductivity values. This process relied on the flow-similarity hypothesis, which assumes that water flow partitioning within pore domains is equivalent across idealized and natural-structure soils. However, the HCF predictions for both models exhibited uncertainties greater than one order of magnitude and R2 values under 0.50. These findings underscore the efficacy of spectroscopy for soil texture characterization, while highlighting persistent limitations in predicting hydraulic properties of structured soils, likely due to violations of flow-similarity assumptions.

Assessing Precipitation Effectiveness in the Context of Drought

The effect of intense rainfall on drought is complex and less well understood than impacts on flooding. Limits to water storage under heavy rainfall can result in less infiltration and more runoff with increasing rainfall intensity. This response to increasing precipitation intensity can reduce the usefulness of widely used precipitation-focused metrics for drought monitoring and early warning. Precipitation effectiveness could provide a useful framework to account for the impact of precipitation intensity on drought conditions and soil moisture recharge. In this study, we assess how precipitation effectiveness varies across space and time and define the impact of rainfall intensity using station observations of precipitation and soil moisture at 3 sites in Illinois. We develop a random forest (RF) model to estimate precipitation effectiveness and compare the model to both observations and precipitation effectiveness estimates using the SCSC Curve Number (CN) method. Observed precipitation effectiveness substantially varies between precipitation events and growing seasons at all three study sites, and this variability is not well represented in total precipitation. We find the RF method can provide a useful measure of the effectiveness of precipitation events for maintaining, improving, or deteriorating drought conditions, especially in lieu of dense, widespread root zone soil moisture observations. Further evaluation and optimization of the RF method for different climates, soil types, and land uses, as well as over larger spatial scales like watersheds is necessary to realize the operational monitoring capabilities of precipitation effectiveness for drought monitoring in a changing climate.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on July 29, 2026.

Journal Paper Digests

2026-07-28T00:00:00-00:00

Journal Paper Digests 2026 #16

‘A flash in the pan’ or a more enduring contribution? A retrospective on Micha Klein’s (1976) ‘Hydrograph peakedness and basin area’
High resolution 4D soil organic carbon stock mapping at farm scale
How the landscape influences soil respiration: Explaining spatio-temporal patterns with interpretable machine learning
Capturing Field-Scale Soil Moisture Dynamics in Ireland Using Cosmic-Ray Neutron Sensing
Combined Effect of Biochar and Microplastics on Soil Greenhouse Gas Emissions: A Meta-Analysis
A Simple Stomatal Model That Unifies the Metabolic and Hydraulic Control of Carbon and Water Flux

‘A flash in the pan’ or a more enduring contribution? A retrospective on Micha Klein’s (1976) ‘Hydrograph peakedness and basin area’

Klein’s 1976 article proposed the hydrograph ‘peakedness index’ (PI), calculated as mean flow as a percentage of highest flow in a drainage basin. In this retrospective, we (1) outline the key findings regarding PI, (2) assess the relevance of PI for fluvial studies today and (3) consider whether PI may provide a useful metric for fluvial studies in future. Using gauging data from three humid area basins (Scotland, Yorkshire, Ohio River basin), Klein’s analysis indicated a ‘break point’ in PI at a basin area of ~300 km2. A division between smaller, ‘peaky’ basins and larger, ‘less peaky’ basins was attributed to the changing relative contributions to channel runoff from rapid overland flow in headwaters and slower subsurface flow farther downbasin. Re-examination using several decades of UK discharge data suggests a more complex picture, although for stations on the River Severn, peakedness is increasing in upstream reaches but decreasing downstream. Temporal trends in PI are likely driven by compounding or counteracting changes to climate (e.g. intense rainfall) and land use/land cover (e.g. urbanisation, damming). Analyses of PI for other basins worldwide could form part of wider efforts to assess the potential for rapid onset, highly peaked floods (flash floods), which are among the world’s most destructive hazards.

High resolution 4D soil organic carbon stock mapping at farm scale

Soil organic carbon (SOC) is intrinsically linked to global carbon balance, climate change mitigation, soil health, and agricultural productivity. Therefore, obtaining accurate information on the spatial-temporal of the soil organic carbon stock (SOCS) is essential. We proposed a four-dimensional (4D) SOCS mapping approach, encompassing space (two dimensions), depth and time. A 100 × 100 m grid sampling was conducted in 1997 and 2022 at two depths (0–20 cm and 80–100 cm) in a sugarcane farm. Dynamic and static covariates representing the soil formation processes were used to fit three Cubist models. We tested three strategies for SOCS spatial-temporal mapping: 1) a model for each year, 2) a model fitted using only the data of the last year (2022) and 3) a multitemporal model using data of both periods. Based on external validation, strategy 1 and 3 produced more accurate and less biased maps, with coefficient of determination (R2) of 0.74, root mean square error (RMSE) of 7.69 ton ha−1 and bias of 3.43 ton ha−1 and R2 of 0.76, RMSE of 7.19 ton ha−1 and Bias of 3.25 ton ha−1 for strategy 3, respectively. Strategy 2 was less efficient (R2 = 0.71; RMSE = 11.06 ton ha−1; Bias = 7.93 ton ha−1). Strategy 3 is particularly useful for SOCS mapping when only limited temporal observations are available. Soil attributes (static) were most important covariates for modeling, followed by a bare soil image (dynamic) and vegetation information (dynamic). An increase in SOCS was observed in most sampling sites and predicted maps. The SOCS dynamic was related to soil type, geology and showed an inverse relationship with bare soil frequency. Finally, the SOCS saturation deficit was assessed by spatio-temporal mapping.

How the landscape influences soil respiration: Explaining spatio-temporal patterns with interpretable machine learning

Soil respiration plays a crucial role in the carbon cycle by representing the greatest flux of carbon from terrestrial ecosystems to the atmosphere. The spatio-temporal variability of soil respiration within a landscape is a result of the patterns of its climatic and environmental drivers. However, despite its importance, the factors driving soil respiration variability within heterogeneous landscapes remain insufficiently understood. To investigate such relationships, we measured soil respiration and determined potential drivers at 166 sites distributed over one year across a 400 km study area in the Fichtelgebirge mountains, Germany. We trained random forest models and applied interpretable machine learning methods to explain and spatio-temporally predict soil respiration. Spatio-temporal patterns of soil respiration were predicted with an RMSE of 61 and an R of 0.39. In the heterogeneous landscape that includes grasslands, arable land, and forests, spatial variability of soil respiration was large, with variations of up to 415 at a single point in time. Spatial patterns of soil respiration followed the patterns of the land use types, were further differentiated by vegetation cover, and were influenced by the topographic position within the landscape. These drivers also influenced patterns of soil temperature, which was the most important driver of soil respiration. Our high-resolution predictions demonstrate pronounced spatial variability in soil respiration at the landscape scale, arising from the interaction of multiple environmental controls, and offer new insights into responses under real-world conditions. Overall, interpretable machine learning showed great potential by explaining the spatio-temporal patterns of soil respiration resulting from complex interactions of its drivers, providing insights into soil respiration on the landscape scale.

Capturing Field-Scale Soil Moisture Dynamics in Ireland Using Cosmic-Ray Neutron Sensing

Cosmic-Ray Neutron Sensing (CRNS) enables non-invasive monitoring of field-scale soil moisture, bridging the spatial gap between point-scale sensors such as Time Domain Reflectometry (TDR) and coarse-resolution satellite products. Its reliability, however, depends on rigorous atmospheric correction, site-specific calibration, and proper scale harmonisation. This study evaluates CRNS performance at four temperate maritime grassland sites in Ireland and conducts a controlled comparison between the classical N0 calibration framework and the physics-based Unified Transport Solution (UTS). Additionally, weighted TDR observations were used as a spatially and vertically harmonised reference. After full correction, CRNS-derived volumetric moisture content (VMC) showed strong agreement with weighted TDR across sites (R2 = 0.78–0.92), capturing seasonal wetting–drying cycles and event-scale infiltration dynamics. Bound hydrogen pools contributed 15%–25% of the total neutron signal, demonstrating their critical role in site-specific calibration. The comparison between N0 and UTS revealed that increased physical complexity does not universally improve performance; For example, UTS provided measurable gains at sites where dynamic pore-water redistribution dominated, whereas at sites with high contributions from relatively static hydrogen pools, improvements were limited. Event-based analysis confirmed that CRNS responses primarily reflected soil layers between 20 and 30 cm depth and integrated transient wetting signals beyond the reach of single-depth sensors. While CRNS exhibited sensitivity to near-surface and interception-related hydrogen during peak wet periods, this behaviour reflects physical signal integration rather than measurement artefacts. Overall, CRNS provides a robust and transferable framework for field-scale soil moisture monitoring in humid temperate grasslands when atmospheric corrections, hydrogen-pool characterisation and footprint-aware validation are consistently applied.

Combined Effect of Biochar and Microplastics on Soil Greenhouse Gas Emissions: A Meta-Analysis

Microplastics (MPs) are pervasive in the soil environment and may contribute to increased soil greenhouse gas (GHG) emissions. Biochar, a common soil amendment, exhibits notable carbon sequestration and emission reduction effects. However, research findings regarding the combined effects of MPs and biochar on soil GHG emissions remain inconsistent. This discrepancy limits a comprehensive assessment of the efficacy of biochar in mitigating GHG emissions from MPs-contaminated soils. This study conducted a meta-analysis to integrate published data to systematically elucidate the effects of MPs, biochar and their coexistence on soil CO2, CH4 and N2O emissions, as well as global warming potential (GWP) and reveal the underlying mechanisms. The results showed that MPs increased CO2, CH4, N2O emissions and GWP, while biochar and combined treatments reduced them, with biochar showing a greater reduction than the combined treatment. This finding confirms that biochar can effectively mitigate the soil greenhouse effect caused by MPs. However, meta-regression results indicate that the effects of biochar and MPs on soil nitrogen conversion, microbiological indicators and GHG emissions are highly dependent on the experimental environment, soil pH, MPs type and biochar feedstock. Among them, biochar feedstock type was the key to determine the direction of CH4 and CO2 emissions: straw biochar reduced CH4 emissions, while sludge biochar increased CH4 emissions; manure biochar increased CO2 emissions, while sludge and straw biochar decreased CO2 emissions. In addition, the effect direction on soil nitrogen was systematically reversed under laboratory incubation and field experiments, but this reversal was not reflected in N2O emissions, suggesting that the mechanisms of N2O mitigation differ under different experimental conditions. The above findings emphasize that when assessing the emission reduction potential of biochar, it is necessary to clarify its feedstock and extrapolate laboratory results to field conditions with caution.

A Simple Stomatal Model That Unifies the Metabolic and Hydraulic Control of Carbon and Water Flux

A striking incongruity has long persisted in the modeling framework typically used to predict CO2 and water vapor exchange between land plants and the atmosphere across scales. Generally, photosynthetic CO2 demand is estimated using process-based models of biochemistry, but the biophysical stomatal constraint on photosynthesis and transpiration (gsw) is estimated using “black box” empirical or optimization-based models. Empirical models of gsw can only be parameterized in the domain of the training data, limiting confidence in predictions made outside that domain; optimization-based models rely on eco-evolutionary “goal functions” about which there remains poor consensus. To resolve this incongruity, we present a novel process-based model for gsw with parameters that all have biophysical meaning, and of which only two require empirical fitting, thus ensuring tractability for application in land-surface models (LSMs). The model successfully reproduces variation in gsw diurnally, globally, and in relation to soil drought and when drought and heat co-occur. The model also has greater functionality than previous models, by predicting stomatal closure under soil drought, the effect of variations in soil-leaf hydraulic conductance, stomatal closure in response to soil and atmospheric drought in darkness, and stomatal opening at high temperatures in both low and high light. With structure and parameters based on physiological processes, this model can translate continuing improvement in understanding of underlying biophysical and molecular genetic causes into predictions for carbon and water exchange, offering greater confidence for predicting the influence of stomata on land-surface exchanges of mass and energy in future climates.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on July 28, 2026.

Journal Paper Digests

2026-07-03T00:00:00-00:00

Journal Paper Digests 2026 #15

NASA’s EMIT hyperspectral observations of phytoplankton ecology in estuarine waters
Bayesian uncertainty analysis of soil organic carbon stocks and stock changes from croplands in the U.S. Midwest
The role of soil structure for the response of CO2 emissions to soil moisture and its relevance for modelling soil carbon dynamics
SMART-Soil: Satellite-based machine learning approach for reliable tracking of soil moisture across CONUS
Coupling near infrared spectroscopy with machine learning algorithms for simultaneously detecting multiple microplastics in soil
Causal Discovery Methods for Functional Performance of Evapotranspiration Models
An independent evaluation of global 1 km soil moisture products using in-situ and airborne observations
Interpolation of large-scale airborne geophysical data with uncertainty quantification

Interpolation of large-scale airborne geophysical data with uncertainty quantification

The collection of airborne geophysical data, ranging from ice sheet thickness measurements to magnetic anomaly detection for critical mineral exploration, continues to be of increasing importance. Interpolation of airborne geophysical data is essential, due to wide gaps between survey flight lines. Traditional interpolations are deterministic, meaning they produce a single map of predicted values between the data locations. This substantially limits the utility of the interpolation, as there is no acknowledgment of the fact that there is no true knowledge (e.g., no data was sampled) at these locations. These deterministic interpolation methods are thus limiting for downstream actions, hindering decision making and obscuring reality, which is uncertain. Geostatistical tools, such as sequential Gaussian simulation, have been deployed to quantify spatial uncertainty via stochastic rastering, but suffer from interpolation artifacts due to the geometric anisotropy of airborne survey data acquisition (e.g., dense along-track, sparse across-track). Existing methods that aim to reduce interpolation artifacts have been developed for deterministic interpolation schemes, but have yet to be carefully considered for stochastic schemes, leaving a clear gap in the interpolation toolbox: a method which properly quantifies spatial uncertainty while simultaneously suppressing unphysical interpolation artifacts. To address this gap, we introduce a new multigrid-based simulation method. This multigrid simulation is a stochastic geostatistical approach that mitigates interpolation artifacts while generating an ensemble of many realizations by treating the domain as a random field. Multigrid simulation is able to handle interpolation across large-scale domains, including non-stationary domains. We demonstrate its effectiveness on two datasets, one synthetic and one from a real field survey.

An independent evaluation of global 1 km soil moisture products using in-situ and airborne observations

High-resolution soil moisture data are essential for applications in agriculture, hydrology, and disaster management. Four global daily SM products at 1 km resolution have recently been developed, being the Seamless Soil Moisture (SSM), Global Surface Soil Moisture (GSSM), Global Land Surface Satellite (GLASS), and a downscaled SMAP product (DSMAP). These products rely on either machine learning or empirical regression models, offering significant potential but raising concerns regarding their generalization capability and spatial fidelity. Previous evaluations of these high-resolution products have relied predominantly on point-scale comparisons using the same in-situ networks employed for model training. Consequently, this study provides an independent evaluation using 1545 global in-situ stations excluded from product development and airborne passive microwave measurements from five field campaigns across North America and Australia. Results reveal that none of the evaluated products met the target unbiased Root Mean Square Error (ubRMSE) of 0.04–0.06 m3/m3, with observed values ranging from 0.097 to 0.104 m3/m3. All products exhibited narrower dynamic ranges (0.10–0.30 m3/m3) than those of in-situ observations (0.05–0.40 m3/m3), particularly underestimating wet and overestimating dry extremes. GLASS (R = 0.576) and DSMAP (R = 0.556) generally outperformed GSSM (R = 0.504) and SSM (R = 0.399) in capturing temporal dynamics relative to ground measurements. Spatially, airborne-based evaluation highlighted limitations in capturing fine-scale heterogeneity, particularly for SSM (mean R = 0.19) and GSSM (mean R = 0.31), which showed a narrow dynamic range and nearly static spatial pattern with weak response to regional rainfall. In contrast, DSMAP effectively captured the temporal dynamics of airborne data (mean R = 0.57) but retained coarse resolution artifacts from its downscaling process. Expanding training datasets, enhancing the generalization capability of the machine learning methods employed, and conducting rigorous spatial evaluations are identified as critical steps to ensure the reliability of high-resolution soil moisture products for operational applications.

Causal Discovery Methods for Functional Performance of Evapotranspiration Models

Evapotranspiration (ET) plays a key role in agricultural water resources management. However, it is challenging to predict as it is driven by water and energy availability as well as soil, vegetation, and meteorological factors, and models vary widely in complexity and assumptions. Causal discovery methods can identify drivers and interactions based on time-series data from both observations and models, and can be used as metrics of model “functional performance” that evaluate how models capture source-target relationships. With many approaches to causal discovery, it is important to compare how functional performance metrics align with predictive accuracy and behave across temporal scales. We compare four methods (Granger causality, Transfer Entropy, PCMCI, and Convergent Cross Mapping) to analyze the functional performance of ET models in a corn-soybean agricultural landscape based on 7 years of eddy covariance measurements, which we use as an empirical reference benchmark. We identify causal sources, among observed weather and soil variables, for Priestly-Taylor (PT), Surface Flux Equilibrium (SFE), Soil Water Balance (SWB), and satellite-based ET products from OpenET, and evaluate how closely model-derived and observation-based causal structures align. Methods consistently identify model forcings as sources, but otherwise vary widely in terms of sources and strengths across sub-hourly to weekly timescales. OpenET products have high functional performance, indicating that they capture key processes although they are not forced by tower observations. Finally, some functional metrics align better with predictive performance than others, which highlights the importance of selecting robust metrics that both capture interactions and align with predictive accuracy.

Coupling near infrared spectroscopy with machine learning algorithms for simultaneously detecting multiple microplastics in soil

Purpose Aiming at the problems of spectral overlap caused by the coexistence of multiple microplastics (MPs) in soil and low efficiency of traditional detection methods, this study explores the feasibility of an efficient detection method combining near-infrared (NIR) spectroscopy and machine learning (ML) for the simultaneous qualitative and quantitative analysis of multiple MPs in soil.

Methods Taking polypropylene (PP), polyethylene terephthalate (PET) and polyvinyl chloride (PVC) as target pollutants, NIR spectra were collected for eight types of samples (MPs-Free, single/two/three types of MPs-contaminated). After spectral preprocessing with the multivariate scatter correction + standard normal variate (MSC + SNV) method, four ML models, namely partial least squares (PLS), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed for the qualitative and quantitative analysis of soil MPs, and their performance was systematically compared.

Results In qualitative classification, all ML models achieved excellent performance with overall accuracies higher than 94%. Among them, PLS performed best, with a macro-averaged F1-score of 98.50 ± 1.16% and an accuracy of 98.55 ± 0.61%, followed by Linear-SVM with 98.02 ± 1.20% accuracy. XGBoost and RF yielded accuracies of 95.00 ± 1.95% and 94.14 ± 1.99%, respectively. In quantitative prediction, model performance was significantly affected by the type and number of coexisting MPs. For single-type MPs, SVM and PLS showed optimal accuracy and stability; for two-type MPs, SVM outperformed other models with higher RPD, lower limit of detection (LOD), and better low-concentration prediction; for three-type MPs, RF and PLS were more suitable for PP and PET, while SVM achieved the best performance for PVC (RPD = 4.6503). Overall, model prediction accuracy and cross-validation stability decreased gradually, while LOD increased slightly with an increasing number of coexisting MPs types.

Conclusion The combination of NIR spectroscopy and ML algorithms can reliably achieve simultaneous qualitative and quantitative analysis of multiple MPs in soil. Different models show distinct adaptability to the complexity of mixed MPs systems: linear models (PLS) excel in single-type and simple mixture scenarios, while SVM presents strong robustness in both binary and ternary mixtures. The established NIR-ML framework offers a simple, efficient, and low-cost strategy for rapid screening of multi-component MPs pollution in soil, with good application potential in environmental monitoring and risk assessment.

SMART-Soil: Satellite-based machine learning approach for reliable tracking of soil moisture across CONUS

Estimation of daily soil moisture (SM) is crucial because it directly affects agricultural productivity, irrigation scheduling, and drought risk assessment. However, most existing satellite-based SM products suffer from two key limitations: (i) strong reliance on radar retrievals, which restricts temporal coverage to the post-2015 period and therefore prevents long-term historical analysis; and (ii) fine-resolution SM maps that are typically obtained via statistical or dynamical downscaling of coarse products, introducing systematic errors and scale-dependent biases. To overcome these limitations, this study proposes a novel framework that estimates daily SM at multiple depths using only land and atmospheric variables from the Moderate Resolution Imaging Spectroradiometer (MODIS). By fully bypassing radar-based inputs and avoiding downscaling of coarse-resolution SM products, our approach natively generates 1-km SM estimates extending back to the early 2000s, enabling consistent, long-term, high-resolution monitoring over the Contiguous United States (CONUS). This study aims to estimate SM in four different depths. The Light Gradient Boosting Machine (LightGBM) optimized with Covariance Matrix Adaptation Evolution Strategy (CMA-ES) was selected to perform the soil moisture estimation on 5, 10, 20 and 50 cm depths. While the increase in depth results in slight accuracy drops, the LightGBM-CMA-ES could perform an accurate estimation. Based on results and considering the surface and deepest soil layer, RMSE ranges from 0.029 to 0.037 m3/m3 while the R2 spans from 0.86 to 0.79, respectively. Spatial analysis of estimations and their associated errors reveals that despite the models’ good estimation accuracy, soils with fine and coarse textures have respectively negative and positive biases. Temporal evaluation showed that the model can perfectly estimate monthly mean SM levels but has some evidence of under-estimation in modelling monthly maxima’s specifically in spring months. Output of this estimation framework would be SM maps with MODIS’s reference spatial resolution of 1 km, which enables better and more sustainable agricultural productivity, allows better water resources management and facilitates more efficient environmental monitoring and disaster risk management.

The role of soil structure for the response of CO2 emissions to soil moisture and its relevance for modelling soil carbon dynamics

Most soil carbon (C) models describe the effects of soil moisture on C mineralization rates using empirical response functions derived from laboratory incubations carried out on sieved soils. This pre-treatment alters the pore space structure controlling solute and oxygen diffusion and may also disturb the spatial distribution of microbial activity. Our objective was therefore to investigate the effects of disruption of the natural soil structure on the soil moisture response function for C mineralization. We measured CO2 emissions at soil water pressure heads ranging from zero to −600 cm for both sieved and intact soil samples taken from tilled and untilled soil horizons at a field site in northern France. The derived soil moisture response curves were then combined with a simple analytical water balance model to predict CO2 emissions in contrasting rainfall climates. We also explored the relationships between the parameters of the moisture response function and soil physical properties as well as metrics of soil structure quantified by X-ray scanning. Sieving significantly affected the shape of the moisture response function. In particular, the optimal degree of saturation for soil CO2 emissions lay much closer to saturation (> 0.8) in the case of intact soil structures. The effects of repeated tillage were like those of sieving, although less pronounced. We identified relationships between some indicators of soil structure and the optimal degree of saturation, which suggests that it may be possible to derive pedotransfer functions linking soil properties to this critical model parameter. Finally, our modelling demonstrated that the use of moisture response functions derived from sieved soils in soil C models can lead to an underestimation of CO2 emissions in wet climates.

Bayesian uncertainty analysis of soil organic carbon stocks and stock changes from croplands in the U.S. Midwest

Quantifying uncertainty in process-based model predictions is essential for evaluating confidence in model predictions and identifying priorities for model improvements. This study applied a Bayesian model analysis framework to quantify and partition multiple sources of uncertainty in DayCent model estimates of soil organic carbon (SOC) stocks and stock changes (0–30 cm) for croplands across the U.S. Midwest from 1990 to 2020. The region gained SOC at an average rate of 10.38 (95% prediction interval (PI) of 4.37–17.83) Tg C year−1, equivalent to 0.27 (95% PI of 0.11–0.46) t C ha−1 year−1 or a relative increase of 0.46% (95% PI of 0.42%–0.57%). Using Monte Carlo simulation, total predictive uncertainty was decomposed into four components: composite structural uncertainty, parameter uncertainty, input uncertainty associated with management practice adoption, and spatial scaling uncertainty associated with National Resources Inventory (NRI) sample design. At the point level, uncertainties for SOC stocks and stock changes were 29.5 and 11.7 t C ha−1, respectively, while at the regional scale they were 10.6 and 0.09 t C ha−1. Uncertainty decomposition showed that the composite structural component was the dominant source of uncertainty for regional SOC stock changes (57.5%), followed by parameters (38.8%), inputs (3.2%), and scaling (0.5%). For SOC stocks, parameter uncertainty dominated at the regional scale (69.8%), followed by composite structural uncertainty (26.0%), inputs (3.7%), and scaling (0.6%). Furthermore, temporal aggregation substantially reduced uncertainty, stabilizing the level of reduction after approximately five years of averaging, whereas spatial uncertainty required aggregation of a relatively large number of sites, about 5,000 to 10,000 sites, to reduce the uncertainty to a stable level. These findings highlight two approaches to reduce parameter and composite structural uncertainties in model-based assessments: advance model development to improve process representation and expand the quantity and quality of SOC observations.

NASA’s EMIT hyperspectral observations of phytoplankton ecology in estuarine waters

NASA’s EMIT hyperspectral spectrometer provides high spectral (∼7.4 nm) and spatial (60 m) resolution, capabilities that have not been routinely applied to water quality monitoring in shallow aquatic systems, where the bio-optical properties are highly complex. In this study, we developed Hyper-MoE-VAE, a deep-learning inversion framework integrating a mixture-of-experts architecture with variational autoencoders for globally applicable hyperspectral water-quality retrievals. The model accommodates diverse water types and addresses one-to-many inversions to retrieve chlorophyll-a (Chl a) and phytoplankton absorption coefficient ( ) from hyperspectral remote sensing reflectance ( ). Hyper-MoE-VAE was trained on a global bio-optical dataset and applied to EMIT imagery over Lake Pontchartrain. Same-day field matchups acquired on 14 April 2025 provide the first validation of EMIT-derived Chl a (MAPE = 7.66 %; MAE = 1.13 in log10 space, unitless) and across all EMIT bands, with representative performance (NRMSE = 0.09–0.11 m−1; ε = 29–36 %), revealing a muted Chl a response under highly turbid freshwater conditions and a clear shift toward chlorophyte dominance following fresh inputs. In addition, PACE-OCI, also hyperspectral with lower spatial resolution, was compared with EMIT for Chl a retrievals. Both sensors show comparable spatial patterns despite differences in spectral and spatial resolution, supporting the Hyper-MoE-VAE’s cross-mission applicability. These findings demonstrate EMIT’s strong potential for characterizing phytoplankton dynamics, both in abundance and community composition, and for monitoring harmful algal blooms (HABs) in aquatic systems.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on July 03, 2026.

Journal Paper Digests

2026-06-22T00:00:00-00:00

Journal Paper Digests 2026 #14

Integrating soil health and manure practices to advance sustainability for US dairy farms
A function-semantic oriented heterogeneous graph aggregation framework with weighted spatial relationships for urban functional zone mapping
Linking Soil Threats and Mitigating Management Practices
Mid-Infrared Spectroscopy to Estimate Ratios of Soil Organic Carbon to Clay
Challenges in reliable bias correction of hydro-climatic and energy data: Towards a reconstruction of datasets

Challenges in reliable bias correction of hydro-climatic and energy data: Towards a reconstruction of datasets

Regular gridded datasets commonly exhibit significant biases and therefore require correction before being used in impact studies. This study highlights the challenges involved in improving the adjustment of climate variables (precipitation and temperature), hydrological variables (soil water content, streamflow, evapotranspiration), and radiative variables (longwave and shortwave radiation). Eighteen bias-correction (BC) methods were selected, covering a wide range of approaches, including classical statistical techniques (e.g., quantile mapping), distribution-based methods (e.g., kernel smoothing), and machine learning approaches (e.g., random forests). BC outputs from CMIP6 models and ERA5 reanalyses were evaluated against observations from contrasting climatic regions of Benin. The evaluation relied on Kling–Gupta Efficiency (KGE), Pearson correlation coefficient, relative-mean-absolute-error (RMAE), and fraction within tolerance (FTI), computed at hourly, daily, and monthly temporal scales. It also considered the representation of diurnal and monthly cycles, as well as consistency with mean values and trends of various climatic extreme indices. Three calibration strategies were explored: (i) using the first half of the time series for calibration to assess the preservation of future trends; (ii) the second half to evaluate the ability to reconstruct missing data; and (iii) 80% of the series to investigate the effect of sample size. Finally, bias corrections were applied by stratifying the data by month and intensity ranges to assess the limitations of global BC methods. Results show that machine learning methods do not systematically outperform classical bias-correction approaches. Performance varies considerably by variable, station, and climatic context. It is therefore important to select methods suited to the specific conditions and intended use, to improve the reliability of future climate and hydrological projections.

Mid-Infrared Spectroscopy to Estimate Ratios of Soil Organic Carbon to Clay

The index based on the ratio of soil organic carbon to clay concentration (SOC/clay) has been confirmed as an effective tool for assessing the organic matter status of mineral soils. The index contains four classes: very good (SOC/clay ≥ 1/8), good (1/10–1/8), moderate (1/13–1/10) and degraded (≤ 1/13). Conventional analyses of SOC and clay concentrations are resource intensive, however. Here, we investigate the use of mid-infrared spectroscopy (MIRS) as an alternative for quantifying SOC/clay ratios and the index. We use data from the National Soil Inventory of England and Wales (n = 1301). We obtained RMSE values of 3.6 and 57 g kg−1 for estimations of SOC and clay concentrations by MIRS, respectively. We found that a simplified three-class index was satisfactory for predictions using MIRS. The accuracy of clay concentration estimations was a limiting factor, particularly for the combined middle class (approx. 60%–70% success). Over 80% of the samples in the other two classes were correctly predicted. The probability that a sample was in the estimated index class can be used to filter out estimates likely to be misclassified. The filtering removed at least twice the number of misclassified samples as the number of correctly classified that had below-threshold probability. We found that uncertainty estimates are more informative if calculated from the component estimates of SOC and clay rather than the SOC/clay ratio. An important source of uncertainty is the mismatch between MIRS estimates of clay concentration based on clay mineralogy and conventional estimates based on particle size fraction. The additional information on clay mineralogy given by MIRS means it provides a better characterisation of SOC–clay interactions affecting soil functioning.

Linking Soil Threats and Mitigating Management Practices

Climate change, population growth, and intensifying land use are exerting increasing pressure on soil resources worldwide. Despite growing knowledge of soil degradation processes, the relationship between soil threats and the effectiveness of management practices remains poorly synthesized. Here we present a comparative assessment of soil expert perspectives on soil threats and mitigation practices based on a survey of 162 soil experts from 38 countries. Across tropical, arid, and temperate climates, soil experts evaluated the importance of soil threats and the perceived effectiveness and use of key soil management practices, whereas also highlighting examples of locally adapted innovations. Across all responses, the most important soil threats were organic matter decline (overall rating of 4.2 on a five-point scale), soil erosion (4.1), and biodiversity loss (3.9), indicating broad agreement on the primary drivers of soil degradation. Mitigating management strategies (crop diversification, reduced tillage, organic inputs, and agroforestry) were consistently perceived as effective across climates (scores from 3.7 to 4.0); however, their implementation was not as widespread (2.6 to 3.2). Differences among climate groups were detectable for some soil threats, such as the higher perceived importance of organic matter decline in tropical climates and the greater relevance of salinization in arid climates. Soil experts also highlighted locally developed climate-smart farming systems, including Milpa intercropped with fruit trees in alternating strips (MIAF), Zero-budget natural farming, and agrivoltaic systems, with potential for upscaling and wider application across contexts. Overall, this global soil expert survey identifies common priorities in soil threats and management practices across three major climate groups, while illustrating how local environmental and socio-economic conditions shape the selection of sustainable soil management practices. These findings provide a comparative perspective to inform future research, monitoring, and soil management strategies.

A function-semantic oriented heterogeneous graph aggregation framework with weighted spatial relationships for urban functional zone mapping

Urban Functional Zones (UFZs) serve as spatial carriers for urban economic and social activities. Accurate and fine-grained UFZ mapping is critical for urban planning, governance, and sustainable development. The complementarity between Remote sensing images and Points of Interest (POIs) provides important support for UFZ mapping. However, existing UFZ identification methods exhibit notable limitations in integrating multimodal features from remote sensing images and POIs due to inherent data differences. These methods rely on pre-defined UFZ units and lack the capability to model spatial relationships between geographic objects, thereby cause functional mixing. Moreover, extracting multi-source heterogeneous features from images and POIs is essential for constructing multidimensional semantic representations of geographic objects. To address these challenges, this study proposes a Function-Semantic Oriented Geographic Object Heterogeneous Graph Aggregation (FOGA) framework for accurate UFZ mapping. FOGA models urban space as a heterogeneous graph composed of an image layer and a POI layer. MGFMamba is designed and Bidirectional Encoder Representations from Transformers (BERT) is employed to extract functional semantic features from images and POIs, respectively. By embedding both types of semantic features into the graph and constructing intra-layer and cross-layer connections, the framework achieves unified modeling of multimodal feature fusion spatial relationships. A Heterogeneous Edge-Attention Relational Graph Netual Network (HEA-RGNN) is proposed to perform UFZ mapping. Within this heterogeneous graph structure, adaptive multimodal feature propagate enables dynamic learning of feature representation ranges and cross-modal semantic weights, orienting the aggregation of geographic objects into UFZs without pre-defined UFZ units. Experimental results from four cities demonstrate the effectiveness and stability of FOGA. FOGA is further applied to construct the first Chinese UFZ dataset that requires no predefined spatial units, at 2.4 m resolution across 31 major cities in 2024, with results outperforming existing products in both mapping accuracy and fine-grained spatial representation. The UFZ data and source code produced in this study have been made public at the following link: https://github.com/haha123haha460/foga.

Integrating soil health and manure practices to advance sustainability for US dairy farms

The US dairy industry has committed to advancing environmental sustainability by reducing greenhouse gas (GHG) emissions, enhancing water use efficiency, and improving water quality. Feed production accounts for approximately 12% of GHG emissions and 99% of consumptive water use from dairy operations, making it a focus area for potential resource use and overall efficiency improvements. However, few studies report changes to GHG emissions or water quantity and quality outcomes from adopting soil health management systems and use of novel manure products for dairy feed production. The Dairy Soil and Water Regeneration (DSWR) project is exploring whether soil health management systems and novel manure products can help advance environmental sustainability outcomes across major dairy-producing regions in the United States. Through a suite of coordinated studies, including regional soil benchmarking and large-plot- to field-scale experiments, DSWR is evaluating the performance and scalability of reduced tillage, cover crops, and novel manure products applied to row crop feed production systems. By integrating high-resolution data on soil, water, GHG emissions, and crop production, this project is generating actionable insights to support decision-making for farmers, farm managers, dairy cooperatives, retailers, and consumer packaged goods companies. We introduce the project by summarizing its purpose, the conceptual framework guiding its design and implementation, and its approaches to hypothesis testing about soil health, hydrology, and yield responses.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on June 22, 2026.

Journal Paper Digests

2026-06-15T00:00:00-00:00

Journal Paper Digests 2026 #14

Chemical structure of soil organic carbon governs formation, stability, and carbon accumulation of soil aggregates under contrasting land uses
Observation-Constrained Agroecosystem Model Inversion Reveals Continental-Scale Variation of Winter Wheat Traits
The potential of poetry for enchanting and communicating geomorphology
A density-based boundary line analysis framework using accessible spatial datasets to identify within-field limitations to crop production
Depth-resolved mapping of soil temperature in Australia using mechanistic and machine-learning approaches
Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping
Zero-shot inference with Tabular Prior-data Fitted Network (TabPFN) for soil MIR spectral analysis

Zero-shot inference with Tabular Prior-data Fitted Network (TabPFN) for soil MIR spectral analysis

The Tabular Prior-Data Fitted Network (TabPFN) is a foundation model, a pretrained, transformer-based neural network, designed for prediction tasks on tabular data. Although TabPFN has demonstrated strong performance relative to state-of-the-art baselines, its generalisability to soil spectral datasets of varying sizes remains unclear. This study evaluates the performance of TabPFN and compares it with partial least squares regression (PLSR), Cubist, and convolutional neural network (CNN) for soil spectral analysis using mid-infrared (MIR) spectroscopy. Soil samples from the Kellogg Soil Survey Laboratory were used to predict three soil properties: total carbon (TC), pH, and Olsen method extractable phosphorus (Olsen-P), representing high, medium and low predictability. An internal dataset from Texas (N = 620) and an external dataset from eastern Australia (N = 387) were used for testing. Models were trained using datasets of varying sizes and spectral similarity to the test sets. TabPFN achieved higher accuracy than all the baseline models in most results, with an average RMSE reduction of 74% relative to PLSR and 39% relative to Cubist when predicting TC. Performance gains were particularly pronounced when trained on medium-sized datasets, and TabPFN also exhibited superior generalisability across spectrally distinct training and test data. Soil property predictability influenced model performance across all models, with higher accuracy for TC than for pH and Olsen-P. For uncertainty quantification, TabPFN produced prediction intervals that closely matched the expected coverage in the external test set, indicating reasonable uncertainty generalisation, although quantile calibration was less reliable. Shapley additive explanations (SHAP) revealed that the wavenumbers contributed to the prediction corresponded to known spectral signatures of soil organic and inorganic carbon, supporting the interpretability of TabPFN. Overall, TabPFN demonstrated high predictive accuracy, improved generalisability compared to conventional methods, and useful uncertainty estimates, highlighting its potential for application to soil spectral libraries, for both large and small sizes.

Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping

Machine learning and geostatistics are two fundamentally different frameworks for the prediction and spatial mapping of soil properties. Geostatistics leverages the spatial structure of soil properties, whereas machine learning models capture the relationship between available environmental features and soil properties. We propose a hybrid framework that augments machine learning with spatial context through the engineering of ‘spatial lag’ features derived from ordinary kriging. We call this approach ‘kriging prior regression’ (KpR), as it reverses the logic of regression kriging by incorperating kriging outputs before and during the regression step. To evaluate this approach, we assessed both the point and probabilistic prediction performance of KpR, using TabPFN across six field-scale datasets from LimeSoDa. These datasets included soil organic carbon, clay content, and pH, along with features derived from remote sensing and in-situ proximal soil sensing. KpR with TabPFN demonstrated reliable uncertainty estimates and accurate predictions in comparison to several other spatial techniques (e.g., regression/residual kriging with TabPFN), as well as to established non-spatial machine learning algorithms (e.g., random forest and categorical boosting). Most notably, it improved the average R2 by 30% relative to machine learning algorithms without spatial context. This improvement was due to the strong prediction performance of the TabPFN algorithm itself and the complementary spatial information provided by KpR features. TabPFN is particularly effective for prediction tasks with small sample sizes, common in precision agriculture, whereas KpR can compensate for weak relationships between sensing features and soil properties when proximal soil sensing data are limited. We conclude that KpR with TabPFN is a robust and versatile modelling framework for digital soil mapping in precision agriculture.

Depth-resolved mapping of soil temperature in Australia using mechanistic and machine-learning approaches

Soil temperature is a key regulator of biogeochemical processes, plant growth, and soil–atmosphere interactions, yet spatially explicit estimates of subsurface temperature is limited at large spatial scales. This study evaluates the performance of a physics-based mechanistic model for mapping annual average soil temperature (ST) across Australia and compares its predictions with two machine-learning (ML) approaches, Support Vector Machine and Extreme Gradient Boosting. The mechanistic model is based on a steady-state analytical solution of soil heat conduction to estimate ST at multiple depths. Model predictions were evaluated using soil temperature observations and compared with ML models trained on a broad set of environmental covariates. The mechanistic model achieved predictive performance comparable to the ML approaches, with an average R2 of 0.94 and RMSE values between 5.5 and 6.0 °C, while providing better physical interpretability and spatial consistency. The resulting maps reveal a clear continental-scale gradient in soil temperature, with northern Australia exhibiting substantially warmer soils than southern coastal regions. Although surface air temperature strongly influences ST patterns, soil thermal properties, particularly thermal conductivity, volumetric heat capacity, and soil moisture, modulate both vertical and lateral temperature variations. On average, annual soil temperature at 1 m depth was approximately 4 °C higher than mean annual surface air temperature, indicating the buffering effect of soil thermal stability. The results suggest that simplified mechanistic models with sensor calibrated approximation can provide annual mean soil temperature patterns at continental scales. Combining physically based approaches with machine-learning methods may further improve soil temperature prediction and support large-scale assessments of ecosystem processes, soil carbon dynamics, and land–atmosphere interactions.

A density-based boundary line analysis framework using accessible spatial datasets to identify within-field limitations to crop production Free

Context. Precision agriculture can benefit from within-field boundary line analysis (BLA) to identify the most limiting factors impacting crop yield. In this study, the BLA was applied at the within-field scale using yield monitor data and key environmental factors, including evapotranspiration (ET), elevation and the apparent soil electrical conductivity (ECa). Aims. This study aimed to develop and evaluate a novel BLA method to estimate Yp and quantify Yg at the within-field scale, and to assess the diagnostic potential of freely available proxy variables for identifying spatially variable yield-limiting factors in dryland wheat production. Methods. A novel approach combined Mahalanobis distance-based filtering for data denoising with two-dimensional kernel density estimation (KDE) and percentile thresholding to select high-density, high-yield points that define the upper yield envelope. A generalised additive model (GAM) was then used to produce the boundary line through these selected points to represent the Yp. Key results. Using two case study fields, the results showed that this approach was robust and less sensitive to noise and outliers in fine-scale datasets. The results also showed that the freely available ET and elevation could be used as proxies to highlight the impact of some limiting factors, such as frost events or waterlogging. The ECa could identify areas where some potential soil related factors (e.g., lower clay content reducing plant available water capacity) could be the limiting factors. Conclusions. The density-based BLA successfully estimated Yp and quantified Yg at the within-field scale using freely available proxy variables. While these proxies effectively indicated potential limiting factors, ground-truth validation is required to confirm the underlying causal mechanisms. Implications. Using this BLA approach, growers could more confidently identify local yield constraints, estimate site-specific Yp and Yg, and fine-tune their inputs, leading to more efficient resource use and improved profitability.

The potential of poetry for enchanting and communicating geomorphology

Increased collaborations between geomorphologists and artists have recently been suggested as possible ways of improving the visibility of geomorphology and of stimulating interest in and engagement with the discipline. As a well-established art form, poetry provides a unique set of challenges and opportunities for such collaborations. Here, we present two original poems about an enigmatic coastal landform and surrounding landscape—Dinas Dinlle hillfort, north Wales—and reflect on the wider potential of poetry to facilitate enchantment with, and communication of, geomorphology. We discuss how poetry’s potential could be employed particularly effectively in two specific sectors: (1) education, through aligning field experiences with creative writing sessions, and by linking with emerging cross-curricular concepts such as cynefin in Wales and ‘greening the curriculum’; and (2) heritage, where links between geomorphology and social and cultural history can be made in collaboration with archaeologists and heritage managers. We contend that poetry can contribute to creating a more diverse and inclusive discipline by fostering increased engagement and facilitating discussions between different actors, including the more-than-human elements of landscape.

Observation-Constrained Agroecosystem Model Inversion Reveals Continental-Scale Variation of Winter Wheat Traits

Understanding how crop trait variability shapes genotype × environment × management (G × E × M) interactions remains a key uncertainty in predicting agricultural performance under a changing climate. Continental-scale crop models commonly rely on spatially uniform parameters, limiting their ability to represent adaptive variation in phenology, allocation, and yield formation. Here we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations. By constraining simulations with satellite-derived photosynthesis and county-level yield records from 2008 to 2022 across ~1000 winter wheat–producing counties, the inversion recovers coherent patterns of maturity group, reproductive capacity, harvest index, and root–shoot allocation. The optimized simulations reproduce observed carbon uptake and yield variability (gross primary productivity r = 0.76–0.88; phenology bias < 2 weeks; > 90% of yields within ±20% of reports) and reveal distinct physiological profiles that align with the geographic distributions of major winter wheat market classes. The inferred controls explain class- and region-specific climate sensitivities: warmer winters reduce vernalization success in late-maturing cultivars, while elevated vapor pressure deficit causes strong yield losses in rainfed Hard Red Winter wheat. The results demonstrate that observation-constrained trait inversion within model–data fusion framework reveals biologically meaningful crop-class variation, thereby providing a scalable, physiologically grounded framework for diagnosing adaptive diversity and climate vulnerability across agroecosystems.

Chemical structure of soil organic carbon governs formation, stability, and carbon accumulation of soil aggregates under contrasting land uses

Purpose This study aimed to elucidate how the chemical structures of soil organic carbon (SOC) govern the formation, stability, and carbon accumulation of soil aggregates under different land uses.

Methods Soil samples were collected from seven representative land uses: cassava plantation (CVP), orchard (OR), sugarcane plantation (SP), corn plantation (COP), forest (FR), pasture (PT), and abandoned land (AL). Soil aggregates were separated into distinct size classes (> 2000, 500–2000, 250–500, 53–250, and < 53 μm), aggregate stability was assessed using mean weight diameter (MWD) and geometric mean diameter (GMD), and SOC accumulation within aggregate fractions was quantified. The chemical composition of SOC was characterized using 13C cross-polarization/magic angle spinning (CPMAS) nuclear magnetic resonance (NMR) spectroscopy.

Results The results revealed pronounced land use-dependent differences in soil aggregation and SOC distribution. The PT land use exhibited a greater proportion of macroaggregates (> 2000 μm; 70% of total), higher aggregate stability (3.6 and 1.4 mm for MWD and GMD, respectively), and higher SOC accumulation in macroaggregate fractions (13.3 mg C g− 1). Whereas, the CVP resulted in greater SOC accumulation in microaggregates (53–250 μm; 10.9 mg C g− 1). The 13C CPMAS NMR results revealed that O-alkyl C was the major C group in the > 2000 μm fraction (23 − 42%), whereas aromatic C was the major C group in the 53 − 250 and < 53 μm fractions (29 − 47% and 35 − 54%, respectively). The formation, stability, and SOC accrual in macroaggregates were positively associated with O-alkyl C and alkyl C components, reflecting the importance of labile organic inputs and chemically hydrophobic features, respectively. Conversely, SOC accumulation in smaller aggregate fractions was positively correlated with aromatic C, suggesting preferential accumulation of recalcitrant compounds.

Conclusions Overall, these findings suggest that land uses with perennial vegetation, such as PT, enhance soil structural stability and SOC accumulation compared to intensively managed croplands, with these advantages being associated with the chemical composition of SOC.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on June 15, 2026.

Journal Paper Digests

2026-05-27T00:00:00-00:00

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.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on May 27, 2026.

Journal Paper Digests

2026-05-19T00:00:00-00:00

Journal Paper Digests 2026 #12

When spectral libraries are too complex to search: Evolutionary subset selection for domain-adaptive calibration
Soil-health frameworks in agri-food systems. A review
Soil carbon markets for climate change mitigation? Pragmatic economists and matters of concern
Demystifying Geographic “Laws” for Soil Mapping via Interactive Geovisualization
Causal network construction and quantification in complex ecosystems
A global synthesis of spectroscopy-based prediction accuracy for soil carbon fractions: A systematic review
Development and validation of physically constrained machine learning for improving remote sensing-based evapotranspiration estimation
Evapotranspiration Everywhere, All the Time: Towards a Unified View From Earth Observation
Soil Organic Carbon Improves Crop Yield and Yield Resilience

Soil Organic Carbon Improves Crop Yield and Yield Resilience

Increasing soil organic carbon (SOC) has been proposed as a strategy to promote crop yield resilience under extreme hydroclimatic stress, particularly drought, due to its positive effect on soil available water-holding capacity. We analyze how SOC mediates the relationship between US rainfed crop yields (1981-2020) and growing season hydroclimatic conditions-defined by soil water supply and atmospheric water demand-over 67,000 county-years across three major crops. We show that higher SOC is consistently associated with increased crop yields and yield resilience, evidenced through reduced interannual variability. Contrary to prevailing expectations, the largest yield gains from SOC occur during moderate water supply conditions-not drought. Under drought, water supply to crops may be more limited by water inputs to soil than by soil water-holding capacity, constraining the benefit provided by SOC regeneration. Furthermore, because moderate conditions are more frequent than drought or wet extremes, the largest production gains from SOC accumulate under moderate conditions. These findings indicate that SOC regeneration can enhance drought resilience to some degree but cannot compensate for extreme water scarcity; the services SOC provides to crops, including water storage, require water for their effective delivery.

Evapotranspiration Everywhere, All the Time: Towards a Unified View From Earth Observation

Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it—especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to monitor sap flow and leaf chambers capturing transpiration. Scaling up to ecosystems became possible through eddy-covariance flux towers and further across landscapes through proximal sensing with drones, aircraft, and, ultimately, with satellites. While enormous progress has been made to measure or estimate ET everywhere and all the time, no single approach has yet achieved both simultaneously. Flux towers help with all the time, but not everywhere. Satellites can do everywhere, but not all the time (except, in part, for geostationary satellites, though with insufficient spatial coverage and resolution). A new advent of smallsat constellations is moving us to everywhere and all the time in detail, though we are only in the beginning of that era. This paper discusses the evolution and revolution of Earth observation for ET, as we advanced from the first Landsat and development of ET models through the progression of increasingly higher spatiotemporal resolution across international space agencies and commercial industry with increasing ET model sophistication, cloud computing, and machine learning. We continue to march ahead towards ET everywhere, all the time, and use that knowledge to better manage water and sustain our planet.

Development and validation of physically constrained machine learning for improving remote sensing-based evapotranspiration estimation

Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding water and carbon cycles. Advances in satellite remote sensing (RS) techniques have greatly prompted the development of ET models, yet their performance varies inconsistently across biomes due to structural and parameterization errors. Machine learning (ML) offers data-driven alternatives, but often lacks physical mechanism-based generalization. Here, we employed automatic ML (AutoML) to develop three model categories: unconstrained ML and deep learning (DL) models, input data-constrained models integrating physical ET estimates into training data, and loss function-constrained models incorporating physical equations into DL loss functions. Validation with site-based observations in the Heihe River Basin (HRB) (75% training, 25% validation) showed all models achieved root mean square errors (RMSEs) below 0.6 mm/d, with physical constraints enhancing generalization under extreme conditions. Out-of-sample tests indicated physical constraints improved spatial extrapolation ability, with DL models benefiting most with RMSEs reduction from 0.32 mm/d to 0.47 mm/d. Utilizing the best unconstrained (MLU), input data-constrained (MLI_SGC), and loss function-constrained (DLL_PM) models, we generated daily ET for the HRB spanning 2000–2021. Water balance-based validation revealed DLL_PM reduced mean absolute percent errors (MAPEs) by 53.9%, 5.2%, and 4.1% in the upper, middle, and lower reaches versus MLU, while outperforming MLI_SGC and two mainstream RS ET products (ETMonitor, PML-V2). Furthermore, we demonstrate that input data constraints enhance physical consistency by increasing the importance of physically based ET features, whereas loss function constraints reshape the DL learning process by modifying network weights and biases. The Akaike Information Criterion (AIC) further indicates that physically constrained models achieve accuracy gains with negligible increases in model complexity during training. These findings represent a meaningful step toward understanding how to effectively integrate physical constraints into ML models for ET estimation, and hold promise for advancing large scale water and energy cycle assessments under changing environmental conditions.

A global synthesis of spectroscopy-based prediction accuracy for soil carbon fractions: A systematic review

Infrared spectroscopy combined with machine learning (ML) offers a rapid and cost-effective approach for quantifying particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) on large spatial scales. However, its predictive accuracy has not been systematically evaluated across diverse ML models, climate zones, and soil types. A systematic search of Web of Science and Google Scholar (before December 2024) identified 108 observations from 27 eligible studies that applied mid-infrared (MIR) or near-infrared (NIR) spectroscopy to mineral soil samples using ML modeling and reported predictive performance and sample size for POC and/or MAOC. This study employed a three-level random-effects model with Fisher’s Z-transformed effect sizes, with no evidence of publication bias detected (Egger’s test, P = 0.71). Our synthesis revealed robust predictive capacities for both fractions, yielding pooled correlation coefficients (r) = 0.88 (95% CI:0.85–0.90) and 0.83 (95% CI:0.78–0.87) for POC and MAOC, respectively. MIR spectroscopy and partial least squares regression (PLSR) achieved the highest prediction performance for POC and MAOC among spectroscopy types and model families. Meta-regression revealed that soil texture and latitude were the dominant drivers of POC prediction, indicating that the prediction performance increased with increasing latitude. No single dominant driver emerged for MAOC, suggesting that the multiple interacting factors governed its predictive performance. The identification of these drivers remains challenging due to the predominance of agricultural studies and the sparse reporting of secondary minerals (e.g., Fe/Al oxides). MIR showed predictive advantage specific to MAOC, while POC prediction performance was less constrained by spectroscopy type and showed a more consistent trend across soil textures. MAOC prediction was highly dependent on soil texture, with higher accuracy in coarse or sandy soils than in medium-textured soils. These findings support fraction-specific spectral strategies for improving large-scale SOC stock estimates.

Causal network construction and quantification in complex ecosystems

Understanding and identifying causal relationships within complex, dynamic ecosystems is essential for elucidating ecological mechanisms and guiding effective ecosystem management. Conventional statistical approaches predominantly quantify correlations among multiple variables, yet fall short of capturing causality. Existing causal-discovery tools in ecology either focus on pairwise interactions, thereby overlooking emergent effects arising from the simultaneous influence of multiple drivers, or fail to provide a direct metric for the strength of causal control. Consequently, there is an urgent need for a framework that can simultaneously reconstruct causal networks among numerous variables and furnish quantitative assessments of causal importance. To address this challenge, we developed a dual-strategy ecological causal-discovery (DS-ECD) model founded on Granger causality that integrated a forward local-search strategy with a backward global-search strategy to detect causal links in long-term time-series data. The local strategy employed a forward greedy search to construct an information set capturing significant individual-level causal relationships, while the global strategy utilized a backward one-step search to uncover group-level causal interactions. In addition, we introduced the Relative Causality-Driven Intensity (RCDI) metric to quantify causal strength by decomposing direct and indirect effects, which complemented existing causal-discovery tools. Simulation experiments demonstrated robust model performance under high noise and high dimensionality. Accordingly, DS-ECD was deployed in ecosystems across different scenarios, rapidly revealing intuitive causal networks together with their associated RCDI values. Being purely data-driven, the approach promptly delivers transparent causal graphs and quantitative intensity values, facilitating cross-validation with experimental or process-model results and offering a reference for ecosystem-management practices.

Demystifying Geographic “Laws” for Soil Mapping via Interactive Geovisualization

“Laws” of geography such as Tobler’s First Law (spatial autocorrelation) and Zhu’s Third Law (environmental similarity) offer fundamental principles for spatial prediction and mapping, yet their implications for digital soil mapping (DSM) are often opaque because the underlying principles and mechanisms of DSM models are rarely inspectable in typical DSM workflows. This study presents an interactive geovisualization portal that demystifies Tobler’s Law, Zhu’s Law, and a combined formulation in spatial prediction processes, using soil organic matter (SOM) concentration prediction in Xuancheng, China, as a case study. The portal integrates multiple DSM frameworks that operationalize two geographic laws—inverse distance weighting (IDW), individual predictive soil mapping (iPSM), an iPSM-IDW hybrid, ordinary kriging (OK), and regression kriging (RK)—and couples them with user-configurable parameters such as neighborhood size, distance-decay factor, and variogram model. The portal provides coordinated, interactive views that link SOM predictions to dynamic map and diagnostic statistical charts for explaining location-level predictions, visualizing the manifestation of geographic laws in constructing local predictions, examining weight allocation patterns, and assessing overall prediction accuracy. Additionally, a built-in sensitivity analysis enables users to investigate and understand the effects of varying the geographic law, modeling framework, and modeling parameters on prediction results. This geovisualization portal advances interpretable DSM by rendering its underlying geographic principles, model mechanics, and parameter influences visually inspectable.

When spectral libraries are too complex to search: Evolutionary subset selection for domain-adaptive calibration

Background: The increasing availability of large spectral libraries offers new opportunities to reduce the costs and efforts required to develop spectroscopy-based sensing techniques for rapidly and non-destructively estimating key properties across environmental and agricultural matrices. These libraries can provide training samples for developing models adapted to specific target domains. However, identifying which samples are most relevant for a given target domain remains challenging. This study introduces gesearch, a non-linear evolutionary algorithm for selecting target-domain-relevant training samples from complex spectral libraries to build accurate and interpretable quantitative models. Results: The gesearch, method was used to extract a subset of relevant samples from a large North American infrared soil spectral library to build predictive models of total carbon for an independent target area in the Democratic Republic of the Congo In this challenging cross-domain test case, simple linear models built with the training samples found by gesearch achieved superior accuracy compared with established modelling approaches, including LOCAL, Cubist, convolutional neural networks, and global partial least squares regression. Significance: The proposed method provides a practical framework for exploiting large heterogeneous spectral libraries when only limited target-domain reference data are available. By selecting samples that are spectrally and compositionally coherent with the target domain, gesearch supports accurate, compact, and interpretable calibration models. It can also operate when only unlabelled target-domain spectra are available.

Soil-health frameworks in agri-food systems. A review

Soil health is central to agroecological transitions, yet guidance for integrating it into agri-food system design and monitoring remains fragmented. Institutions increasingly use frameworks to define indicators, guide interventions, and report progress against climate, biodiversity, and food-security agendas. However, to our knowledge, there is no integrative soil health framework which coherently links biophysical diagnostics, socio-institutional enablers, and multiscale accountability. This leaves critical gaps in design, sequencing, and measurement of agroecological transitions. Here we review how soil health is operationalized within agroecology and agri-food systems and translate these patterns into an actionable programming guide. We reviewed 64 frameworks and extracted 652 indicators across 12 agroecological principles to build a framework-by-principle evidence matrix. Frameworks were classified by use-orientation (theory, practice, analysis), and indicator thematic profiles were analyzed using hierarchical clustering with adaptive branch detection. The major findings are as follows: (1) framework evolution exhibits four chronological waves with shifts from conceptual foundations to operational measurement and outcome reporting, alongside changes in global and regional agenda setting and a rising demand for comparable indicators; (2) clustering identified five soil health design domains separating biophysical and socio-economic principles and revealing stable micro-constellations beyond earlier pathway framing. These include soil management and input stewardship, soil-health assessment, agroecological and ecosystem-based, integrated landscape and livelihood, and policy- and outcome-based. These findings were translated into a sequenced, multi-domain programming architecture that operationalizes complementarity across diagnostics, stewardship implementation, ecosystem safeguards, landscape–livelihood embedding, and iterative learning, thereby closing the gaps between farm practices, governance mechanisms, and outcome monitoring for soil health.

Soil carbon markets for climate change mitigation? Pragmatic economists and matters of concern

Soil carbon markets are increasingly promoted as climate mitigation instruments, yet their emergence is uneven, contested, and shaped by complex socio-technical configurations. Through comparative research in Taiwan and the United Kingdom, this paper examines how these markets are actively constructed rather than pre-given, highlighting the critical but often overlooked role of mediators whom we conceptualise as pragmatic economists. Drawing on interviews, workshops, and stakeholder mapping, we show how pragmatic economists translate scientific and metrological knowledge, assemble infrastructures for measurement and certification, and align agricultural, policy, and commercial interests. Their practices extend but also complicate Callon’s concept of economists in the wild, revealing marketisation as a situated, relational, and performative process. Across both sites, we identify key matters of concern, including scientific simplification, fragmented governance, unequal power relations, and new dependencies between farmers and mediators. By foregrounding pragmatic economists, the paper advances debates on the political economy of environmental markets and underscores the need for more reflexive, ecologically attentive, and socially just approaches to governing soil carbon within wider decarbonisation strategies

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on May 19, 2026.

Journal Paper Digests

2026-05-11T00:00:00-00:00

Journal Paper Digests 2026 #11

How Will Higher Interannual Precipitation Variability Intensify Water Stress Under a Drying Climate?
PGDM: Physically guided diffusion model for land surface temperature downscaling

How Will Higher Interannual Precipitation Variability Intensify Water Stress Under a Drying Climate?

Climate change is expected to alter both the mean and variability of precipitation. Hydrological consequences of higher precipitation variability remain less understood than that induced by changes in precipitation mean. This study evaluates the combined effects of changes in annual precipitation mean and variability on runoff and water supply system across south-east Australia (SEA) through sensitivity experiments and hydrological projections informed by climate change signals from global climate models. Runoff is modeled using three rainfall-runoff models and water supply system performance is modeled using a concatenated behavior analysis of a virtual reservoir, using climate and streamflow data from 392 catchments across the region. The results show that annual runoff variability will increase but by less than the increase in precipitation variability due to the effects of catchment storage. The higher annual runoff variability will reduce water supply reliability. The impact on regulated systems is smaller as there are storages to buffer the variability in inflow. This is particularly so in this region, which will be much more influenced by the significant projected decline in rainfall and runoff. Median projections, for ∼2060 relative to ∼1990, from hydrological modeling informed by change signals from climate models indicate that mean annual runoff will decline by 18% in the southern part of the region and by 6% in the northern part, and the reliability of water supply systems will fall by 13% and 7% in the south and north respectively.

Plain Language Summary Climate change will impact long-term average rainfall and year-to-year rainfall variability. Higher rainfall variability can further accentuate water stress in regions that are drying. While the effects of changes in average rainfall are reasonably well studied, the consequences of higher rainfall variability are less well understood. In this study, we assessed how changes in both average rainfall and rainfall variability can influence water availability, reliability, resilience, and vulnerability across south-east Australia, a region that is becoming hotter and drier. The results show that higher rainfall variability, and therefore catchment runoff variability, will reduce water supply reliability particularly in unregulated areas. The impact on regulated systems is smaller as there are storages to buffer the variability in inflow. This is particularly so in this region, which will be much more influenced by the significant projected decline in rainfall and runoff. Median projections, for ∼2060 relative to ∼1990, from hydrological modeling informed by change signals from climate models indicate that mean annual runoff will decline by 18% in the southern part of the region and by 6% in the northern part, and the reliability of water supply systems will fall by 13% and 7% in the south and north respectively.

PGDM: Physically guided diffusion model for land surface temperature downscaling

Land surface temperature (LST) is a fundamental parameter in thermal infrared remote sensing, while current LST products are often constrained by the trade-off between spatial and temporal resolutions. To mitigate this limitation, numerous studies have been conducted to enhance the resolutions of LST data, with a particular emphasis on the spatial dimension (commonly known as LST downscaling). Nevertheless, a comprehensive benchmark dataset tailored for this task remains scarce. In addition, existing downscaling models face challenges related to accuracy, practical usability, and the capability to self-evaluate their uncertainties during inference. To overcome these challenges, this study first compiled three representative datasets, including one dataset over mainland China containing 22,909 image patches for model training and evaluation, as well as two datasets covering 40 heterogeneous regions worldwide for external evaluation. Subsequently, grounded in the geophysical reasoning results, we proposed the physically guided diffusion model (PGDM) for LST downscaling. In this framework, the downscaling task was formulated as an inference problem, aiming to sample from the posterior distribution of high-spatial-resolution (HR) LST conditioned on low-spatial-resolution (LR) LST observations and a suite of HR geophysical priors. Comprehensive evaluations demonstrate the effectiveness of PGDM, which generates high-quality downscaling results and outperforms existing representative interpolation, kernel-driven, hybrid, and deep learning approaches. Finally, by exploiting the inherent stochasticity of PGDM, the scene-level standard deviation of multiple generations was computed, revealing a strong positive linear correlation with the actual downscaling error. This property enables PGDM to self-assess its downscaling uncertainty, constituting an additional key advantage over conventional deterministic downscaling models. The codes and data will be released at https://github.com/cas222huan/PGDM.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on May 11, 2026.

Journal Paper Digests

2026-04-30T00:00:00-00:00

Journal Paper Digests 2026 #10

DeepProfile: An inverse fusion framework for root zone soil moisture profile estimation
Alternatives to Equivalent Soil Mass in Monitoring, Reporting and Verification of Changes in Soil Carbon
Organic Carbon and Texture Control Moisture Dependence of Soil Shortwave Infrared Reflectance

Organic Carbon and Texture Control Moisture Dependence of Soil Shortwave Infrared Reflectance

Moisture content affects soil reflectance in the optical domain (400–2500 nm), acting as a confounding factor in soil property prediction models. Soil reflectance needs to be simulated efficiently for varying levels of soil moisture, in order to aid soil property prediction efforts and inform physical land surface models. Here, we built on previous work that investigated how soil reflectance decreases with increasing soil moisture. We explored how the relationship between the reflectance and soil moisture content changes as a function of wavelength and soil characteristics. For this purpose, we acquired the spectra of 28 soil samples from various locations across Europe in a laboratory setting, at different levels of soil moisture. The soil reflectance-moisture relationship was found to be wavelength-dependent and best represented by decreasing exponential functions. The rates of exponential decrease, however, varied across soil samples and were normalised to isolate effects of different soil characteristics. It was found that organic carbon (OC), clay and silt content displayed a statistically significant relationship with the normalisation factor, a proxy for how quickly soil ‘darkens’ with increasing soil moisture content. A multiple linear regression model was used to describe the normalisation factor based on OC content and soil textural information. The resulting model was able to explain 67% of the variance, with OC and clay content accounting for almost 70% of the relative feature importance. Our findings call for the inclusion of OC content and textural information, especially clay content, in physical models of soil moisture-reflectance, for more efficient simulations of soil reflectance at varying levels of soil moisture, to support climate models and soil property predictions efforts based on field and remotely sensed data.

Alternatives to Equivalent Soil Mass in Monitoring, Reporting and Verification of Changes in Soil Carbon

Sequestering carbon in soils is a key action to address climate change and food security. Schemes incentivising farmers to change land management practices to sequester more carbon in soils are underpinned by soil monitoring protocols. Accurate estimation of soil organic carbon (SOC) stocks is essential for the integrity of such carbon credit schemes. Common SOC estimation methods like sampling to fixed depth are prone to errors due to changes in bulk density over time, particularly under changing management practices. Equivalent Soil Mass (ESM), utilising a reference mass rather than a reference volume for SOC estimation, arguably alleviates this. In practice, the potentially large variation in sampled core lengths (and thus masses) still introduces substantial variability into SOC estimates. This work compares four approaches to SOC estimation: (1) ESM10, based on the 10th percentile of sampled masses, currently implemented in the Australian Soil Carbon Method; (2) ESMμ, based on the mean of sampled masses, and (3) EBD10 and (4) EBDμ, built on the concept of equivalent bulk density (EBD), based on either the 10th percentile or mean of the average soil sublayer bulk densities. Variability in ESM and EBD and in resulting SOC estimates was quantified using soil cores from nine intensively sampled farms in eastern Australia. The proposed alternatives, particularly ESMμ and EBDμ offered more stable and accurate SOC estimates, reducing variance by up to 38%–86% compared to ESM10. These findings can be applied to support the evolution of improved methods of soil carbon monitoring, reporting and verification in Australia and internationally.

DeepProfile: An inverse fusion framework for root zone soil moisture profile estimation

Root zone soil moisture (RZSM) is a critical variable for understanding land–atmosphere interactions, hydrological processes, and agricultural productivity. Direct remote sensing of RZSM remains challenging due to the shallow sensing depth and the ill-posed nature of inversing a profile, with the existing global RZSM products mainly derived from model-based data assimilation. These products offer valuable information but exhibit inconsistent accuracy and disparate vertical discretizations. As no single root-zone soil moisture product is superior globally, fusing them offers a means to integrate their complementary strengths into a unified and consistent framework. In this study, a DeepProfile framework was proposed for estimating the continuous soil moisture profile throughout the top 100 cm layer of soil by integrating three widely used RZSM products; Soil Moisture Active Passive level 4 (SMAP L4), Global Land Data Assimilation System (GLDAS) version 2, and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5-land). Unlike traditional fusion methods requiring harmonized inputs, DeepProfile treats parent products as learning targets, optimizing the integral of a polynomial profile to match heterogeneous layers without enforcing identical vertical or spatiotemporal coverage. This yields a continuous analytical profile for flexible depth extraction, utilizing location-specific triple collocation weights to optimally balance product contributions. Evaluation against in-situ measurements from 2373 stations across 45 global networks demonstrated strong agreement in near-surface and intermediate layers (≤50 cm), with median RMSE values below 0.06 m3/m3 and correlation coefficients (R) exceeding 0.72. The use of SMAP near-surface soil moisture was found critical for the satisfactory results for the top 50 cm. The model also showed promising performance at deeper layers of >50 cm (R > 0.65), although accuracy declined with depth due to weaker observational constraints. The proposed DeepProfile offers a scalable and transferable solution for generating depth-resolved soil moisture estimates, with potential applications in hydrological modeling, drought monitoring, and weather forecasting.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on April 30, 2026.

Journal Paper Digests

2026-04-28T00:00:00-00:00

Journal Paper Digests 2026 #9

Bridging single-species research and mixture reality: Emerging contaminants fate and transport in vadose zones
From soil to gas – high resolution insights into plant-soil interactions by integrating planar oxygen optodes, porewater chemistry, soil microbial analysis and trace soil gas flux using a rhizobox approach
Mechanisms of soil aggregate stability and disintegration response to soil internal forces: Especially under water-level fluctuation zones
Modeling Soil Organic Carbon Changes Using Signal-To-Noise Analysis: A Case Study Using European Soil Survey Datasets

Modeling Soil Organic Carbon Changes Using Signal-To-Noise Analysis: A Case Study Using European Soil Survey Datasets

Soil organic carbon (SOC) is a key indicator of soil health and a crucial component of climate mitigation, making its reliable monitoring increasingly important. While Digital Soil Mapping (DSM) based on Machine Learning and Earth Observation (EO) data enables the generation of time series of spatially explicit SOC predictions, detecting temporal changes from these model predictions remains challenging due to the relatively large associated uncertainties. Although prediction uncertainties are now commonly reported, few studies have explicitly accounted for them when assessing SOC change. This study introduces a model-based signal-to-noise ratio (SNR) framework to assess the detectability of SOC change using both the state-first approach—modeling SOC states at each time point and then deriving change—and the change-first approach—modeling SOC change directly from repeated measurements. SNR is defined as the ratio of predicted SOC (concentration, g/kg) change to its modeled uncertainty, enabling evaluation of change-model reliability at pixel levels. Applied to repeated SOC observations from the pan-European Land Use and Coverage Area Frame Survey, this framework assesses the reliability of SOC change modeling across multiple land-cover types using Random Forest and Quantile Regression Forests. At the site level, prediction accuracy was poor and SNR values were consistently low. An illustrative aggregation analysis showed that spatial averaging improved SNR, supporting SOC change assessments at broader scales. However, further work is needed to incorporate land use and management information and to systematically examine how different aggregation schemes affect the results in various contexts, ensuring that aggregated outcomes remain meaningful and policy-relevant. As an internal metric based on model predictions and their estimated uncertainty, SNR provides a practical diagnostic of change-model confidence, especially when repeated ground-truth SOC measurements are not available. We advocate for routine SNR reporting to enhance the transparency and credibility of DSM-based SOC change monitoring.

Mechanisms of soil aggregate stability and disintegration response to soil internal forces: Especially under water-level fluctuation zones

With the intensification of global climate change, extreme hydrological events increasingly drive soil erosion in water-level fluctuation zones. Soil aggregates represent the fundamental units of soil structure, and their disintegration marks the onset of soil erosion. Although numerous studies have examined externally driven disintegration mechanisms, growing evidence indicates that soil internal forces, including van der Waals forces, electrostatic forces, and hydration forces, play critical roles in governing aggregate disintegration. However, a comprehensive review specifically addressing the mechanisms of soil aggregate disintegration response to soil internal forces in water-level fluctuation zones remains absent. In this study, we systematically reviewed the mechanisms underlying soil aggregate disintegration. Four major mechanisms are identified as differential swelling, slaking, raindrop impact, and physicochemical dispersion, each contributing to aggregate disintegration to varying extents. Among these, physicochemical dispersion, driven by soil internal forces, emerges as the dominant pathway leading to complete aggregate disintegration. We further traced the development of research on soil internal forces and elucidated the mechanistic processes by which these forces mediate aggregate disintegration. In addition, we discussed the regulatory roles of specific ion effects on modifying soil internal forces and aggregate stability. Importantly, in WLFZs, hydrological fluctuations (such as dry–wet cycles, wave action, and inundation) induce strong temporal variability in soil moisture and solution chemistry, resulting in dynamically regulated soil internal forces and enhanced aggregate disintegration. This study established a mechanistic framework linking soil internal forces to aggregate disintegration, providing a theoretical foundation for predicting and mitigating soil erosion under hydrological fluctuations.

From soil to gas – high resolution insights into plant-soil interactions by integrating planar oxygen optodes, porewater chemistry, soil microbial analysis and trace soil gas flux using a rhizobox approach

Ecosystem trace gas fluxes (CO2, CH4, N2O) are a critical component of the global greenhouse gas cycle, but uncertainty remains regarding the important mechanisms driving variability across the soil-plant-atmosphere interface. This is due in part to a lack of techniques that can integrate measurements across these interfaces at high spatial and temporal resolution under controllable experimental conditions. To improve upon these experimental techniques, we present a novel approach in which custom-made rhizoboxes, integrated with state-of-the-art planar oxygen (O2) optode sensors and outfitted with water, soil and gas samplers, allow for integration of porewater chemistry, soil microbiology, plant-soil trace gas flux, belowground root dynamics, along with a spatially and quantitatively resolved O2 profile (i.e., planar optode). We demonstrate our experimental design with a case study using three rhizoboxes at controlled water levels, one with soil only and two transplanted with Carex acutiformis, a wetland plant known to transport O2 belowground through the roots (i.e., radial oxygen loss). Our case study clearly illustrates that high spatially and temporally resolved data can be captured using planar chemical sensors and integrated with simultaneous measurements of soil, water, plant and gas variables. We find clear evidence for radial O2 loss, a mechanism occurring at the millimeter scale whereby roots emit O2 belowground. Additionally, we find that plant-soil gas fluxes are correlated to porewater chemistry (i.e., redox potential, pH, O2 concentration), soil microbial relative abundances and planar O2 optode profiles, underscoring the ability of this experimental design to simultaneously monitor a variety of measurement types with minimal disturbance. We find that CO2 uptake from the plants increases significantly (p = 0.003, R2 = 0.78) with belowground root radial O2 loss, indicating a tight coupling between above and belowground plant dynamics. Additionally, the bacterial genus Hydrogenophaga, often associated with denitrification, increased in abundance with a corresponding decrease in N2O flux over time. Finally, we find that conventional porewater O2 measurements provide an inaccurate characterization of soil O2 concentration when compared to planar optodes. Our rhizobox design is a promising strategy for solving fundamental knowledge gaps and mechanisms in biogeochemistry. Our hope is that this will be a useful tool for the community in generating data for improved ecosystem modeling since the setup can be modified to simulate variable environmental conditions and characterize a wide variety of plant-soil systems.

Bridging single-species research and mixture reality: Emerging contaminants fate and transport in vadose zones

Vadose zones serve as interfaces controlling contaminant transport from the land surface to underlying aquifers. While traditional research has largely focused on individual contaminant species, real-world contamination often involves complex mixtures containing dozens to thousands of chemical species. This disconnect creates considerable challenges for predicting contaminant behavior, assessing environmental risks, and developing effective remediation strategies. This review examines complex contaminant mixtures in vadose zones, with emphasis on per- and polyfluoroalkyl substances, pharmaceuticals and personal care products, and hydraulic fracturing fluid additives and contaminants. We synthesize recent advances in understanding mixture behavior, including competitive sorption at air-water interfaces and solid surfaces, co-facilitated transport mechanisms, and transformation dynamics under co-contaminant conditions. Field observations from contaminated sites reveal that mixture effects alter transport rates, retention capacities, and degradation pathways relative to single-species predictions. While vadose zones function as persistent secondary sources, transient saturation conditions can enhance contaminant transport by an order of magnitude relative to constant-flow predictions. Current modeling frameworks remain limited in their capability to account for complex physicochemical interactions in contaminant mixtures, particularly under transient flow and heterogeneous environments. We identify and describe six priority areas for new or enhanced research to bridge current knowledge gaps: mixture sorption and competitive transport, transformation products and reaction pathways, field-scale validation studies, multi-mechanism remediation technologies, integrated mixture toxicity assessment, and climate change and evolving land use impacts.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on April 28, 2026.

Journal Paper Digests

2026-03-16T00:00:00-00:00

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.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on March 16, 2026.

Journal Paper Digests

2026-03-03T00:00:00-00:00

Journal Paper Digests 2026 #7

A Mineral Protection Paradigm for Soil Organic Carbon Fractionation: Iron and Calcium as a Geochemical Bridge in Arid and Semi-Arid Grasslands
Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice
An innovative mapping framework for soil erodibility integrating spatial association dimensions and machine learning
Uncertainties of enhanced rock weathering for climate-change mitigation

Uncertainties of enhanced rock weathering for climate-change mitigation

Enhanced rock weathering (ERW) on agricultural soils is under consideration as a long-term carbon dioxide removal (CDR) strategy. In this Perspective, we evaluate uncertainties related to ERW around feedstock availability, plant–soil system impacts, CDR efficiency along the land–ocean continuum and socio-economic considerations. The composition of (ultra)mafic rocks places constraints on the availability of suitable feedstock when considering their potential for CDR and toxic element contents. For ERW application at scale, dedicated mining for suitable feedstock seems unavoidable. ERW can positively and negatively affect soil structure, hydrology, and overall carbon and nutrient cycles, and so optimal ERW will require site-specific assessment of effective CDR and mitigation of potential negative impacts. Additionally, the fate of weathering products along the land–ocean continuum in rivers remains poorly constrained, which is a challenge for verifying successful CDR. The socio-economic effects and constraints of ERW regarding financing and risk responsibility are also uncertain. Ultimately, large-scale ERW deployment seems limited by substantial challenges throughout its application, from its initial set-up to final CDR. Future research prioritizing site-specific assessments, long-term monitoring along the land–ocean continuum, and system modelling to constrain uncertainties and address socio-economic factors is needed to ensure that ERW deployment is effective, equitable, and sustainable.

An innovative mapping framework for soil erodibility integrating spatial association dimensions and machine learning

Accurate spatial mapping of soil erodibility (K) is essential for assessing erosion risks and formulating conservation strategies. However, existing empirical models and spatial prediction face challenges, including underestimating spatial variability, static local environmental associations, and limited regional adaptability. This study proposed an innovative framework integrating empirical models, the second dimension of spatial association (SDA, incorporating multi-scale neighborhood features), and machine learning to select the optimal K values mapping. First, based on soil surveys and laboratory analyses in the Northeast China Black Soil (Mollisols) Region, three empirical models: the erosion-productivity impact calculator (K_EPIC), the Shirazi (K_Shirazi), and the Torri (K_Torri), were used to calculate K values. Second, SDA reconstructed environmental covariates by extracting quantile features (0−1) within radius-defined neighborhoods (100–3000 m), capturing multi-scale spatial dynamics. Third, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) were employed for digital mapping, while per configuration generated 40 ensemble models (10 random seeds × 4-fold cross-validation) to enhance model robustness. Results demonstrated that SDA-based models improved R2 by 12–89 % compared to conventional static local association models. Considering data distribution, model accuracy, and spatial prediction, K_Shirazi demonstrated optimal regional representation (R2=0.4562, in SDA-GBDT). Moreover, climate and landscape are important driving factors for K_EPIC and K_Shirazi, while topography additionally influences K_Torri. The proposed framework offers a scientific and effective method to create the optimal pathway for soil erodibility mapping through multi-scale environmental feature extraction and integrated machine learning modeling, which could be transferred to other regions or fields.

Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice

Soil carbon mapping (SCM) is rapidly becoming a cornerstone of soil science and environmental decision-making, from precisionagriculture to national carbon inventories. Yet SCM is at a crossroads: the methods that often promise high-accuracy metrics canmask structural weaknesses that limit generalization and undermine policy relevance. Similar problems apply to larger DigitalSoil Mapping (DSM). In this article, using soil organic carbon (SOC) as an illustrative example, we highlight three systematicsources of error that consistently inflate SCM performance: depth, bulk density, and spatial autocorrelation. Soil profile depth isoften mishandled when profile increments are split between training and test sets, leading to inflated accuracy estimates. Bulkdensity (BD), essential for converting concentrations to stocks, is inconsistently applied and rarely accompanied by uncertaintyestimates. SOC stocks at sampling locations are often derived using BD, and when BD is reintroduced as a predictor in machinelearning models, it inflates reported accuracy and the model’s predictive skill. Spatial autocorrelation further exaggerates accu-racy when conventional random splits are used, while spatial blocking reveals much lower and more realistic predictive skill.Drawing on recent literature and our own analysis, we argue that SCM must adopt more rigorous practices, including profile-level validation, spatially aware blocking, standardized reporting of assumptions, and alignment with policy-relevant depth in-tervals. These steps will enhance comparability across studies and ensure that SCM outputs are credible for carbon accounting,climate mitigation, and land management purposes. The future of SCM and DSM depends on both new algorithms and method-ological rigor and transparency

A Mineral Protection Paradigm for Soil Organic Carbon Fractionation: Iron and Calcium as a Geochemical Bridge in Arid and Semi-Arid Grasslands

Mineral association is widely recognized as a fundamental mechanism of soil organic carbon (SOC) stabilization; however, its relative importance versus climatic and vegetation drivers, and the key controlling geochemical factors remain poorly quantified in arid and semi-arid grasslands (mean annual precipitation, MAP < 400 mm). Combining a regional survey across the Mongolian Plateau (n = 260) with a global data synthesis (n = 2,097), we quantified the overwhelming dominance of mineral-associated organic carbon (MAOC), which constituted 79.8 ± 0.6% of SOC and established a benchmark for Eurasian drylands. More critically, we establish a hierarchical framework for MAOC accumulation: macro-scale environmental parameters (C:P ratio, pH) set the stabilization capacity, whereas localized geochemical actors (Fe, Ca) actuate this capacity via direct physiochemical interactions. In contrast, POC (particulate organic carbon) and CPOC (coarse particulate organic carbon) fractions were predominantly regulated by the C:P ratio, mean annual precipitation minus potential evapotranspiration (MAP-PET), and aboveground plus belowground biomass (AGB + BGB), suggesting a stronger dependence on recent carbon inputs and decomposition. Effective moisture (MAP-PET) served as the principal indirect control modulating both carbon inputs and mineral weathering. We thus propose a “mineral protection paradigm” for these ecosystems, wherein Fe and Ca directly enhance SOC sequestration through adsorption and cation bridging, forming a geochemically driven core process that is indirectly amplified by climate (MAP-PET) through its influence on vegetation drivers (AGB + BGB). This study establishes a synergistic Climate-Geochemistry-Vegetation framework that provides a scientific basis for SOC management in arid grassland ecosystems.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on March 03, 2026.

Journal Paper Digests

2026-02-23T00:00:00-00:00

Journal Paper Digests 2026 #6

The influence of rewetting intensity on soil priming after drought
Interactions between soil environmental factors and microbial communities consistently predict plant health
Commentary: Structural equation models and causal claims in soil science and biogeochemistry – An equation-free “how to”
Knowledge-Guided Machine Learning for Global Change Ecology Research
Spatiotemporal Trade-Offs and Synergies Among Environmental Footprints of Grain Crop Production in China
Combining physical models and machine learning for enhanced soil moisture estimation
Potential for improving micronutrient supply and environmental sustainability by using underutilized crops in China
Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models

Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models

Characterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks. Rasters of soil geochemical distributions for the conterminous United States, however, are limited. We present a Bayesian modeling workflow and tool for generating predictive geochemical and mineralogy distribution maps for the conterminous United States using integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach. By modeling soil geostatistical data with environmental covariates (soil properties, topography, climate, and land cover), we generate predictive distributions of soil geochemistry that can be mapped or extracted for further analyses. As an example, we model the spatial distribution of trace elements in soil relevant to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) and provide a workflow that can be used to generate and visualize predictive distributions of 39 other major and trace elements and 21 minerals of the soil survey, supporting a variety of ecological, environmental, and agricultural applications. Bayesian Modeling: Uses R-INLA to predict soil geochemistry across large spatial extents. Covariate Integration: Incorporates environmental variables to increase predictive accuracy. Raster Generation: Produces continuous geospatial layers of mineral and element distributions of the conterminous United States for a variety of applications.

Potential for improving micronutrient supply and environmental sustainability by using underutilized crops in China

Rice and wheat provide the bulk of calories in diets globally. However, foods made from these cereals are commonly in refined forms and are low in micronutrients and dietary fiber. Increasing the consumption of more nutrient-dense, underutilized cereals and beans (UCBs), such as millet, sorghum, mung bean, along with unrefined rice and wheat, could improve diet quality. Compared with rice and wheat, UCBs are generally cultivated using less intensive methods, resulting in a lower environmental impact, though their productivity is generally lower. This study explores how reallocating rice and wheat areas to UCBs, either alone or combined with greater use of unrefined rice and wheat, could potentially enhance micronutrient supply (iron, thiamin, riboflavin, calcium, zinc), while reducing water use and greenhouse gas emissions in China. A strategy combining area reallocation and greater use of unrefined rice and wheat increased micronutrient supply and dietary fiber by 12–82%, reduced environmental impact by 11–12%, and slightly increased energy supply (3%). These outcomes were achieved by reallocating 7.9 million hectares (Mha) of rice area (26% of the current total) and 1.7 Mha of wheat area to sorghum (+5.5 Mha), millet (+2.5 Mha), beans (+1.4 Mha), and oats (+0.2 Mha). As a result, the supply of UCBs and unrefined rice and wheat products increased, supporting healthier diets. Reallocating only 5% of the rice area would still yield improvements, especially for dietary fiber and iron ( 27%). These findings offer insights for rethinking the value of UCBs and supporting their integration into future food system strategies.

Combining physical models and machine learning for enhanced soil moisture estimation

Estimating soil moisture is crucial for agricultural management, water resource planning, and environmental monitoring. Traditional methods, whether based on physical models or machine learning, face limitations, with physical models suffering from reduced accuracy due to parameter sensitivity and environmental variability, and machine learning models struggling with interpretability and generalization. To address these challenges, this study introduces a novel hybrid approach that leverages the strengths of both physical modeling and machine learning to enhance soil moisture estimation. The hybrid model, ML-Phy-meteo, integrates the physical model (Hydrus-1D) results and meteorological data into a LightGBM framework, achieving optimal estimation accuracy across various soil depths. Quantitatively, ML-Phy-meteo exhibits superior performance across all depths, achieving an average root mean square error (RMSE) between 0.020 and 0.026 cm3/cm3, an average Nash–Sutcliffe Efficiency (NSE) ranging from 0.195 to 0.811, and an average Kling–Gupta Efficiency (KGE) from 0.623 to 0.860, thereby outperforming both standalone physical models and purely machine learning-based approaches. Notably, ML-Phy-meteo achieves high-precision predictions even in the absence of detailed soil texture and stratification data, with the machine learning component effectively compensating for the simplifications of the physical model. Among the machine learning methods used in the hybrid model, tree-based models (LightGBM and Random Forest) outperform deep learning models (LSTM) in terms of accuracy and robustness in handling noise and missing data, despite the latter’s smoother prediction profiles. These findings highlight the potential of hybrid models to overcome the inherent limitations of standalone physical or machine learning approaches, providing new ideas for future research and applications in soil moisture estimation.

Spatiotemporal Trade-Offs and Synergies Among Environmental Footprints of Grain Crop Production in China

Human agricultural activities have exacerbated multiple types of natural resource depletion and environmental impacts through complex interactions with land, water, carbon, and nutrient cycles, which can be measured as corresponding environmental footprints (EFs). However, the spatiotemporal trade-offs and synergies among multiple EFs in agricultural systems remain under-quantified, hindering effective mitigation strategies. Here, we propose an assessment framework of spatiotemporal trade-offs and synergies among multiple EFs of crop production, with a case study on blue water, green water, land, carbon, nitrogen, and phosphorus footprints for wheat, maize, rice, and soybean production across 31 Chinese provinces over 2000–2018. In total, 3630 pairwise EFs were analyzed. Results show that, although the EFs of unit mass crop production generally declined across provinces, national total EFs increased, with land, carbon, and phosphorus footprints rising by 16%, 17%, and 23%, respectively, during the study period. Synergistic interactions among EFs prevailed, comprising 50% positive and 32% negative synergies. The spatial distribution of trade-offs and synergies varies by crop and region. Land use intensity is the main factor limiting the positive EF synergies.

Knowledge-Guided Machine Learning for Global Change Ecology Research

Global change ecology demands predictive models that reconcile data-driven learning with mechanistic theory to address complex, interconnected ecosystem challenges. Traditional process-based approaches struggle with spatiotemporal parameterization, while purely data-driven machine learning approaches suffer from extrapolation, interpretability, and physical consistency. Knowledge-guided machine learning (KGML) bridges this divide by systematically integrating ecological principles (e.g., physical first principles, stoichiometry, process understanding, disturbance regimes) into how models are designed, trained, and adjusted to generalize across different ecosystems. The emerging KGML paradigm offers tremendous opportunities to advance the research of global change ecology. This review synthesizes KGML’s transformative potential, showcasing its capacity to enhance the prediction of carbon-water-nutrient cycles and other ecological processes and lay groundwork for ecological foundation models. Emerging applications in decision support and symbolic regression further illustrate its role in deriving actionable insights and novel theoretical hypotheses. Future directions emphasize adaptive integration of data and knowledge, uncertainty quantification, causal embedding in foundation models, and interdisciplinary collaboration to align KGML innovations with sustainability goals. By uniting ecological theory with AI advances, KGML offers a robust pathway to encompass ecosystem responses to global change, fostering scientific discovery and actionable solutions.

Commentary: Structural equation models and causal claims in soil science and biogeochemistry – An equation-free “how to”

Structural equation modeling (SEM) is a set of approaches that have seen exponential usage in the soil sciences as well as the related fields of agriculture and biogeochemistry. When correctly used and interpreted, SEM can be a powerful and flexible tool to test complex hypotheses on causality. However, the recent explosion of SEM usage in the soil sciences facilitated by user-friendly statistical programs has not been fully met by statistical expertise of users, reviewers and editors, ultimately leading to widespread contamination of the literature with inappropriate modeling and inflated or unfounded causal claims. The rise of such “SEM slop” poses a serious risk of an unreliable knowledge base and also undermines efforts and standards on what constitutes causality in the soil sciences. To address this, we diagnose major pitfalls in SEM, with an eye towards considerations specific to soil sciences, categorizable as three types: (1) Causal claims, including not satisfying causal criteria, lack of justified a priori models, not considering counterfactuals, and unqualified causal language; (2) Experimental design, including use in randomized complete block designs without complete pooling or multi-level models, inappropriate data type (e.g., ontological misalignment), and insufficient sample size; and, (3) Assessing the model, including incomplete or inappropriate model evaluation, non-qualified use of modification indices, and lack of robustness tests. There is a dual imperative for users as well as reviewers and editors to better implement and evaluate SEMs and claims of causality made with SEMs. To support this, we offer best practices and practical considerations on these three major pitfalls. These best practices will help SEM be appropriately employed as a powerful, nuanced statistical tool that benefits the soil science community.

Interactions between soil environmental factors and microbial communities consistently predict plant health

Intensive agricultural practices cause dysbiosis in soil nutrient levels and microbial communities, significantly affecting plant health and productivity. However, the mechanisms underlying the interactions between soil environmental factors and microbial communities, and their role in determining and predicting plant health, remain poorly understood. In this study, we collected soils planted with tomato in different health conditions, including healthy and bacterial wilt, Fusarium wilt, and nematode diseases, to identify key abiotic and biotic factors influencing plant health. Additionally, We fitted machine learning models using multidimensional data to classify plant health status. Our results revealed that diseased soils (bacterial wilt, Fusarium wilt, and nematode disease) exhibited significantly higher AP levels compared to healthy soils. Moreover, increased Amplicon Sequence Variants (ASVs) in diseased soils had lower network connectivity and were positively correlated with soil nutrient contents, pathogen abundance, and pathogen-supportive soil microbial functions, while being negatively correlated with plant defense-associated soil microbial functions. Both soil nutrient levels and the increased ASVs in diseased soil were stronger correlates of disease occurrence than other soil indicators. Optimal classification performance was observed when both soil environmental factors and microbial communities were considered, with AP emerging as the most influential indicator. In conclusion, excessive accumulation of AP was associated with disrupted microbial community structures, destabilized microbial networks, enhanced pathogen abundance, and impaired microbial functions, which collectively correlated with higher disease occurrence. These findings highlight the potential importance of optimizing soil nutrient management for supporting plant health.

The influence of rewetting intensity on soil priming after drought

Soil moisture is a key driver of soil organic matter (SOM) decomposition and the global carbon (C) cycle, and climate warming-induced extremes of rainfall and drought are intensifying the dynamics of soil C stocks. Although addition of labile C pools can trigger strong priming effects (PE) by stimulating decomposition of recalcitrant SOM, how varying rewetting intensity interacts with these exogenous inputs of C pools to affect PE remains unclear. In this study, we used an isotopic approach to examine the effects of rewetting intensity (40%, 60%, and 80% water holding capacity (WHC)) on PE during a 21-day incubation following intensive drought (20% WHC). Our results show that glucose addition induced a strong positive PE, with higher moisture (60% and 80% WHC) resulting in greater PEs. High moisture regulated microbial community composition and boosted microbial activity and turnover. These changes heightened microbial nitrogen demand, accelerated nitrogen mining, and intensified decomposition of stable C, leading to net soil-C loss. The correlation analysis shows that enhanced biosynthetic and degradative activity under higher moisture conditions facilitates the turnover of labile C, whereas reduced microbial diversity and metabolic intensity promote the stabilization of more persistent C forms. This study underscores the significant role of moisture in shaping PEs and soil C dynamics in the subtropical forest soils, offering insights into soil C sequestration in response to climate change.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 23, 2026.

Journal Paper Digests

2026-02-17T00:00:00-00:00

Journal Paper Digests 2026 #5

Future societal developments provide a challenge for pedology as an integrative activity within soil science
Observation-Driven Forecast of Global Terrestrial Water Storage and Evaluation for 2010–2024
Multi-link network modeling of water resource systems: identifying critical linkages driving resilience dynamics
alidation of high-resolution surface soil moisture time series retrieved by means of SAR interferometry
Airborne and spaceborne imaging spectroscopy capture belowground microbial communities and physicochemical characteristics in invaded grasslands

Airborne and spaceborne imaging spectroscopy capture belowground microbial communities and physicochemical characteristics in invaded grasslands

Belowground properties, including belowground microbial communities and physicochemical characteristics, play a crucial role in ecosystem functioning. Developing scalable approaches to map these properties across large spatial domains is essential for advancing our understanding of ecosystem functioning. However, large-scale approaches for mapping belowground properties, particularly in vegetated ecosystems, have yet to be developed. In this study, we aimed to develop approaches to map belowground microbial communities (bacterial and fungal) and physicochemical characteristics in an extensive grassland ecosystem affected by invasive plants using airborne and spaceborne imaging spectroscopy (hyperspectral remote sensing). We focused on Lespedeza cuneata (L. cuneata), an invasive plant threatening grasslands of the U.S. Southern Great Plains. We developed structural equation models to determine aboveground-belowground linkages. We used airborne hyperspectral data to estimate aboveground characteristics from partial least squares regression and then mapped belowground properties using aboveground characteristics through generalized joint attribute models. We also assessed the capability of spaceborne data in mapping the spatial distribution of belowground properties through fusing coarse spatial resolution DLR’s DESIS hyperspectral data with fine spatial resolution PlanetScope multispectral data. Our findings showed that there are linkages between percent cover of L. cuneata, aboveground characteristics, and belowground properties. Large-scale analysis using airborne hyperspectral data showed that belowground properties varied across increasing percent cover of L. cuneata. Similar results were observed when using fused spaceborne data. Our findings indicated that (1) spectral information can reveal belowground properties and (2) fusing spaceborne data can be an effective approach to mapping belowground properties in grassland ecosystems.

Validation of high-resolution surface soil moisture time series retrieved by means of SAR interferometry

This paper presents a novel algorithm for high-resolution soil moisture retrieval based on Synthetic Aperture Radar (SAR) interferometry and closure phases. The proposed method efficiently processes long SAR time series with minimal computational cost, generating a soil moisture measurement for each acquisition. Soil moisture data were derived from Sentinel-1 SAR imagery and validated across seven different test sites. Retrieval results were compared with modeled soil moisture data from land surface models, alternative remote-sensing products, and in situ measurements. The algorithm demonstrates strong correlations with modeled soil moisture, particularly in areas characterized by high interferometric coherence. However, performance was expectedly limited in regions with low interferometric coherence due to factors such as vegetation cover or snow cover. Looking ahead, this study identifies some relevant directions for future research, including the integration of backscatter information alongside phase data and the adaptation of the algorithm for SAR missions operating at different frequencies (e.g., L-band) or with very dense acquisition schedules (e.g., geosynchronous platforms). These advancements would further enhance the applicability and accuracy of soil moisture retrieval using SAR-based techniques.

Multi-link network modeling of water resource systems: identifying critical linkages driving resilience dynamics

Under changing environmental conditions, river basins face increasing water scarcity and heightened ecological vulnerability, posing greater challenges to water security. The resilience of water resource systems is recognized as offering a novel research perspective and analytical approach for addressing water security issues. The resilience of water resource systems is affected by numerous factors with significant cascading effects. Multi-link network approaches quantitatively capture the complex interrelationships among these factors, offering a scientific basis for effective management and improved water security. In this study, the Fenhe River Basin is selected as the study area to construct a multi-link network centered on hydrometeorological, socioeconomic, and engineering regulation links. The PCMCI algorithm is employed to quantify causal strengths among indicators within this complex network across different time lags, revealing the intertwined interactions, dynamic transmission patterns, and complex nonlinear relationships of the system. Based on the principle of multivariate transfer entropy, key links and critical transmission pathways underlying the evolution of water resource system resilience are identified. The role and effect of these key links on system resilience are analyzed, providing a quantitative assessment of their influence on the overall resilience of the water resource system. The results indicated that: (1) Total water resources, the proportion of secondary industry, and domestic water use indicators have the highest causal connection strengths among the links in the multi-link network of the water resources system; (2) The normalized transfer entropy values of hydrometeorological and socioeconomic links were found to be essentially comparable, whereas the engineering regulation link exhibited the highest normalized transfer entropy value for water resource system resilience. This indicates that the engineered regulation link constitutes the key link driving the evolution of water resource system resilience.

Observation-Driven Forecast of Global Terrestrial Water Storage and Evaluation for 2010–2024

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE/-FO) satellite missions have provided unprecedented measurements of terrestrial water storage changes (TWSC). These data are essential for monitoring the global water cycle, supporting drought and flood risk management, and informing water-related decision-making. However, GRACE products are typically released with a latency of several months, limiting their utility for real-time and operational forecasting applications. In this study, we use machine learning to forecast GRACE-like TWSC up to 12 months ahead, relying solely on observational and reanalysis-based inputs. The observation-driven forecast approach is evaluated over the period 2010–2024 and benchmarked against seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF)’s new long-range forecasting system (SEAS5). Our results show that the developed method offers improved accuracy and robustness compared to the ECMWF forecasts, providing a viable data-driven alternative for operational TWSC forecasting. We generate global forecast data sets at 1° resolution, creating a robust, publicly available resource that extends GRACE-like insights into the near future. The study addresses the latency of GRACE/-FO products by offering real-time TWSC forecasts to support applications such as drought early warning, sea level prediction, hydrological model validation, and geodetic applications such as forecasting Earth orientation parameters via hydrological angular momentum excitation or estimating loading corrections in GNSS and altimetry data analysis. The hindcast data set (2010–2024) evaluated in this study and the regularly updated semi-operational forecast data set (from 2024 onward) are publicly available at: https://doi.pangaea.de/10.1594/PANGAEA.973113 and https://www.igg.uni-bonn.de/apmg/de/data-and-models/grace-fo-forecasting.

Future societal developments provide a challenge for pedology as an integrative activity within soil science

In contrast to earlier periods, the soil science profession receives major attention from the policy arena in Europe as evidenced by recent major financial research support (e.g. EC, 2023). Writing research proposals and running research programs, sometimes with participants from many countries, is highly time consuming, also because legal requirements are increasingly imposed. In addition, scientific activities are affected by elaborate internal procedures, adopted from the business community, focused on financial and human resource management. As a result, not enough time, it seems, is left for reflection as to desirable future developments of the soil science profession in a rapidly changing world. This discussion paper has therefore the objective to contribute to a discussion about the future role of soil science in society and sketch possible future scenarios. A 5-level scheme will be followed in the discussion, which relates research in general and soil science and pedology in particular to governmental and various stakeholder group actions (Fig. 1), emphasizing the need to consider developments in the outside world when assessing the future role of soil science in a societal context. Any discussion about future activities of the soil science discipline should preferably be framed in such a broad societal context, avoiding a too limited, self-centered approach.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 17, 2026.

Journal Paper Digests

2026-02-10T00:00:00-00:00

Journal Paper Digests 2026 #4

Unearthing historical pedology: An analysis of soil science concepts in 1200 years of Persian poetry
The economics of conservation agriculture within a conceptual and methodological assessment framework
Pasture management in Ferralsols drives mineral-associated organic matter storage, exceeding native soil carbon stocks and enhancing cation exchange capacity
Release of potassium from soils under different cultivations using wood vinegar: emphasis on clay mineralogy

Release of potassium from soils under different cultivations using wood vinegar: emphasis on clay mineralogy

Context The release of potassium (K) from K-bearing minerals in the soils of arid and semi-arid regions is crucial.

Aims This study aims to investigate the role of wood vinegar in K release from soils under various land uses and to compare its effectiveness with other extractants.

Methods The experimental treatments included three different land use types (grape, wheat, and rangeland) and four extractants [hydrochloric acid (HCl), calcium chloride (CaCl2), oxalic acid, and wood vinegar] at a concentration of 0.01 M over 10 consecutive half-hour periods. The experimental release data were fitted to various kinetic equations.

Results Analysis of variance revealed that both the extractant and land use significantly affected the amount of released K (P < 0.01). The wood vinegar extractant had the highest release rate (3794 mg kg−1), while CaCl2 had the lowest release rate (156.1 mg kg−1). Power function and parabolic diffusion equations provided the best fit for the data (r2 = 0.99). The results indicate that the ‘b’ coefficient across all applications adheres to the following sequence: wood vinegar > HCl > oxalic acid > CaCl2. The highest release rate was for grape cultivation, followed by rangeland and wheat cultivation, as described by the parabolic diffusion equation. The clay mineralogy analysis indicated a transformation of vermiculite clay to smectite–illite and illite–vermiculite mixed clays when using the oxalic acid extractant, and completely into illite or dissolution in the soil sample using the wood vinegar extractant.

Conclusions It is recommended that wood vinegar be used to supply K in K-depleted soils containing K-bearing minerals.

Implications Wood vinegar is introduced as sustainable strategy for releasing potassium from soil mineral sources.

Pasture management in Ferralsols drives mineral-associated organic matter storage, exceeding native soil carbon stocks and enhancing cation exchange capacity

Pasture management is pivotal for enhancing soil organic carbon (SOC) storage in tropical grasslands, yet SOC recovery is often considered merely as the replenishment of historical losses following land-use change. It remains unclear whether managed Ferralsols can surpass the SOC stocks of native vegetation (NV) and which mechanisms drive such gains. We evaluated SOC pools, chemical composition, and nutrient-holding capacity after 24 years under unmanaged degraded pasture (DP) and fertilized managed pasture (MP), relative to NV. SOC storage in these systems was primarily mediated by the mineral-associated organic matter (MAOM) pool. Compared to NV, DP soils exhibited reduced MAOM stocks (119 vs. 92 Mg C ha−1), whereas MP soils stored 148 Mg C ha−1. In DP, soil acidity, low nutrient availability, and poor forage inputs induced microbial stress (as revealed by phospholipid fatty acid profiles), likely constraining MAOM formation and yielding MAOM enriched in carbohydrates with fewer carbonyl groups. In contrast, liming and fertilization in MP alleviated the Ferralsol’s low pH and nutrient deficiencies, enhancing forage yields and reducing microbial stress, likely promoting MAOM with more microbially processed signatures. NanoSIMS analyses revealed microscale organic matter patches sparsely covering clay-sized particles, indicating that SOC storage is decoupled from mineral surface area and highlighting the role of organic inputs and microbial activity in MAOM formation. Higher MAOM under MP not only increased SOC stocks but also enhanced cation exchange capacity, demonstrating that targeted pasture management can exceed native SOC stocks while improving nutrient retention.

The economics of conservation agriculture within a conceptual and methodological assessment framework

Conservation agriculture (CA) involves the simultaneous adoption of three agroecological practices: no-tillage or reduced tillage, maintenance of soil organic cover, and crop diversification. This paper develops a conceptual and methodological framework for assessing the economic benefits of CA. More than 150 studies were reviewed to create a conceptual diagram that identifies and links the key economic and environmental effects of adopting CA. The review reveals contradictory impacts of CA on production costs. Labor and machinery costs are significantly reduced, but these savings may be offset by increased pesticide costs due to greater weed pressure. Evidence is also mixed regarding whether CA adoption increases or decreases crop yields and water pollution. However, implementing CA is likely to promote biodiversity, reduce soil erosion, mitigate global warming, and improve soil quality in the long term. These effects vary depending on the CA practices adopted, experiment duration, climatic conditions, soil textures, and crop types. Adopting no-tillage alone may be ineffective at controlling soil erosion and is likely to result in yield losses or insignificant yield gains. CA impacts extend from individual farms to national and global levels and involve various risks and uncertainties. In light of these findings, a methodological approach is proposed to assess the probability distributions of the private and public benefits that CA generates. Assessing these benefits will help farmers and policymakers make informed decisions, thereby ensuring successful transitions to CA practices.

Unearthing historical pedology: An analysis of soil science concepts in 1200 years of Persian poetry

Literature serves as a cultural repository for enduring knowledge, offering unique insights into the historical evolution of scientific and philosophical thought. This study investigates the semantic understanding of soil, from the perspective of pedology, across 1200 years of Persian poetry. The word khāk (soil) was analyzed in the works of major Persian poets from the third to the fourteenth century SH. Poems demonstrating a strong thematic relevance with soil science principles were selected and systematically categorized according to the discipline’s specialized subfields. The results yielded ten distinct thematic categories, encompassing soil’s agricultural functions (e.g., soil-water-plant relationships), ecological challenges (e.g., salinity, erosion), physical and biological properties, and its role in human health. The analysis of poetic themes reveals a significant focus on soil erosion, and the soil-water-plant relationship was the next most significant theme. Conversely, topics such as soil quality and land capability and modern humanity and soil were minimally represented. The findings reveal that soil holds a dual status in Persian poetry: it is simultaneously a tangible, natural element and a profound symbolic concept. On one hand, it is recognized as the substrate for life, reflecting practical agricultural knowledge and principles of sustainable resource management. On the other, its physical and chemical properties inspire rich metaphors for life, death, humility, and other core ontological themes. This long-standing integration of scientific observation and artistic expression demonstrates a deep-rooted connection between humanity and the environment. It also provides a valuable historical framework for contemporary interdisciplinary fields such as ecopoetry and offers a model for bridging the humanities and the natural sciences.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 10, 2026.

Journal Paper Digests

2026-02-02T00:00:00-00:00

Journal Paper Digests 2026 #3

Federated earth-observation models for collaborative farm-scale soil mapping
Ensuring accuracy and reliability in spectroscopic diagnostics: the role of quality control systems
Prediction of potentially toxic trace elements (PTEs) in soil and sediments using vis-NIR spectroscopy: a review
Air-drying of soil preserves microbial and faunal eDNA abundance and diversity regardless of land-use type or management intensity
Analysis of soil thermal property measurements in double-layered soils with the heat pulse sensor vertically crossing a soil horizon interface

Analysis of soil thermal property measurements in double-layered soils with the heat pulse sensor vertically crossing a soil horizon interface

The growing demand for studying coupled hydrothermal transport processes in layered soils comes with a need for accurate estimations of thermal properties using the heat pulse (HP) sensor. In the case where a HP sensor is installed vertically in a double-layered soil with the sensor crossing a soil horizon interface, its measurements are affected by different upper and lower layered properties. This study combined laboratory and numerical experiments to quantify the effect of the soil horizon interface on HP measurements, and to develop a parameterized cylindrical perfect conductor (PCPC) model that accounts for the interface position and layered properties. Results indicated that the effect of the layered soil properties on HP measurements depended on the soil horizon interface position, specifically when the soil horizon interface was within 15 mm vertically above or below the thermocouples in the HP sensor. A sigmoid function was used to quantify the effects of soil layer properties and soil horizon interface position on HP measurements. The developed PCPC model, based on the sigmoid function, exhibited strong agreement with the numerical simulations, yielding soil thermal property estimates all within a maximum relative error of −3.1%. The PCPC model effectively captured the combined effects of soil horizon interface and thermal properties of soil layers on the HP measurements in a double-layered soil system. This model provides a theoretical basis for the inversion of soil thermal property in such a double-layered soil environments with a HP sensor vertically crossing a soil horizon interface.

Air-drying of soil preserves microbial and faunal eDNA abundance and diversity regardless of land-use type or management intensity

Soil biodiversity monitoring requires standardized and practical sample storage methods, particularly for large-scale surveys. Yet, the influence of the soil storage conditions on eDNA-based assessments of microbial and faunal communities remains a key concern. Here, we assessed whether air-drying of soils at room temperature alters microbial (prokaryotes, fungi, micro-eukaryotes) and faunal (nematodes, annelids, micro-arthropods) abundance and diversity compared to freezing at −20 °C across different land-use types and management intensities through quantitative polymerase chain reaction (qPCR) and multi-marker DNA metabarcoding. We sampled topsoil (0–10 cm) from 42 sites of the Swiss Central Plateau spanning forests, grasslands, arable lands, orchards, wetlands, and urban areas. Forests, grasslands and arable lands were sampled in sites varying in management intensities. Across land-use types and management intensities, air-drying of soil followed by four to eight weeks of storage at room temperature or at −20 °C and freezing soil directly yielded comparable gene abundances, alpha-diversity, and community structure for all microbial and faunal groups. Moreover, microbial and faunal community structure were consistently shaped by land-use types and soil physicochemical variables regardless of the soil storage method used. These findings demonstrate that air-drying is a cost-effective and reliable method for short-term storing soil samples in large-scale biodiversity monitoring without compromising data quality.

Prediction of potentially toxic trace elements (PTEs) in soil and sediments using vis-NIR spectroscopy: a review

The potentially toxic trace elements (PTEs) in soil/sediments are a severe environmental problem. It is necessary to better understand and evaluate the distribution of PTEs in soil/sediments. Visible-near infrared (Vis-NIR) spectroscopy has great advantages of being green, rapid, and highly operational for large-scale monitoring, to be an efficient alternative of traditional methods in the inversion of PTEs in soil/sediments. This article reviews the progress on the application of VIS-NIR technology in predicting PTEs content in soil/sediments, including the prediction mechanism, the main factors affecting prediction accuracy, spectral data processing and modeling methods and the present shortcomings or challenges. Also, this article points out the future research directions to improve the application of Vis-NIR in predicting PTEs content, including modeling of PTEs in different forms, spectral feature selection, prediction model optimization, interdisciplinary cooperation and communication, and the spectral data accessibility and standardization. The purpose is to provide an overview and outlook on the application of Vis-NIR technology in predicting PTEs content in soil/sediments, promoting the scientific research and practical applications.

Ensuring accuracy and reliability in spectroscopic diagnostics: the role of quality control systems

Spectroscopic diagnostics have great potential in clinical applications, enabling the assessment of biological tissues and fluids through their spectral signatures. The accuracy and reliability of these techniques are paramount for their integration into routine clinical workflows. Several challenges—such as instrumental drift, environmental fluctuations, and operator-related variability—can compromise spectral consistency. Quality control (QC) systems serve as essential safeguards against these challenges, ensuring that instruments operate within predefined performance parameters through calibration, validation, standardized protocols, and contamination detection. This paper explores the fundamental role of QC frameworks in spectroscopic diagnostics, where maintaining measurement integrity is critical for patient safety and regulatory compliance. The practical implementation of QC principles is demonstrated through a case study on a noninvasive NIR system designed for glycation assessment in nail keratin as a screening tool for diabetes risk. The study highlights the importance of automated error detection, real-time calibration verification, and robust statistical quality assurance in ensuring diagnostic reliability. Aligning spectroscopic QC measures with the unmet needs of healthcare professionals and tested individuals is crucial for fostering trust in these technologies. By addressing real-world clinical challenges and advancing regulatory oversight, spectroscopy-based diagnostics can improve accessibility to timely and cost-effective medical assessments, particularly in resource-limited settings.

Federated earth-observation models for collaborative farm-scale soil mapping

Accurate, privacy-respecting soil information is essential for site-specific nutrient management and carbon accounting, yet the cost of laboratory analyses limits many farms to relatively sparse sampling grids. We propose a collaborative framework that couples a national Sentinel-2 bare-soil composite with FL to produce high-resolution clay and soil organic carbon (SOC) maps while keeping all local data on-premise. A one-dimensional convolutional neural network was first pre-trained on a 53,570-sample Brazilian archive and then fine-tuned across 50 farms through synchronous Federated Averaging. We benchmarked this hybrid configuration against (i) a purely centralized model trained on the same archive and (ii) a fully decentralized FL model initialized at random. Across farm-level validation sets, pre-trained FL lowered median RMSE by 42% for clay and 31% for SOC relative to the centralized baseline, while increasing median RPIQ by 33% and 25%, respectively. On farms with samples, the gains remained substantial, confirming that the approach remains effective when local datasets are modest compared with national archives. Error distributions differed significantly between scenarios (Friedman and Wilcoxon tests), and the pre-trained FL maps removed most spatial artefacts observed in the centralized outputs while preserving fine-scale gradients. Because only encrypted weight updates are exchanged, sensitive information never leaves the farm, satisfying GDPR/LGPD-style constraints and allowing late-joining clients to inherit an increasingly mature global model. Taken together, these results indicate that continental pre-training followed by federated fine-tuning reconciles global generality with local specificity and offers a scalable blueprint for privacy-preserving, high-resolution soil mapping in settings where sample densities are often substantially lower than in experimental setups, without compromising data sovereignty.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 02, 2026.