Soil Inventory Research Articles

Allometric Models of Aboveground Biomass in Mangroves Compared with Those of the Climate Action Reserve Standard Applied in the Carbon Market

The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass.

The centennial legacy of land-use change on organic carbon stocks of German agricultural soils.

Converting natural vegetation for agriculture has resulted in the loss of approximately 5% of the current global terrestrial soil organic carbon (SOC) stock to the atmosphere. Increasing the agricultural area under grassland may reverse some of these losses, but the effectiveness of such a strategy is limited by how quickly SOC recovers after conversion from cropland. Using soil data and extensive land-use histories gathered during the national German agricultural soil inventory, this study aims to answer three questions regarding agricultural land-use change (LUC): (i) how do SOC stocks change with depth following LUC; (ii) how long does it take to reach SOC equilibrium after LUC; and (iii) what is the legacy effect of historic LUC on present day SOC dynamics? By using a novel approach that substitutes space for time and accounts for differences in site properties using propensity score balancing, we determined that sites that were converted from cropland to grassland reached a SOC equilibrium level 47.3% (95% confidence interval (CI): 43.4% to 49.5%) above permanent cropland levels 83 years (95% CI: 79 to 90 years) after conversion. Meanwhile, sites converted from grassland to cropland reached a SOC equilibrium level -33.6% (95% CI: -34.1% to -33.5%) below permanent grassland levels after 180 years (95% CI: 151 to 223 years). We estimate that, over the past century, today's German agricultural soils (16.6 million ha) have gained about 40 million Mg C. Furthermore, croplands with historic LUC from grassland are losing SOC by -0.26 Mg ha-1 year-1 (10% of agricultural land) while grasslands historically converted from cropland are gaining SOC by 0.27 Mg ha-1 year-1 (18% of agricultural land). This study shows that even long-standing temperate agricultural sites likely have ongoing SOC change as a result of historical LUC.

Downward migration of 137Cs promotes self-cleaning of forest ecosystem by reducing root uptake of Japanese cedar in Fukushima

Approximately 70 % of the area highly 137Cs-contaminated by the Fukushima Daiichi Nuclear Power Plant accident is forested. Decontamination works in most of these forests have not progressed, and the forestry industry remains stagnant. Although the long-term dynamics of 137Cs in the forest ecosystem will be controlled by the amount of 137Cs absorbed by roots in the future, temporal changes in 137Cs of tree roots have rarely been reported. In the present study, we monitored the depth distribution of 137Cs in the soil and absorptive very fine (VF) roots of 0.5 mm or less in a Japanese cedar forest from 2011 to 2023. As a result, the 137Cs inventory in the mineral soil increased over time due to the migration from the forest canopy and litter layers, whereas that in the VF roots tended to decrease since 2020, although there was a large variation. Temporal decrease in the exchangeable 137Cs fraction with fixation and temporal increase in VF root biomass with their growth were not clearly observed, the 137Cs concentration in the VF roots at 0–2 cm decreased with the decrease in 137Cs concentration in the litter layers. Although the 137Cs concentration in the VF roots below 2 cm tended to increase with increasing 137Cs concentration in the soil at the same depth, the downward migration of 137Cs within the soil can reduce the amount of 137Cs absorbed by roots because the VF root biomass decreases exponentially with depth. In other words, 137Cs can be removed from the long-term active cycles of forest ecosystems as they migrate deeper into the soil. This natural migration process can be regarded as a “self-cleaning” of the forest ecosystem, the green and sustainable remediation using such self-cleaning should be actively adopted for the future forest management.

Integrative assessment of in situ combined bioremediation strategies applied to remediate soils spilled with sewage sludges

Landfills and waste disposal sites in the Basque Country are summarized in the inventory of soils that either currently support or have supported potentially polluting activities or facilities (Law 4/2015). Notably, “Landfill 17,” located in Gernika-Lumo, has been receiving, for decades, sewage sludges from the local wastewater treatment plant (WWTP) as agricultural amendment. In order to decontaminate and recover soil functionality, a combination of bioremediation (which involved bioagumentation and phyto- and vermitechnologies) and complementary bioremediation strategy (i.e., promotion and maintenance of the native vegetation) was implemented in situ. Physicochemical and ecotoxicological characterization were achieved. Furthermore, an ecotoxicological assessment of the soils upon flora and fauna was carried out through the application of different bioassays and biomarkers. Additionally, an integrative biomarker response (IBR/n) index was calculated to provide a holistic view of the soil general status. Critical pollutants [Cd, Cr, Ni, Pb, benzo(a)pyrene, and dieldrin] were observed in most of the treated sites. Microbial parameters did not present remarkable differences among sites. However, plant indicators pointed the non-treated site (MN8) as the unhealthiest. This was also observed in earthworms’ immune system, where cytotoxicity appears when exposed to non-treated soils. In conclusion, this field study showed that the combination of bioaugmentation, phytoremediation with native species, and vermiremediation is highly useful in eliminating mixed contamination, improving soil health, and ultimately restoring ecosystem functionality and biodiversity.

Dasometric variables that determine fuel load in oak forests in Guerrero, Mexico

Climate change and the lack of fuel management in oak forests promote favorable conditions for the occurrence of forest fires. The objective of the present research was to know the relationship of dasometric variables, terrain slope (Pe), depth of leaf litter (Ph) and depth of mulch (Pm) on the fuel load in oak forests in the Mountain region of Guerrero. The dasometric variables mean diameter (Dm), mean height (Am), number of trees ha-1 (Na) and basal área ha-1 (Ab) were evaluated according to the sampling design of the 2015 National Forest and Soil Inventory; At each sampling site, three planar intersection lines were established for sampling woody material (Ml), leaf litter (Ho) and mulch (Ma) were collected in 0.09 m2. The total fuel load ranged from 23.2 to 46.5 t ha-1 (p < 0.05), the Ma presented the highest load with 43%, followed by woody material (Ml) and leaf litter (Ho) with 31 and 26%. The total charge (Ct) showed a relationship with Ma, Ho and Ml (r = 0.81 to 0.41), Ho with Ph and Pe (r = 0.71 and -0.72). The c1h showed a relationship with Na, Ma, Pe and Dm (r = 0.72 to -0.41), the c10h showed a correlation with Ab, Pe and Na (r = 0.51 to 0.42). The dasometric variables only explain the fuel load with a diameter less than or equal to 2.5 cm.

Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

137Cs inventories in soil in the Qaidam Basin, Tibetan Plateau

This paper presents measurements of cesium-137 (137Cs) in the Qaidam Basin during 2019 with 39 soil samples across the landscape. The aim here is to use the results of 137Cs inventories for the Qaidam Basin to subsequently estimate soil wind erosion. The 137Cs inventories in the surface soil vary from lower limit detection (LDD) to 1,072 Bq m−2, with a mean of 266 Bq m−2. Overall, the 137Cs inventories in the Qaidam Basin decreases from southeast to northwest. The highest 137Cs inventories was found in farmland, and the lowest was found in Gobi. Wetlands had higher 137Cs inventories than their neighboring sites.

No detectable upper limit of mineral-associated organic carbon in temperate agricultural soils.

Soil organic carbon (SOC) sequestration is a promising climate change mitigation option. In this context, the formation of the relatively long-lived mineral-associated organic carbon (MAOC) is key. To date, soils are considered to be limited in their ability to accumulate MAOC, mainly by the amount of clay and silt particles present. Using the comprehensive German Agricultural Soil Inventory, we selected 189 samples with a wide range of SOC (5-118 g kg-1 ) and clay contents (30-770 g kg-1 ) to test whether there is a detectable upper limit of MAOC content. We found that the proportion of MAOC was surprisingly stable for soils under cropland and grassland use across the whole range of bulk SOC contents. Soil texture influenced the slope of the relationship between bulk SOC and MAOC, but no upper limit was observed in any texture class. Also, C content in the fine fraction (g C kg-1 fraction) was negatively correlated to fine fraction content (g kg-1 bulk soil). Both findings challenge the notion that MAOC accumulation is limited by soil fine fraction content per se.

A simple soil organic carbon level metric beyond the organic carbon‐to‐clay ratio

AbstractSoil is a precious and non‐renewable resource that is under increasing pressure and the development of indicators to monitor its state is pivotal. Soil organic carbon (SOC) is important for key physical, chemical and biological soil properties and thus a central indicator of soil quality and soil health. The content of SOC is driven by many abiotic factors, such as texture and climate, and is therefore strongly site‐specific, which complicates, for example, the search for appropriate threshold values to differentiate healthy from less healthy soils. The SOC:clay ratio has been introduced as a normalized SOC level metric to indicate soils' structural condition, with classes ranging from degraded (<1:13) to very good (>1:8). This study applied the ratio to 2958 topsoils (0–30 cm) in the German Agricultural Soil Inventory and showed that it is not a suitable SOC level metric since strongly biased, misleading and partly insensitive to SOC changes. The proportion of soils with SOC levels classified as degraded increased exponentially with clay content, indicating the indicator's overly strong clay dependence. Thus, 94% of all Chernozems, which are known to have elevated SOC contents and a favourable soil structure, were found to have either degraded (61%) or moderate (33%) normalized SOC levels. The ratio between actual and expected SOC (SOC:SOCexp) is proposed as an easy‐to‐use alternative where expected SOC is derived from a regression between SOC and clay content. This ratio allows a simple but unbiased estimate of the clay‐normalized SOC level. The quartiles of this ratio were used to derive threshold values to divide the dataset into the classes degraded, moderate, good and very good. These classes were clearly linked to bulk volume (inverse of bulk density) as an important structural parameter, which was not the case for classes based on the SOC:clay ratio. Therefore, SOC:SOCexp and its temporal dynamic are proposed for limited areas such as regions, states or pedoclimatic zones, for example, in a soil health monitoring context; further testing is, however, recommended.

Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data.

The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

Soil and vegetation factors affecting carbon storage in a coastal forest in NE Germany

A large part of carbon in forest ecosystems is stored in the soil. Large-scale inventories of soil C stocks in European forests have been published. Nevertheless, detailed site-specific studies are still necessary, because C storage is spatially highly variable and depends on site specific soil, ecological and management factors, which cannot be captured by large scale studies.Here, we assess C stocks in a coastal forest at the Baltic Sea with different tree species, stand ages, management, and soils, to elucidate possible causes for differences in carbon storage. Soil samples were taken down to 1 m depth both from soil profile pits with 250 cm3 cores and by auger cores in the years 2017–2019 in forest treatment plots which varied with respect to forest management, tree species (beech, birch, oak), and tree age. Although the abiotic parameters soil type (mainly gleysol and podsol), soil texture (mainly sandy), pH value, groundwater depth and distance to the coast were not part of the treatment design, these factors were – along with the biotic factors – evaluated to foster our understanding of the importance of possible drivers of carbon storage in forests. Soil carbon stocks were evaluated for 0–1 m soil depth using measured C concentrations in the soil samples and bulk densities. Carbon stocks in the vegetation were determined by detailed stand inventories of the trees on the forest plots.Total ecosystem carbon stocks were on average 50 kg C m−2, i.e., about threefold higher than the reported average values for forest ecosystems worldwide in temperate climates. Soil C stocks were on average 32 kg C m−2 (0–1 m depth). This is distinctly higher than soil C stocks reported for most other forest sites in similar climatic conditions, and is likely caused by widespread occurrence of anaerobic conditions in the soil, but also by high bioproductivity due to good water availability and low drought stress. Variability of soil carbon stocks at this site is predominantly governed by soil factors (soil type, soil texture, soil pH), whereas tree carbon stocks are mainly determined by tree species and tree age.

Calibrating Nepal's scientific forest management practices in the measure of forest restoration

Various restoration programs have been implemented worldwide to recover degraded ecosystems. Nepal implemented a signature policy— Scientific Forest Management (SciFM)—with the aim of restoring and enhancing forest conditions, as well as increasing employment, timber production and economic growth in the last decade. While SciFM might achieve these objectives, it may conflict with Nepal’s own biodiversity and emission reduction policies. So far, various aspects of SciFM, such as stakeholders' opinion, policy issues, and financial analysis have been conducted. However, the effect of SciFM in achieving various restoration objectives remains unexplored. Taking a case of Sal (Shorea robusta) dominated forest which resembles productive forests in the lowlands of Nepal, we evaluated land productivity, carbon, and biodiversity outcomes of SciFM. Employing the methods of policy review (n = 6), literature review (n = 35), expert consultations (n = 15), and forest and soil inventory, we found that SciFM has been beneficial for establishing regeneration of Sal. However, in the short-run, SciFM was found to be counterproductive to achieve national objectives of REDD+ (reducing carbon emissions from forestry sector), biodiversity conservation, and land productivity enhancement. Based on our analysis, we have discussed some learning areas from SciFM practices to align with the restoration objectives at a national and global scale. Implications of the findings in the other parts of the world, who are also implementing intensive forest management programs, are highlighted.

Forest Structure and Fine Root Biomass Influence Soil CO2 Efflux in Temperate Forests under Drought

Soil respiration is rarely studied at the landscape scale where forest and soil properties can be important drivers. We performed forest and soil inventories in 150 temperate forest sites in three German landscapes and measured in situ soil CO2 efflux with the soda-lime method in early summer 2018 and 2019. Both years were affected by naturally occurring summer droughts. Our aim was to investigate the impact of forest structural and compositional properties, soil properties and climate on soil CO2 efflux at the landscape. Forest properties explained a large portion of soil CO2 efflux variance (i.e., 14% in 2018 and 20% in 2019), which was comparable or larger than the portion explained by soil properties (i.e., 15% in 2018 and 6% in 2019), and much larger than that of climate. Using Structural Equation Modeling, we found that forest structural properties, i.e., tree density and basal area, were negatively linked to soil CO2 efflux, while forest composition, i.e., conifer share and tree species richness, was not important. Forest structure effects on soil CO2 efflux were either direct or mediated by fine root biomass under dry summer conditions. Summer soil CO2 efflux was positively linked to fine root biomass but not related to total soil organic carbon stocks or climate. Forest structural properties influence soil CO2 efflux under drought events and should be considered when predicting soil respiration at the landscape scale.

Provenance of sediments derived from karst and non-karst areas and deposited behind sequential check dams in a small watershed

This study uses a composite fingerprinting technique, supported by 137Cs and 210Pbex chronologies, to determine the proportion of sediment derived from karst (limestone) and non-karst (purple sandstone) areas and deposited behind sequential check dams within a small watershed in the Three Gorges Reservoir Region and to calculate the sediment deposition rates (DRs) derived from the two lithological areas. Nine sediment cores were extracted from three terraces behind the sequential check dams, and a total of 115 samples were taken from the cores. In addition, 67 and 99 soil samples were collected from the karst and non-karst areas, respectively. Geochemical elements were used as the fingerprints to identify the sediment provenance. The results indicate that the sediment contribution from the karst area generally decreases in the upcore direction, whereas that from the non-karst area shows the opposite trend. The karst and non-karst areas occupy 60% and 37% of the watershed, respectively, while contributing 26% and 74% of the sediment in the nine cores overall, respectively. The DRs from both lithological areas first increase and then decrease through three stages (1920–1954, 1955–1963, and 1964–2020) over the last 100 years. However, the increased proportion of the DR from the karst area during the second stage was less than that from the non-karst area, while the decreased proportion of the DR from the karst area during the final stage was greater than that from the non-karst area. Overall, the sediment provenance, DRs, and grain-size distributions determined for the nine cores show little change in the downstream direction across the three check dams. The sediment provenance and DRs conjointly indicate the sediment contributions from the karst and non-karst areas, and deposition behind the sequential check dams within a small watershed influenced by human activity over the last 100+ years. They reflect the characteristic patterns of sediment yields from the two areas with contrasting lithology, which originate from the fact that the soil inventory and human activity are generally both limited in the karst area but not in the non-karst area. The grain-size sorting across the check dams, and its influence on the sediment provenance, are minimal, which is related to the small size of the watershed and the hydrological interception and retention of the check dams. This study provides new insights into sediment contributions from karst and non-karst areas, as well as sediment deposition behind sequential check dams, at the scale of a small watershed. It also improves our understanding of surface processes on karst hillslopes experiencing rocky desertification and on non-karst hillslopes subjected to human disturbance, as well as differences between the two.

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

AbstractA 3–4D soil model represents a logical step forward from one‐dimensional soil columns (1D), two‐dimensional soil maps (2D), and three‐dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3–4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3–4D soil model. A 3–4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3–4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing.This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3–4D soil model is a truly an ambitious, but promising task. The core of such a model is the bookkeeping of the solid mass together with soil structure, while accounting for biogeochemical and mechanical processes. The presented concepts are ambitious in context for research avenues toward the improvement of soil modeling by conjoining methods from a wide range of disciplines, including geological, geophysical, pedological, and remote sensing and visualization applications. The paper reviews and outlines research tools and needs for the 3‐D, spatially continuous representation of relevant soil properties and the modeling to represent the dynamics of soil properties and soil functions.

High manganese redox variability and manganate predominance in temperate soil profiles as determined by X-ray absorption spectroscopy

Manganese speciation is a key to understanding the fate of contaminants, nutrients, and organic matter in soils. To date, quantification of Mn species in bulk soils has been performed mainly by sequential extraction methods and rarely supported by spectroscopic analysis. In order to obtain quantitative information on the Mn species inventory of soils, we investigated 46 soil horizons (<2-mm fraction, 45.1–2,280 mg/kg Mn) of nine typical Central European soils (Cambisols, Chernozems, Luvisols, Podzol, Stagnosol) by chemical Mn analyses and Mn K-edge X-ray absorption spectroscopy, and related speciation results to major soil properties. Amounts of Mn2+, Mn3+, and Mn4+ and the average oxidation state of Mn were evaluated by linear combination fitting (LCF) of X-ray absorption near edge structure (XANES) spectra. Additionally, we used extended X-ray absorption fine structure (EXAFS) spectroscopy to identify and quantify major Mn species. For this, EXAFS spectra of 20 organic and mineral soil samples from five soils (Cambisols, Chernozem, Luvisol) were analyzed by LCF and shell fitting. XANES analyses revealed a high Mn redox variability in organic surface layers, with Mn2+ being most abundant (≤100%, x¯ = 54%), followed by Mn3+ (≤80%, x¯ = 32%) and Mn4+ (≤55%, x¯ = 14%). Mineral soil horizons contained significantly less Mn2+ (≤56%, x¯ = 23%), about equal quantities of Mn3+ (≤68%, x¯ = 31%), and were enriched in Mn4+ (≤89%, x¯ = 46%). EXAFS analyses implied the presence of six major Mn species groups: manganates, organically complexed Mn, Mn(III) oxyhydroxides, silicate-bound Mn, Mn oxides without tunnel- or layer structure, and physisorbed Mn. In litter horizons, Mn was mainly present in organic complexes (58–91%, x¯ = 78%) and as physisorbed Mn (≤15%), but individual horizons also comprised manganates, Mn(III) oxyhydroxides, and silicate-bound Mn. Manganates, likely mixtures of phyllomanganates with hexagonal layer symmetry and tectomanganates, dominated in all mineral soil horizons (37–94%, x¯ = 67%). Correlation analysis showed that manganates dissolve completely during dithionite-citrate and acid ammonium oxalate extractions, and suggested that Mn4+-rich manganates preferentially form under less acidic soil conditions, partly by oxidation of organically complexed Mn(II), and that they are enriched in the soil clay fraction. Mineral soil horizons also contained minor quantities of organically complexed Mn (≤39%, x¯ = 11%), silicate-bound Mn (≤30%, x¯ = 8%), Mn(III) oxyhydroxides (≤37%, x¯ = 7%), Mn oxides without tunnel- or layer structure (≤18%, x¯ = 5%), and physisorbed Mn (≤14%, x¯ <1%). The detection of Mn(III) oxyhydroxides such as feitknechtite (β-MnOOH) or groutite (α-MnOOH) as well as the spinel hausmannite (Mn3O4) in acidic soils is remarkable, since their formation is normally linked to neutral or alkaline pH conditions. Minor contributions of silicate-bound Mn indicate the release of Mn from primary minerals already at early stages of soil formation, and low concentrations of physisorbed Mn suggest that exchangeable Mn is rapidly converted into manganates in oxic soils. The predominance of manganates in mineral soils has far-reaching implications for the functioning of soils and biogeochemical element cycles, as these minerals play an important role in metal binding, plant nutrition, and redox-related processes.

Flower strips as a carbon sequestration measure in temperate croplands

PurposeFlower strips have been shown to increase insect biodiversity and improve agricultural yields through increased pollination and pest predation. Less is known about their potential to increase soil organic carbon (SOC). We aimed to investigate the biomass production and SOC sequestration potential of flower strips as a sustainable management option of temperate agricultural soils.Methods23 flower strips across varying soil types and climatic regions in Germany were sampled for aboveground and belowground peak biomass in order to estimate the annual carbon input to the soil. Those were used as 23 scenarios to model the potential SOC sequestration of the flower strips compared to a business-as-usual scenario for 1533 sites of the German Agricultural Soil Inventory using the RothC model.ResultsOn average, flower strips sequestered 0.48 ± 0.36 Mg C ha−1 year−1 in the initial 20-year period after establishment. Converting 1 % of the total German cropland area into flower strips would thus lead to a mitigation of 0.24 Tg CO2 year−1, which equals 0.4 % of current agricultural greenhouse gas emissions in Germany.We found a negative correlation between C sequestration rate and the number of plant species in the flower strips, mainly related to grasses outcompeting herbaceous species.ConclusionFlower strips are one overlooked option for increasing SOC stocks of croplands that has multiple benefits for agro-ecosystems. However, within a flower strip it might not be possible to maximise both plant biodiversity and SOC sequestration.

Recognition of arable soils from photographs obtained as part of crowdsourcing technologies

The study focuses on the possibilities of using photographs obtained using crowdsourcing technologies for the operational inventory of arable soils. The object of the study is the spectral reflectance of the open surface of arable soils of the test plots, measured using a HandHeld-2 spectroradiometer operating in the range of 325–1 075 nm, and their image in photographs taken with conventional cameras. Test sites are located in the Tula, Moscow and Tver regions. The soils of the test plots are sod-podzolic, gray forest, and leached chernozems. Based on the analysis of photographs of the surface and information obtained using a spectroradiometer, a set of spectral parameters in the RGB, YMC and HSI color systems, as well as their ratios (45 parameters), was calculated. These parameters were used to separate the analyzed soil types using classification trees. The accuracy of classification based on the results of validation varies from 63–100%. At the same time, the parameters of the HSI and YMC color systems turned out to be more informative than the parameters of the RGB color system. The established classification rules can later be used to determine the classification position of soils from images collected using crowdsourcing technologies.

Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Abstract. As the largest terrestrial carbon pool, soil organic carbon (SOC) has the potential to influence and mitigate climate change; thus, SOC monitoring is of high importance in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new opportunities to develop these maps due to its ability to data mine large datasets. The aim of this study was to apply three algorithms commonly used in digital soil mapping – random forest (RF), boosted regression trees (BRT), and support vector machine for regression (SVR) – on the first German agricultural soil inventory to model the agricultural topsoil (0–30 cm) SOC content and develop a two-model approach to address the high variability in SOC in German agricultural soils. Model performance is often limited by the size and quality of the soil dataset available for calibration and validation. Therefore, the impact of enlarging the training dataset was tested by including data from the European Land Use/Cover Area frame Survey for agricultural sites in Germany. Nested cross-validation was implemented for model evaluation and parameter tuning. Grid search and the differential evolution algorithm were also applied to ensure that each algorithm was appropriately tuned . The SOC content of the German agricultural soil inventory was highly variable, ranging from 4 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The results showed that SVR produced the best performance, with a root-mean-square error (RMSE) of 32 g kg−1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modelled separately, with the best result from SVR presenting an RMSE of 21 g kg−1. The model performance was enhanced by up to 1 % for mineral soils and by up to 2 % for organic soils. Despite the ability of machine learning algorithms, in general, and SVR, in particular, to model SOC on a national scale, the study showed that the most important aspect for improving the model performance was to separate the modelling of mineral and organic soils.

Digital mapping of soil organic carbon hotspots in nature conservation areas in the region of Flanders, Belgium

Climate-smart land management offers an opportunity to reconcile biodiversity goals with climate change mitigation. An important first step towards this, is an accurate estimate of the soil organic carbon (SOC) stock and the identification of locations with a large share in storage. General-purpose, regional assessments are not always useful to identify such hotspots, since they tend to even out extremes of SOC stocks in the landscape. The objectives of this study were to (i) develop and validate a digital soil mapping procedure to assess the SOC stock of nature conservation areas in Flanders, N. Belgium, and (ii) identify soil carbon hotspots in these areas. To this end, different soil inventories, encompassing 864 soil profiles, were combined in a digital soil mapping approach.The resulting boosted regression trees model was able to explain 75% of the variability in the training dataset (R2 = 0.75, RMSE = 9.83 kg m−2) and 59% after 10-fold cross-validation (R2 = 0.59, RMSE = 12.38 kg m−2). Reference Soil Group according to World Reference Base (WRB), vegetation type, highest groundwater level and clay fraction were identified as key predictors of the SOC stock in the upper 100 cm. The total SOC stock stored under 86,828 ha of nature conservation area was estimated to be 21,565 kton OC, or 24.8 kg m−2 on average. According to the presented hotspot definition, taking the SOC stock of the surrounding landscape into account, 10.5–11.3% of the nature conservation areas was classified as a hotspot. However, validation of the results in five study sites showed the local variability in predictive performance, with relatively accurate predictions in the two data-rich sites in the eastern part of the territory and poor predictions in the three remaining sites. Local, site-specific variations, together with uncertainties in the input data and unmapped elements can hamper the accurate estimation of SOC stocks and hotspots.