Smart Digital Agriculture

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.

Journal Paper Digests

2026-01-27T00:00:00-00:00

Journal Paper Digests 2026 #2

Scale dependence of genome-derived microbial functional diversity informing soil functions
AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping
A radiometrically and spatially consistent super-resolution framework for Sentinel-2
Multi-sensor (since 1997) global soil moisture mapping with enhanced Spatio-temporal coverage through machine learning framework fusion
A Century of Drought Research (1900–2023): Scientific Developments, Methodological Innovations, and Emerging Frontiers
Multiple Global Change Stressors Boost Soil Greenhouse Gas Emissions Worldwide
Effects of microplastics on farmland soils and plants: a review

Effects of microplastics on farmland soils and plants: a review

Microplastics (MPs) are plastic particles smaller than 5 mm in size, which are widely present and have become one of the major pollutants in the natural environment, and are increasingly recognised as emerging pollutants in agricultural ecosystems. Due to their small size and high mobility, MPs can easily migrate into farmland soils and attach to plant surfaces, thereby altering the physical, chemical and microbial properties of the soil. These changes may affect seed germination, plant growth, and physiological and biochemical functions. This review systematically synthesises current research on the impact of MPs on agricultural soil, focusing on their effects on soil structure, chemical properties and microbial diversity. The positive and negative effects of MPs on plant seed germination, growth, and physiological and biochemical processes are critically analysed. Furthermore, the potential ecological risks of MPs to soil and plant health are discussed. Mitigation strategies and future research priorities are proposed to address MPs contamination in agricultural systems. This study aims to provide both theoretical insights and practical references to support the prevention and control of MPs pollution in farmland soils, thereby contributing to sustainable agricultural development and soil ecosystem resilience.

Multiple Global Change Stressors Boost Soil Greenhouse Gas Emissions Worldwide

Soil carbon greenhouse gas (GHG) emissions are integral to climate security worldwide. Global change is known to impact soil GHG emissions; yet, the contribution of an increasing number of global change factors (GCFs) to the rates of carbon GHG emissions remains virtually unknown, challenging our capacity to forecast the trajectory of climate change. Here, we synthesize 1803 observations on soil CO2 and CH4 fluxes across 21 types of GCFs spanning a wide range of ecosystems (i.e., forests, grasslands, farmland, wetlands, tundras, and deserts) and found that an increasing number of GCFs will result in significant increases in CO2 and CH4 emissions. The impacts of GCFs on GHG emissions were largely explained by climate, biome types, and GCF-induced changes in soil moisture, providing potential tools for managing global change. Our work provides critical insights, emphasizing that the number of global change stressors needs to be immediately reduced to help minimize the negative impacts of carbon greenhouse gas emissions on climate change.

A Century of Drought Research (1900–2023): Scientific Developments, Methodological Innovations, and Emerging Frontiers

Drought significantly affects water resources, agriculture, energy, and ecosystems, revealing enduring socio-economic vulnerabilities over the centuries. This review synthesizes a century of development and recent advances in drought research (1900–2023), drawing on a bibliometric analysis of over 152,000 peer-reviewed publications. The review begins by exploring ancient and historical droughts, their climatic drivers, and societal impacts, then examines the evolving disciplinary landscape, shifting research priorities, and the progression of drought research over the past century. Key methodological advances are discussed, including statistical and probabilistic modeling, machine learning, and deep learning. Technical milestones such as satellite remote sensing, hydrological and land surface modeling, and global climate modeling have greatly expanded both the scope and precision of drought studies. Research on climate change has deepened understanding of drought processes by examining changes in climate variability and teleconnections, attributing events to human influence, and projecting future risks. Simultaneously, there has been a notable shift from reactive approaches to resilience-oriented management, enhancing preparedness. In the past decade, increasing attention has focused on emerging societal challenges such as environmental degradation, public health risks, social inequities, and resource conflicts. Despite significant progress, critical gaps remain, including the need for stakeholder-informed indicators, improved flash drought detection, a deeper understanding of cascading processes, integration of human-driven factors, enhanced interpretability of AI models, next-generation satellite monitoring, and comprehensive risk management for drought-related compound hazards. This synthesis consolidates a century of progress and presents a forward-looking framework aimed at strengthening resilience and guiding actionable drought risk governance.

Multi-sensor (since 1997) global soil moisture mapping with enhanced Spatio-temporal coverage through machine learning framework fusion

The successful deployment of multiple satellites equipped with passive microwave sensors has been pivotal for monitoring global soil moisture. Despite their importance, limitations including varying service durations, orbital scanning gaps, and differences in retrieval algorithms result in poor spatio-temporal consistency and coverage. This study introduces a two-stage paradigm to overcome the inconsistency of multi-sensors: Firstly, high-precision soil moisture is generated from SMAP L-band observations through the multi-channel collaborative algorithm (MCCA) as the physically anchored training target. Then, a long short-term memory (LSTM) network specifically designed for global gridded soil moisture dynamics is trained based on cross-calibrated brightness temperature observations (inclined orbit satellite sensors (TMI and GMI) and polar orbit satellite sensors (AMSR-E and AMSR2)) to obtain the high-quality retrieval accuracy of MCCA SMAP. Finally, the daily global soil moisture product (25 km resolution, 1997–2023) is provided by fusing the instantaneous soil moisture data of the four sensors from the model output. The study performed extensive validation with ground measurements and cross-validation with other datasets for both temporal and spatial consistency. The results indicate that the spatial distribution and seasonal variation patterns of MCCA-ML closely match those of MCCA SMAP, reflecting global climatic and geographic features. Verified by 24 dense global observation networks, the global r value of MCCA-ML SM is 0.76, the RMSE is 0.068 m3/m3, and the ubRMSE is 0.059 m3/m3, which well inherits the excellent performance of SMAP. During the service period of two or more satellites, the daily global land coverage of MCCA-ML SM usually exceeds 80 %, and it has a good ability to detect soil moisture.

A radiometrically and spatially consistent super-resolution framework for Sentinel-2

Deep learning-based super-resolution (SR) models offer a promising approach to enhancing the effective spatial resolution of optical satellite images. However, existing SR implementations have shown that, while these models can reconstruct fine-scale details, they often introduce undesirable artifacts, such as nonexistent local structures, reflectance distortions, and geometric misalignment. To mitigate these issues, fully synthetic data approaches have been explored for training, as they provide complete control over the degradation process and allow precise supervision and ground-truth availability. However, challenges in domain transfer have limited their effectiveness when applied to real satellite images. In this work, we propose SEN2SR, a new deep learning framework trained to super-resolve Sentinel-2 images while preserving spectral and spatial alignment consistency. Our approach harmonizes synthetic training data to match the spectral and spatial characteristics of Sentinel-2, ensuring realistic and artifact-free enhancements. SEN2SR generates 2.5-meter resolution images for Sentinel-2, upsampling the 10-meter RGB and NIR bands and the 20-meter Red Edge and SWIR bands. To ensure that SR models focus exclusively on enhancing spatial resolution, we introduce a low-frequency hard constraint layer at the final stage of SR networks that always enforces spectral consistency by preserving the original low-frequency content. We evaluate a range of deep learning architectures, including Convolutional Neural Networks, Mamba, and Swin Transformers, within a comprehensive assessment framework that integrates Explainable AI (xAI) techniques. Quantitatively, our framework achieves superior PSNR while maintaining near-zero reflectance deviation and spatial misalignment, outperforming state-of-the-art SR frameworks. Moreover, we demonstrate maintained radiometric fidelity in downstream tasks that demand high-fidelity spectral information and reveal a significant correlation between model performance and pixel-level model activation. Qualitative results show that SR networks effectively handle diverse land cover scenarios without introducing spurious high-frequency details in out-of-distribution cases. Overall, this research underscores the potential of SR techniques in Earth observation, paving the way for more precise monitoring of the Earth’s surface. Models, code, and examples are publicly available at https://github.com/ESAOpenSR/SEN2SR.

AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping

Climate change and population growth intensify the demand for precise agriculture mapping to enhance food security. Such mapping tasks require robust modeling of multi-scale spatiotemporal patterns from fine field textures to landscape context, and from short-term phenology to full growing-season dynamics. Existing methods often process spatial and temporal features separately, limiting their ability to capture essential agricultural dynamics. While transformer-based remote sensing foundation models (RSFMs) offer unified spatiotemporal modeling ability, most of them remain suboptimal: they either use fixed windows that ignore multi-scale crop characteristics or neglect temporal information entirely. To address these gaps, we propose AgriFM, a multi-source, multi-temporal foundation model for agriculture mapping. AgriFM introduces a synchronized spatiotemporal downsampling strategy within a Video Swin Transformer backbone, enabling efficient handling of long and variable-length satellite time series while preserving multi-scale spatial and phenological information. It is pre-trained on a globally representative dataset comprising over 25 million samples from MODIS, Landsat-8/9, and Sentinel-2 with land cover fractions as pre-training supervision. AgriFM further integrates a versatile decoder specifically designed to dynamically fuse multi-source features from different stages of backbone and accommodate varying temporal lengths, thereby supporting consistent and scalable agriculture mapping across diverse satellite sources and task requirements. It supports diverse tasks including agricultural land mapping, field boundary delineation, agricultural land use/land cover mapping, and specific crop mapping (e.g., winter wheat and paddy rice) with different data sources. Comprehensive evaluations show that AgriFM consistently outperforms existing deep learning models and general-purpose RSFMs across multiple agriculture mapping tasks. Codes and models are available at https://github.com/flyakon/AgriFM and https://glass.hku.hk

Scale dependence of genome-derived microbial functional diversity informing soil functions

The relationship between soil multifunctionality and microbial diversity is well established, and using genomic data to link microbial diversity with soil functions is increasingly recognized as a reliable approach, despite challenges such as horizontal gene transfer, functional redundancy, and transcriptional uncertainty. Here, we investigated how microbial taxonomic and functional diversities derived from metagenomic data explain soil multifunctionality across soil profiles. We conducted analyses across four seasons and two contrasting hydrological habitats: wetland and cropland. We found that microbial functional diversity captured soil functions more effectively than taxonomic diversity, and its explanatory power depended on scale, strongest at broader classification levels (phylum/module) and higher data hierarchies (cosmopolitan). Microbial functional diversity explained 95 % and 79 % of individual soil functions in wetland and cropland, respectively, and showed a closer association with overall soil multifunctionality. The relationship remained consistent across spatial (0–100 cm soil profiles), temporal (four seasons), and hydrological (wetland and cropland) gradients, demonstrating greater stability than taxonomic diversity. By linking microbial diversity to soil functions across space and time, our findings show that genome-derived microbial functional diversity provides a robust and reliable framework for explaining soil functions, reinforcing the potential of genome-based microbial modeling.

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

Journal Paper Digests

2026-01-21T00:00:00-00:00

Journal Paper Digests 2026 #1

Upscaling Models for the Large-Scale Assessment of Soil Functions
Autistic voices are an overlooked minority in geosciences
Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events
Hydrologic connectivity as a predictor of degradation thresholds across semiarid sites with different vegetation patterns
Spectra-based predictive mapping of soil erodibility and analysis of its influence mechanism: A typical case study for Northeast China
Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe
Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes
A Novel Hybrid Predictive Model Based on Mixture Density Networks With Weighted Conformal Inference Strategy for Runoff Interval Prediction Across Australia

A Novel Hybrid Predictive Model Based on Mixture Density Networks With Weighted Conformal Inference Strategy for Runoff Interval Prediction Across Australia

Abstract Accurate runoff forecasting helps mitigate flooding and drought risks and ensure water security under changing conditions. Compared to deterministic prediction models, interval prediction can more effectively quantify uncertainty, enhancing practical applicability. However, the Mixture Density Network (MDN) model—a state-of-the-art probabilistic modeling approach in hydrology—is susceptible to bias from distributional misspecification, and its prediction intervals are often overly wide, reducing practical utility. We therefore innovatively incorporated the Weighted Conformal Inference (WCI) strategy, which accounts for distributional shifts in runoff sequences, and integrated it with MDN to develop the WCI-MDN model for runoff interval prediction. To validate the effectiveness of the WCI strategy, we constructed six models in total: MDNs and WCI-MDNs under three distributions—Gaussian Mixture (GMM), Laplace Mixture (LMM), and Countable Mixtures of Asymmetric Laplacians (CMAL)—and evaluated their accuracy and robustness using data from 222 basins in the CAMELS-AUS data set. Results indicated that among the three MDN models, the LMM distribution achieved the best interval prediction performance, followed by the CMAL and GMM distributions. After introducing the WCI strategy, the coverage width-based criterion (CWC) for GMM, LMM, and CMAL distributions decreased by approximately 61.1%, 48.7%, and 54.3%, respectively, across all basins, demonstrating that the WCI-MDNs achieved higher prediction reliability. Furthermore, compared to the MDNs, the standard deviation of the CWC for the WCI-MDNs was reduced by 66.7%–81.8%, indicating higher robustness. Thus, the study improved the existing MDNs, providing a promising new approach for runoff interval prediction.

Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes

Global warming is increasing the number and intensity of many extreme weather and climate events. Here we argue that extreme day-to-day temperature changes, exceeding the 90th percentile threshold of historical records, are an independent, but largely ignored, aspect of extreme weather events. Such extreme temperature changes have a stronger impact on human health in many locations than do diurnal temperature variations. Global observations show that such events have become more frequent since the 1960s in low and mid-latitudes but decreased at high latitudes, primarily due to GHG forcing. Climate models project a further amplification of extreme day-to-day temperature changes under warming, with frequency, amplitude and total intensity rising by ~17%, ~3% and ~20%, respectively, by 2100 in regions covering 80% of global population. Increased extreme day-to-day temperature changes are associated with drier soil and increased variability in pressure and soil moisture, posing substantial risks to societal and ecosystem resilience and adaptation.

Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe

Comprehensive evaluation of satellite and model-based soil moisture (SM) products is essential for their further development and application. With the advent of Cosmic Ray Neutron Sensing (CRNS), which has an observation radius of 130–240 m, the spatial representativeness mismatch between these grid-based SM products and ground single-point observations during the evaluation process can be feasibly relieved. In this study, we systematically evaluated 23 gridded SM products, including single-sensor satellite, multi-sensor merged, and model-based products, using 68 CRNS measurement sites across the Europe. Our evaluation revealed that the SMAP-INRAE-BORDEAUX (SMAP-IB) SM retrievals showed the superior consistency with CRNS measurements among all analyzed products, demonstrating both high correlation (R = 0.80) and low unbiased root mean square error (ubRMSE = 0.050 m3/m3). The CCI/C3S combined active-passive SM products ranked second in performance (R > 0.75, ubRMSE <0.060 m3/m3). In the bias analysis, 17 products had negative bias (−0.003 m3/m3 to −0.190 m3/m3) against CRNS measurements, while AMSR2-LPRM at C1 and C2 bands and CCI/C3S at active and passive products had positive bias (0.011 m3/m3 to 0.161 m3/m3). It was also found that the capabilities of all SM products retrievals degraded in terms of R and ubRMSE with increasing vegetation density, topographic complexity and soil wetness. Most products showed the lowest ubRMSE and highest R values in cropland compared to other land cover types. Our study emphasizes the substantial potential of cosmic field-scale SM observations for the validation of satellite- and model-based SM products, and our findings have the potential to advance algorithm refinement, product improvement, and hydrometeorological applications.

Spectra-based predictive mapping of soil erodibility and analysis of its influence mechanism: A typical case study for Northeast China

Soil erosion in Northeast China’s black soil region poses serious challenges to agricultural productivity and ecosystem sustainability. This study proposes a novel framework for high-resolution (10 m) mapping of soil erodibility by integrating Sentinel-2 spectral data with a gradient boosting decision tree (GBDT) model. A comprehensive soil erodibility index (CSEI) was developed to represent the combined effects of soil texture, structure, and organic stability. The GBDT model was used to identify the dominant environmental drivers and their nonlinear relationships with CSEI. Results indicate that the normalized difference tillage index (NDTI), soil moisture, and mean annual precipitation are the key influencing factors, collectively explaining 69.3 % of the spatial variability in soil erodibility. Threshold effects were observed, including an inverse S-curve for soil moisture and an inverted-U response to precipitation, reflecting shifts in erosion mechanisms under varying surface conditions. These findings provide quantitative evidence for targeted soil conservation and land-use optimization, supporting management strategies such as conservation tillage, slope-specific terracing, and vegetation restoration to mitigate erosion risks in vulnerable landscapes.

Hydrologic connectivity as a predictor of degradation thresholds across semiarid sites with different vegetation patterns

Dryland landscapes typically display a two-phase mosaic consisting of densely vegetated patches interspersed with low-cover or bare soil areas. The extent and spatial patterns of these patches have a direct effect on ecosystem function and disturbances, such as over grazing, can disrupt the original structure of vegetation and lead to degradation. This work investigates changes in the hydrologic connectivity (i.e., the degree to which areas of the landscape connect to each other) of Mulga landscapes induced by land degradation. Mulga is a keystone ecosystem of the Australian drylands and is characterised by a patchy vegetation cover, which can vary considerably from site to site. We analyse 31 plots with different degrees of degradation (or vegetation cover) in four Mulga sites with different precipitation, slope and vegetation and we quantify hydrologic connectivity combining high-resolution binary vegetation maps and DEMs. Results indicate that connectivity increases as vegetation cover decreases, but this relation is significantly non-linear with a clear threshold at 38 % vegetation cover below which connectivity (and loss of resources due to runoff out of the system) increases dramatically leading to degradation. A site with a pattern of vegetation strands concentrated along drainage lines showed consistently higher connectivity (due to longer connected paths) compared to the other sites where vegetation was more uniformly scattered or presented banded pattern perpendicular to drainage lines. Outputs from a vegetation thinning algorithm on patch edges consistent with grazing effects confirm the existence of the observed threshold in vegetation cover and the influence of vegetation patterns on connectivity. Our results indicate that connectivity is a strong indicator to detect degradation thresholds over a variety of vegetation arrangements typical of dryland systems.

Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events

Ecosystems are not only affected by current climate but are also shaped by antecedent climate through their influences on vegetation growth and environmental conditions. These lagged responses, known as memory effects, can either exacerbate or mitigate the impacts of climate extremes on ecosystem functions. However, the direction, strength and influential duration of memory effects on ecosystem productivity remain poorly understood. Here we implement an interpretable machine-learning framework based on eddy covariance data to model ecosystem gross primary productivity over the period 1995–2020 and further investigate the characteristics of memory effects on positive and negative extremes of ecosystem productivity. Our results show a large contribution from antecedent climate conditions (38.2%) to ecosystem productivity during extremes, with precipitation accounting for 42.2% of the memory effects, followed by temperature (22.1%) and vapour pressure deficit (20.8%). Extreme events conditioned by long-term climatic variations often cause higher productivity losses than short-term extremes, with semi-arid ecosystems exhibiting the largest productivity anomalies and prolonged memory effects. Our results highlight the role of memory effects in regulating carbon flux variations and provide an observation-constrained benchmark for these effects.

Upscaling Models for the Large-Scale Assessment of Soil Functions

The characterization and assessment of soil functions is a prerequisite for agricultural and environmental policies aimed at soil health. However, there is a lack of satisfactory models for the assessment of soil functions supply to support national and intergovernmental initiatives. In this study we fill this gap by restructuring models developed to assess the multifunctionality of agricultural soils at the field scale. The multi-criteria decision models rely on soil properties, site characteristics and management information to assess the following five soil functions: (1) water regulation, (2) climate regulation, (3) nutrient cycling, (4) primary productivity and (5) provision of habitat for biodiversity. We develop models to assess soil functions supply at regional and national scales by adapting their structure to cope with the general lack of information on soil management at larger geographical scales. The restructured models are verified and a sensitivity analysis of the new model structure is performed. We further applied a comparison of the upscaled models with results from validated field-scale models using real data from 94 sites spanning across 13 European countries. We found that the upscaled models showed a similar sensitivity to the variability of the input data from the 94 sampling sites as the base models from which they were developed and that their overall supply is expected to be comparable. We describe the model structure of the upscaled models as well as their qualitative scales and integration rules. We propose the application of the models can serve for large-scale assessment of soil functions supply as part of soil health assessment for regional and national environmental and agricultural policies.

Autistic voices are an overlooked minority in geosciences

Autism remains an under-recognized and under-represented aspect of inclusivity conversations in geosciences. We highlight an urgent need for support and recognition of autistic learners, alongside a need to integrate autistic voices in learning and teaching practices.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on January 21, 2026.

Journal Paper Digests

2024-03-07T00:00:00-00:00

Journal Paper Digests 2024 #5

Global cropland nitrous oxide emissions in fallow period are comparable to growing-season emissions
Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils
Why make inverse modeling and which methods to use in agriculture? A review

Why make inverse modeling and which methods to use in agriculture? A review

Inverse modeling (IM) is a valuable tool in agriculture for estimating model parameters that aid in decision-making. It is particularly useful when parameters cannot be directly measured or easily estimated due to logistical constraints in agricultural settings. Unlike other estimation methods, IM combines a mechanistic model with observations of its outputs to derive the parameters of interest, allowing for the integration of various sources of knowledge. The availability of numerous data sources, such as remote sensing and crowdsourcing, with high spatial and temporal resolution, has expanded the potential of IM in agriculture. Practitioners can now incorporate the spatial and temporal footprint of observational data into parameter estimation. However, common IM techniques currently applied in agriculture often struggle to account for effectively spatial and temporal variability. Relevant IM methods that address these challenges are usually isolated within specific developer and user communities and are not well known within the agricultural community. There is a lack of comprehensive reviews focusing on IM methods suitable for handling spatial and temporal data in agriculture. In parallel, the process of conducting IM in agriculture remains under-formalized. Typically, specific IM methods are chosen for specific combinations of models and types of observational data, but the rationale behind their selection is rarely explained in publications. The relationship between IM methods, models, and observational data is unclear, making it overwhelming for new practitioners to choose an appropriate method. This complex problem, along with the diversity of IM methods, has yet to be adequately addressed while taking into account the specificities of agricultural applications. To address these challenges, this review aims to provide a structured classification of IM methods based on the practical needs of new practitioners in agriculture. It examines a wide range of inversion methods applied in agriculture-related domains and covers four key topics: i) the essential elements and general process of IM, ii) the main families of IM methods in agriculture and their characteristics, iii) the circumstances in which practitioners prefer using IM over other approaches, and their motivations, and iv) practical guidance on choosing a method family based on operational criteria. The review aims to help readers develop a clear understanding of the practice of inverse modeling, gain insights into the diversity of IM methods, and make informed choices when selecting a method family for their agricultural applications.

Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils

With widespread nitrogen fertilizer use and complex N2O sources in flooded paddy soils, understanding N2O emission dynamics is crucial but largely understudied. We investigate fungal, bacterial, and chemical denitrification pathways in N2O production after rewetting and nitrogen fertilizer application across southeastern China. Findings suggest significant fungal dominance, challenging previous undervaluations, with bacterial and chemical processes as secondary contributors. The spatial heterogeneity is linked to soil properties, particularly organic carbon and total nitrogen, laying the foundation for predictive models of future global N2O emissions from paddy soils.

Global cropland nitrous oxide emissions in fallow period are comparable to growing-season emissions

Fallow-period N2O emissions have been neglected in the estimation of whole-year greenhouse gas inventories for decades. It is estimated that the mean contribution of fallow period to whole-year N2O emissions (Rfallow) was 44% globally, with hotspots mainly in the northern high latitudes. The dominant driver of global variation in Rfallow was soil pH. To accurately estimate N2O emissions for national greenhouse gas inventories, it is crucial to update current EFs with full consideration of the fallow-period N2O emissions in the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on March 07, 2024.

Journal Paper Digests

2024-02-28T00:00:00-00:00

Journal Paper Digests 2024 #4

Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing
Spatial evaluation of the soils capacity and condition to store carbon across Australia
Suitability of microbial and organic matter indicators for on-farm soil health monitoring
Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada
Flexible marked spatio-temporal point processes with applications to event sequences from association football
Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.

Flexible marked spatio-temporal point processes with applications to event sequences from association football

We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively on the space of marks, allowing a separate model specification for the occurrence times. We develop a Bayesian framework for their inference and prediction that can naturally accommodate covariate information to drive cross-excitations, offering broad flexibility for real-world applications. The framework is applied to in-game event sequences from association football, resulting in inferences about previously unquantified characteristics of game dynamics, extraction of event-specific team abilities and predictions for event occurrences, such as goals or fouls in a specified interval of time.

Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada

Adoption of soil health indicators to assess physical, biological, and chemical properties involves adapting their interpretation for a specific region using scoring functions. Accordingly, we used data provided from 1166 soil samples distributed between fine-, medium-, and coarse-textured soils, collected in agricultural areas across the province of Quebec, Canada, and analyzed for 15 soil health indicators. Scoring functions were calculated according to the means and standard deviations obtained for each soil health indicator by textural group. Three scoring types were used: “more-is-better”, “less-is-better”, and “optimum-is-best”. The results showed that 12 indicators were significantly influenced by soil texture and need separate scoring functions, except for wet aggregate stability, penetration resistance of the surface hardness (0–15 cm), and pH. This led to the development of one to three scoring functions for each soil health indicator. Correlation analysis between soil health indicators was also investigated to better understand relationships between soil physical, biological, and chemical properties. We observed that soil biological indicators were moderately to strongly correlated with each other (r = 0.59–0.74) and with soil physical indicators (r = 0.60–0.76). Overall, the results of this study led to the development of new scoring functions based on soil texture to interpret soil health indicators objectively and accurately for the benefit of Quebec farmers and agricultural stakeholders. The findings of this study demonstrated the need to adapt scoring functions to better account for the impact of regional factors on agricultural soils for the interpretation of soil health indicators.

Suitability of microbial and organic matter indicators for on-farm soil health monitoring

In addition to standard laboratory testing of soil samples, on-farm soil health monitoring methods are needed to help farmers assess progress in adopting new management practices. However, there is currently a lack of studies evaluating the suitability of semi-quantitative on-farm indicators to accurately rank target soil properties according to laboratory results. Therefore, this study assessed methods with potential for field use compared to common laboratory approaches for the determination of (i) soil organic carbon (SOC), (ii) carbon (C) fractions and (iii) microbial activity. The comparison allowed the evaluation of the validity, practicality and cost-effectiveness of the approaches. For this purpose, three sites in north-eastern Austria with contrasting soil textures (light, medium, heavy) and two different management systems (namely ‘pioneer’ and ‘standard’) were selected. Pioneer soils are managed long-term according to principles of soil health using conservation agricultural practices while neighbouring fields under standard management represent conventional practices. Beyond texture and site differences, both laboratory and field-adapted approaches revealed differences between the pioneer and standard systems. Overall, management-specific differences were most pronounced in the light and heavy textured soil. Although the laboratory methods provided more accurate results with less variability, the field-based approaches still identified trends in soil health parameters in the pioneer system. Our study can thus serve as a guide for the selection of suitable parameters and methods for assessing soil health in different areas of research and practical application.

Spatial evaluation of the soils capacity and condition to store carbon across Australia

The soil security concept has been put forward to maintain and improve soil resources inter alia to provide food, clean water, climate change mitigation and adaptation, and to protect ecosystems. A provisional framework suggested indicators for the soil security dimensions, and a methodology to achieve a quantification. In this study, we illustrate the framework for the function soil carbon storage and the two dimensions of soil capacity and soil condition. The methodology consists of (i) the selection and quantification of a small set of soil indicators for capacity and condition, (ii) the transformation of indicator values to unitless utility values via expert-generated utility graphs, and (iii) a two-level aggregation of the utility values by soil profile and by dimension. For capacity, we used a set of three indicators: total organic and inorganic carbon content and mineral associated organic carbon in the fine fraction (MAOC) estimated via their reference value using existing maps of pedogenons and current landuse to identify areas of remnant genosoils (total organic and inorganic carbon) and the 90th percentile for MAOC. For condition we used the same set of indicators, but this time using the estimated current value and comparing with their reference-state values (calculated for capacity). The methodology was applied to the whole of Australia at a spatial resolution of 90 m 90 m. The results show that the unitless indicator values supporting the function varied greatly in Australia. Aggregation of the indicators into the two dimensions of capacity and condition revealed that most of Australia has a relatively low capacity to support the function, but that most soils are in a generally good condition relative to that capacity, with some exceptions in agricultural areas, although more sampling of the remnant genosoils is required for corroboration and improvement. The maps of capacity and condition may serve as a basis to estimate a spatially-explicit local index of Australia’s soil resilience to the threat of decarbonization.

Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 28, 2024.

Journal Paper Digests

2024-02-27T00:00:00-00:00

Journal Paper Digests 2024 #2

Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing
Spatial evaluation of the soils capacity and condition to store carbon across Australia
Suitability of microbial and organic matter indicators for on-farm soil health monitoring
Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada
Flexible marked spatio-temporal point processes with applications to event sequences from association football
Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Flexible marked spatio-temporal point processes with applications to event sequences from association football

Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada

Suitability of microbial and organic matter indicators for on-farm soil health monitoring

Spatial evaluation of the soils capacity and condition to store carbon across Australia

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on February 27, 2024.

Journal Paper Digests

2024-01-17T00:00:00-00:00

Journal Paper Digests 2024 #2

Physics-Informed Neural Networks for solving transient unconfined groundwater flow
Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning
An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter
Estimating soil organic carbon content at variable moisture contents using a low-cost spectrometer

Estimating soil organic carbon content at variable moisture contents using a low-cost spectrometer

Research-grade spectrometers such as ASD are widely used in the lab to estimate soil properties, but they are bulky, heavy, and not easily deployable to measure field soils. The newer FT-NIR spectrometers are compact, lightweight, and robust, suitable for developing portable sensors for emerging applications such as field-based soil carbon stock assessment. In this study, we investigated the usefulness of an FT-NIR spectrometer (NanoQuest) for estimating SOC content while correcting for the effect of soil moisture using External Parameter Orthogonalization (EPO), and its performance was compared to that of ASD. To develop EPO transformation, five levels of soil moisture were used at 0, 0.07, 0.13, 0.18, 0.24, and 0.30 g g−1. We tested two modeling approaches: Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR). The results showed that EPO was more effective in correcting for the moisture effect as samples became drier. ASD gave a better performance in estimating SOC with SVR (R2: 0.17 to 0.84, RMSE: 6.1 to 3.9 g C kg−1, bias: −0.3 to 0.1 g C kg−1) after EPO transformation. NanoQuest gave slightly lower, but still satisfactory performance in SOC estimation (R2: 0.17 to 0.70, RMSE: 9.2 to 5 g C kg−1, bias: −0.3 to 0.1 g C kg−1). EPO substantially reduced the bias of the SOC models for both ASD and NanoQuest. This study demonstrates the usefulness of low-cost FT-NIR spectrometers for SOC measurement at varying moisture contents and their great potential for field-deployable soil sensor development.

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments’ dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments’ characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures.

Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning

Globally, the strategic use of nitrogen (N) is important in optimizing economic returns and reducing soil nitrogen losses to the environment. Incorporating reliable estimates of nitrogen (N) mineralized over a growing season (GSN) into N-fertilizer rate prescriptions is critical, but may often lack a direct measurement. For this purpose, Pedotransfer functions (PTFs) of total nitrogen (TN) – representing the stable pool from which N is mineralized and biological nitrogen availability (BNA) – representing the labile pool of N mineralization were used to estimate GSN. GSN was calculated based on TN and BNA results from a soil health database (SHD), which also includes a suite of related soil health parameters (n = 2222). Using a process of recursive feature elimination (RFE) and cost-benefit feature elimination (CBFE), the best predictors of TN, BNA, and GSN were identified using a suite of machine learners (MLs) and regression analysis. For TN, RFE revealed that BNA, active carbon (AC), sand (Sa), and soil organic matter (OM) were the best predictors yielding a Lin’s concordance correlation coefficient (CCC) of 0.80 and a reduction in theoretical cost of 41 % compared to the control. CBFE resulted in AC, soil respiration (SR), clay, Sa, and OM as the most cost-effective predictors of TN with a CCC of 0.79 and a theoretical cost savings 49 % below the cost of using all appropriate soil health parameters in the SHD. With respect to BNA, the best predictors from RFE were aggregate stability (AS), AC, SR, and TN with a CCC of 0.78 and a theoretical cost reduction of 23 %. CBFE retained AC, SR, S, TN, OM and pH as predictors of BNA with a CCC of 0.78 and reduction of 29 % in theoretical cost. Finally, GSN results from RFE identified AS, AC, SR, OM and pH as the best predictors with a 0.82 CCC and 17 % reduction in theoretical cost. CBFE, on the other hand, identified AC, SR, sand, OM, and pH as the most cost-efficient predictors while maintaining a CCC of 0.82 and theoretical cost reduction of 29 %. Of the MLs used for pattern recognition (i.e., cubist, random forest, support vector machine, and stochastic gradient boosting), cubist model outperformed the others for the majority of iterations of the RFE and CBFE processes. The cost-effective framework, and the N-related PTFs developed in this study will greatly enhance our ability to predict of soil N-pool dynamics and the ability to incorporate GSN estimates into N-fertilizer recommendations for producers worldwide. Improvements in predictive strength could be achieved by incorporating climate and soil management practices into PTF development. Another area for improvement and future study would include addition of spatial and landscape variability related to N-measures via digital soil mapping applications.

Physics-Informed Neural Networks for solving transient unconfined groundwater flow

Neural networks excel in various machine learning applications; however, they lack the physical interpretability and constraints crucial for numerous scientific and engineering problems. This limitation hinders their ability to accurately capture and predict complex physical systems’ behavior, potentially yielding inaccurate or unreliable results. Physics-Informed Neural Networks (PINNs) are a class of machine learning models that integrate the power of neural networks with the physical laws governing natural phenomena. PINNs provide an effective tool for solving intricate physical problems, ranging from fluid dynamics to materials science, by incorporating physical constraints into the neural network architecture. PINNs can substantially enhance the accuracy and efficiency of model predictions, even in data-limited situations. This work offers insight into recent developments in the PINN field, including their mathematical formulation and training algorithms, and emphasizes their application in solving transient unconfined groundwater flow. In this context, the phreatic surface acts as a spatiotemporally varying boundary condition, and properly accounting for its position is vital for precise predictions of unconfined groundwater flow and related environmental and engineering applications. The study’s objective is to develop a reliable model for estimating the phreatic surface and the spatiotemporal distribution of piezometric heads in a vertical cross-section of an unconfined aquifer. Two cases are examined: the first involves a homogeneous and isotropic aquifer, while the second comprises a mildly heterogeneous and anisotropic one. The challenges and opportunities arising from this emerging research area are also explored, and essential directions for future research are underscored.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on January 17, 2024.

Journal Paper Digests

2024-01-12T00:00:00-00:00

Journal Paper Digests 2024 #1

Temporal Gap-Filling of 12-Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting
Carbon sequestration in soils and climate change mitigation—Definitions and pitfalls
Pyrogeography in flux: Reorganization of Australian fire regimes in a hotter world
Distinct, direct and climate-mediated environmental controls on global particulate and mineral-associated organic carbon storage
Stabilisation of soil organic matter with rock dust partially counteracted by plants
A technical evaluation on the mathematical attitudes and fitting accuracy of soil moisture retention curve models
Glacial rock flour reduces the hydrophobicity of Greenlandic cultivated soils
Determination of soil water retention curves from thermal conductivity curves, texture, bulk density, and field capacity
Incorporating soil knowledge into machine-learning prediction of soil properties from soil spectra

Incorporating soil knowledge into machine-learning prediction of soil properties from soil spectra

Various machine-learning models have been extensively applied to predict soil properties using infrared spectroscopy. Beyond the interpretability and transparency of these models, there is an ongoing discussion on the reliability of the prediction of soil properties generated from soil spectra. In this review, we contribute to this discussion by advocating for the integration of soil knowledge into machine-learning models. By doing so, researchers can delve deeper into the underlying soil constituents, ultimately enhancing prediction accuracy. Our review explores the soil information present in spectral data, the fallacy of model interpretability, methods to incorporate soil knowledge into machine-learning techniques, and the ways in which machine learning and soil spectroscopy can assist soil science. The combination of machine learning and domain knowledge is recommended to develop more meaningful models for predicting soil properties within the field of soil science.

Determination of soil water retention curves from thermal conductivity curves, texture, bulk density, and field capacity

The soil water retention curve (SWRC) is frequently expressed using the van Genuchten (VG) model, which has four parameters: saturated water content (θs), residual water content (θr), α, and m (1–1/n). Soil thermal conductivity (λ), which is linked to the hydraulic properties of unsaturated soil, has been a proxy variable used to estimate SWRC. In this study, we present a new approach to estimate the VG model parameters. Parameters θs, α and m are calculated from the information of soil texture, bulk density (ρb), and a measured water content at field capacity (θfc, at −33 kPa or −10 kPa), and θr is estimated from the thermal conductivity versus water content curve, λ(θ), based on similarities between SWRCs and λ(θ) curves. The new approach was evaluated with laboratory and field measurements on 23 soils of various textures, ρb values, and θ values. Results showed that for repacked core samples, intact core samples, and in situ field soils, the new approach estimated SWRCs with average root mean square errors (RMSEs) of 0.042, 0.030, and 0.049 m3 m‐3, respectively. The new approach offers a quick and effective way to estimate SWRCs accurately with measured λ(θ) curves, texture, bulk density, and θ at field capacity.

Glacial rock flour reduces the hydrophobicity of Greenlandic cultivated soils

Applying fine-grained glacial rock flour (GRF) may alleviate highly hydrophobic subarctic soils. Moisture-dependent hydrophobicity was assessed in two field trials in South Greenland. Parameters evaluated included both total area under the hydrophobicity curve and single-point values. Hydrophobicity was reduced at GRF applications ≥300 ton ha−1, particularly in the less clayey field. Normalizing the level of hydrophobicity to the level of water retention enabled comparisons between soils.

A technical evaluation on the mathematical attitudes and fitting accuracy of soil moisture retention curve models

Numerous mathematical equations have been formulated in the literature of different researchers for describing soil moisture retention curve (SMRC), which can be applied to simulate and solve soil hydraulic modeling problems. The primary concern lies in selecting an efficient model to simulate accurately the S-shaped curve or sigmoid-type of the SMRC for soils with different textures. Therefore, the objective of this study was a comprehensive and technical evaluation of 50 developed models of the SMRC based on the influence of parameters on the behavior of the curve and the ability of their fitting accuracy on 728 soil samples of the UNSODA dataset, that has not been investigated so far. Statistical criteria including corrected Akaike’s information criterion (AICc), root mean square error (RMSE) and coefficient of determination (R2) together with Duncan’s multiple range test and cluster analysis were employed to assess the fitting accuracy of SMRC models to measured data. Results from fitting accuracy on the UNSODA dataset indicated that Brutsaert model provided the best fit to the measured data compared to other models in 14.6% of the soil samples with RMSE = 0.0125 and AICc = -94.18. This model was classified in the same cluster with Groenevelt and Grant (GG1, GG2 and GG3), Dexter, Mualem, and Fredlund and Xing models and did not have a significant difference in terms of RMSE. Also, Brutsaert model had the highest fitting accuracy in 67% of different soil textural classes compared to other models. Finally, the technical evaluation in terms of accuracy, flexibility and simplicity of the fitting process showed that the Brutsaert, Mualem, Dexter and GG3 models can be selected for better simulation of the SMRC in water and soil research.

Temporal Gap-Filling of 12-Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Twelve-hourly satellite soil moisture (SM) data were gap-filled using a water balance based on SM and precipitation observations

Gap-filled data had good accuracy and temporal consistency with in situ data and captured SM peaks to heavy rainfall

Exclusive fill-in SM values exhibited comparable performance to the Soil Moisture Active Passive observations

Carbon sequestration in soils and climate change mitigation—Definitions and pitfalls

The term carbon (C) sequestration has not just become a buzzword but is something of a siren’s call to scientific communicators and media outlets. Carbon sequestration is the removal of C from the atmosphere and the storage, for example, in soil. It has the potential to partially compensate for anthropogenic greenhouse gas emissions and is, therefore, an important piece in the global climate change mitigation puzzle. However, the term C sequestration is often used misleadingly and, while likely unintentional, can lead to the perpetuation of biased conclusions and exaggerated expectations about its contribution to climate change mitigation efforts. Soils have considerable potential to take up C but many are also in a state of continuous loss. In such soils, measures to build up soil C may only lead to a reduction in C losses (C loss mitigation) rather than result in real C sequestration and negative emissions. In an examination of 100 recent peer-reviewed papers on topics surrounding soil C, only 4% were found to have used the term C sequestration correctly. Furthermore, 13% of the papers equated C sequestration with C stocks. The review, further, revealed that measures leading to C sequestration will not always result in climate change mitigation when non-CO2 greenhouse gases and leakage are taken into consideration. This paper highlights potential pitfalls when using the term C sequestration incorrectly and calls for accurate usage of this term going forward. Revised and new terms are suggested to distinguish clearly between C sequestration in soils, SOC loss mitigation, negative emissions, climate change mitigation, SOC storage, and SOC accrual to avoid miscommunication among scientists and stakeholder groups in future.

Pyrogeography in flux: Reorganization of Australian fire regimes in a hotter world

Changes to the spatiotemporal patterns of wildfire are having profound implications for ecosystems and society globally, but we have limited understanding of the extent to which fire regimes will reorganize in a warming world. While predicting regime shifts remains challenging because of complex climate–vegetation–fire feedbacks, understanding the climate niches of fire regimes provides a simple way to identify locations most at risk of regime change. Using globally available satellite datasets, we constructed 14 metrics describing the spatiotemporal dimensions of fire and then delineated Australia’s pyroregions—the geographic area encapsulating a broad fire regime. Cluster analysis revealed 18 pyroregions, notably including the (1) high-intensity, infrequent fires of the temperate forests, (2) high-frequency, smaller fires of the tropical savanna, and (3) low-intensity, diurnal, human-engineered fires of the agricultural zones. To inform the risk of regime shifts, we identified locations where the climate under three CMIP6 scenarios is projected to shift (i) beyond each pyroregion’s historical climate niche, and (ii) into climate space that is novel to the Australian continent. Under middle-of-the-road climate projections (SSP2-4.5), an average of 65% of the extent of the pyroregions occurred beyond their historical climate niches by 2081–2100. Further, 52% of pyroregion extents, on average, were projected to occur in climate space without present-day analogues on the Australian continent, implying high risk of shifting to states that also lack present-day counterparts. Pyroregions in tropical and hot-arid climates were most at risk of shifting into both locally and continentally novel climate space because (i) their niches are narrower than southern temperate pyroregions, and (ii) their already-hot climates lead to earlier departure from present-day climate space. Such a shift implies widespread risk of regime shifts and the emergence of no-analogue fire regimes. Our approach can be applied to other regions to assess vulnerability to rapid fire regime change.

Stabilisation of soil organic matter with rock dust partially counteracted by plants

Soil application of Ca- and Mg-rich silicates can capture and store atmospheric carbon dioxide as inorganic carbon but could also have the potential to stabilise soil organic matter (SOM). Synergies between these two processes have not been investigated. Here, we apply finely ground silicate rock mining residues (basalt and granite blend) to a loamy sand in a pot trial at a rate of 4% (equivalent to 50 t ha−1) and investigate the effects of a wheat plant and two watering regimes on soil carbon sequestration over the course of 6 months. Rock dust addition increased soil pH, electric conductivity, inorganic carbon content and soil-exchangeable Ca and Mg contents, as expected for weathering. However, it decreased exchangeable levels of micronutrients Mn and Zn, likely related to the elevated soil pH. Importantly, it increased mineral-associated organic matter by 22% due to the supply of secondary minerals and associated sites for SOM sorption. Additionally, in the nonplanted treatments, rock supply of Ca and Mg increased soil microaggregation that subsequently stabilised labile particulate organic matter as organic matter occluded in aggregates by 46%. Plants, however, reduced soil-exchangeable Mg and Ca contents and hence counteracted the silicate rock effect on microaggregates and carbon within. We suggest this cation loss might be attributed to plant exudates released to solubilise micronutrients and hence neutralise plant deficiencies. The effect of enhanced silicate rock weathering on SOM stabilisation could substantially boost its carbon sequestration potential.

Distinct, direct and climate-mediated environmental controls on global particulate and mineral-associated organic carbon storage

Identifying controls on soil organic carbon (SOC) storage, and where SOC is most vulnerable to loss, are essential to managing soils for both climate change mitigation and global food security. However, we currently lack a comprehensive understanding of the global drivers of SOC storage, especially with regards to particulate (POC) and mineral-associated organic carbon (MAOC). To better understand hierarchical controls on POC and MAOC, we applied path analyses to SOC fractions, climate (i.e., mean annual temperature [MAT] and mean annual precipitation minus potential evapotranspiration [MAP-PET]), carbon (C) input (i.e., net primary production [NPP]), and soil property data synthesized from 72 published studies, along with data we generated from the National Ecological Observatory Network soil pits (n = 901 total observations). To assess the utility of investigating POC and MAOC separately in understanding SOC storage controls, we then compared these results with another path analysis predicting bulk SOC storage. We found that POC storage is negatively related to MAT and soil pH, while MAOC storage is positively related to NPP and MAP-PET, but negatively related to soil % sand. Our path analysis predicting bulk SOC revealed similar trends but explained less variation in C storage than our POC and MAOC analyses. Given that temperature and pH impose constraints on microbial decomposition, this indicates that POC is primarily controlled by SOC loss processes. In contrast, strong relationships with variables related to plant productivity constraints, moisture, and mineral surface availability for sorption indicate that MAOC is primarily controlled by climate-driven variations in C inputs to the soil, as well as C stabilization mechanisms. Altogether, these results demonstrate that global POC and MAOC storage are controlled by separate environmental variables, further justifying the need to quantify and model these C fractions separately to assess and forecast the responses of SOC storage to global change.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on January 12, 2024.

Journal Paper Digests

2023-12-18T00:00:00-00:00

Journal Paper Digests 2023 #24

Physics-Informed Neural Networks for solving transient unconfined groundwater flow
A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia
Visible and near infrared spectroscopy for predicting soil nitrogen mineralization rate: Effect of incubation period and ancillary soil properties
Real-Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human-Machine Interactions
A Complete Water Balance of a Rain Garden
Agricultural value chains and food security in the Pacific: Evidence from Papua New Guinea and Solomon Islands

Agricultural value chains and food security in the Pacific: Evidence from Papua New Guinea and Solomon Islands

Small island developing states in the Pacific face multiple development challenges driven by rapid population growth and high transportation costs due to remoteness and isolation. Combined with the adverse consequences of extreme weather events and climate change, these challenges exacerbate poverty and food insecurity. Agricultural value chain development presents a pathway to poverty reduction and food security. In this paper, we assess the impacts of two value chain development projects in Papua New Guinea and Solomon Islands on dietary diversity and food security of small-scale producers. Project impacts on dietary diversity are positive and significant in both countries, but improved food security is only observed in Solomon Islands. These impacts are mainly driven by crop yields, value of crop production and sales, crop diversification and share of crop sales. We find that treatment households are more likely to consume less nutritious foods such as sweets and oils. Our findings expand the literature in a data-scarce region and caution that value chain interventions without nutrition-focused components to induce behavioral change may have unintended impacts on healthy diets.

A Complete Water Balance of a Rain Garden

A bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a 5-min timeseries resolution and with instrumentation that would enable a water balance calculation. This 20-year data set allows for an in-depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real-time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed. Analysis shows the rain garden captures 73.5% of runoff, resulting in a post-implementation management of 86.2% of all rainfall in its watershed. In comparison to the hydrology of other land covers, implementing the rain garden resulted in the management of 37.6% more rainfall than pre-implementation, producing a hydrological signature similar to that of cultivated land or low development levels (e.g., 30% impervious). Additionally, with the long data record, several statistical techniques were applied to determine the amount of monitoring needed for a certain level of precision in system performance assessment. For 5% uncertainty, approximately 3 years of continuous data is needed to assess performance. This analysis not only facilitates understanding the function of rain garden systems, but also provides conclusions and methodology for understanding the uncertainty associated with the extent of monitoring performed on these green stormwater infrastructure systems. These findings provide practical knowledge as monitoring of stormwater management infrastructures is becoming a more standard part of their operation.

Real-Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human-Machine Interactions

Real-time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real-time irrigation scheduling requires reliable present soil-crop-atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer-based tools are rarely connected in a closed-loop and farmers’ feedback are usually not incorporated into a real-time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real-time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human-machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human-computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers’ own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers’ direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers’ feedbacks show interest in using the tool in real-world irrigation scheduling and providing meaningful suggestions to improve the tool for real-world application.

Visible and near infrared spectroscopy for predicting soil nitrogen mineralization rate: Effect of incubation period and ancillary soil properties

Soil nitrogen mineralization rate (SNMR) influences crop N uptake and nitrate leaching leading to environmental pollution. This study aims at (i) examining whether visible and near-infrared reflectance spectroscopy (vis-NIRS) can predict SNMR and (ii) investigating if incubation periods and ancillary soil attributes can improve the prediction accuracy. Total 133 soil samples collected from seven fields were incubated under aerobic conditions for 60 days with seven batches of sub-samples. Mineral N was measured at regular time intervals and soil samples were scanned using a vis-NIRS sensor (Tec5 Technology, Germany) parallelly. SNMR was determined by fitting a zero-order kinetic to the net mineralized N as a function of the incubation time. Soil total nitrogen (TN), total carbon (TC) and electrical conductivity (EC) were determined once. Partial least squares regression (PLSR) models were calibrated individually for each field both for vis-NIR spectra and its combinations with TN, TC and EC. Six out of seven batches of sub-samples were used for calibrating PLSR when remaining one batch was used for model validation, and it rotated across all seven batches. Vis-NIRS alone predicted SNMR with moderate accuracy in five of seven fields (coefficient of determination, 0.53 ≤ R2 ≥ 0.66, ratio of prediction to deviation, 1.51 ≤ RPD ≥ 1.76), while models were poor in two fields (R2 = 0.23–0.26, RPD = 1.18 – 1.20). Inclusion of soil TC, TN and/or EC was expected to improve accuracy, but improvements varied across fields (R2 = 0.23–0.79, RPD = 1.18 – 2.26). Similarly, the incubation period increased vis-NIRS prediction accuracy, but frequently occurred among 2nd to 6th batches (R2 = 0.35–0.82, RPD = 1.28 – 2.44). Even incorporating secondary properties and increasing incubation duration hardly improved predictions, improvement can be compromised since it is not significant mostly and often underperformed or remained unchanged. Considering the time and effort required to incubate and analyze soil properties, this study suggests using a vis-NIRS sensor to estimate SNMR in fresh soil conditions i.e., without incubation and incorporation of secondary properties.

A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia

Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA.

Physics-Informed Neural Networks for solving transient unconfined groundwater flow

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on December 18, 2023.

Journal Paper Digests

2023-12-08T00:00:00-00:00

Journal Paper Digests 2023 #23

Equalizing urban agriculture access in Glasgow: A spatial optimization approach
Reproducing computational processes in service-based geo-simulation experiments
Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model
Cropping intensity map of China with 10 m spatial resolution from analyses of time-series Landsat-7/8 and Sentinel-2 images
Diffuse reflectance mid-infrared spectroscopy is viable without fine milling
Optimising POXC effective sensitivity as a soil indicator in Australian soils
A Blue Water Scarcity-Based Method for Hydrologically Sustainable Agricultural Expansion Design
A global indicator of soil macroinvertebrate community composition, abundance and diversity
Fifty years after deep-ploughing: Effects on yield, roots, nutrient stocks and soil structure

Fifty years after deep-ploughing: Effects on yield, roots, nutrient stocks and soil structure

Deep-ploughing far beyond the common depth of 30 cm was used more than 50 years ago in Northern Germany with the aim to break root-restricting layers and thereby improve access to subsoil water and nutrient resources. We hypothesized that effects of this earlier intervention on soil properties and yields prevailed after 50 years. Hence, we sampled two sandy soils and one silty soil (Cambisols and a Luvisol) of which half of the field had been deep-ploughed 50 years ago (soils then re-classified as Treposols). The adjacent other half was not deep-ploughed and thus served as the control. At all the three sites, both deep-ploughed and control parts were then conventionally managed over the last 50 years. We assessed yields during the dry year 2019 and additionally in 2020, and rooting intensity at the year of sampling (2019), as well as changes in soil structure, carbon and nutrient stocks in that year. We found that deep-ploughing improved yields in the dry spell of 2019 at the sandy sites, which was supported by a more general pattern of higher NDVI indices in deep-ploughed parts for the period from 2016 to 2021 across varying weather conditions. Subsoil stocks of soil organic carbon and total plant-available phosphorus were enhanced by 21%–199% in the different sites. Root biomass in the subsoil was reduced due to deep-ploughing at the silty site and was increased or unaffected at the sandy sites. Overall, the effects of deep-ploughing were site-specific, with reduced bulk density in the buried topsoil stripes in the subsoil of the sandy sites, but with elevated subsoil density in the silty site. Hence, even 50 years after deep-ploughing, changes in soil properties are still detectable, although effect size differed among sites.

A global indicator of soil macroinvertebrate community composition, abundance and diversity

Macroinvertebrate communities are highly sensitive indicators of physical and chemical soil qualities. Their evaluation in field conditions is rather simple and could serve as proxy for soil-based ecosystem services that farmers and field technicians could use. We tested the hypothesis that an indicator of soil macroinvertebrate communities, with 14 taxonomic groups characterized at the order level, could be used in any region of the world to assess these communities. A synthetic indicator was calculated using data from 9 reference sites from tropical, subtropical and temperate regions, a set of 3694 data of the open access Macrofauna database and a new site for validation. Invertebrates were extracted with the standard ISO/TSBF methodology and characterized with a set of 14 large taxonomic units, plus an index of taxonomic richness and total density. At each of the 9 reference sites, 27 to 252 sample points, representing different types of plant covers and/or soil management options, were considered and compared with their respective local synthetic GISQ indicator values. These indicators, elaborated from Principal Component Analysis of the sample points data, are set to vary from 0.1 to 1.0 according to the composition, diversity and abundance of the community. Analyses showed great similarities among sites, with Factors 1 (from 21.9 to 36.9 % variance explained) expressing the overall abundance and diversity of the communities and Factors 2 (8.9 to 15.83 %) opposing sites with dominant soil ecosystem engineer populations (Earthworms, Ants, Termites and some Coleoptera) to sites dominated by litter transformer populations (Diplopoda, Isopoda and others). Indicator formulae were designed based on PCA analyses of each data set and a global formula was established with the Macrofauna database. Very high correlations (>0.95) were obtained among values calculated with local data set and the general formula calculated with data of the Macrofauna database, in 8 of 9 sites and in the validation site. We discuss the importance of having a single formula to transform data obtained with a simple standard field method in the building of public policies for soil-based ecosystem services payment.

A Blue Water Scarcity-Based Method for Hydrologically Sustainable Agricultural Expansion Design

A new methodology for designing sustainable agricultural expansion while preventing water scarcity is developed

The methodology selects areas with high water availability while ensuring that neither local nor downstream water scarcity is triggered

An application on coffee expansion in Kenya finds more areas than foreseen by policy, leaving action space for further selection criteria

Optimising POXC effective sensitivity as a soil indicator in Australian soils

The continuum of soil organic carbon is currently not well represented by any single metric. Permanganate oxidizable carbon (POXC) has been widely utilized as a soil condition indicator due to its correlation with biological indicators and sensitivity to management effects over relatively short time periods. However, the ability of POXC to represent the continuum of soil organic carbon, and how this could improve the characterization of management effects, has not been sufficiently explored. This study investigated the relationship between permanganate concentration and POXC across nine permanganate concentrations ranging from 3 to 300 mM. An initial investigation was performed on ten cropped and uncropped topsoil pairs representing a diverse range of soil types across New South Wales, Australia, and an additional 52 sites were investigated in a farm-scale study. POXC was observed to increase monotonically and non-linearly with increasing permanganate concentration. POXC characteristics were developed by fitting a logistic function to the observed data, which facilitated calculation of the area under the curve (POXCAUC) and the theoretical maximum POXC (POXCmax). The utility of the POXC characteristic was demonstrated with highly significant differences (p ≤ 0.002) observed in POXCAUC between cropped and uncropped sites. POXCAUC also displayed larger probability test statistics compared to any single permanganate concentration. The investigation also revealed that if a single concentration were to be utilized for australian soils, the 50 mM concentration was more effective at discerning land use effects in clayey (p = 0.000) and sandy (p = 0.049) sites compared to the widely adopted 20 mM concentration (p = 0.001; p = 0.312).

Diffuse reflectance mid-infrared spectroscopy is viable without fine milling

While diffuse reflectance Fourier transform mid-infrared spectroscopy (mid-DRIFTS) has been established as a viable low-cost surrogate for traditional soil analyses, the assumed need for fine milling of soil samples prior to analysis is constraining the commercial appeal of this technology. Here, we reevaluate this assumption using a set of 2380 soil samples collected across North American agricultural soils. Cross-validation indicated that the best preprocessing (standard normal variate) and model form (memory-based learning) resulted in very good and nearly identical predictions for the <2 mm preparation and fine-milled preparation of these soils for total organic carbon (TOC), clay, sand, pH and bulk density (BD). Application of larger models built from the USDA NRCS mid-DRIFTS library also resulted in minimal performance differences between the two sample preps. Lower predictive performance of the existing library was attributed to less-than-perfect spectral representativeness of the library. Regardless of model form, there was very little variability between replicates of the <2 mm prep, suggesting that the lack of fine milling did not lead to more heterogeneous subsamples. Additionally, there was no relationship between residual error and soil texture, implying these results should be robust across most soil types. Overall, in agreement with other recent findings, these results suggest that routine scanning of standard <2 mm preparation does not degrade predictive performance of mid-DRIFTS-based inference systems. With good standard operating procedures including quality control and traditional analysis on a small percent of samples, mid-DRIFTS can become a routine tool in commercial soil laboratories.

Cropping intensity map of China with 10 m spatial resolution from analyses of time-series Landsat-7/8 and Sentinel-2 images

Cropping intensity maps at high spatial resolution play a crucial role in guiding agricultural policies and ensuring food security. So far, most of nationwide cropping intensity maps have been developed from satellite images at moderate or coarse resolutions. In this study, we first assembled and integrated time-series dataset with high spatial resolution, specifically Landsat-7, Landsat-8 and Sentinel-2 imagery in 2017. We then used an object- and phenology-based algorithm and integrated images to create a 10-m resolution cropping intensity map over China. The map evaluation results revealed an overall accuracy of 96.68 ± 0.01 % and a Kappa coefficient of 0.90. In 2017, single cropping dominated the agricultural practices in China, with an approximate area of 1.189 × 106 km2 ± 7.90 × 103 km2, constituted 79.26 % of the entire cropland area. Simultaneously, double and triple cropping covered approximately 0.306 × 106 km2 ± 8.03 × 103 km2 and 5.00 × 103 ± 1.75 × 103 km2, corresponding to 20.41 % and 0.33 % of the entire cropland area, respectively. On average, the national multiple cropping index (MCI) was 1.21. The results in the study prove the reliability of the generated mapping products and high potential of the developed mapping framework (the algorithm and integrated datasets), which can be readily applied to quantify the interannual changes of cropping pattern on a nationwide level with a high spatial resolution.

Prediction of soil organic matter by Kubelka-Munk based airborne hyperspectral moisture removal model

Obtaining high-precision soil organic matter (SOM) spatial distribution information is of great significance for applications such as precision agriculture. But in the current hyperspectral SOM inversion work, soil moisture greatly influences the representation of the sensitive information of SOM on the spectrum. Therefore, a Kubelka-Munk theory based spectral correction model for soil moisture removal is proposed to improve the spectral sensitivity of SOM. Firstly, the soil moisture content was obtained by the use of a Kubelka-Munk based physical soil moisture model and an unmixing method. Then, the spectral correction model for soil moisture removal was implemented based on the quantitative description of the Beer-Lambert law. The results show that the proposed spectral correction model for soil moisture removal can significantly enhance the expression of the sensitive spectral features of SOM, especially for the short-wave infrared range. After moisture removal, the imaging spectral data were used for inversion, using the sensitive band at 0.69 μm and a support vector machine regression (SVR) modeling method. The Kubelka-Munk moisture removal model for soil moisture removal can improve the accuracy of SOM inversion by at least 22% comparing with the 0.69 μm original spectral inversion model, with R2 of 0.42. Moreover, the proposed model can also improve the accuracy of SOM inversion by 19% for the SVR statistical regression method, with R2 of 0.69. Finally, the SOM distribution maps based on sensitive band model and SVR regression method were analyzed. Findings show that the two methods have high consistency, but the statistical method obtains better details of the SOM spatial distribution, due to its higher accuracy.

Reproducing computational processes in service-based geo-simulation experiments

Geo-simulation experiments (GSEs) are experiments allowing the simulation and exploration of Earth’s surface (such as hydrological, geomorphological, atmospheric, biological, and social processes and their interactions) with the usage of geo-analysis models (hereafter called ‘models’). Computational processes represent the steps in GSEs where researchers employ these models to analyze data by computer, encompassing a suite of actions carried out by researchers. These processes form the crux of GSEs, as GSEs are ultimately implemented through the execution of computational processes. Recent advancements in computer technology have facilitated sharing models online to promote resource accessibility and environmental dependency rebuilding, the lack of which are two fundamental barriers to reproduction. In particular, the trend of encapsulating models as web services online is gaining traction. While such service-oriented strategies aid in the reproduction of computational processes, they often ignore the association and interaction among researchers’ actions regarding the usage of sequential resources (model-service resources and data resources); documenting these actions can help clarify the exact order and details of resource usage. Inspired by these strategies, this study explores the organization of computational processes, which can be extracted with a collection of action nodes and related logical links (node-link ensembles). The action nodes are the abstraction of the interactions between participant entities and resource elements (i.e., model-service resource elements and data resource elements), while logical links represent the logical relationships between action nodes. In addition, the representation of actions, the formation of documentation, and the reimplementation of documentation are interconnected stages in this approach. Specifically, the accurate representation of actions facilitates the correct performance of these actions; therefore, the operation of actions can be documented in a standard way, which is crucial for the successful reproduction of computational processes based on standardized documentation. A prototype system is designed to demonstrate the feasibility and practicality of the proposed approach. By employing this pragmatic approach, researchers can share their computational processes in a structured and open format, allowing peer scientists to re-execute operations with initial resources and reimplement the initial computational processes of GSEs via the open web.

Equalizing urban agriculture access in Glasgow: A spatial optimization approach

Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agriculture in Glasgow and found that the current configuration of urban agriculture projects is mostly located centrally in the city, covering 36 % of the total population (approximately 635,000) within 10-minute walking distance. We also found a positive correlation (r = 0.13, p = 0.0003) between the walking travel time to the nearest urban agriculture project and the food desert status. To increase urban agriculture access across the city, we used the Maximal Covering Location Problem (MCLP) model to optimally situate new urban agriculture projects on vacant and derelict land to maximize the covered population. We identified that a minimum of 15 new urban agriculture projects could increase the population coverage to 49 % and equalize the access disparity to a statistically non-significant level. This research shows that converting vacant and derelict land in Glasgow into urban agriculture projects could both help with the city’s problem of vacant and derelict land and bring many potential benefits to local communities.

Journal Paper Digests was originally published by Smart Digital Agriculture at Smart Digital Agriculture on December 08, 2023.