The study of pedological maps from the Armorican Massif evidenced the effect of recent tectonics (500 000–700 000 years BP) on the regional hydromorphic soil distribution. Blocs in relative uplift were characterized by a low proportion of hydromorphic soils, whereas a higher proportion marked blocs in relative downlift. Such clear differences can be related to the denudation regime which affects topography and saprolite properties, two soil formation factors. Improvements in soil modelling may be achieved by taking into account the regional trends of soil waterlogging and hydromorphy. L'analyse de cartes pédologiques du Massif armoricain suggère un effet de la tectonique récente (500 000–700 000 ans BP) sur la distribution régionale des sols hydromorphes. Les blocs tectoniques en voie de surrection présentent une faible proportion de sols hydromorphes, tandis que les blocs en voie d'affaissement sont caractérisés par une proportion plus forte. Ces différences peuvent s'expliquer par la topographie et la qualité des altérites, deux facteurs majeurs de l'organisation des sols, en relation étroite avec le rythme de denudation. Cette étude permet de mieux comprendre la distribution des sols et d'envisager une amélioration des modèles de prédiction de cette distribution.
Because soils are both a source and a sink for atmospheric CO2, there is an increasing need to characterize the spatial distribution of soil C pools. Large amounts of organic carbon (OC) accumulate in hydric bottom-lands soils. In the Armorican Massif (Western France) where these soils represent 20% of the total surface area, the spatial characterization of OC pools is difficult to assess due to methodological problems such as high spatial variability. Soil color indexes, which combine various characteristics of soil horizons or profiles, are an alternative approach for quantifying the differences in OC storage. In addition, terrain attributes derived from Digital Elevation Models (DEM) may be useful in characterizing the distribution of soil color indexes over large areas. Thus, the overall goal of this work was the development and application of a model for use in predicting the organic carbon (OC) content of soil areas. To accomplish this, extensive examination of soil morphology combined with selected terrain attributes measured in the field and calculated from a digital elevation model (DEM) were used. Soil samples were collected in Western France from a 2-ha agricultural parcel that forms the major part of a hillslope. The results indicate that OC stocks of the entire profile were correlated highly to a soil hydromorphic index (HI) (r2 = 0.80). HI is a function of the percent of the total soil profile depth constituted by horizons with some degree of hydromorphic feature development and the moist color of the surface A horizon. Using a stepwise regression technique, we constructed a prediction model of HI distribution by using the relations between HI and (i) the elevation above the stream bank (ES) (r2 = 0.80); (ii) the downslope gradient (DG) (r2 = 0.55); and (iii) the upslope contributing area (AMU) (r2 = 0.60). Validation of this model on a second site showed that topographical attributes explained up to 75% of the profile OC stock variability. These results confirmed that the integration of a soil index and topographical information is a useful tool for prediction of OC distribution. In addition, the use of soil morphologic indexes could significantly improved the construction and the validation of soil-landscape models because it would minimize laboratory measurements of OC reservoirs.
Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.
The spatial or temporal variability of soil has been extensively considered in the literature using either experimental or modeling approaches. However, only a few studies integrate both spatial and temporal dimensions. The aim of this paper is to present a method for field‐scale simulations of the spatio‐temporal evolution of topsoil organic C (OC) at the landscape scale over a few decades and under different management strategies. A virtual landscape with characteristics matching part of Brittany (France) was considered for the study. Stochastic simulations and regression analysis were used to simulate spatial fields with known spatial structures: short‐range, medium‐range, and long‐range variability. These were then combined using an additive model of regionalization. Agricultural land use was simulated considering four different land uses: permanent pasture, temporary pasture, annual cereal crops, and maize ( Zea mays L.). Land use evolution over time was simulated using transition matrices. Evolution of soil organic matter was estimated each year for each pixel through a rudimentary balance model that accounts for land use and the influence of soil waterlogging on mineralization rates. This spatio‐temporal simulation approach at the landscape level allowed the simulation of several scales of soil variability including within‐field variability. Spatial variability decreased drastically over time when only the influence of land use was considered. This effect on soil variability over the landscape may have implications for site‐specific soil management and precision agriculture. The presence of redoximorphic conditions was found to maintain soil spatial variability.
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