Accurate soil organic carbon content estimation is critical as a proxy for carbon sequestration, and as one of the indicators for soil health. Here, we collected 497 soil samples during 2015 and 2019, as well as five environmental covariates (organic carbon (OC) input from the crops, normalized difference vegetation index (NDVI), elevation, clay content and precipitation) at a resolution of 30 m. We then aggregated these to represent agricultural fields and compiled a soil organic carbon (SOC) content map for the agricultural soils of Wallonia using Gradient Boosting Machine. We calculated OC input from both main crops and cover crops for each individual field. As the cover crops do not occur in the agricultural census, we identified cover crops based on long time series of NDVI values obtained from the Google Earth Engine platform. The quality of the SOC predictions was assessed by validation data and we obtained an R2 of 0.77. The Empirical Mode Decomposition indicated that OC input and NDVI were the dominant factors at field scale, whereas the remaining covariates determined the distribution of SOC at the scale of the entire Walloon region. The SOC map showed an overall northwest to southeast trend i.e. an increase in SOC contents up to the Ourthe river followed by a decrease further to the South. The map shows both regional trends in SOC and effects of differences in land use and/or management (including crop rotation and frequency of cover crops) between individual fields. The field-scale map can be used as a benchmark and reference to farmers and agencies in maintaining SOC contents at an appropriate level and optimizing decisions for sustainable land use.

Sustainable management of agricultural systems is a major challenge for ensuring food security of the growing world population. Organic farming and reduced tillage are assumed to be sustainable agricultural practices improving soil quality relative to conventional management strategies. However, assessment of soil quality is often restrained to either physical, chemical or biological parameters. Soil organic carbon (SOC) is the most widely used indicator of soil quality, but it is not necessarily reactive to change, nor representative of the functioning of soil systems, in particular in relation to the realization of soil microbial processes, as it is composed of fractions with different availability for microbial activity and decomposition. The objective of this study was to assess the influence of three major cropping systems: organic agriculture and conservation agriculture (no/reduced-tillage) vs. conventional agriculture on SOC fractions and microbial processes related to C and N cycling and to establish relationships between carbon fractions and microbial processes in order to identify the most relevant indicator of soil functioning. We hypothesized that 1) organic farming and conservation agriculture would improve soil functioning, 2) labile organic fractions would be better indicators of soil functioning. We measured C and N in physical and chemical fractions (bulk, <20 µm, 20-2000µm, dissolved organic C, hot water extractable C, water soluble C, K2SO4 extractable C, microbial biomass C) and microbial processes (respiration potential, net N mineralization, metabolic diversity of soil bacteria) in 16 cropland sites in Wallonia, south Belgium (CARBIOSOL project). Preliminary data analyses indicate improved soil functioning in organic managed sites relative to sites under conventional and conservation management and reveal hot water extractable carbon as a promising proxy for monitoring changes in soil functioning in response to agricultural practices. Final detailed data analyses will be presented.

Artificial radionuclides including radiocesium (134Cs and 137Cs) and radiosilver (110mAg) were released into the environment following the Fukushima Dai-ichi nuclear power plant accident in March 2011. These particle-bound substances deposited on soils of north-eastern Japan, located predominantly within a ∼3000 km2 radioactive fallout plume and drained by several coastal rivers to the Pacific Ocean. The current dataset (Evrard et al., 2021), which can be accessed at https://doi.org/10.1594/PANGAEA.928594, compiles gamma-emitting artificial radionuclide activities measured in 782 sediment samples collected from 27 to 71 locations across catchments draining ∼6450 km2 during 16 fieldwork campaigns. These campaigns were conducted in Japan between November 2011 and November 2020 in river catchments draining the main radioactive plume. This database may be useful to evaluate and anticipate the post-accidental redistribution of radionuclides in the environment and for the spatial validation of models simulating the transfer of radiocesium across continental landscapes.

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data.

In sub-Saharan Africa, market gardening relies on the intensive use of organic amendments, but little is known about the impact of these practices on soil organic carbon (SOC) dynamics. Recent studies have demonstrated that a better understanding of carbon dynamics can be achieved by considering different carbon pools. Here we used a simple method of physical SOC fractionation to assess the impact of market gardening practices on the evolution of total SOC and its stable and labile sub-fractions over a period of several decades. The study was conducted at the Kuinima market gardening zone in Bobo-Dioulasso (Burkina Faso). Composite soil samples (0-15 cm depth) were collected from a chronosequence of 69 fields including control plots and plots farmed for more than 50 years. Samples were wet sieved at 20 mu m after agitation or sonication to assess carbon content in the fine (i.e. stable) and coarse fractions. Our results show an asymptotic increase in total carbon content, from 9 g C kg(-1) for uncultivated control plots to 28 g C kg(-1) for plots cultivated for more than 50 years. This increase tends to stabilize after 30 years. A similar trend is observed for C content in the coarse fraction > 20 mu m. In contrast, carbon content in the fraction < 20 mu m increased linearly over time, and this increase was greater in sonicated than in agitated samples. Based on the comparison of the two fractionation methods, a strong contribution of micro-aggregation to the physical stabilization of SOC was hypothesized. This stabilization appears favored by short-range-order (SRO) iron and aluminum oxides and/or metal-humus complexes, as suggested by the strong correlation between stable C content and oxalate-extractable Fe and Al. Large and repeated additions of organic amendments in market gardening thus appear to lead to increased SOC content and promote SOC storage in the stable fraction, thereby contributing to soil quality improvement and the mitigation of climate change.

The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 mu m and analysing the SOC concentration in the soil as well as in the fine fraction (< 20 mu m). Eight biological indicators measured in an earlier study across the same monitoring network for the 0-10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers' fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r(2) = 0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5-10 years).

The protection of agricultural soil quality is critical to environmental sustainability and requires relevant indicators. Total soil organic carbon (SOC) is of importance for soil quality but its slow dynamic and inherent variability do not allow early detection of changes. The project CARBIOSOL provides a data set from agricultural soils in Wallonia (Southern Belgium), of total SOC, SOC fractions and biological indicators, selected for their relevance as indicators of soil quality. Two land uses (sampled in 2013), five agricultural regions (2015), seasonal variability in croplands (2016) and four management types (2017) were studied. Soil organic carbon content (total, stable fine fraction <20 μm, labile coarse fraction >20 μm), cold and hot water extractable carbon and nitrogen contents, total nitrogen, pHKC l , pHH 2O , potential respiration, microbial biomass carbon and nitrogen, net nitrogen mineralization, metabolic potential of soil bacteria, earthworm density and biomass, and two ecophysiological quotients (metabolic and microbial quotient) were measured for a total of 415 samples. The present data set provides an important contribution for establishing a reference system of soil quality in Wallonia and eventually for large-scale studies through its integration into a global database. Moreover, the present data set could be used to support the interpretation of measurements of fractions of SOC and biological indicators by soil analyses laboratories, which will be useful for farmers and decision makers to evaluate the effect of different management practices. Information contained in this publication or product may be reproduced, in part or in whole, and by any means for personal or public non-commercial uses, without charge or further permission, unless otherwise specified. Users are required to exercise due diligence in ensuring the accuracy of the material reproduced, indicate the complete title of the material produced and refer to this publication (including author names), indicate that the reproduction is a copy/uses official work financed by the SPW-DGO3. Commercial reproduction and distribution is prohibited, except with written permission from SPW-DGO3 and publication authors.

Summary Quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site‐specific management at local or regional scales. In this context, soil spectroscopy is a well‐consolidated and widespread method to estimate soil variables, and in particular SOC content, at a low cost for routine analysis. The increasing number of large soil spectral libraries collected worldwide reflects the importance of spectroscopy in soil science. These large libraries contain soil samples derived from a large number of pedological regions and thus from different parent materials and soil types. In the light of the huge variation in the spectral responses to SOC content and composition, a rigorous process is necessary to subdivide large spectral libraries to avoid calibration with global models that fail to predict local variation in SOC content. Here, we propose to classify the European LUCAS topsoil database with a cluster analysis based on a large number of soil properties. The soil samples collected from arable land in the LUCAS database were chosen to apply a standardized multivariate calibration approach, valid for large areas, to calibrate local models without the need for further field and laboratory work. Cluster analysis detected seven soil classes and the samples belonging to each class were used to calibrate specific partial least squares regression (PLSR) models to estimate SOC content in three spectral libraries collected in Belgium and Luxembourg. Soil organic carbon was predicted with good accuracy, both within each library (root mean square error (RMSE), 1.2–5.1 g kg −1 ; ratio of performance to prediction (RPD), 1.41–2.24) and for the samples of the three libraries together (RMSE, 3.7 g kg −1 ; RPD, 2.54). The proposed approach could enable SOC to be estimated for arable soils in Europe with only the spectra of soil samples and without the need for laboratory analyses. Highlights We investigated the potential of the LUCAS database to estimate SOC in spectral libraries. We proposed a routine approach to estimate SOC with less laboratory work. The classification of the LUCAS topsoil dataset improved estimation accuracy of SOC SOC content can be predicted from soil spectra with models calibrated on the LUCAS database.

Tools that will enable the assessment of agricultural soil quality and include measurements of biological indicators, such as soil respiration or nitrogen mineralisation, are increasingly in demand. Such tools require the establishment of reference systems to provide comparative 'baseline' or 'normal' values. In this study, we measured the spatial and seasonal variability of eight biological indicators (including two eco-physiological quotients) in order to establish a reference system at the regional level of Wallonia (Southern Belgium). Respiration potential, microbial biomass carbon, microbial C/N ratio, net nitrogen mineralisation, metabolic potential of soil bacteria, earthworm abundance, microbial quotient, and metabolic quotient were measured at 60 sites across contrasting agricultural regions (different soil types and climate) in both grasslands and croplands. Additionally, the same biological indicators were measured four times during the vegetation period (April, June, August, and October) in 11 cropland sites to assess seasonal variability. Reference ranges were defined for each biological indicator, based on the addition of variances (seasonal and spatial) and the calculation of cumulative distribution functions. Land use was the most useful classification variable to define a reference system in Wallonia. Two separate reference systems, one for grasslands and one for croplands, were thus appropriate for Wallonia. Sampling season had a significant effect on all biological indicators. The inclusion of seasonal variability resulted in reference ranges 1.1-5.7 times wider than ranges accounting only for spatial variability. The reference system provides a basis for a first comparative assessment of soil quality for most agricultural soils of Wallonia, independent of sampling period.

Description of the subject. The paper presents and discusses a method for fractionating bulk soil organic carbon (SOC) in meaningful SOC fractions to better assess SOC status and its related soil ecosystem functions. Objectives. The objective is to perform an evaluation of ecosystem functions of soil organic matter at plot scale and compare it to the normal operative range of the local agro-ecological region. Method. By separating carbon associated with clay and fine silt particles (stable carbon with slow turnover rate, u003c 20 µm) and carbon non-associated with this fraction (labile and intermediate carbon with higher turnover rates, ≥ 20 µm), effects of management can be detected more efficiently at different scales. Conclusions. Soil organic carbon fractions, used as proxies for soil ecosystem functions, can be helpful because they represent SOC functional pools. This paper proposes to apply fractionation on samples taken at plot and regional scale. It is therefore possible to establish a normal operative range for a specific agro-region for comparison with the values in individual plots. This allows drawing a baseline for SOC fractions status in a specific agricultural unit. This approach provides valuable information to study and evaluate the impact of agricultural management in the context of enhancing soil quality and functions.

To best utilize the electrical'resistivity data and slope intensity derived from a Digital Elevation Model, the kriging spatial components technique was applied to separate the nuggets and small- and large-scale structures for both resistivity and slope intensity data. The spatial structures in the resistivity and slope intensity data, which are poorly correlated with soil thickness (ST), are then filtered out prior to integrating the resistivity data and slope intensity into soil thickness estimation over a 12 ha area located in the south-western Parisian Basin (France). ST was measured at 650 locations over the study area by manual augering. Twenty percent of the observations (131 points) were randomly selected to constitute the validation dataset The remaining 80% of the dataset (519 points) was used as the prediction dataset The resistivity data represent a set of 7394 measurement points for each of the three investigated depths over the study area. The methodology involves successively (1) a principal component analysis (PCA) on the electrical measurements and (2) a geostatistical filtering of the small-scale component and noise in the first component (PC1) of the PCA. The results show that the correlation between ST and PC1 is greatly improved when the small-scale component and noise are filtered out, and similarly, the correlation between ST and slope intensity is greatly improved once the geostatistical filtering is carried out on the slope data. Thus, the large scales of both slope intensity and the electrical resistivity's PC1 were used as external drifts to predict ST over the entire study area. This prediction was compared with ordinary kriging and kriging either with a large scale of slope intensity or with a large scale of the electrical resistivity's PC1 taken as an external drift. The first prediction of ST by ordinary kriging, which was considered as our reference, was also compared to those achieved by kriging using the raw secondary variables: PC1 and slope intensity as external drifts; slope intensity as an external drift; and PC1 as an external drift. The results indicate a reasonably low bias of prediction for all of the methods, in particular in the case of kriging using the large scales of both slope intensity and PC1 as external drifts. The root mean square error shows that kriging accounting for the large scales of two secondary exhaustive variables is the most accurate prediction method. The relative improvement of the accuracy is at least equal to 29% between the approach accounting for both large scale components of secondary attributes in the spatial estimates of ST and the other approaches of estimates considered in this study. (C) 2017 Elsevier B.V. All rights reserved.

Description of the subject. Soil organisms and their activities are essential for soil ecosystem functioning and they can thus be used as pertinent indicators of soil quality. Recent efforts have been undertaken to include biological indicators of soil quality into regional/national monitoring networks. Objectives. The aim of this study was to provide a first dataset of six biological indicators and two eco-physiological quotients for two landscape units in Wallonia. These spatial units are characterized by homogeneous climate conditions, soil type, land-use and management (here, grasslands in the Ardennes, and croplands in the Loam Region). Method. Respiration potential, microbial biomass carbon and nitrogen, net nitrogen mineralization, metabolic potential of soil bacteria and earthworm abundance were measured at a total of 60 sites in two different landscape units (LSU). Variability within each LSU was studied. Data was synthesized through calculation of a comprehensive score and presentation as radar plots. Results. All selected biological indicators were significantly higher under grassland than under cropland soils, highlighting the biological indicators’ power of discrimination between main land use types. Variability within LSU depended on the biological indicator and was generally higher in grassland than in cropland soils. Each site could unambiguously be assigned to its landscape unit based on its calculated comprehensive score. Radar plots allowed an assessment of the distribution of values within a landscape unit at a glance. Conclusions. The pilot-study defined the first baseline values for agricultural soils in Wallonia and laid the foundation for a monitoring network of biological soil quality.

The quantification and the spatialisation of reliable SOC stocks (MgCha−1) and total stock (TgC) baselines and associated uncertainties are fundamental to detect the gains or losses in SOC, and to locate sensitive areas with low SOC levels. Here, we aim to both quantify and spatialize SOC stocks at regional scale (southern Belgium) based on data from one non-design-based or model-based sampling scheme. To this end, we developed a computation procedure based on Digital Soil Mapping techniques and stochastic simulations (Monte-Carlo) allowing the estimation of multiple (here, 10,000) independent spatialized datasets. The computation of the prediction uncertainty accounts for the errors associated to both the estimations of i) SOC stocks and ii) parameters of the spatial model. Based on these 10,000 individuals, median SOC stocks and 90% prediction intervals were computed for each pixel, as well as total SOC stocks and their 90% prediction intervals for selected sub-areas and for the entire study area. Hence, a Generalised Additive Model (GAM) explaining 69.3% of the SOC stock variance was calibrated and then validated (R2=0.64). The model overestimated low SOC stock (below 50MgCha−1) and underestimated high SOC stock (especially those above 100MgCkg−1). A positive gradient of SOC stock occurred from the northwest to the center of Wallonia with a slight decrease on the southernmost part, correlating to the evolution of precipitation and temperature (along with elevation) and dominant land use. At the catchment scale higher SOC stocks were predicted on valley bottoms, especially for poorly drained soils under grassland. Mean predicted SOC stocks for cropland and grassland in Wallonia were of 26.58TgC (SD 1.52) and 43.30TgC (2.93), respectively. The procedure developed here allowed to predict realistic spatial patterns of SOC stocks all over agricultural lands of southern Belgium and to produce reliable statistics of total SOC stocks for each of the 20 combinations of land use/agricultural regions of Wallonia. This procedure appears useful to produce soil maps as policy tools in conducting sustainable management at regional and national scales, and to compute statistics which comply with specific requirements of reporting activities.

Description of the subject. Soil organisms and their activities are essential for soil ecosystem functioning and they can thus be used as pertinent indicators of soil quality. Recent efforts have been undertaken to include biological indicators of soil quality into regional/national monitoring networks. Objectives. The aim of this study was to provide a first dataset of six biological indicators and two eco-physiological quotients for two landscape units in Wallonia. These spatial units are characterized by homogeneous climate conditions, soil type, land-use and management (here, grasslands in the Ardennes, and croplands in the Loam Region). Method. Respiration potential, microbial biomass carbon and nitrogen, net nitrogen mineralization, metabolic potential of soil bacteria and earthworm abundance were measured at a total of 60 sites in two different landscape units (LSU). Variability within each LSU was studied. Data was synthesized through calculation of a comprehensive score and presentation as radar plots. Results. All selected biological indicators were significantly higher under grassland than under cropland soils, highlighting the biological indicators' power of discrimination between main land use types. Variability within LSU depended on the biological indicator and was generally higher in grassland than in cropland soils. Each site could unambiguously be assigned to its landscape unit based on its calculated comprehensive score. Radar plots allowed an assessment of the distribution of values within a landscape unit at a glance. Conclusions. The pilot-study defined the first baseline values for agricultural soils in Wallonia and laid the foundation for a monitoring network of biological soil quality.