Soil microbial diversity plays a crucial role in plant health, influencing pathogen suppression and biocontrol efficacy. This study investigated how soil microbial diversity modulates interactions between the pathogen and the biocontrol bacterium in the wheat rhizosphere. Using a dilution-to-extinction method, we established five soil microbial diversity levels: natural soil, dilutions at 10, 10, 10, and fully autoclaved soil. This gradient allowed us to evaluate disease severity, plant growth, and rhizosphere microbiome shifts. Inoculation with significantly reduced disease severity caused by , particularly in low-diversity soils, emphasizing the effectiveness of in these simplified environments where microbial competition is reduced. Despite higher pathogen abundance in low-diversity soils, effectively mitigated disease severity, likely through direct antagonistic activity. Alpha diversity indices confirmed a reduction in microbial diversity across the gradient, while beta diversity analyses revealed distinct shifts among treatments. Although , and were significantly enriched in natural soils with inoculation of the , statistically significant disease suppression was not observed under these higher-diversity conditions. On the other hand, in low-diverse soils (autoclaved soil), where disease is suppressed with inoculation, showed a significant enrichment when compared with the treatment inoculated only with the pathogen, suggesting that this bacterial taxon can play a role in disease suppression along with the inoculant. These findings underscore the critical role of the soil microbial diversity in shaping the success of biocontrol interventions.

Climate warming is expected to have contrasting impacts on soil respiration in dryland ecosystems, with responses ranging from positive to negative across short-, mid-, and long-term timescales. However, the long-term (>10 yr) effects of warming and their underlying mechanisms remain understudied in biocrust-dominated dryland ecosystems. In this study, we investigated the effects of 10-13 years of experimental warming on soil respiration and its underlying regulatory mechanisms at microsites with contrasting biocrust cover in a dryland ecosystem in southeastern Spain. We also examined how long-term warming and biocrust cover influenced the accumulation rate of soil organic carbon (SOC) in the surface layer (0-1 cm). Our results showed that initial and mid-term reductions in soil respiration induced by warming were transient at microsites with low biocrust cover, where respiration rates eventually returned to control levels. In contrast, the suppressive effect of warming on soil respiration persisted over the long term at microsites with high biocrust cover. At low biocrust cover microsites, soil respiration dynamics were primarily regulated by changes in SOC stocks and the activity of carbon-degrading enzymes such as β-glucosidase and β-D-cellobiosidase. Conversely, at high biocrust cover microsites, the long-term response of soil respiration appeared to be more closely associated with shifts in biocrust cover rather than enzymatic activity. Notably, SOC accumulation rates were not significantly affected by either long-term warming or biocrust cover. Overall, our findings underscore the value of long-term experimental studies for capturing delayed or persistent ecosystem responses and reducing uncertainties in projections of soil respiration and carbon-climate feedbacks under global warming.

Recently, the QuantiSlakeTest (QST) was developed which records mass loss or gain of intact soil samples during submergence, but it is yet unclear how slaking relates quantitatively to soil structure. This study was set up to determine if the QST can be a quick and cost-effective alternative to established structure tests and can be linked to soil porosity, determined by the soil water retention curve, or to the water-stable aggregate (WSA, >250 µm) fraction obtained by wet sieving. This was performed in a comparative study with 22 soils collected in arable and pasture land with contrasting properties. The mass loss due to aggregate breakdown in the QST was smaller as soil organic carbon (SOC) content increased (r = 0.59), while SOC correlated weaker with the WSA fraction (r = 0.45) or with relative meso + macroporosity (> 30 µm; r = 0.53). The WSA correlated strongly to oxalate extractable Fe (Fe) in the soil (r = 0.63), conforming to earlier studies, but no such trend was found for Quantislake parameters, indicating that the QST captures a different aspect of soil structure than the wet sieving. The mass loss in the Quantislake decreased with increasing relative meso + macroporosity (r = 0.68), suggesting that soil resistance to aggregate breakdown is higher in soils with a large fraction of large pores than in soils with smaller average pore sizes. In a subset of 10 soils, root mass within each Kopecky ring was recorded. It showed that the QST and relative microporosity strongly correlated with the root mass in the sample (r = 0.77 and 0.71, respectively), showing the importance of roots on soil structure. The Quantislake parameters have a much larger variability among sampling replicates (CV = 75---100 %) than the parameters of the two other methods. However, the Quantislake parameters differed much more among soils, leading to similar statistical power as wet sieving and the water retention curves. It is concluded that the QST is a useful additional index of soil structure in undisturbed soils. It even outperforms the wet sieving method to indicate mesoporosity and is thus better suited for studying, e.g., preferential flow.

Soils are a major player in the global carbon (C) cycle and climate change by functioning as a sink or a source of atmospheric carbon dioxide (CO). The largest terrestrial C reservoir in soils comprises two main pools: organic (SOC) and inorganic C (SIC), each having distinct fates and functions but with a large disparity in global research attention. This study quantified global soil C research trends and the proportional focus on SOC and SIC pools based on a bibliometric analysis and raise the importance of SIC pools fully underrepresented in research, applications, and modeling. Studies on soil C pools started in 1905 and has produced over 47,000 publications (>1.7 million citations). Although the global C stocks down to 2 m depth are nearly the same for SOC and SIC, the research has dominantly examined SOC (>96 % of publications and citations) with a minimal share on SIC (<4%). Approximately 40 % of the soil C research was related to climate change. Despite poor coverage and publications, the climate change-related research impact (citations per document) of SIC studies was higher than that of SOC. Mineral associated organic carbon, machine learning, soil health, and biochar were the recent top trend topics for SOC research (2020-2023), whereas digital soil mapping, soil properties, soil acidification, and calcite were recent top trend topics for SIC. SOC research was contributed by 151 countries compared to 88 for SIC. As assessed by publications, soil C research was mainly concentrated in a few countries, with only 9 countries accounting for 70 % of the research. China and the USA were the major producers (45 %), collaborators (37 %), and funders of soil C research. SIC is a long-lived soil C pool with a turnover rate (leaching and recrystallization) of more than 1000 years in natural ecosystems, but intensive agricultural practices have accelerated SIC losses, making SIC an important player in global C cycle and climate change. The lack of attention and investment towards SIC research could jeopardize the ongoing efforts to mitigate climate change impacts to meet the 1.5-2.0 °C targets under the Paris Climate Agreement of 2015. This bibliographic study calls to expand the research focus on SIC and including SIC fluxes in C budgets and models, without which the representation of the global C cycle is incomplete.

Weathering processes are recognized as drivers of soil and water resource sustainability, but how pedogenesis stage impacts contaminant reactivity and mobility in soils has been minimally investigated. The primary goal of this study was to quantify how soil development influences contaminant reactivity. To achieve this goal, soils from two depths (30 and 100 cm) across a chronosequence (ages 3.5, 20, 69, 140, 200, and 908 ky) in the Oregon Coast Range were subjected to arsenic (As) adsorption isotherms, with As removal from solution serving as a proxy for soil-contaminant reactivity. Langmuir models were applied to isotherm data to quantify relationships between contaminant retention capacity, soil age and soil physicochemical properties, and data revealed that 20 ky soils from a 30-cm-depth had the greatest affinity for As sorption (8,474.5 mg kg). Chemical extractions revealed that amorphous (oxy)hydroxides were the dominant mineral phases governing As sorption, even in the presence of abundant crystalline oxides. Micro-X-ray fluorescence spectroscopy revealed a strong spatial correlation between As and Fe in reacted soils. The abundance of amorphous minerals within soils is controlled by the balance between their production from weathering of primary minerals and their loss from ripening to crystalline minerals, and because the mode, extent and minerals governing contaminant sorption determine solid-aqueous phase partitioning, this knowledge will assist in improving models for predicting Critical Zone processes that govern the sustainability of soil and water quality.

Acid tropical soils may become more productive when treated with agricultural lime, but optimal lime rates have yet to be determined in many tropical regions. In these regions, lime rates can be estimated with lime requirement models based on widely available soil data. We reviewed seven of these models and introduced a new model (LiTAS). We evaluated the models' ability to predict the amount of lime needed to reach a target change in soil chemical properties with data from four soil incubation studies covering 31 soil types. Two foundational models, one targeting acidity saturation and the other targeting base saturation, were more accurate than the five models that were derived from them, while the LiTAS model was the most accurate. The models were used to estimate lime requirements for 303 African soil samples. We found large differences in the estimated lime rates depending on the target soil chemical property of the model. Therefore, an important first step in formulating liming recommendations is to clearly identify the soil property of interest and the target value that needs to be reached. While the LiTAS model can be useful for strategic research, more information on acidity-related problems other than aluminum toxicity is needed to comprehensively assess the benefits of liming.

Inadvertent oral ingestion is an important exposure pathway of arsenic (As) containing soil and dust. Previous researches evidenced health risk of bioaccessible As from soil and dust, but it is unclear about As mobilization mechanisms in health implications from As exposure. In this study, we investigated As release behaviors and the solid-liquid interface reactions toward As(V)-containing iron minerals in simulated gastrointestinal bio-fluids. The maximum As release amount was 0.57 mg/L from As-containing goethite and 0.82 mg/L from As-containing hematite at 9 h, and the As bioaccessibility was 10.8% and 21.6%, respectively. The higher exposure risk from hematite-sorbed As in gastrointestinal fluid was found even though goethite initially contained more arsenate than hematite. Mechanism analysis revealed that As release was mainly coupled with acid dissolution and reductive dissolution of iron minerals. Proteases enhanced As mobilization and thus increased As bioaccessibility. The As(V) released and simultaneously transformed to high toxic As(III) by gastric pepsin, while As(V) reduction in intestine was triggered by pancreatin and freshly formed Fe(II) in gastric digests. CaCl reduced As bioaccessibility, indicating that calcium-rich food or drugs may be effective dietary strategies to reduce As toxicity. The results deepened our understanding of the As release mechanisms associated with iron minerals in the simulated gastrointestinal tract and supplied a dietary strategy to alleviate the health risk of incidental As intake.

Methane (CH) is an important greenhouse gas that contributes to climate change and one of its major sources is rice cultivation. The main aim of this paper was to compare two well-established biogeochemical models, namely Daily Century (DAYCENT) and DeNitrification-DeComposition (DNDC) for estimating CH emissions and grain yields for a double-rice cropping system with tillage practice and/or stubble incorporation in the winter fallow season in Southern China. Both models were calibrated and validated using field measured data from November 2008 to November 2014. The calibrated models performed effectively in estimating the daily CH emission pattern (correlation coefficient, r = 0.58-0.63, p < 0.001), but model efficiency (EF) values were higher in stubble incorporation treatments, with and without winter tillage (treatments S and WS) (EF = 0.22-0.28) than that in winter tillage without stubble incorporation treatment (W) (EF = -0.06-0.08). We recommend that algorithms for the impacts of tillage practice on CH emission should be improved for both models. DAYCENT and DNDC also estimated rice yields for all treatments without a significant bias. Our results showed that tillage practice in the winter fallow season (treatments WS and W) significantly decreased annual CH emissions, by 13-37 % (p < 0.05) for measured values, 15-20 % (p < 0.05) for DAYCENT-simulated values, and 12-32 % (p < 0.05) for DNDC-simulated values, respectively, compared to no-till practice (treatments S), but had no significant impact on grain yields.

Invasive plants can modify the diversity and taxonomical structure of soil microbiomes. However, it is difficult to generalize the underlying factors as their influence often seems to depend on the complex plant-soil-microbial interactions. In this paper, we investigated how impacts on the soil microbiome across two soil horizons in relation to native woodland. Five paired adjacent invaded vs native vegetation plots in a managed forest in southern Poland were investigated. Soil microbial communities were assessed along with soil enzyme activities and soil physicochemical parameters, separately for both organic and mineral horizons, as well as forest stand characteristics to explore plant-soil-microbe interactions. Although did not significantly affect pH, organic C, total N, available nutrients nor enzymatic activity, differences in soil abiotic properties (except C to N ratio) were primarily driven by soil depth for both vegetation types. Further, we found significant differences in soil microbiome under invasion in relation to native vegetation. Microbial richness and diversity were lower in both horizons of vs control plots. Moreover, increased relative abundance of unique amplicon sequence variants in both horizons and thereby significantly changed the structure of the core soil microbial communities, in comparison to the control plots. In addition, predicted microbial functional groups indicated a predominant soil depth effect in both vegetation plots with higher abundance of aerobic chemoheterotrophic bacteria and endophytic fungi in the organic horizon and greater abundance of methanotrophic and methylotrophic bacteria, and ectomycorrhizal fungi in the mineral horizon. Overall, our results indicate strong associations between invasion and changes in soil microbiome and associated functions, a finding that needs to be further investigated to predict modifications in ecosystem functioning caused by this invasive species.

Grazing-based production systems are a source of soil greenhouse gas (GHG) emissions triggered by excreta depositions. The adoption of forages (formerly known as ) with biological nitrification inhibition (BNI) capacity is a promising alternative to reduce nitrous oxide (NO) emissions from excreta patches. However, how this forage affects methane (CH) or carbon dioxide (CO) emissions from excreta patches remains unclear. This study investigated the potential effect of soils under two forages with contrasting BNI capacity on GHG emissions from cattle dung deposits. Additionally, the NO and CH emission factors (EF) for cattle dung under tropical conditions were determined. Dung from cattle grazing star grass (without BNI) was deposited on both forage plots: cv. Mulato and cv. Tully, with a respectively low and high BNI capacity. Two trials were conducted for GHG monitoring using the static chamber technique. Soil and dung properties and GHG emissions were monitored in trial 1. In trial 2, water was added to simulate rainfall and evaluate GHG emissions under wetter conditions. Our results showed that beneath dung patches, the forage genotype influenced daily CO and cumulative CH emissions during the driest conditions. However, no significant effect of the forage genotype was found on mitigating NO emissions from dung. We attribute the absence of a significant BNI effect on NO emissions to the limited incorporation of dung-N into the soil and rhizosphere where the BNI effect occurs. The average NO EFs was 0.14%, close to the IPCC 2019 uncertainty range (0.01-0.13% at 95% confidence level). Moreover, CH EFs per unit of volatile solid (VS) averaged 0.31 g CH kgVS, slightly lower than the 0.6 g CH kgVS developed by the IPCC. This implies the need to invest in studies to develop more region-specific Tier 2 EFs, including farm-level studies with animals consuming forages to consider the complete implications of forage selection on animal excreta based GHG emissions.

A crucial decision in designing a spatial sample for soil survey is the number of sampling locations required to answer, with sufficient accuracy and precision, the questions posed by decision makers at different levels of geographic aggregation. In the Indian Soil Health Card (SHC) scheme, many thousands of locations are sampled per district. In this paper the SHC data are used to estimate the mean of a soil property within a defined study area, e.g., a district, or the areal fraction of the study area where some condition is satisfied, e.g., exceedence of a critical level. The central question is whether this large sample size is needed for this aim. The sample size required for a given maximum length of a confidence interval can be computed with formulas from classical sampling theory, using a prior estimate of the variance of the property of interest within the study area. Similarly, for the areal fraction a prior estimate of this fraction is required. In practice we are uncertain about these prior estimates, and our uncertainty is not accounted for in classical sample size determination (SSD). This deficiency can be overcome with a Bayesian approach, in which the prior estimate of the variance or areal fraction is replaced by a prior distribution. Once new data from the sample are available, this prior distribution is updated to a posterior distribution using Bayes' rule. The apparent problem with a Bayesian approach prior to a sampling campaign is that the data are not yet available. This dilemma can be solved by computing, for a given sample size, the predictive distribution of the data, given a prior distribution on the population and design parameter. Thus we do not have a single vector with data values, but a finite or infinite set of possible data vectors. As a consequence, we have as many posterior distribution functions as we have data vectors. This leads to a probability distribution of lengths or coverages of Bayesian credible intervals, from which various criteria for SSD can be derived. Besides the fully Bayesian approach, a mixed Bayesian-likelihood approach for SSD is available. This is of interest when, after the data have been collected, we prefer to estimate the mean from these data only, using the frequentist approach, ignoring the prior distribution. The fully Bayesian and mixed Bayesian-likelihood approach are illustrated for estimating the mean of log-transformed Zn and the areal fraction with Zn-deficiency, defined as Zn concentration <0.9 mg kg , in the thirteen districts of Andhra Pradesh state. The SHC data from 2015-2017 are used to derive prior distributions. For all districts the Bayesian and mixed Bayesian-likelihood sample sizes are much smaller than the current sample sizes. The hyperparameters of the prior distributions have a strong effect on the sample sizes. We discuss methods to deal with this. Even at the mandal (sub-district) level the sample size can almost always be reduced substantially. Clearly SHC over-sampled, and here we show how to reduce the effort while still providing information required for decision-making. R scripts for SSD are provided as supplementary material.

Rice is the primary crop in Bangladesh and rice yield is diminished due to the buildup of arsenic (As) in soil from irrigation with high-As groundwater. Soil testing with an inexpensive kit could help farmers target high-As soil for mitigation or decide to switch to a different crop that is less sensitive to As in soil. A total of 3,240 field kit measurements of As in 0.5 g of fresh soil added to 50 mL of water were compared with total soil As concentrations measured on oven-dried homogenized soil by X-ray fluorescence (XRF). For sets of 12 soil samples collected within a series of rice fields, the average of kit As measurements was a linear function of the average of XRF measurements (r=0.69). Taking into account that the kit overestimates water As concentrations by about a factor of two, the relationship suggests that about a quarter of the As in paddy soil is released in the kit's reaction vessel. Using the relationship and considering XRF measurements as the reference, the 12-sample average determined correctly whether soil As was above or below a 30 mg/kg threshold in 86% of cases where soil As was above the threshold and in 79% of cases where soil As was below the threshold. We also used a Bayesian approach using 12 kit measurements to estimate the probability that soil As was above a given threshold indicated by XRF measurements. The Bayesian approach is theoretically optimal but was only slightly more accurate than the linear regression. These results show that rice farmers can identify high-As portions of their fields for mitigation using a dozen field kit measurements on fresh soil and base their decisions on this information.

Soil mineral compositions are often complex and spatially diverse, with each mineral exhibiting characteristic chemical properties that determine the intrinsic total concentration of soil nutrients and their phyto-availability. Defining soil mineral-nutrient relationships is therefore important for understanding the inherent fertility of soils for sustainable nutrient management, and data-driven approaches such as cluster analysis allow for these relations to be assessed in new detail. Here the fuzzy-c-means clustering algorithm was applied to an X-ray powder diffraction (XRPD) dataset of 935 soils from sub-Saharan Africa, with each diffractogram representing a digital signature of a soil's mineralogy. Nine mineralogically distinct clusters were objectively selected from the soil mineralogy continuum by retaining samples exceeding the quantile of the membership coefficients in each cluster, yielding a dataset of 239 soils. As such, samples within each cluster represented mineralogically similar soils from different agro-ecological environments of sub-Saharan Africa. Mineral quantification based on the mean diffractogram of each cluster illustrated substantial mineralogical diversity between the nine groups with respect to quartz, K-feldspar, plagioclase, Fe/Al/Ti-(hydr)oxides, phyllosilicates (1:1 and 2:1), ferromagnesians, and calcite. Mineral-nutrient relationships were defined using the clustered XRPD patterns and corresponding measurements of total and/or extractable (Mehlich-3) nutrient concentrations (B, Mg, K, Ca, Mn, Fe, Ni, Cu and Zn) in combination with log-ratio compositional data analysis. Fe/Al/Ti/Mn-(hydr)oxides and feldspars were found to be the primary control of total nutrient concentrations, whereas 2:1 phyllosilicates were the main source of all extractable nutrients except for Fe and Zn. Kaolin minerals were the most abundant phyllosilicate group within the dataset but did not represent a nutrient source, which reflects the lack of nutrients within their chemical composition and their low cation exchange capacity. Results highlight how the mineral composition controls the total nutrient reserves and their phyto-availability in soils of sub-Saharan Africa. The typical characterisation of soils and their parent material based on the clay particle size fraction (i.e. texture) and/or the overall silica component (i.e. acid and basic rock types) alone may therefore mask the intricacies of mineral contributions to soil nutrient concentrations.

While the importance of soils in agriculture cannot be overlooked, plot level soil data remain scarce in the current data landscape. Large-scale household surveys efforts are increasing in low-income countries and assessing the accuracy, scalability and cost-effectiveness of available methods is crucial. Here, we firstly explore soil data requirements for a set of objectives that include identifying a soil constraint, improving recommendation domain studies and capturing soil metrics as covariates, or as outcomes. We then expose the lessons learned from a methodological experiment in rural Ethiopia, where different approaches - farmer's self-elicitation and miniaturized spectrometers - are compared against laboratory benchmarks for a set of soil parameters: soil texture, soil pH and soil organic C. With the exception of soil particle sizes, we find that soil parameters captured through farmer's elicitation do not converge with objective metrics. Miniaturized spectrometers can provide reasonably accurate data for the identification of soil constraints - soil acidity, low organic C or sandy soils. Approximate quantitative predictions can also be delivered for soil pH (R = 0.72) and organic C (R = 0.60). The additional costs of plot sampling and analysis are in the range of $19-$23 per sample, with the additional percentage of plots with correct data equivalent to 10% for the identification of sandy soils, 75% for low organic C and 89% of acidic soils.

The development of a robust method to non-invasively visualize root morphology in natural soils has been hampered by the opaque, physical, and structural properties of soils. In this work we describe a novel technology, low field magnetic resonance imaging (LF-MRI), for imaging energy sorghum ( (L.) Moench) root morphology and architecture in intact soils. The use of magnetic fields much weaker than those used with traditional MRI experiments reduces the distortion due to magnetic material naturally present in agricultural soils. A laboratory based LF-MRI operating at 47 mT magnetic field strength was evaluated using two sets of soil cores: 1) soil/root cores of Weswood silt loam (Udifluventic Haplustept) and a Belk clay (Entic Hapluderts) from a conventionally tilled field, and 2) soil/root cores from rhizotrons filled with either a Houston Black (Udic Haplusterts) clay or a sandy loam purchased from a turf company. The maximum soil water nuclear magnetic resonance (NMR) relaxation time T (4 ms) and the typical root water relaxation time T (100 ms) are far enough apart to provide a unique contrast mechanism such that the soil water signal has decayed to the point of no longer being detectable during the data collection time period. 2-D MRI projection images were produced of roots with a diameter range of 1.5-2.0 mm using an image acquisition time of 15 min with a pixel resolution of 1.74 mm in four soil types. Additionally, we demonstrate the use of a data-driven machine learning reconstruction approach, Automated Transform by Manifold Approximation (AUTOMAP) to reconstruct raw data and improve the quality of the final images. The application of AUTOMAP showed a SNR (Signal to Noise Ratio) improvement of two fold on average. The use of low field MRI presented here demonstrates the possibility of applying low field MRI through intact soils to root phenotyping and agronomy to aid in understanding of root morphology and the spatial arrangement of roots .

While soil microbial ecology, soil organic carbon (SOC) and soil physical quality are widely understood to be interrelated - the underlying drivers of emergent properties, from land management to biochemistry, are hotly debated. Biological binding agents, microbial exudates, or 'extracellular polymeric substances' (EPS) in soil are now receiving increased attention due to several of the existing methodological challenges having been overcome. We applied a recently developed approach to quantify soil EPS, as extracellular protein and extracellular polysaccharide, on the well-characterised soils of the Highfield Experiment, Rothamsted Research, UK. Our aim was to investigate the links between agricultural land use, SOC, transient binding agents known as EPS, and their impacts on soil physical quality (given by mean weight diameter of water stable aggregates; MWD). We compared the legacy effects from long-term previous land-uses (unfertilised grassland, fertilised arable, and fallow) which were established > 50 years prior to investigation, crossed with the same current land-uses established for a duration of only 2.5 years prior to sampling. Continuously fallow and grassland soils represented the poorest and greatest states of structural integrity, respectively. Total SOC and N were found to be affected by both previous and current land-uses, while extractable EPS and MWD were driven primarily by the current land-use. Land-use change between these two extremes (fallow → grass; grass → fallow) resulted in smaller SOC differences (64% increase or 37% loss) compared to MWD (125% increase or 78% loss). SOC concentration correlated well to MWD (adjusted  = 0.72) but the greater SOC content from previous grassland was not found to contribute directly to the current stability (p < 0.05). Our work thus supports the view that certain distinct components of SOC, rather than the total pool, have disproportionately important effects on a soil's structural stability. EPS-protein was more closely related to aggregate stability than EPS-polysaccharide ( values of 0.002 and 0.027, respectively), and ranking soils with the 5 greatest concentrations of EPS-protein to their corresponding orders of stability (MWD) resulted in a perfect match. We confirmed that both EPS-protein and EPS-polysaccharide were transient fractions: supporting the founding models for aggregate formation. We suggest that management of transient binding agents such as EPS -as opposed to simply increasing the total SOC content- may be a more feasible strategy to improve soil structural integrity and help achieve environmental objectives.

The combination of concurrent soil degradation and restoration scenarios in a long-term experiment with contrasting treatments under steady-state conditions, similar soil texture and climate make the Highfield land-use change experiment at Rothamsted Research unique. We used soil from this experiment to quantify rates of change in organic matter (OM) fractions and soil structural stability (SSS) six years after the management changed. Soil degradation included the conversion of grassland to arable and bare fallow management, while soil restoration comprised introduction of grassland in arable and bare fallow soil. Soils were tested for clay dispersibility measured on two macro-aggregate sizes (DispClay 1-2 mm and DispClay 8-16 mm) and clay-SOM disintegration (DI, the ratio between clay particles retrieved without and with SOM removal). The SSS tests were related to soil organic carbon (SOC), permanganate oxidizable C (POXC) and hot water-extractable C (HWC). The decrease in SOC after termination of grassland was greater than the increase in SOC when introducing grassland. In contrast, it was faster to restore degraded soil than to degrade grassland soil with respect to SSS at macro-aggregate scale. The effect of management changes was more pronounced for 8-16 mm than 1-2 mm aggregates indicating a larger sensitivity towards tillage-induced breakdown of binding agents in larger aggregates. At microscale, SSS depended on SOC content regardless of management. Soil management affected macroscale structural stability beyond what is revealed from measuring changes in OM fractions, underlining the need to include both bonding and binding mechanisms in the interpretation of changes in SSS induced by management.

The soil water retention curve is one of the most important properties used to predict the amount of water available to plants, pore size distribution and hydraulic conductivity, as well as knowledge for drainage and irrigation modeling. Depending on the method of measurement adopted, the water retention curve can involve the application of several wetting and drying (W-D) cycles to a soil sample. The method assumes soil pore structure is constant throughout however most of the time soil structure is dynamic and subjected to change when submitted to continuous W-D. Consequently, the pore size distribution, as well as other soil morphological properties can be affected. With this in mind, high resolution X-ray Computed micro-Tomography was utilized to evaluate changes in the soil pore architecture following W-D cycles during the procedure of the water retention curve evaluation. Two different soil sample volumes were analyzed: ROI (whole sample) and ROI (the region close to the bottom of the sample). The second region was selected due to its proximity to the hydraulic contact of the soil with the water retention curve measurement apparatus. Samples were submitted to the following W-D treatments: 0, 6 and 12 W-D. Results indicated the soil changed its porous architecture after W-D cycles. The image-derived porosity did not show differences after W-D cycles for ROI; while for ROI it increased porosity. The porosity was also lower in ROI in comparison to ROI Pore connectivity improved after W-D cycles for ROI, but not for ROI. W-D cycles induced more aligned pores for both ROIs as observed by the tortuosity results. Pore shape showed changes mainly for ROI for the equant and triaxial shaped pores; while pore size was significantly influenced by the W-D cycles. Soil water retention curve measurements showed that W-D cycles can affect water retention evaluation and that the changes in the soil morphological properties can play an important role in it.

This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO), C:N ratio, nitrogen (N), phosphorus (P) and potassium (K). The LUCAS survey collected harmonised data on changes in land cover and the state of land use for the European Union (EU). Among the 270,000 land use and cover observations selected for field visit, approximately 20,000 soil samples were collected in 24 EU Member States in 2009 together with more than 2000 samples from Bulgaria and Romania in 2012. The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. GPR was selected for its capacity to assess model uncertainty and the possibility of adding prior knowledge in the form of covariance functions to the model. The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union.

Biochar has been reported to reduce emission of nitrous oxide (NO) from soils, but the mechanisms responsible remain fragmentary. For example, it is unclear how biochar effects on NO emissions are mediated through biochar effects on soil gross N turnover rates. Hence, we conducted an incubation study with three contrasting agricultural soils from Kenya (an Acrisol cultivated for 10-years (Acrisol10); an Acrisol cultivated for over 100-years (Acrisol100); a Ferralsol cultivated for over 100 years (Ferralsol)). The soils were amended with biochar at either 2% or 4% w/w. The N pool dilution technique was used to quantify gross N mineralization and nitrification and microbial consumption of extractable N over a 20-day incubation period at 25 °C and 70% water holding capacity of the soil, accompanied by NO emissions measurements. Direct measurements of N emissions were conducted using the helium gas flow soil core method. NO emissions varied across soils with higher emissions in Acrisols than in Ferralsols. Addition of 2% biochar reduced NO emissions in all soils by 53 to 78% with no significant further reduction induced by addition at 4%. Biochar effects on soil nitrate concentrations were highly variable across soils, ranging from a reduction, no effect and an increase. Biochar addition stimulated gross N mineralization in Acrisol-10 and Acrisol-100 soils at both addition rates with no effect observed for the Ferralsol. In contrast, gross nitrification was stimulated in only one soil but only at a 4% application rate. Also, biochar effects on increased NH immobilization and NO consumption strongly varied across the three investigated soils. The variable and bidirectional biochar effects on gross N turnover in conjunction with the unambiguous and consistent reduction of NO emissions suggested that the inhibiting effect of biochar on soil NO emission seemed to be decoupled from gross microbial N turnover processes. With biochar application, N emissions were about an order of magnitude higher for Acrisol-10 soils compared to Acrisol-100 and Ferralsol-100 soils. Our NO and N flux data thus support an explanation of direct promotion of gross NO reduction by biochar rather than effects on soil extractable N dynamics. Effects of biochar on soil extractable N and gross N turnover, however, might be highly variable across different soils as found here for three typical agricultural soils of Kenya.