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Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns.

Soil erosion by water is a major cause of land degradation in the highlands of Ethiopia and anywhere else in the world, but its magnitude and variability are rarely documented across land uses and climatological conditions. The purpose of this study was to examine runoff and soil loss responses under cropland (CL) and grazing land (GL) management practices in three climatic regions of the Ethiopian highlands: semi-arid (Mayleba), dry sub-humid (Gumara), and humid (Guder). We measured runoff and soil loss using runoff plots with and without soil and water conservation (SWC) measures (trenches, stone/soil bunds [embankments] with trenches on the upslope side, and exclosure) during the rainy season (July-September). The results revealed significant variation in runoff and soil loss amounts across land uses, SWC measures, and climatic regions. At Mayleba, seasonal runoff and soil loss in control plot were far higher from GL (280 mm, 26.5 t ha-1) than from CL (108 mm, 7.0 t ha-1) largely due to lack of protective vegetation cover and soil disruption because of intense grazing. In contrast, at Gumara and Guder, seasonal soil loss values were much higher from CL (21.4-71.2 t ha-1) than from GL (0.6-24.2 t ha-1) irrespective of runoff values. This was attributed to the excessive tillage/weeding operations involved in cultivation of teff (cereal crop) at Gumara and potato at Guder. Although SWC measures (practices) substantially reduced runoff and soil loss (decreased by 23%-86%) relative to control plot, seasonal soil loss under GL uses with trenches at Mayleba (12.6 t ha-1), CL with soil bunds and trenches at Gumara (22.1 t ha-1), and Guder (21.4 t ha-1) remained higher than the average tolerable soil loss rate (10 t ha-1 year-1) proposed for the Ethiopian highlands. This suggests that SWC measures should be carefully designed and evaluated specific to land use and climatic conditions. Overall, the results of this study can help improve SWC planning in regions where land use and climate impact on soil erosion vary across geographical areas, as they do in Ethiopia and anywhere else. However, further investigation is crucial with replication of measurements over years and locations to provide more accurate information on land use, management and climate controls on hydrological and soil erosion processes. ispartof: JOURNAL OF ENVIRONMENTAL MANAGEMENT vol:326 issue:Pt A ispartof: location:England status: published

The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 μm and analysing the SOC concentration in the soil as well as in the fine fraction (< 20 μm). Eight biological indicators measured in an earlier study across the same monitoring network for the 0–10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers’ fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r²=0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5–10 years)

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

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

Visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy is being increasingly used for soil organic carbon (SOC) assessment. Common practice consists of scanning soil samples using a single spectrometer. Considerations have rarely been documented of the effects of using multiple instruments and scanning conditions on SOC model calibration that occur when merging soil spectral libraries (SSLs), particularly in soils with low SOC concentration and using both field spectroradiometers and laboratory fixed spectrometers. To address this gap, we scanned 143 low-SOC-content soil samples using three spectrometers (ASD FieldSpec 3, ASD FieldSpec 4, and FOSS XDS) and four setup features - FOSS, contact probe (CP), dark-box (DB), and open laboratory (LAB) - at three laboratories. The application of an internal soil standard (ISS) to align one laboratory spectrum with another for spectral correction and spectral merging of various SSLs was examined. SOC models were developed using i) data from each single spectrometer – single laboratory separately and ii) merged data from multiple spectrometers – different laboratories, applying the 1st derivatives of spectra and random forest (RF) regression. The results indicate that the spectral shape and wavelength position of key features obtained from all spectrometers and setups did not show any noticeable differences, though spectra based on FOSS setup, particularly on low-SOC samples, demonstrated greater range in absolute derivative values regardless of ISS application. The derivative ISS-corrected spectra showed less variation among different spectrometers compared to their uncorrected raw reflectance spectra. All single spectrometer models predicted SOC reasonably well. However, the spectra acquired by the FOSS setup predicted SOC more accurately (R2 = 0.77, RPIQ = 3.30, RMSE = 0.22 %, and SD = 0.04) than the spectra acquired by the other setups. The models derived from merged uncorrected raw reflectance spectra yielded poor results (R2 = 0.48, RPIQ = 2.33, RMSE = 0.33 %, and SD = 0.10); nevertheless, assessment of SOC using the 1st derivative ISS-corrected merged SSLs considerably improved the prediction accuracy (R2 = 0.70, RPIQ = 3.10, RMSE = 0.25 %, and SD = 0.06). Hence, the derivative spectra coupled with the ISS correction improved the accuracy of SOC prediction models obtained from the merged soil spectra collected in different environments using different instruments. We therefore recommend application of the ISS spectral alignment method linked to the 1st derivative approach to enhance the compilation of SSLs at the regional and global scales for SOC assessment.

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.

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 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.

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.

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.

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 (

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.

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 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.

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.

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.

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.

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.

The spatial distribution of soil properties often displays complex and multiscale patterns of variation. It results from multiple soil processes acting simultaneously but at different scales. Hence, characterizing the influence of a given controlling factor on the soil property is made more difficult by the variation due to other controlling factors. In this context, separating the variation of the soil properties by spatial scales could allow disentangling the combined effect of controlling factors and would provide a qualitative and quantitative characterization of controlling factors separately. In this paper, geostatistical tools have been used to separate the scales of variation of two soil properties (i.e. SOC and texture) coming from a legacy dataset in the Belgian Loess Belt. Scale components were predicted separately and the relationships between soil properties were analyzed at different scales. Results illustrated that the contents of a given soil property in different depth layers were typically more correlated when only the long range components were compared. Similarly, the link between SOC and texture components was also clearer for the long range components. This means that soil processes acting at local or landscape scale influence soil properties differently according to their nature or to the depth considered. Eliminating the variation at this scale allows to better characterize the relationships between depth layers and soil properties. The study gives insights for further spatial mapping of SOC by focusing on more appropriate variables at specific spatial scales. Furthermore, we raise the interest of spatial filtering for detecting inconsistencies inside composite datasets.

The global carbon cycle has an important influence on global change. Soil organic carbon (SOC), the largest component of the terrestrial carbon pool, plays a vital role in the terrestrial carbon cycle and therefore knowledge of soil organic carbon pools and fluxes is required. This paper assesses typical values and measures of variation for soil organic carbon contents of soil-land use combinations in Belgium, further termed landscape units (LSU). Data from several heterogeneous data sets collected around the years 1990 and 2000 are used. Their heterogeneity is related to the purpose for which the soil carbon data were collected, the measurement technique, the nature and total depth of the sampled layers and the level of spatial aggregation with which the data were made available. In order to make integrated use of the data and present a spatial and temporal uncertainty assessment of the SOC contents of LSU, we exploit all available information on variability using simple statistical rules. Two thirds of the LSU under arable or grassland have lost SOC between 1990 and 2000 for the upper 20 cm. Approximately one third of these LSU show significantly decreased SOC stocks. The decreases are more pronounced in northern Belgium (region of Flanders), which is possibly due to specific manure regulations. The average SOC content of cropland in the upper 100 cm decreased from 8.8 kg C m(-2) in 1990 to 8.4 kg C m(-2) in 2000. A comparable decrease is observed under grassland, namely from 13.9 to 13.0 kg C m(-2). For soils, the highest SOC losses occur in the sandy soil associations of northern Belgium. LSU under forest could only be assessed for the year 2000. Forest soils store on average between 14.8 and 15.5 kg C m(-2) in the upper 100 cm of mineral soil, depending on forest type. These values are comparable to grassland but significantly higher than cropland. (c) 2004 Elsevier B.V. All rights reserved.

Sequestration of carbon in soils and vegetation can contribute to meeting greenhouse gas emission reduction targets for individual countries under the Kyoto Protocol. In this paper, the carbon mitigation potential of a series of agricultural land-management practices that are relevant to Belgian agriculture is quantified. Reforestation of a part of the total agricultural area is also considered, and the sequestration potential is corrected for practices already in use before the 1990 baseline. Existing and future agro-environmental policies as well as regional policies aimed at reducing environmental side effects of spreading organic amendments were considered when calculating the areas for which sequestration measures could be applied. The results are brought together in a realistic scenario. The mean sequestration potential expressed in Gg C per year for Belgium is as follows: additional bio-energy crops (96.2), spreading farmyard manure formerly applied to grassland on arable land (84.4) woodland regeneration (31.7), adopting no-till farming on suitable soils over a period of 20 years (15.8), the use of cover crops following winter cereals (15.2, adopted over 20 years), improved management of farmed peat soils (13.4) and organic fanning (2.2). These figures suggest that, by 2010, Belgium can only expect a reduction in CO2 emissions ranging from 0.47 to 0.90% of the 1990 greenhouse gas emissions by improving agricultural management. These measures should not however be neglected as they will have other positive effects on soil properties, and they could offer a solution to reduce emissions from the agricultural sector by 4.7-9.0% of the 1990 values. (C) 2003 Elsevier B.V. All rights reserved.

Tillage erosion is increasingly recognised as an important soil erosion process on agricultural land. In view of its potential significance, there is a clear need to broaden the experimental database for the magnitude of tillage erosion to include a range of tillage implements and agricultural environments. The study discussed in this paper sought to address the need for such data by examining tillage erosion by a duckfoot chisel plough in stony soils on steep slopes in a semi-arid environment. Results of the investigation of coarse fraction (rock fragment) translocation by tillage in this environment have been presented elsewhere and the paper focuses on tillage translocation and erosion of the fine earth. Tillage translocation was measured at 10 sites, representing both upslope and downslope tillage by a duckfoot chisel plough on five different slopes, with tangents ranging from 0.02 to 0.41. A fine-earth tracer, comprising fine earth labelled with 134 Cs, was introduced into the plough layer before tillage. After a single pass of the plough, incremental samples of plough soil were excavated and sieved to separate the fine earth from the rock fragments. Translocation of the fine-earth tracer was established by analysing the 134 Cs content of the samples of fine earth. These data were used to establish translocation distances for each combination of slope and tillage direction. Translocation distances of the fine earth were not significantly different from translocation distances of the coarse fraction. For all sites, except uphill on the 0.41 slope, translocation distances were found to be linearly related to slope tangent. The soil flux due to tillage for each site was calculated using the translocation distance and the mass per unit area of the plough layer. For slopes with tangents

The spatial variation of rock fragment cover (Rc) and rock fragment size (Rs) along semiarid hillslopes and transects in the Mediterranean is largely controlled by hillslope gradient. Total rock fragment cover (Rc>5 mm) often increases in a convex upward curve with hillslope gradient while the D50 of the surface rock fragments >5 mm increases linearly with hillslope gradient. On south-facing slopes, Rc>5 mm is slightly higher than on north-facing slopes. Lithology controls the size distribution of the stone pavement rather than its cover percentage. Spatial variation of rock fragment cover reflects spatial variation in past erosion and deposition rates. Hillslope sections that are steep, south-facing, or have been abandoned a long time ago have undergone intense interrill and rill erosion, and thus have high rock fragment covers. Tillage erosion leads to high rock fragment covers on convex hillslopes in intensively cultivated areas. Thus, using information on hillslope gradient, aspect, lithology and landuse, we have been able to describe the spatial variation of rock fragment cover and size along semiarid hillslopes in southeast Spain. Such information is crucial for understanding and modelling the spatial variation of the hydrological and erosion response of semiarid hillslopes under environmental change, especially in semiarid environments of the Mediterranean where vegetation cover is predicted to decrease due to climatic or landuse changes and rock fragments at the surface become the only soil surface stabilisers.

The growing awareness of the potential of traditional agricultural techniques has resulted in a recent scientific interest in these techniques. As modernisation and mechanisation often lead to the abandonment of these measures, it is vital to . document them. Gravel mulching is such a traditional technique that is still practised in Chamoson southern Switzerland . As no written information about this technique in Chamoson was found, an inquiry was set up amongst the wine-growers of Chamoson. The main objective of this inquiry is to document gravel mulching, based on the experiences of the wine-growers themselves. The results of the inquiry showed that these practitioners in Chamoson consider the effects of the gravel mulch . . on the thermal temperature and hydraulic infiltration and evaporation characteristics of the soil, to be the most important for wine-growing. In addition to the inquiry, field experiments were set up to investigate the effects of a gravel mulch on soil temperature and evaporation during summer. The results of these experiments show that a gravel mulch causes a significant increase in the soil temperature at two measured depths mean increase of 0.978C at 3-cm depth and 1.58 Ca t 10 cm; . . measuring period: 21r07r94-14r08r94 and at the surface mean increase of 2.28C; measuring period: 24 h . As for the effect on evaporation, field experiments show an increase of the cumulative evaporation depth, due to the presence of a gravel mulch. This unexpected result can be explained when considering the soil characteristics of the mulched field i.e., a . very stony topsoil with a discontinuous mulch and the rainfall regime during the measuring period limited amounts of . rainfall; non-saturated soils . The results of the evaporation experiment in the field, agree with the results from similar laboratory experiments reported in the literature. q 1998 Elsevier Science B.V. All rights reserved. .

Intensively cultivated areas in the upper part of the Guadalentin catchment (southeast Spain) show a systematic spatial pattern of surface rock fragment cover (Rc). The objective of this paper is to quantify and to explain this spatial rock fragment cover pattern. Therefore, a map of an intensively cultivated area of 5 km2 was digitised, and for each pixel total topographic curvature was calculated. Next, rock fragment cover was determined photographically at 35 sites with a range of total slope curvatures. A linear relation between total curvature and rock fragment cover was found, except for narrow concavities. It was hypothesised that this pattern can be explained by a significant net downslope movement of rock fragments and fine earth by tillage. The displacement distances of rock fragments by tillage with a duckfoot chisel were measured by monitoring the displacement of tracers (painted rock fragments and aluminium cubes) on 5 sites having different slopes. The rare of tillage erosion for one tillage pass with a duckfoot chisel, expressed by the diffusion constant (k), equals 282 kg/m for up and downslope tillage and only 139 kg/m for contour tillage. Nomograms indicate that mean denudation rates in almond groves due to tillage erosion (3 to 5 tillage passes per year) can easily amount to 1.5–2.6 mm/year for contour tillage and up to 3.6–5.9 mm/year for up- and downslope tillage for a field, 50 m long and having a slope of 20%. These figures are at least one order of magnitude larger than reported denudation rates caused by water erosion in similar environments. Hence tillage erosion contributes significantly to land degradation. The downslope soil flux induced by tillage not only causes considerable denudation on topographic convexities (hill tops and spurs) and upper field boundaries but also an important sediment accumulation in topographic concavities (hollows and valley bottoms) and at lower field boundaries. Kinetic sieving (i.e. the upward migration of rock fragments) by the tines of the duckfoot chisel also concentrates the largest rock fragments in the topsoil in such a way that a rock fragment mulch develops in narrow valleys and at the foot of the slopes. These results clearly indicate that tillage erosion is the main process responsible for the observed rock fragment cover pattern in the study area. Since the study area is representative for many parts of southern Spain where almond groves have expanded since 1970, the results have a wider application. They show to what extent intensive tillage of steep slopes has contributed to the increase in soil degradation, to changes in hillslope morphology (i.e. strong denudation of convexities, development of lynchets and rapid infilling of narrow valley bottoms) and to the development of rock fragment cover patterns which control the spatial variability of the hydrological and water erosion response within such landscapes.

Decomposition of leaf litter and the distribution of elements in the humus layer were studied in mediterranean deciduous, sclerophyllous and coniferous forests on acid rocks. The results indicate a clear difference in relative decomposition rate between pine needles (Pinus pinaster: 0.12 year−1) and leaves of deciduous and sclerophyllous species (Quercus cerris, Quercus suber and Arbutus unedo: 0.30 year−1). The concentrations of N, P, S and Ca increase upon decomposition, whereas that of K decreases by initial leaching, and those of Mg, Mn (Fe, Al) remain unchanged except for an increase resulting from mineral contamination. In deciduous and sclerophyllous litter, absolute amounts of N, P, S and Ca increase until a critical concentration level is reached, after which net mineralization occurs. For pine needles net mineralization was not observed within 915 days. In analogy with the situation during the litter bag experiments, elemental concentrations are highest in the lower more decomposed part of the humus profiles. In deciduous and sclerophyllous forests net mineralization of N, P, S and Ca starts in the lower part of the fermentation layer. In the coniferous forest elemental concentrations are much lower and no indications of N, P, S and Ca mineralization were found in the ectorganic horizons.