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The capacity of soils to store organic carbon represents a key function of soils that is not only decisive for climate regulation but also affects other soil functions. Recent efforts to assess the impact of land management on soil functionality proposed that an indicator- or proxy-based approach is a promising alternative to quantify soil functions compared to time- and cost-intensive measurements, particularly when larger regions are targeted. The objective of this review is to identify measurable biotic or abiotic properties that control soil organic carbon (SOC) storage at different spatial scales and could serve as indicators for an efficient quantification of SOC. These indicators should enable both an estimation of actual SOC storage as well as a prediction of the SOC storage potential, which is an important aspect in land use and management planning. There are many environmental conditions that affect SOC storage at different spatial scales. We provide a thorough overview of factors from micro-scales (particles to pedons) to the global scale and discuss their suitability as indicators for SOC storage: clay mineralogy, specific surface area, metal oxides, Ca and Mg cations, microorganisms, soil fauna, aggregation, texture, soil type, natural vegetation, land use and management, topography, parent material and climate. As a result, we propose a set of indicators that allow for time- and cost-efficient estimates of actual and potential SOC storage from the local to the regional and subcontinental scale. As a key element, the fine mineral fraction was identified to determine SOC stabilization in most soils. The quantification of SOC can be further refined by including climatic proxies, particularly elevation, as well as information on land use, soil management and vegetation characteristics. To enhance its indicative power towards land management effects, further “functional soil characteristics”, particularly soil structural properties and changes in the soil microbial biomass pool should be included in this indicator system. The proposed system offers the potential to efficiently estimate the SOC storage capacity by means of simplified measures, such as soil fractionation procedures or infrared spectroscopic approaches.

Remote Sensing, 13 (7), ISSN:2072-4292

Investigations into the spatial dynamics of soil aggregate stability (AS) are urgently needed to better target areas that have undergone soil degradation. However, due to the lack of efficient alternatives to the conventional labor-intensive methods to quantify AS, detailed information on its spatial structure across scales are scarce. The objective of this study was to explore the possibility of using hyperspectral remote sensing imagery to rapidly produce a high-resolution AS map at regional scale. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 230 km2 in the Belgian loam belt were used together with a local topsoil dataset. Partial least squares regression (PLSR) models were developed for three AS indexes (i.e., mean weight diameter (MWD), microaggregate and macroaggregate fractions) and soil organic carbon (SOC), and evaluated against an independent validation dataset. The prediction models were then applied to more than 700 bare soil fields for the production of high resolution (2×2 m) MWD and SOC maps. The PLSR models had a satisfactory level of accuracy for all four variables (R2 >0.5, RPD > 1.4), and the predicted maps were capable of capturing the fine-scale as well as the between-field variabilities of soil properties. Variogram analysis on the spatial structure of MWD showed a clear spatial organization at the catchment scale (range: 1.3 km) that is possibly driven by erosion-induced soil redistribution processes. Further analysis in restricted areas displayed contrasting spatial structures where spatial auto-correlation of AS was only found at field scale, thus highlighting the potential of hyperspectral remote sensing as a promising technique to investigate the spatial variability of AS across multiple scales.

Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2)= 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.

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 μm 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 μm. In contrast, carbon content in the fraction < 20 μm 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.

We thank the authors for their thought-provoking comments on our paper. Most of the commentators agree that soil organic carbon (SOC) sequestration is important for improving the quality of soil, however they argue that we have overstated the potential of soil carbon sequestration. We welcome the comments and appreciate that the issue of SOC sequestration has always been somewhat factious (Schlesinger, 2000). We shall address the significance of the quantity “4 per mille”, reported sequestration rates, the limitation of carbon sequestration with time, and nutrient requirements. We clarify that our paper (Minasny et al., 2017) mainly deals with potentials for the 20 countries and regions, where SOC sequestration can also be seen as a way to improving the resilience of the soil to future climate change, that is, improving adaptation rather than mitigation. We believe that in some parts of the world where food security is threatened, the benefit of soil carbon management for adaptation should be stressed more than for mitigation. This is the reason why the 4 per mille initiative explicitly includes food security (Chabbi et al., 2017; Soussana et al., 2015). We need to add that the “4 per mille Soils for Food Security and Climate” initiative is just one of many national and global initiatives on SOC sequestration for mitigating climate change. The Intergovernmental Technical Panel on Soils (ITPS) of the Global Soil Partnership (GSP) discussed incorporating the topic of SOC in the IPCC Assessment Report (ARs), from AR6 onwards. The IPCC has also put a focus on soil in their upcoming special report “Climate Change and Land” (http://www.ipcc.ch/report/sr2/). The recent FAO Global Symposium (GSOC17) assembled experts engaged in FAO, GSP and its ITPS, IPCC, UNCCD-SPI and WMO activities to work together for the common goal of appropriate SOC management as part of overall sustainable soil management within the climate change mitigation and adaptation, sustainable development, Land Degradation Neutrality (LDN) and food security agendas (http://www.fao.org/about/meetings/soil-organic-carbon-symposium/en/). The Global Research Alliance on Agricultural Greenhouse Gases (GRA) focused on opportunities to reduce agricultural greenhouse gas emissions and increase soil carbon sequestration while still helping to meet food security objectives (http://globalresearchalliance.org/about/). The Common Agriculture Policy in the EU is currently being revised to include the potential use of SOC as an indicator. The 4 per mille initiative was launched at COP21, where the Paris Agreement was adopted, and one of the main aims of the Paris Agreement is to stop the planet from warming an additional two Celsius degrees. The two-degree target, although suggested by scientists through modelling work, was chosen more for political and pragmatic reasons whereby countries could agree on a target that they could work towards (Tollefson, 2015). And of course, there are many scientific critiques of this target (Knutti et al., 2016). Similarly, the 4 per mille initiative comes from a politically-driven aspiration, and our paper (Minasny et al., 2017) is a response to such an aspiration, to seek and outline possibilities based on current knowledge. The important concept is that soil and agriculture are part of the solution, and it is an interim and evidence-based solution that we can implement. Now we shall respond to each of the commentaries.

Copyright © 2017 John Wiley & Sons, Ltd. The implementation of soil and water conservation (SWC) measures in the Ethiopian Highlands is a top priority to reduce soil erosion rates. However, the effectiveness of these measures for different hillslope gradients and land use conditions remains poorly understood. This study addresses this knowledge gap by determining support practice (P) and cover-management (C) factors of the Revised Universal Soil Loss Equation for commonly used SWC structures in semi-arid environments. The factor values were calculated on the basis of soil loss data collected with 21 large runoff plots installed in rangeland and cropland sites. The P- and C-factors were calculated following the recommended procedures. Results show P-factors ranging from 0.32 to 0.74 for stone bunds, from 0.07 to 0.65 for trenches, and from 0.03 to 0.22 for stone bunds with trenches. Reduced storage capacities due to sediment deposition resulted in significant declines of the effectiveness of SWC structures over time. For example, the average P-factor value for trenches increased from 0.1 in the first year after installation to 0.51 after 3 years. C-factor values ranged from 0.23 to 0.82 in rangeland and from 0.03 to 0.35 in cropland. For rangeland, this large variability is due to vegetation cover changes caused by grazing. In cropland, C-factors vary with crop types and tillage practices. The results of this study not only aid in modelling and quantifying the short-term impacts of SWC structures on soil erosion rates but also highlight the importance of considering temporal variations of the effectiveness of SWC measures. ispartof: Geophysical Research Abstracts vol:19 ispartof: location:Vienna status: published

The quantification and the spatialisation of reliable SOC stocks (Mg C ha − 1 ) and total stock (Tg C) 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 (R 2 = 0.64). The model overestimated low SOC stock (below 50 Mg C ha − 1 ) and underestimated high SOC stock (especially those above 100 Mg C kg − 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 26.58 Tg C (SD 1.52) and 43.30 Tg C (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.

Soil erosion severely threatens the soil resource and the sustainability of agriculture. After decades of research, this problem still persists, despite the fact that adequate technical solutions now exist for most situations. This begs the question as to why soil conservation is not more rapidly and more generally implemented. Studies show that the implementation of soil conservation measures depends on a multitude of factors but it is also clear that rapid change in agricultural systems only happens when a clear economic incentive is present for the farmer. Conservation measures are often more or less cost-neutral, which explains why they are often less generally adopted than expected. This needs to be accounted for when developing a strategy on how we may achieve effective soil conservation in the Global South, where agriculture will fundamentally change in the next century. In this paper we argue that smart intensification is a necessary component of such a strategy. Smart intensification will not only allow for soil conservation to be made more economical, but will also allow for significant gains to be made in terms of soil organic carbon storage, water efficiency and biodiversity, while at the same time lowering the overall erosion risk. While smart intensification as such will not lead to adequate soil conservation, it will facilitate it and, at the same time, allow for the farmers of the Global South to be offered a more viable future.

Soil organic carbon (SOC) loss is one of the main causes of soil degradation in croplands. Thus, spatial and temporal monitoring of SOC is extremely important, both from the environmental and economic perspective. In this regard, the high temporal, spatial, and spectral resolution of the Sentinel-2 data can be exploited for monitoring SOC contents in the topsoil of croplands. In this study, we aim to test the effect of the threshold for a spectral index linked to soil moisture and crop residues on the performance of SOC prediction models using the Multi-Spectral Instrument (MSI) Sentinel-2 and the European Land Use/cover Area frame Statistical survey (LUCAS) topsoil database. The LUCAS spectral data resampled according to MSI/Sentinel-2 bands, which were used to build SOC prediction models combining pairs of the bands. The SOC models were applied to a Sentinel-2 image acquired in North-Eastern Germany after removing the pixels characterized by clouds and green vegetation. Then, we tested different thresholds of the Normalized Burn Ratio 2 (NBR2) index in order to mask moist soil pixels and those with dry vegetation and crop residues. The model accuracy was tested on an independent validation database and the best ratio of performance to deviation (RPD) was obtained using the average between bands B6 and B5 (Red-Edge Carbon Index: RE-CI) (RPD: 4.4) and between B4 and B5 (Red-Red-Edge Carbon Index: RRE-CI) (RPD: 2.9) for a very low NBR2 threshold (0.05). Employing a higher NBR2 tolerance (higher NBR2 values), the mapped area increases to the detriment of the validation accuracy. The proposed approach allowed us to accurately map SOC over a large area exploiting the LUCAS spectral library and, thus, avoid a new ad hoc field campaign. Moreover, the threshold for selecting the bare soil pixels can be tuned, according to the goal of the survey. The quality of the SOC map for each tolerance level can be judged based on the figures of merit of the model.

Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.

Soil aggregate stability (AS) reflects a soil's resistance to external erosive forces and is an indicator that varies with changing elementary soil properties across space and time. However, the quantification of AS via conventional wet-sieving is too resource-demanding a task to be carried out at large scales. We explored the possibility of using laboratory Visible-Near infrared (Vis-NIR) spectroscopy to predict aggregate mean weight diameter (MWD), and three aggregate size fractions (i.e. clay + silt, microaggregate (63–250 μm) and macroaggregate (>250 μm)) resulting from wet-sieving. Two spectra-based approaches, one (SPF approach) that built direct linkage between soil spectra and four AS indexes via partial least squares regression and the other (SPF + PTF approach) that established pedotransfer functions based on spectroscopically predicted elementary soil properties, were developed on a total of 83 topsoil samples collected in the Belgian Loam Belt. These two approaches were later compared to a third approach (PTF approach) that built pedotransfer functions using measured elementary soil properties. Results show that, with the PTF approach consistently giving the best performance, all three approaches produced good prediction models (R2: 0.62–0.85; RPD: 1.59–2.46) for MWD, microaggregate and macroaggregate fractions, while no correct model was developed for the clay + silt fraction using the two spectra-based approaches, due to the rather homogenous soil texture in the study area. Soil organic carbon was a major factor that controlled the variations in AS, and a critical threshold of 2% SOC content was found to be the level that separated the “stable” and “unstable” AS classes. This study demonstrated that Vis-NIR spectroscopy is a promising technique that enables large-scale assessment of AS and aggregate size fractions, especially considering that future space-borne hyperspectral imagers will provide high-resolution Vis-NIR spectral data at unprecedented scales.

Traditional methods to assess the soil organic carbon (SOC) content based on soil sampling and analysis are timeconsuming and expensive, and the results are influenced by the sampling design. The aim of this study was toinvestigate the potential of UAS (Unmanned Aerial Systems) multi-spectral imagery (480–1000 nm) for estimatingthe SOC content in bare cultivated soils at a high spatial resolution (12 cm).We performed UAS analysis onthe Hoosfield Spring Barley experiment at Rothamsted (UK) where adjacent plots with distinctly different SOCcontents, due to different long-term management practices, provide a valuable resource to evaluate thisapproach. We acquired images (wavelength: 480–550–670–780–880–1000 nm) at an altitude of 120 m overan area of 2 ha using a multi-spectral camera mounted on an UAS. The high-resolution images captured smallscalevariations at the soil surface (e.g. shadows, tillage andwheels marks). After a projection in newdimensionsby a PCA, we calibrated a support vector machine regression using observations fromconventional soil samplingand SOC measurements. The performance of the calibration had a R2 of 0.98 and a RMSE of 0.17%C. A crossvalidationshowed that the model was robust, with an average R2 of 0.95 and a RMSE of 0.21%. An externalvalidation dataset was used to evaluate the predicted spatial patterns of SOC content and a good fit with anRMSE 0.26%C was obtained. Although this study shows that the methodology has a clear potential for use inprecision agriculture or monitoring important soil properties following changes in management,we also identifyand discuss its limitations and current shortcomings.

Land abandonment is the dominant land use change in the Mediterranean and the resulting vegetation recovery has a strong influence on soil organic carbon (SOC). Yet, the gradual changes in SOC pools during secondary succession remain poorly studied. As SOC is a mixture of pools with distinct functional properties, isolating these pools may provide a better understanding of the decadal SOC dynamics. Topsoil samples were collected along a chronosequence of cropland abandonment in the region north of Malaga (Spain). Five fractions were isolated: particulate organic matter (POM), dissolved organic carbon (DOC), SOC bound to silt and clay (s + c), SOC attached to sand particles or occluded in aggregates (S + A) and a chemically resistant fraction (rSOC). The significant increase in the S + A mass over time indicated aggregation processes along the vegetation recovery stages. Carbon concentrations within S + A or rSOC fractions did not change over time. In contrast, carbon in the s + c fraction significantly increased after three decades from 7.9 g C·kg− 1 to 20.4 g C·kg− 1 at the final stage of the chronosequence. Hence, in addition to the frequently reported increases in POM, carbon was also allocated to the more stable SOC fractions. Taking active carbon (DOC + POM; high turnover rate) and intermediate carbon (s + c, S + A; low turnover rate) as indicators for carbon turnover, the proportion of active carbon increased from 17% to 39% along the chronosequence. Consequently, the proportion of slow cycling carbon decreased from 72% to 54%. These results show that the proportion of labile carbon increases along the secondary succession while, at the same time, the slow cycling carbon fractions sequester carbon (from 0.6 to 1.12 kg C·m− 2).

International audience; The identification of soil management strategies as well as the evaluation of their effectiveness requires detailed information on the spatial and temporal patterns of soil organic carbon storage. High-resolution SOC profile data are generally not available and traditional methods for collecting these are time consuming and costly. Recent studies use geo-statistical approaches to assess the three-dimensional patterns of SOC storage. However, there is still a large discrepancy between the continuous and high resolution mapping of the horizontal SOC variability on the one hand, and the coarse and discontinuous mapping of the vertical SOC profile on the other. In this study, we combine spectroscopic techniques with spatial modeling in a small, cultivated catchment in Germany and we evaluate the contribution of soil redistribution processes and topographical parameters to the observed spatial and vertical patterns. Using high-resolution data from soil cores, we evaluated the robustness of a third order polynomial function to model the vertical SOC profile. Using a crossvalidation, our results show that this approach results in a robust model (RSME = 0.24%) and performs better than the widely used exponential depth model (RMSE = 0.39%). In a next step, we evaluated the relationship between the parameters of the SOC depth model and co-variables including soil redistribution (inferred from 137Cs data) and topographical indices using a multiple linear regression model. The performance was calculated by cross-validation and we found a low robustness of the models because of the low number of profiles (i.e. n = 19). A statistical evaluation of the co-variables highlighted two key factors influencing the SOC vertical distribution. Soil redistribution processes mainly influenced the surface SOC content (first centimeters) whereas the shape of the depth distribution was controlled by slope curvature alone. The mapping of polynomial parameters was validated using an external SOC profile dataset and showed a poor prediction of the surface content but a good prediction of the depth distribution once the surface SOC content is known (RMSE = 0.15–0.25%C). This suggests that estimating the vertical SOC profile from topsoil data by applying remote sensing data, in combination with our SOC profile model, is promising and can will result in an accurate mapping of 3D SOC patterns at a very high resolution.

Fractionation of soil organic carbon (SOC) is crucial for mechanistic understanding and modeling of soil organic matter decomposition and stabilization processes. It is often aimed at separating the bulk SOC into fractions with varying turnover rates, but a comprehensive comparison of methods to achieve this is lacking. In this study, a total of 20 different SOC fractionation methods were tested by participating laboratories for their suitability to isolate fractions with varying turnover rates, using agricultural soils from three experimental sites with vegetation change from C3 to C4 22-36 years ago. Enrichment of C4-derived carbon was traced and used as a proxy for turnover rates in the fractions. Methods that apply a combination of physical (density, size) and chemical (oxidation, extraction) fractionation were identified as most effective in separating SOC into fractions with distinct turnover rates. Coarse light SOC separated by density fractionation was the most C4-carbon enriched fraction, while oxidation-resistant SOC left after extraction with NaOCl was the least C4-carbon enriched fraction. Surprisingly, even after 36 years of C4 crop cultivation in a temperate climate, no method was able to isolate a fraction with more than 76% turnover, which challenges the link to the most active plant-derived carbon pools in models. Particles with density > 2.8 g cm(-3) showed similar C4-carbon enrichment as oxidation resistant SOC, highlighting the importance of sesquioxides for SOC stabilization. The importance of clay and silt sized particles (< 50 mu m) for SOC stabilization was also confirmed. Particle size fractionation significantly outperformed aggregate size fractionation, due to the fact that larger aggregates contain smaller aggregates and organic matter particles of various sizes with different turnover rates. An evaluation scheme comprising different criteria was used to identify the most suitable methods for isolating fractions with distinct turnover rates, and potential benefits and trade-offs associated with a specific choice. Our findings can be of great help to select the appropriate method(s) for fractionation of agricultural soils.

The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability.

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.

International audience; The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha−1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year−1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

Soil organic carbon (SOC) is considered to influence important processes affecting soil, air, and water quality. The management of this valuable resource could be assisted by remote sensing techniques able to provide highresolution spatial estimates of SOC. Such estimations are usually based on empirical regressions that are likely to have poor extrapolation abilities and hence it is important to properly estimate their accuracy in unsampled fields. Based on an imaging spectroscopy image acquired over the Luxembourg (c. 420 km2), several multivariate calibration models (partial least square [PLSR], penalized-spline signal [PSR], and support vector machine [SVMR] regressions) were developed to predict SOC content of topsoil bare agricultural fields and compared. The performance of the models was evaluated by means of cross-validation (k-fold[KFO], leave-one-out [LOO], leave-one-group-out [LOGO], and leave-one-field-out [LOFO]) and these estimates were compared with model performance obtained by validation. The validation set excluded the fields used in the training set, to provide realistic measures of prediction error in unsampled fields. All cross-validation techniques, except LOFO, strongly underestimate validation error. In large areas, training samples are often not a representative subset of the soil and spectral variation. Leave-one-field-out cross-validation, by repeatedly leaving samples belonging to one field out of the calibration, better simulates model error at unknown locations than other cross-validation strategies. The root mean square error (RMSE) of the best models, obtained with a stringent validation procedure (leave-fields-out), was equal to 4.7 g C kg-1. This is higher than most of previous studies using imaging spectroscopy for SOC prediction, suggesting that measures of accuracy obtained by KFO, LOO, and LOGO are likely over-optimistic in large areas. Finally, a SOC content map for the topsoil of croplands was produced that may assist soil monitoring and/or management efforts in this region in the future.

Land-use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites in the temperate zone, carbon response functions (CRFs) were derived to model the temporal dynamic of SOC after five different LUC types (mean soil depth of 30±6cm). Grassland establishment caused a long lasting carbon sink with a relative stock change of 128±23% and afforestation on former cropland a sink of 116±54%, 100 years after LUC (mean±95% confidence interval). No new equilibrium was reached within 120 years. In contrast, there was no SOC sink following afforestation of grasslands and 75% of all observations showed SOC losses, even after 100 years. Only in the forest floor, there was carbon accumulation of 0.38±0.04Mgha-1yr-1 in afforestations adding up to 38±4Mgha-1 labile carbon after 100 years. Carbon loss after deforestation (-32±20%) and grassland conversion to cropland (-36±5%), was rapid with a new SOC equilibrium being reached after 23 and 17 years, respectively. The change rate of SOC increased with temperature and precipitation but decreased with soil depth and clay content. Subsoil SOC changes followed the trend of the topsoil SOC changes but were smaller (25±5% of the total SOC changes) and with a high uncertainty due to a limited number of datasets. As a simple and robust model approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC. © 2011 Blackwell Publishing Ltd.

Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible spectrometers could provide the large amount of spatial data needed. In particular, the ability of airborne imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, airborne imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions. Here, five hyperspectral images acquired with the AHS-160 sensor (430 nm-2540 nm) were analysed with the objective to map soil organic carbon (SOC) at a regional scale. The study area, covering a surface of similar to 420 km(2) and located in Luxembourg, is characterized by different soil types and a high variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and support vector machine regression (SVMR). The performance of these statistical tools was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). Under global calibration, the Root Mean Square Error in the Predictions reached 5.3-6.2 g C kg(-1). Under local calibrations, this error was reduced by a factor up to 1.9. SOC maps of bare agricultural fields were produced using the best calibration model. Two map excerpts were shown, which display intra- and inter-field variability of SOC contents possibly related to topography and land management. (C) 2009 Elsevier B.V. All rights reserved.

During the previous decade, 68% of the municipalities in the Belgian loam belt have been confronted with muddy floods from agricultural catchments after intense rainfall. A muddy flood means that runoff concentrates in a dry valley causing damage to infrastructure and housing property downstream. A typical problem area is the village of Velm where a permanent river is constrained by a culvert designed to accommodate its peak discharge. However, the design of the culvert does not take the local flooding from seven dry valleys just upstream into account. This study focuses on peak discharge from one of these agricultural catchments (ca. 300 ha). The Meshed Hydrological Model (MHM) is used to evaluate the effectiveness of mitigation measures to reduce flooding. Seasonal variation of soil cover in cropland and difference in land use patterns, i.e. before and after land consolidation is explicitly taken into account. The land cover spatial pattern was mapped at regular intervals during 2003. The largest potential of runoff generation occurs in December, and therefore represents a worst-case scenario. Mitigation measures implemented after the extreme event of August 2002 (i.e. a 12 ha grassed waterway and a retention dam in the thalweg) alleviate the flooding risk in Velm. The model simulates a peak discharge and a runoff volume reduction of more than 40%. The retention pond would buffer the generated runoff volume entirely for the worst-case scenario. Land consolidation carried out in the 1970s has led to an increase in peak discharge by 33% and in runoff volume by 19%. The major role played by a new consolidation road built in the thalweg on runoff concentration is highlighted. Implementation of additional soil conservation measures is therefore needed to limit runoff generation within the catchment. (c) 2006 Elsevier B.V. All rights reserved.

International audience; During the last decades, the European loess belt has been confronted with a significant increase in environmental problems due to erosion on agricultural land. Spatially distributed runoff and erosion models operating at the catchment scale are therefore needed to evaluate the impact of potential mitigation measures. Expert-based models offer an alternative solution to process-based and empiricalmodels, but their decision rules are only valid for the local conditions forwhich they have beenderived. The STREAMmodel,whichwas developed inNormandy (France), has been applied in two Belgian catchments having a similar soil texture, as well as in a catchment of southern France differing by soil, land use and climate characteristics. The performance of hydrological models can be assessed for instance by calculating the Nash–Sutcliffe efficiency criterion (ENS).When applied to Belgium, the model results are satisfactory to good after an adaptation of the decision rules (0.90bENSb0.93 for runoff predictions and 0.85bENSb0.89 for erosionpredictions).Given the important environmental differences between Normandy and southern France, the model rules were also adapted for application in the latter environment. Unfortunately, the quality of runoff predictions was insufficient to simulate erosion in southern France. In conclusion, STREAMis a reliablemodel providing satisfactory runoff and erosion predictions in the regionswhere hortonian overland flowdominates.Nevertheless, an adaptation of decision rules based on localmulti-scale (plot, field, catchment) data is needed, before running themodel. STREAMcan then serve as a decision support tool to design for instance flood control measures.

Muddy floods, i.e. runoff from cultivated areas carrying large quantities of soil, are frequent and widespread in the European loess belt. They are mainly generated in dry zero-order valleys and are nowadays considered as the most likely process transferring material eroded from Cultivated hillslopes during the Holocene to the flood plain. The huge Costs Of Muddy flood damages justify the urgent installation of control measures. In the framework of the 'Soil Erosion Decree' of the Belgian Flemish region, a 12 ha-grassed waterway and three earthen dams have been installed between 2002-2004 in the thalweg of a 300-ha cultivated dry valley in the Belgian loess belt. The measures served their purpose by preventing any Muddy flood in the downstream village, despite the Occurrence of several extreme rainfall events (with a maximum return period of 150 years). The catchment has been intensively monitored from 2005-2007 and 39 runoff events were recorded in that period. Peak discharge (per ha) was reduced by 69% between the upstream and the downstream extremities of the grassed waterway (GWW). Furthermore, runoff was buffered for 5-12 h behind the dams, and the lag time at the outlet of the catchment was thereby increased by 75%. Reinfiltration was also observed within the waterway, runoff coefficients decreasing by a mean of 50% between both extremities of the GWW. Sediment discharge was also reduced by 93% between the GWW's inflow and the Outlet. Before the installation of the control measures, specific sediment yield (SSY) of the catchment reached 3.5 t ha(-1) yr(-1) and an ephemeral gully was observed nearly each year in the catchment. Since the control measures have been installed, no (ephemeral) gully has developed and the SSY of the catchment dropped to a mean of 0.5 t ha(-1) yr(-1). Hence, sediment transfer from the cultivated dry valley to the alluvial plain should dramatically decrease. Total cost of the control measures that are built fora 20 year-period is very low (126 (sic) ha(-1)) compared to the mean damage cost associated with muddy floods in the study area (54 (sic) ha(-1) yr(-1)). Similar measures should therefore be installed to protect other flooded villages of the Belgian loess belt and comparable environments. (C) 2008 Elsevier B.V. All rights reserved.

Numerous villages in the European loess belt are confronted with floods caused by runoff from agricultural land. Seventy-nine percent of the municipalities in central Belgium experienced at least one muddy flood during the last decade. Of these flooded municipalities, 22% have been affected more than 10 times during this period. Twenty municipalities have been selected for a detailed analysis. A database of 367 locations affected by muddy floods has been compiled, and the connectivity between cultivated areas and inhabited zones could be assessed for 100 flooded locations. Roads and drainage network facilitate runoff transfer between cultivated and inhabited areas in 64% of cases. Three types of areas producing muddy floods have been identified: hillslopes (1-30 ha) without thalweg where runoff is generally dominated by sheet flow; small catchments (10-300 ha) characterised by runoff concentration in the thalweg and medium catchments (100-300 ha) with multiple thalwegs dominated by concentrated runoff. About 90% of muddy floods are generated on hillslopes and in small catchments. A critical area-slope threshold for triggering muddy floods has been computed for hillslopes. A logistic regression shows that muddy floods are generated in small and medium catchments with 99% probability after 43 mm rainfall. Rainfall depths required to trigger muddy floods are lower in May and June (25 12 mm) than between July and September (46 20 mm), because of different surface conditions (crusting, roughness and crop cover). Each year, muddy floods lead to a total societal cost of 16 x 10(6) - 172 x 106 46 in central Belgium, depending on the extent and intensity of thunderstorms and monetary values damaged. Recent datasets suggest that the phenomenon is becoming more frequent in central Belgium, because of land consolidation, urban sprawl and expansion of row crops, sown in spring, at the expense of winter cereals. The huge costs induced by muddy floods justify the installation of erosion control measures. It is suggested to install a grassed buffer strip at the downslope edge of cultivated hillslopes to protect houses and roads. In small and medium catchments, it is preferred to install a grassed waterway and earthen dams in the thalweg. (c) 2006 Elsevier B.V. All rights reserved.

Laboratory spectroscopy has proved its reliability for estimating soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic carbon located in the VIS-NIR-SWIR (350&ndash, 2500 nm) region. While this approach provides SOC estimates at specific sampling points, geo-statistical or interpolation techniques are required to infer continuous spatial information. UAS-based proximal or remote sensing has the potential to provide detailed and spatially explicit spectral sampling of the topsoil at the field or even watershed scale. However, the factors affecting the quality of spectral acquisition under outdoor conditions need to be considered. In this study, we investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two small-form multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), to predict SOC content. We collected spectral data under both controlled laboratory and outdoor conditions, with the latter being affected by variable illumination and atmospheric conditions and sensor-sample distance. We also analysed the transferability of the prediction models between different measurement setups by aligning spectra acquired under different conditions (laboratory and outdoor) or by different instruments. Our results indicate that UAS-compatible small-form sensors can be used to reliably estimate SOC. The results show that: (i) the best performance for SOC estimation under outdoor conditions was obtained using the VIS-NIR range, while the addition of the SWIR region decreased the prediction accuracy, (ii) prediction models using only the narrow bands of multispectral cameras gave similar or better performances than those using continuous spectra from the STS hyperspectral sensors, and (iii) when used in outdoor conditions, the micro hyperspectral sensors substantially benefitted from a laboratory model calibration followed by a spectral transfer using an internal soil standard. Based on this analysis, we recommend VIS-NIR portable instruments for estimating spatially distributed SOC data. The integration of these sensors in UAS-mapping devices could represent a cost-effective solution for soil research and precision farming applications when high resolution data are required.

• Carbon sequestration in forest ecosystems is an important though still uncertain process in the global greenhouse gas balance. • We computed biomass organic carbon (BOC) stocks of spatially explicit forested landscape units (LSU) in Belgium based on data collected in the regional forest inventories of 1984 (Wallonia region only) and 2000 (Wallonia and Flanders). C stock changes between 1984 and 2000 were estimated for Wallonia. • The total BOC pool stored in Belgian forests in 2000 amounts to 57.8 Mt C in 6 222 km 2 , or 10.0 kg C m −2 in broadleaf, 9.5 kg C m −2 in coniferous and 8.7 kg C m −2 in mixed forest. Based on previous soil organic carbon (SOC) analysis for the same LSU, BOC and SOC stock per LSU appeared only weakly correlated. The total BOC sequestration between 1984 and 2000 equals 5.7 Mt C over an area of 5 107 km 2 , resulting in a flux of 0.07 kg Cm −2 y −1 . The BOC content of broadleaf forest in Wallonia increased with 6%, of coniferous forest with 32% and of mixed forest with 11%. • The observed regional differences in BOC stocks and in BOC sequestration rates are explained by the forest age-class distribution and site productivity. The strength of the spatially explicit approach lies in the fact that BOC and SOC data originating from diverse sampling strategies can be combined for spatial or temporal comparison of C stocks. forest inventory / Belgium / forest biomass / soil / carbon sequestration