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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.

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.

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), 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.

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.

Soil and Water Conservation (SWC) structures, in particular stone bunds and conservation trenches, have been extensively installed in Tigray since the 1980's. As the effectiveness of stone bunds and trenches in reducing runoff and soil loss depends on their retention capacities, it can be expected that this effectiveness declines over time due to infilling with sediment. However, little is known about the rate of this decline during subsequent years. We therefore assessed the effectiveness of SWC structures for two land use types, three slope classes and during three consecutive rainy seasons. Rainfall, runoff and soil loss were measured using 21 large (600-1,000m2) runoff plots at Mayleba catchment. Results show that all studied SWC structures are more effective in reducing soil loss than runoff. Conservation trenches are generally more effective in reducing runoff and soil loss than stone bunds. However, due to their infilling with sediment, their effectiveness quickly declines over time. By the end of the third rainy season, their effectiveness was reduced to about one third of their initial effectiveness. The effectiveness of stone bunds remained fairly constant during three consecutive rainy seasons. These findings have important implications, as they demonstrate that many of the installed SWC structures (especially in rangelands) are only very effective for short periods (one to two years). Regular sediment removal from conservation trenches is therefore crucial to preserve their effectiveness over longer periods.

Evidences exist that the spatial variability of soil organic carbon (SOC) in cropland is partially controlled by environmental or human factors acting on a field basis (e.g., agricultural management, landuse history, landscape structure). However, few studies have quantified the relative importance of the fields-related variability at the regional scale. Recent airborne hyperspectral imagery methods provide SOC estimates at high resolution and over large surfaces. They may be used to quantify and explain the spatial variation of SOC. In this study we used a SOC hyperspectral image over Luxembourg to separate SOC variation in three components: the effect of the texture class (as defined by a texture map), the effect of fields (as defined by a cadastral map) and spatially dependent residuals. The relative variance of these components and the spatial structure of the residuals were rigorously assessed by restricted maximum likelihood (REML). Results indicated that 65.7 ± 0.3% of the variance of SOC in the study area was explained by texture classes. The intensity of the field effect was largely dependent on the location. In some sub-areas of homogeneous texture class, no significant effect could be observed while in others, field explained up to 68.8 ± 12.0% of the variance. In contrast with other methods like ANOVA, the method developed here measures the variation related to spatial units (soil map units or fields) while taking explicitly into account spatial dependencies. As soon as the boundaries of these spatial units and a spatially extensive (or at least very dense) knowledge of a soil property over a region of interest are available, it allows rigorously determining if the soil property depends on these spatial units. In the present application, findings pointed out the importance of considering fields-related variability in SOC modeling and mapping studies.

Land abandonment has been the main land use change in rural Mediterranean areas over the last decades. The secondary succession following land abandonment is strongly affected by precipitation, which in turn determines the change of soil organic carbon (SOC) and other soil properties. However, SOC consists of different fractions with contrasting resistance to decomposition that are differently affected by land abandonment. The aim of this study is assessing the evolution of different carbon fractions after land abandonment along a West–East precipitation gradient in southern Spain (Gaucin (GAU): 1080.5 mm y − 1 –Almogia: 650 mm y − 1 –Gergal (GER): 350 mm y − 1 ). In each site, samples from a chronosequence of land abandonment were size fractionated, yielding three fractions with increasing degrees of carbon stability (coarse, intermediate, fine). Following land abandonment, there was a transitional grassland state in GAU and ALM that promoted a quick SOC gain of 0.87 and 0.42 kg m − 2 , respectively. The majority of this gain, i.e. 79.2% in GAU and 71.4% in ALM, occurred in the most stable fractions. SOC was rapidly stabilised due to the presence of litter with a low C:N ratio. The colonisation of grasslands, in GAU and ALM, or croplands, in GER, by woody vegetation triggered SOC accumulation of 0.07, 0.78 and 0.74 kg m − 2 . However, in GAU there was at the same time a SOC loss in the most stable fractions (0.23 kg m − 2 ). In ALM and GER a reverse trend was observed: SOC accumulated in the intermediate and fine fractions. SOC accumulation in the coarse fraction was related to the low quality of the litter from woody vegetation, that had a higher C:N ratio than litter from grasslands. The largest effect of precipitation was found in the fine fraction, whereas SOC in the coarse fraction was mostly controlled by land abandonment and related factors, such as vegetation, litter quality and soil conditions.

Agricultural management has received increased attention over the last decades due to its central role in carbon (C) sequestration and greenhouse gas mitigation. Yet, regardless of the large body of literature on the effects of soil erosion by tillage and water on soil organic carbon (SOC) stocks in agricultural landscapes, the significance of soil redistribution for the overall C budget and the C sequestration potential of land management options remains poorly quantified. In this study, we explore the role of lateral SOC fluxes in regional scale modelling of SOC stocks under three different agricultural management practices in central Belgium: conventional tillage (CT), reduced tillage (RT) and reduced tillage with additional carbon input (RT+i). We assessed each management scenario twice: using a conventional approach that did not account for lateral fluxes and an alternative approach that included soil erosion-induced lateral SOC fluxes. The results show that accounting for lateral fluxes increased C sequestration rates by 2.7, 2.5 and 1.5 g C m(-2) yr(-1) for CT, RT and RT+i, respectively, relative to the conventional approach. Soil redistribution also led to a reduction of SOC concentration in the plough layer and increased the spatial variability of SOC stocks, suggesting that C sequestration studies relying on changes in the plough layer may underestimate the soil's C sequestration potential due to the effects of soil erosion. Additionally, lateral C export from cropland was in the same of order of magnitude as C sequestration; hence, the fate of C exported from cropland into other land uses is crucial to determine the ultimate impact of management and erosion on the landscape C balance. Consequently, soil management strategies targeting C sequestration will be most effective when accompanied by measures that reduce soil erosion given that erosion loss can balance potential C uptake, particularly in sloping areas.

A large amount of soil organic carbon (SOC) is laterally redistributed by agricultural erosion. Recent studies have shown that this leads to strong horizontal (i.e., spatial) and vertical (i.e., with soil depth) gradients in SOC stock and C pool distribution in eroding landscapes. However, the mechanisms leading to these gradients in relation to erosion and deposition are still poorly documented. In particular, the effect of the inherent properties of SOC (as controlled by the SOC pool composition) versus the effect of depth-related soil environmental condition (i.e., differences in soil humidity, temperature, aeration, etc.) on the persistence of SOC in eroding landscapes is uncertain. Nonetheless, a detailed understanding of these factors is important to correctly assess landscape-scale soil C turnover and vulnerability to disturbance from human activities. This study utilizes observational data on long-term erosion/deposition rates and C pool composition derived from soil C fractionation experiments along an eroding agricultural hillslope to constrain a coupled erosion-SOC dynamics model. The simulation results show that the data set used can result in a robust parameter estimation of a multipool C model for an eroding landscape with parameter values that are consistent with incubation experiments. A scenario analysis, where we evaluate the contribution of different processes, demonstrates that soil redistribution is essential to explain the observation that depositional locations contain more SOC in subsoils, while the SOC content of the surface layer is similar to those observed along an eroding hillslope. The spatial variability of plant production could explain some of the observed variability in SOC content, but our results suggest that the spatial variability of SOC pool composition is mainly related to soil redistribution. Finally, we suggest that environmental factors may play a more important role than the inherent properties of SOC in determining the vertical variation of SOCmineralization. This implies that depositional C stocks might be highly vulnerable to disturbance from human activities that may reconnect the buried SOC with the atmosphere.

Land abandonment is the dominant form of land use change in the Mediterranean over the last decades, and determines the soil organic carbon (SOC) evolution during the secondary succession following abandonment. However, the rate of succession strongly depends on climatic conditions and the extent to which these determine the SOC dynamics is largely unknown. The aim of this study is determining these dynamics along a precipitation gradient (1085-650-350 mm yr1) on noncalcareous rocks in southern Spain. Fields abandoned in different periods, as verified on aerial photographs taken in 1956, 1977, 1984, 1998, 2001, 2004 and 2009, were selected using a chronosequence approach. SOC was determined using a spectrometer, vegetation was described, and NDVI calculated from Landsat images. SOC and NDVI evolution were analysed subsequently. In the two wettest sites SOC increased after land abandonment until it approached a plateau. Mean accumulation rates were 0.11 kg C m2 y1 for the wettest and 0.06 kg C m2 y1 for the intermediate site. These sites reached the long-term state, similar to the stocks in (semi) natural fields, in c.a. 10 years (wettest) and c.a. 35 years (intermediate). SOC and NDVI followed parallel trends, so SOC stocks were mainly driven by inputs from vegetation. At the dry end of the gradient, where NDVI’s (

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.

Detecting soil organic carbon (SOC) gain or loss is challenging due to large uncertainties deriving from temporal and spatial variability of organic carbon concentrations, even at the field scale. In order to reduce these uncertainties, we used the organic carbon associated with clay and fine silt particles (fine fraction) rather than SOC in bulk soil for assessing decadal changes. This approach reduces the impact of the inherent variability of labile carbon on SOC estimates. We analysed archived soil samples taken in 1970 and recent ones taken in 2012 from an on-going long-term field trial in the Hesbaye region in Belgium. The experiment started in 1959 and contains three contrasting management practices (36 replicates): residue export (RE), farmyard manure (FYM) and residue restitution (RR). After 42 years, there are no significant differences in bulk soil organic carbon concentrations between treatments (RE = 9.2 g C kg1 soil; FYM = 10.4 g C kg1 soil; RR = 10.1 g C kg1 soil). In contrast, there are significant differences (p < 0.05) in stable carbon concentration (C associated to the fine fraction) between treatments over the same time period (RE = 13.2 g C kg1 clay andfine silt; FYM = 16.6 g C kg1 clay and fine silt; RR = 15.4 g C kg1 clay and fine silt). Moreover, we can be 99% confident that stable carbon in the fine fraction increased between 1970 and 2012 in FYM (+19%, p < 0.01) and RR plots (+14%, p < 0.01). There was a small, but significant, change of stable carbon in RE plots over the same period. In 1970, no differences in stable carbon concentration were detected between residue treatments. Labile carbon did not change significantly from 1970 to 2012 but its variability increased for all plots except for the RE treatment. We used the Rothamsted carbon model (RothC-26.3) to describe SOC changes under the different residue treatments. For bulk soil, observed trends in FYM and RR SOC concentrations are in line with the ones predicted. Modeled SOC changes from 1962 to 2012 are 14% (RE) and +10% (FYM). We also used RothC-26.3 to understand the evolution of the sensitive and slow fractions over time. On the one hand, we found that RothC was not capable to simulate the range of observed SOC concentrations inter- annual variability. On the other hand, the increase of the RothC pool with slow decomposition (HUM) was similar to the trend in the carbon associated with the fine fraction observed in the FYM and RR plots. This finding highlights that residue management can increase the storage of C in more stable fractions in agricultural soils, even when no changes are detected in bulk soil C.

We investigated the effect of both the calibration set size (number of samples) and the calibration sampling strategy on the performance of vis–NIR models to predict clay content and exchangeable Ca (Ca++). We evaluated the following calibration sampling algorithms: Kenard–Stone (KSS), conditioned Latin hypercube (cLHS) and fuzzy c-means (FCMS), which are commonly used in spectroscopy and digital soil mapping. These algorithmswere tested separately using a field-scale dataset and a regional scale dataset. For each datasetwe randomly selected a validation subset and the remaining samples were used as candidates for calibration sampling. The accuracy of vis–NIRmodels of clay content and Ca++were compared on the basis of the sampling algorithms used for selecting the calibration samples.We also tested 38 different calibration set sizes varying from10 to 380 samples. The vis–NIR models were calibrated by using the support vector regression machine (SVM) algorithm. The training root mean square error (RMSE), the normalized RMSE and the prediction RMSE were used to evaluate the sensitivity of the models to both the sampling algorithmand the calibration set size. In addition, we investigated the sample representativeness of each algorithm and we suggest a novel and simple methodology to identify an adequate calibration set size based only on the vis–NIR data (i.e. without prior knowledge of the response variables). As expected, our results showthat the error of the soil vis–NIRmodels depends on the calibration set size.When the number of calibration samples is relatively small the sampling algorithm may play an important role on the accuracy of the vis–NIRmodels.On the other hand, if the calibration set size is large enough, the samplingmethod is not a critical issue. Concerning the sample representativeness, we found for all the algorithms that the original distribution of the vis–NIR data can be better replicated by increasing the calibration set size. The results indicate that the calibration samples selected by the cLHS and by the FCMSalgorithms better replicate the original vis–NIR distribution of all the samples, in comparison to those samples selected by the KSS algorithm.

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples andmultivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect. A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas. Moreover, the study of laboratory ASDreflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350 2500 nm] spectral range was computed to translate this differential influence. Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa.

The heterogeneity of the spatial distribution of soil organic carbon (SOC) at the landscape scale is generally not considered in regional or national SOC dynamics models. In cropland this heterogeneity is controlled largely by topography, which influences the distribution of water, energy and sediments, and thus the SOC dynamics. Sediment redistribution rates have increased strongly since the mechanization of agriculture. The over-simplification of landscape processes in regional models of C dynamics may add to the uncertainty in C balances. Therefore, a better characterization of the importance of landscape-scale effects on the SOC distribution throughout a region is needed. This study characterized the relative importance of geomorphology in the SOC horizontal and vertical variability across croplands in the Belgian loess belt region. A large legacy dataset of soil horizons was exploited together with 147 recently sampled profiles. Mean SOC depth profiles for different soil types were compared. Various topographic attributes were computed from a digital elevation model, and their influence on SOC was quantified through simple linear models. Finally, SOC content was mapped at three depth layers through multiple linear models, and results were cross-validated. The legacy dataset allowed identification of significant differences in the mean SOC profile according to texture, drainage or profile development classes. A clear relationship between SOC content and topographic attributes was demonstrated, but only for the recently sampled profiles. This may be explained by a substantial error in the location of the profiles of the legacy dataset. This study thus shows evidence that the major control of the vertical distribution of SOC is related to topography in a region where observed heterogeneities for other commonly involved factors are limited. However, the large amount of unexplained variability still limits the usefulness of the spatial prediction of SOC content, and suggests the importance of additional influencing factors.

Due to the large spatial variation of soil organic carbon (SOC) content, assessing the current state of SOC for large areas is costly and time consuming. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring SOC based on empirical equations and spectral libraries. While the approach has been demonstrated to yield accurate predictions for databases containing samples belonging to soils with similar characteristics such as mineralogy, texture, iron, and CaCO3 content, spectroscopic calibrations have been less successful when applied to large and diverse soil spectral libraries. The scope of this study was to predict SOC using a local partial least square regression approach. In total, 19,969 topsoil (0-20cm) samples collected all over the European Union were analyzed for physical and chemical properties, and scanned with a Vis-NIR spectrometer in a single laboratory. The local regression method builds a different multivariate model for each sample to predict. Each local model is trained with neighbours' samples selected from a large spectral library, based on their spectral similarity with the sample to predict. We modified the local regression procedure by including other covariates (geographical and texture information) in the computation of the distance between samples. The results showed good prediction ability for mineral soils under cropland (RMSE=3.6gCkg-1) and grassland (RMSE=7.2gCkg-1). Predictions of mineral soils under woodland (RMSE=11.9gCkg-1) and organic soils (RMSE=51.1gCkg-1) were less accurate. The use of sand content in the computation of the sample similarities provided the most accurate SOC predictions due to its influence on light scattering properties of soils. In large datasets, using additional soil or environmental information allows to select neighbours that have overall the same soil composition as the samples to predict, resulting in more accurate models. This study shows that (i) it is possible to realize low-cost estimations of SOC at continental scale using large spectral libraries with a reasonable accuracy, and (ii) the local approach is a valuable tool to deal with large datasets, especially if existing soil property maps or soil legacy data could be used as covariates in the SOC prediction models.

The outcomes of the discussion in the four working sessions showed that although there is an urgent need to improve soil carbon management and stocks, and despite the existing knowledge about good agricultural practices to achieve this goal, these are not put into practice effectively and globally. The apparent contradiction has to do with a mismatch of policies at different societal and geographical scales, and the low policy profile of SOC. All participants agreed in the need to bring SOC into the core of environmental policies at all levels and to improve the governance of policy actions by addressing the stakeholders in a more effective way. Fil: Banwart, Steven. University of Sheffield. Kroto Research Institute; Reino Unido Fil: Black, Helaina. James Hutton Institute; Reino Unido Fil: Cai, Zucong. Nanjing Normal University; China Fil: Gicheru, Patrick. Kenya Agricultural Research Institute; Kenia Fil: Joosten, Hans. University Greifswald; Alemania Fil: Victoria, Reynaldo. Universidade de Sao Paulo; Brasil Fil: Milne, Eleanor. State University Of Colorado Boulder; Estados Unidos Fil: Noellemeyer, Elke Johanna. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Pascual, Unai. Basque Centre for Climate Change; España Fil: Nziguheba, Generose. Columbia University; Estados Unidos Fil: Vargas, Rodrigo. University of Delaware; Estados Unidos Fil: Bationo, Andre. International Fertilizer Development Center; Ghana Fil: Buschiazzo, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina Fil: de Brogniez, Delphine. European Commission; Italia Fil: Melillo, Jerry. Ecosystems Centre of The Marine Biological Laboratory; Estados Unidos Fil: Richter, Dan. University of Duke; Estados Unidos Fil: Termansen, Mette. University Aarhus; Dinamarca Fil: van Noordwijk, Meine. International Centre for Research in Agroforestry; Indonesia Fil: Goverse, Tessa. United Nations Environment Programme. Division of Early Warning and Assessment; Kenia Fil: Ballabio, Cristiano. European Commission; Italia Fil: Bhattacharyya, Tapan. Indian Council of Agricultural Research. National Bureau of Soil Survey and Land Use Planning. Division of Soil Resource Studies; India Fil: Goldhaber, Marty. United States Geological Survey; Estados Unidos Fil: Nikolaidis, Nikolaos. Technical University of Crete; Grecia Fil: Zhao, Yongcun. Chinese Academy of Sciences; República de China Fil: Funk, Roger. Leibniz Centre for Agricultural Landscape Research. Institute for Soil Landscape Research; Alemania Fil: Duffy, Chris. State University of Pennsylvania; Estados Unidos Fil: Pan, Genxing. Nanjing Agricultural University; China Fil: la Scala, Newton. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: Gottschalk, Pia. Potsdam Institute for Climate Impact Research; Alemania Fil: Batjes, Niels. International Soil Reference and Information Centre. World Soil Information; Bélgica Fil: Six, Johan. Eidgenössische Technische Hochschule Zürich; Suiza Fil: van Wesemael, Bas. Université Catholique de Louvain; Bélgica Fil: Stocking, Michael. University of East Anglia; Reino Unido Fil: Bampa, Francesca. Università di Padova; Italia Fil: Bernoux, Martial. French Research Institute for Development; Francia Fil: Feller, Christian. French Research Institute for Development; Francia Fil: Lemanceau, Philippe. Institut National de la Recherche Agronomique; Francia. University of Burgundy; Francia Fil: Montanarella, Luca. European Commission; Italia

The effects of soil redistribution on the carbon (C) cycle and the need for spatially and depth-explicit C estimates at large scales have recently been receiving growing attention. In eroding agricultural landscapes, C gets transported from erosional to depositional landscape elements forming a heterogeneous pattern in quantity and quality of the distributed carbon. At present, methods and research to characterize this horizontal and vertical variability are either limited to local slope scales or, if applied to larger scales, to surface soil horizons with large uncertainties when extrapolated to deeper layers. In this study, we used soil profile data collected in two zones of differing soil texture (loam and clay-rich soils) in Luxembourg, to calibrate a linear mixed-effect model to predict the 3D soil C stock distribution on a regional scale for cropping systems using a set of spatially-explicit hydrologic, climatic, pedologic and geomorphologic variables. We demonstrate that due to a high spatial variability of C stocks it is mandatory to consider various environmental processes to predict C accurately on a regional scale, especially in deeper soil layers, and to avoid simple depth extrapolation of topsoil C data as has been done earlier in flat landscapes. Using estimates of topsoil C contents derived from hyperspectral remote sensing, we predict spatial patterns of C stocks for cropland on a regional scale and provide new insights into the spatial heterogeneity of soil C storage covering a large area. The variability of C stocks in the two texture zones expressed as values larger or smaller than the mean ± standard deviation is hereby lower in the loam zone (26.2%) than in the clay zone (38.7%). We estimate a mean C stock (to 100 cm soil depth) of 9.4 ± 3.1 kg/m2 for the clay-rich soils and 11.3 ± 2.4 kg/m2 for loamy soils. This represents the first regional estimate for C stocks for the research area using continuous spatial explicit datasets.

Land degradation and recurrent drought are the major threats to rain-fed agriculture in the semi-arid Ethiopian highlands. Water harvesting has become a priority in the Tigray region since 1990. However, the success of water harvesting in reservoirs is limited due to reduced inflow. The aim of this study was to investigate the effects of typical land-use types, slope gradients, and different soil and water conservation (SWC) structures on runoff and soil loss at the runoff-plot scale. Six runoff measuring sites, corresponding to three slope gradients, were established for cropland (cultivated land for annual crop production) and rangeland (heavily grazed land on hillslopes with high rock-fragment cover) at Mayleba catchment in Tigray, Ethiopia. SWC structures tested were stone bunds, trenches, and stone bunds with trenches, in addition to control plots. In total, 21 large runoff plots (with lengths of 60 to 100 m) were monitored daily for runoff production and soil loss during the main rainy season (July-September) in 2010. The results show that the seasonal runoff coefficient (RCs) representing the fraction of rainfall measured as runoff was much higher for rangeland (0.38 < RCs < 0.50) compared to that for cropland (0.11 < RCS < 0.15). Seasonal soil loss (SLs) values were five to six times larger on rangeland (28.6 < SLs < 50.0 ton ha-1) compared to that for cropland (4.6 < SLs < 11.4 ton ha-1). Stone bunds with trenches were the most effective SWC structures in reducing runoff and soil loss. With the same SWC structures installed, RCs and SLs for both rangeland and cropland tend to decrease with increasing slope gradient mainly due to a corresponding increase in rock-fragment cover. The effects of SWC structures on runoff production and soil loss are considerable; hence, it is crucial to consider these effects for optimal design of water-harvesting schemes such as micro-dams that collect and store surface runoff for irrigation development in the Ethiopian highlands.

Silicon (Si) is a key element in global biogeochemical cycling and recent research has shown that changes in the biological component of the Si cycle may lead to more rapid variations in the land-ocean Si transfer than previously thought. The objective of this paper is to better understand the controls on temporal Si dynamics in terrestrial ecosystems, by studying Si fluxes from a small forested catchment in central Belgium. An intensive monitoring program (2008-2010) of dissolved and amorphous silica (DSi and ASi) concentrations and load patterns show that DSi concentrations are signif- icantly lower during winter-spring periods than during summer-autumn periods. In contrast to what was found in other studies, seasonal dynamics in Meerdaal forest are not controlled by variations in biological uptake or temperature, but mainly by the more important supply of pore-water to the groundwater table in winter-spring periods. Analysis of seasonal and event fluctuations in stream water DSi concentra- tions showed that final stream water is a mixture of old, DSi rich water pushed out of the soil, and new, DSi poor water delivered by quick flow. The mixing of old and new water finally resulted in streamwater DSi concentrations responding only moderately to varia- tions in discharge (near-chemostatic behaviour). We estimated the total DSi export from the system to be ca. 65.1 9 10 3 mol km -2 year -1 . Because Si deliv- ery is biologically regulated through an important Si cycle in the vegetation-soil continuum, an anthropo- genic (e.g. agricultural expansion) or climatic distur- bance of terrestrial ecosystems may alter both water residence times through shifts in hydrological regimes and the DSi chemical equilibrium concentration in soils. In turn, these perturbations will potentially alter long-term DSi and ASi inputs to aquatic systems.

Visible and near infrared diffuse reflectance spectroscopy has produced promising results to infer soil organic carbon (SOC) content in the laboratory. However, using soil spectra measured directly in the field or with airborne imaging spectrometers remains challenging due to uncontrolled variations in surface soil conditions, like vegetation cover, soil moisture and roughness. In particular, soil moisture may dramatically degrade predictions of SOC content when using an empirical approach. This study aims to quantify the effect of soil moisture on the accuracy of SOC predictions, and propose a method to determine SOC content for moist samples with unknown moisture content. More than 100 soil samples were collected along a transect, in the Grand-Duchy of Luxembourg. The soil samples were air-dried, moistened in steps of 0.05 g water g soil - 1 until saturation, and scanned in the laboratory with a visible and near infrared diffuse reflectance spectrometer. We computed the normalized soil moisture index (NSMI) to estimate the soil moisture content of the samples (R 2 = 0.74), and used it to spectrally classify the samples according to their moisture content. SOC content was predicted using separate partial least square regressions developed on groups of samples with similar NSMI values. The root mean square error of prediction (RMSE) after validation was always below 5 g C kg - 1, with a ratio of prediction to deviation (RPD) greater than 2. The SOC content prediction models with a-priori knowledge of soil moisture gave similar RMSE as the ones after the NSMI classification. Hence, the NSMI might be used as a proxy of moisture content to improve SOC content prediction for spectral data acquired outside the laboratory since the method is simple and does not need other data than a simple band ratio of the spectra. © 2012 Elsevier B.V. All rights reserved.

While the implementation of soil conservation depends on a multitude of factors, it is also clear that rapid change in agricultural systems only happens when a clear economic incentive is present. This fact, as well as the fact that agriculture will change fundamentally in the Global South over the next decades, need to be accounted for when developing a vision on how we may achieve effective soil conservation in the Global South. In this paper we argue that smart intensification is a necessary component of such strategy. Smart intensification will not only allow to make soil conservation more economical, but will also allow to make significant gains in term 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 to offer the farmers of the Global South a viable future.

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.

Semiarid karst landscapes represent an important ecosystem surrounding the Mediterranean Basin for which little is known on runoff generation. Knowledge of the sources and patterns of variation in infiltration-runoff processes and their controls is important for understanding and modelling the hydrological functions of such ecosystems. The objectives of this paper are to determine the infiltration rates and their controls in a representative mountain karst area (Sierra de Gador, SE Spain) at micro-plots and to investigate the integrated response of rainfall on a typical hillslope. Rainfall simulations in micro-plots and natural rainfall-runoff monitoring on a hillslope were carried out complementarily. We investigated the role of soil surface components (vegetation, rock outcrop, fracture, and soil crust), topographic position, antecedent soil moisture, and rainfall characteristics in regulating infiltration-runoff processes. Results of rainfall simulation revealed the importance of vegetation cover and the presence of rock fractures in promoting the infiltration in the limestone karst landscape, while bare patches and rock outcrops acted as sources for runoff. All plots with >50% vegetation cover had no runoff with up to 55mmh-1 of simulated rain. In contrast, nearly all bare plots had runoff under the same simulated rain, with runoff coefficients ranging from 3.1 to 20.6% on dry soil surface conditions, and from 2.0 to 65.4% on wet soil surfaces. Runoff coefficients amounted to 59.0-79.5% for rock outcrops without cracks, but were drastically reduced by the presence of cracks. The surfaces with rock fragments resting on the soil (generally located in the middle of the slopes) prevented more effectively the runoff generation than those surfaces where rock fragments were embedded in the top soil. Antecedent soil moisture had significant impact on runoff generation, with wet soil having doubled runoff coefficient, shortened time to runoff, and increased runoff rate compared to the same but dry soil. Linear regressions indicated that the main controls for constant infiltration rate were the cover percentages of vegetation and litter, plus rainfall intensity; while the major controls for runoff coefficient were the bare soil and vegetation coverage, plus rainfall intensity. High infiltration rates measured at the micro-plots agreed with low intra-event runoff coefficients (mostly below 1%) observed under natural rainfalls at the hillslope. Runoff depth and coefficient at the hillslope was significantly correlated with rainfall depth, maximum hourly rainfall intensity and antecedent precipitation over 20days (AP20). During the 1.5-year monitoring period from Sep-2003 to Mar-2005, the overall infiltration was 41% of the total rainfall amount and the maximum infiltration rate was almost 94% of the largest single rainfall event. The results from this study contribute to improved understanding of the magnitude and controls of the surface runoff in semiarid karst mountain areas. © 2011 Elsevier B.V.

Inventories of soil organic carbon (SOC) stocks for 1960, 1990 and 2000 have recently become available for Belgium. From these inventories we selected three agricultural regions (Polders, Loam belt and Condroz) with 60 to 80 % of the agricultural area under cropland in order to analyse the driving forces of the changes in SOC stocks over time. The observed values of SOC stocks of typical soil associations for each agricultural region are compared to simulations with the RothC soil carbon model. After estimating the local parameters by fitting the model to SOC values from a long term experiment in central Belgium, the model was run from 1960 to 2000 for typical soil profiles of soil associations in the three agricultural regions. The main factors inducing changes in SOC stocks are the increase in plough depth as a result of continued mechanisation in the 1960's and the sustained input of organic amendments in the form of farm yard manure and slurry. In contrast to earlier publications on CO2 emissions from agricultural soils, the model did not predict a decrease in SOC stocks for the period 1990-2000. The decrease in animal manure production observed during the 1990's for those regions with a concentration of intensive livestock breeding in Flanders suggests that SOC stocks in croplands will continue to decrease. This will lead to an emission of 0.41 Mt CO2 per year for the three main cropland areas of Belgium in the near future and hence partly offset the carbon sequestration potential of improved cropland management (0.95 Mt CO2 per year).

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.

As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach).

Amorphous, biogenic Si (ASi) is stored in large amounts in terrestrial ecosystems. The study of terrestrial ASi mobilization remains in the pioneer research stage: most Si budget studies have not included the biogenic amorphous Si stock and fluxes. This hampers our ability to accurately quantify terrestrial mobilization of Si, which isthrough ocean carbon burial and CO2 uptake during terrestrial Si weatheringintricately linked to global carbon budgets. We studied detailed concentration and load patterns of dissolved (DSi) and ASi during several high-discharge events in eight first-order river basins. Based on high frequency discharge measurements and concurrent analysis of ASi and DSi concentrations at base flow and during intense precipitation events, we were able to attribute a percentage of yearly ASi and DSi fluxes to both base flow and precipitation event related surface run-off. Our results show ASi and DSi concentrations in upstream river basins to be intricately linked to each other and to discharge, and ASi transport constitutes an important part to the total transport of Si even through first-order river basins (up to 40%). Based on our observations, increased occurrence of peak-discharge events with global climatic changes, and lowered importance of base flow, will coincide with drastic changes in ASi and DSi dynamics in the river continuum. Our work clearly shows ASi dynamics should be incorporated in global Si budgets now, even in low-order small river basins.

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.

Soil monitoring programmes face significant challenges as there is an important trade-off between detecting significant changes in soil properties on the one hand (which can be achieved by minimizing variability by higher sampling density or stratification approaches), and identifying the driving forces responsible for these changes on the other hand (which requires enough variability). This study aims to reconcile these two objectives by identifying the driving forces of soil organic carbon (SOC) evolution over a long period, based on an extensive but stratified soil monitoring programme. Data at both the finest level (questionnaires to the farmers) and the large scale (agricultural census, climate and soil databases for southern Belgium) were used in a cluster analysis, multiple linear regressions and mixed odels in order to discriminate between the driving forces involved. Results indicated that the negative 'baseline effect' (i.e. the inversely proportional effect of the initial SOC content on the SOC evolution) was responsible for an important part of the SOC variability. Consequently, the systems are not at steady state when starting the observations, although this assumption is used by most SOC dynamic models. Moreover, the baseline effect resulted in a trend of the soils to converge towards a regional SOC stock which significantly differed according to land use (36.4 t C ha-1 for the plough depth of cropland and 92.2 t C ha-1 for the 0-30 cm layer of grassland). Despite this strong effect, the main driving forces of the SOC decrease of cropland (-0.2 t C ha-1 yr-1) and SOC increase of grassland (+0.2 t C ha-1 yr-1) over a period of 50 years were discriminated. The agricultural management (cropland) and the clay content (grassland), together with the change in precipitation (to a lesser degree for cropland) were highlighted as the predominant factors involved in SOC evolution, when land use change is excluded. The use of questionnaires allowed to better understanding the impact of an intensive agricultural management on the SOC content, as the lowest SOC stocks were associated to the most intensively managed fields. The mixed models partly succeeded in predicting SOC evolution as they presented still large uncertainties after validation (mean error from 3% to 25%, root mean square error of prediction from 21% to 242%). While SOC monitoring schemes are increasingly being implemented, our results will likely apply to those using a similar design. It was shown that this strategy succeeded to reconcile both the SOC change detection and the distinction of the driving forces involved at the regional scale.

The effectiveness of water harvesting systems collecting surface runoff form rangeland hillslopes in semi-arid regions is difficult to predict, since the hydrological response at the outlet depends on the heterogeneity of hydrological processes. The lack of continuous runoff pathways, due to the irregular spatial patterns of soil properties and the variety of antecedent soil moisture conditions directly influence runoff generation and control discharge into the water harvesting cisterns. The aim of this paper is to evaluate the effectiveness of semi-arid hillslopes in generating runoff for water harvesting systems. Runoff was estimated by the STREAM expert-based model which was applied to three semi-arid hillslopes (0.4 to 6 ha). On the one hand the STREAM model rules were adapted to the regional conditions i.e. an antecedent precipitation index was adjusted to local soil moisture conditions and the rainfall duration was defined as the total rainfall event quantity and the effective rainfall duration (P-tot/t(eff)). On the other hand, the distribution of rock outcrop and vegetation cover along the slopes was used to define homogeneous hydrological units. Final infiltration capacities were attributed to these hydrological units based on values found in the literature. The prediction performances are acceptable for the three water harvesting systems with an RMSE of 13.9 m(3). It was shown that the rainfall/runoff model was more sensitive to the duration of the storm than to the antecedent soil moisture conditions. The use of a unique set of hydrological parameters for the three water harvesting systems on representative hillslopes allows the runoff prediction from any rangeland hillslope within the same region. Furthermore, the spatial patterns of soil surface characteristics are crucial for collecting runoff at the outlet of the system. Model runs demonstrated that degradation of vegetation and sealing of very small areas within flow paths can lead to an increase of annual runoff by as much as a factor two. (C) 2009 Elsevier B.V. All rights reserved.

The evolution of SOC stock over time is difficult to assess at a regional scale due to the small magnitude of the changes, to the important spatial variability of SOC and the lack of detailed information on present and past management practices. This paper aimed to detect changes in SOC stocks of agricultural soils of southern Belgium over a long time period (1955-2005), and to determine the driving forces of SOC evolution. The stratification of the study area into homogeneous units (based on land use, soil type, climate and agricultural region) and the re-sampling of soil profiles from the 1950s allowed detection of significant changes in SOC stocks. The use of equivalent masses for SOC stock comparisons based on the plough depth of 2005 allowed excluding dilution effect from changes in plough depth or in bulk density. For units under cropland, an average decrease of 5.8 t C ha(-1) was measured in the plough layer (from an initial equivalent SOC stock of 46.4 t C ha(-1)), while for units under grassland, an average increase of 21.9 t C ha(-1) was observed in the 0-30 cm depth (from an initial equivalent SOC stock of 61.2 t C ha(-1)). Explanatory factors include human driving forces (land management) and inherent soil properties. The decrease in mass of farmyard manure and slurry applied on cropland along with the change in the types of crops cultivated (progressive replacement of cereals by root crops and fodder) could explain the decrease in SOC stocks observed for cropland, while the increase in livestock density per grazing area has lead to an increase in the SOC stocks of grassland. The increase in plough depth for cropland (+1.5 cm) was slightly correlated to the silt content (r=-0.14) but not to the decrease in SOC content. The impact of erosion or climate on SOC changes remained uncertain. Soil texture was not highlighted as a driving force in the SOC changes, while a strong negative relationship existed between the initial SOC content and the change in SOC content. Therefore, any attempt to increase SOC content in agricultural soils should mainly focus on farming practices through adapted regulations and policies. (c) 2007 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.

Isotope signatures in precipitation from the Global Network for Isotopes in Precipitation around the Mediterranean basin and literature data are compared with isotopic data from a large karstic aquifer in southeast Spain to explain the origin and type of the precipitation events dominating recharge. Analysis of the deuterium excess d at the scale of the Mediterranean basin and at the regional scale allows us to understand the isotopic context of the study area: Campo de Dalias and the Sierra de Gador (Almerfa province). The origin of precipitation can be determined from its d value. The d value changes as a function of the initial evaporation condition. It depends on the relative humidity and temperature during the evaporation producing the water vapour of the clouds. The water vapour, which dominates the study area, is generated in two areas: the Atlantic Ocean (d = 10 parts per thousand) and the western Mediterranean basin (d = 15 parts per thousand). With increasing precipitation volume, the western Mediterranean character dominates. These heavier storms contribute mainly to recharge, as illustrated by the d value of 13.6 parts per thousand in deep groundwater of the Campo de Dalias. Weighted d values increase with the volume of precipitation, giving a significant relationship for the southern and eastern coasts of the Iberian Peninsula. This selectivity of d to monthly precipitation was used to estimate the return period of precipitation leading to aquifer recharge at 0.9-4.9 years. Moderate rainfall, which occurs more frequently, still represents similar to 60-90% of the total precipitation. One of the challenges to meet ever-growing water demands is to increase recharge from moderate events yielding intermediate quantities per event, but forming the bulk of the annual precipitation. Copyright (c) 2006 John Wiley & Sons, Ltd.

International audience; The soil science community is facing a growing demand of regional, continental, and worldwide databases in order to monitor the status of the soil. However, the availability of such data is very scarce. Cost-effective tools to measure soil properties for large areas (e.g., Europe) are required. Soil spectroscopy has shown to be a fast, cost-effective, environmental-friendly, nondestructive, reproducible, and repeatable analytical technique. The main aim of this paper is to describe the state of the art of soil spectroscopy as well as its potential to facilitating soil monitoring. The factors constraining the application of soil spectroscopy as an alternative to traditional laboratory analyses, together with the limits of the technique, are addressed. The paper also highlights that the widespread use of spectroscopy to monitor the status of the soil should be encouraged by (1) the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and (2) the scanning of existing soil archives, reducing the need for costly sampling campaigns. Finally, routine soil analysis using soil spectroscopy would be beneficial for the end users by a reduction in analytical costs, and an increased comparability of results between laboratories. This ambitious project will materialize only through (1) the establishment of local and regional partnerships among existent institutions able to generate the necessary technical competence, and (2) the support of international organizations. The Food and Agriculture Organization (FAO) of United Nations and the Joint Research Centre of the European Commission are well placed to promote the use of laboratory and field spectrometers for monitoring the state of soils.