During the last 10 years, several attempts to map soil attributes at the scale of mainland France have been realised. We exemplify them by seven major outputs: maps of organic C stocks, trace ...elements (TE), microbial density and diversity, soil thickness, available water capacity (AWC), extractable P, and changes in soil pH. We first briefly describe the data and the methods used to produce these maps and summarise their main results. We then focus on their impacts on various categories of the public, i.e. the general public and citizens; farmers; private companies; non-governmental organisations; agricultural development organisations, stakeholders, and national agencies; French governmental bodies; and international organisations. We also analyse the demands that came to the French National Soil Information Centre from 2008 to 2018 and the impact that our activities had in various media. Soil organic C had the largest impact in nearly all categories of end-users, which may be linked to the recent ‘4 per 1000’ initiative launched by the French governmentduring the COP21 and to the fact that farmers are interested in increasing the organic matter content of their soil for increasing the fertility. TE obtained high scores, which may be related to citizens' care about health and to the fact that governmental bodies and national agencies have a major interest in site contamination assessments. The soil P content, pH, and AWC exhibited major impacts on the agricultural sector. Maps of the soil P content and pH were used as geomarketing tools by private companies selling fertilisers and soil amendments, whereas the AWC was already incorporated into decision-making aid tools for irrigation management developed by development organisations for farmers. Microbial diversity generated collaborations with a large network of farmers and had a large media impact. Nevertheless, the visibility of soil information to the general public should be increased. This can be done by using new multimedia and interactive tools. Overall, these selected examples of digital soil mapping of soil attributes at the national scale in France clearly indicate that the soil attributes have substantial impact on various categories of end-users, such as farmers, professional organisations, stakeholders, and policymakers at different levels of decision-making, among others. However, the impacts on the general public and citizens are more difficult to quantify, and increasing the soil awareness of the general public should be of high priority.
Human societies face six existential challenges to their sustainable development. These challenges have been previously addressed by a myriad of concepts such as soil conservation, soil quality, and ...soil health. Yet, of these, only soil security attempts to integrate the six existential challenges concurrently through the five biophysical and socio-economic dimensions of capacity, condition, capital, connectivity and codification. In this paper, we highlight past and existing concepts, and make a proposal for a provisional assessment of soil security. The proposal addresses three roles of soil: soil functions, soil services and threats to soil. For each identified role, we indicate a potential, but not exhaustive, list of indicators that characterise the five dimensions of soil security. We also raise issues of quantification and combination of indicators briefly. We found that capacity and condition are theoretically easier to measure and quantify than connectivity and codification. The dimension capital might be conveniently assessed using indicators that relate to the economic value of soils. The next step is to test this proposal for which we make recommendations on potential study cases and examples. We conclude that the five dimensions of soil security can potentially be assessed quantitatively and comprehensively using indicators that characterise each role, but also found that there is need for further work to devise an operational measurement methodology to estimate connectivity of people to soil.
Plant available water capacity (AWC) refers to the maximum amount of water that a soil can store and provide to plant roots. Spatial predictions of AWC through digital soil mapping at high resolution ...and national extent provide relevant information for upscaling ecological and hydrological models, and assessment of the provision of ecosystem services like water quantity and quality regulation, carbon sequestration, and provision of food and raw materials. However, the spatial predictions of AWC are prone to errors and uncertainties. Moreover, this digital soil mapping process requires using pedotransfer functions (PTFs) due to the lack of sufficient georeferenced measurements of the upper (i.e., soil moisture at field capacity, θFC) and lower (i.e., soil moisture at permanent wilting point, θPWP) limits of soil moisture contents defining AWC. This adds an additional source of uncertainty to the final estimates of AWC. The objectives of this study were: 1) to predict AWC for mainland France following the GlobalSoilMap (GSM) project specifications on depth intervals and uncertainty, and 2) to quantify the uncertainty of AWC accounting for uncertainty of the soil input variables and the PTFs' coefficients. We first predicted the soil input properties by GSM layer (0–5, 5–15, 15–30, 30–60, 60–100, 100–200 cm), and then applied PTFs for estimating θFC, θPWP, and volumetric AWC (cm3 cm−3). The volume of coarse elements by GSM layer was subtracted before aggregating AWC to estimated soil depth for a maximum of 2 m. The uncertainty of AWC was quantified by first-order Taylor analysis. Independent evaluation indicated that clay had the lowest R2 (clay R2 = 0.27, silt R2 = 0.43 and sand R2 = 0.46) and RMSE (clay RMSE = 128 g kg−1, silt RMSE = 139 g kg−1 and sand RMSE = 172 g kg−1) from the three particle size fractions. However, the model for coarse elements had the worst predictive performance (R2 = 0.14 and RMSE = 21%) among all AWC input variables. The performance of the GSM predictions for θFC and θPWP had a R2 of 0.21 and 0.29. When the PTFs were applied to the spatial predictions of sand and clay, the RMSE for θFC and θPWP had a relative increase of 25% and 36% respectively compared to when they were applied to measured horizon data. Across the majority of mainland France, the main sources of uncertainty of elementary AWC were coarse elements and soil texture, but the contribution of uncertainty of PTFs' coefficients increased in areas dominated by very sandy and clayey textures. An advantage of the produced maps of θFC, θPWP and AWC is that the end users can incorporate associated uncertainties into ecological and agricultural modelling, and decision-making processes involved in soil and water planning.
•We mapped soil available water capacity and its associated uncertainty for mainland France.•We accounted for the uncertainty of soil input variables and pedotransfer functions coefficients.•The spatialization of soil texture increased the RMSE of soil moisture estimates 25–36%.•The main sources of uncertainty were coarse elements and particle size fractions.
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•Pedogenon classes are created by K means clustering on soil forming factors.•Pedogenons define unique soils and characterise the pedodiversity of the study area.•Soil properties of ...genosoil and phenosoil are predicted using VisNIR spectra.•The result shows that genosoils had twice the variation of phenosoils.
Mapping soil classes can support the understanding of soil origin and development, subsequently the soil classes can be used to support monitoring and assessing soil change due to human influence. Pedogenon was proposed as a conceptual soil taxon derived from a set of quantitative state variables representing the soil-forming factors for a given reference time. This study aims to test the pedogenon concept in the Edgeroi region in New South Wales. This paper developed local pedogenons, designed a sampling scheme to capture soil variation under natural conditions and under intensive human activities, and tested the hypothesis that pedogenon is an efficient method of stratifying the landscape to capture soil variation. This study derived the 14 pedogenons by employing layers of soil-forming factors (soil, climate, organism, topography, and parent material and age) using an unsupervised classification technique (k-means clustering). Within each pedogenon, genosoils were identified based on areas with native vegetation, while phenosoils were identified as areas with cropping practises. One meter soil cores were collected for each genosoil and phenosoil, and scanned using Vis-NIR spectrometer for predicting soil properties (clay, sand, cation exchange capacity, pH, and organic carbon). Results show that each pedogenon was characterised by a soil type formed under a dominant parent material occupying a unique position in the landscape. Redundancy discriminant analysis of the soil properties as a function of pedogenon and depth of observations show that pedogenon significantly explained the variation in soil properties. Variance partitioning analysis confirmed that pedogenon explained a large proportion of the variation (49 %) as opposed to landuse (5 %). Principal component analysis of the soil properties shows that genosoils had twice the variation of phenosoils. The results indicate that agricultural activities homogenised the variation of soil profiles. This study demonstrated that pedogenon clasess can effectively characterise soil variation and be used as a benchmark to compare how human activities have altered soil conditions.
The impact from humans on soils, particularly in terms of intensive agriculture, has been most noticeable in the last 200 years. Intensive agricultural activities have caused soil organic carbon ...(SOC) to decline in many parts of the world. However, there is a dearth of approaches that can spatially estimate the change of SOC due to human influence. Here, we used the concept of Pedogenon to stratify the landscape into soil classes called Pedogenons. Within each Pedogenon, we sampled representative soils under native vegetation and soils under intensive human management. We surveyed the lower Namoi Valley area, NSW, Australia (1700 km2), comprising 13 Pedogenons (soil classes) and analysed SOC on 99 soil cores. Using Digital Soil Mapping techniques, the SOC data were used for mapping SOC every 10 cm down to 1 m using environmental covariates. Sampling points under native vegetation were used to map SOC under the native state, and all data were used for mapping SOC current state. By comparing the SOC maps at two states (native and current), we assessed SOC change. The results show that the SOC loss in irrigated cropping areas was the largest, with surface SOC content decreased by 38%, followed by non-irrigated cropping (30% loss), and pasture (19% loss). All cropping areas show a decrease in SOC stock content at least 5 t C ha−1. SOC loss was greatest in the surface soils and decreased exponentially with depth. We further demonstrate that each Pedogenon can be used to define SOC sequestration potential. Understanding SOC change can provide information on areas under SOC loss threat and require immediate remediation.
Soil data are most commonly available as point-support measurements at known spatial locations. However, several potentially useful databases do not retain precise geographic coordinates, but instead ...attribute each measurement to some unit (a region or municipality); these data can be summarized by the mean, variance and number of observations by unit. We investigate methods for mapping based on such datasets using methods based on area-to-point kriging and the linear mixed model (LMM), so that part of the variation is explained using secondary variables. We compare approaches that (i) account for the within-unit variances of primary and secondary variables, (ii) disregard variance data of the primary variable, (iii) disregard variances of the secondary variables, and (iv) disregard both. The approaches are compared through simulation and synthetic studies, before being applied for a real case study, predicting soil K2O in the French Région Centre based on measurements attributed to municipalities. Results suggest that although approach (i) is best in theory (simulations), it is less robust than approach (iii) in practice (synthetic and real case studies) when certain model assumptions might be violated. We therefore recommend approach (iii) that utilizes within-unit variances of the primary variable, but disregards those of the secondary variables.
The French soil-test database (Base de Données d'Analyses de Terre: BDAT) is populated with analytical results of agricultural topsoil samples requested by farmers for fertilization planning. The ...coordinates of the farms are unknown due to data confidentiality policies, and the best available georeference is at level of municipality. We compared four approaches for mapping soil texture of agricultural land in Region Centre (France) using BDAT data: 1) a reference approach of mapping the mean of the aggregated data by municipality, 2) a boosted regression tree (BRT) model fitted with the municipality-averaged data, 3) area-to-point cokriging (AToP CK), and 4) a regression kriging version of this (AToP RCK, for which the BRT predictions were used to give the trend). Specifically, parameters for these last two approaches were fitted through the summary statistics approach to AToP kriging, which accounts for the full set of municipality summary statistics data (i.e. the mean, variance and number of measurements from each municipality). We could thus determine whether more complex and statistically-challenging approaches improve our knowledge on the spatial distribution of soil texture compared with maps of data aggregated by municipality. Texture data from 105 sites form the French soil monitoring network (Réseau de Mesures de la Qualité des Sols: RMQS) were used for independent validation. In general, the R2 was greater for sand (average R2 = 0.69) and silt (average R2 = 0.72) than for clay (average R2 = 0.40). The three methods for disaggregating the summary statistics data (BRT, AToP CK, and AToP RCK) showed similar prediction accuracies—although BRT predictions showed the greatest bias—and were better than the BDAT reference approach. AToP RCK was able to give similar prediction accuracy to BRT modelling alone, reduced the bias considerably, and gave a reasonable (although slightly conservative) assessment of prediction uncertainty. The results indicate that geostatistical methods for change of support expand the utility of aggregated data from soil-test databases.
Quacking aspen (Populus tremuloides Michx.) is an iconic species in western United States that offers multiple ecosystem services, including carbon sequestration. A shift in forest cover towards ...coniferous species due to natural succession, land management practices, or climate change may modify soil organic carbon (SOC) dynamics and CO2emissions. The objectives of this study were to: (i) assess the effects of overstory composition on SOC storage and stability across the aspen-conifer ecotone, (ii) use Fourier transform infrared spectroscopy attenuated total reflectance (FTIR-ATR) to assess whether SOC storage is associated with preferential adsorption of certain organic molecules to the mineral surfaces, and (iii) develop models using near-infrared reflectance spectroscopy (NIRS) to predict aspen- and conifer-derived SOC concentration. Mineral soils (0 - 15 cm) were sampled in pure and mixed aspen and conifer stands in Utah and subjected to physical fractionation to characterize SOC stability (i.e., SOC protected against microbial decomposition), long term laboratory incubations (i.e., SOC decomposability), and hot water extractions (i.e., SOC solubility). Vegetation cover had no effect on SOC storage (47.0 ± 16.5 Mg C ha−1), SOC decomposability (cumulative released CO2-C of 93.2 ± 65.4 g C g−1 C), SOC solubility (9.8 ± 7.2 mg C g−1 C). Mineral-associated SOC (MoM) content was higher under aspen (31.2 ± 15.1 Mg C ha−1) than under mixed (25.7 ± 8.8 Mg C ha−1) and conifer cover (22.8 ± 9.0 Mg C ha−1), indicating that aspen favors long-term SOC storage. FTIR-ATR spectral analysis indicated that higher MoM content under aspen is not due to higher concentration of recalcitrant compounds (e.g., aliphatic and aromatic C), but rather to stabilization of simple molecules (e.g., polysaccharides) of plant or microbial origin. NIRS models performed well during calibration-validation stage (ratio of standard deviation of reference values to standard error of prediction (RPD) ≥ 2). However, model performance decreased during independent validation (RPD = 1.2 - 1.6), probably due to the influence of soil texture, mineralogy, understory vegetation, and land history on SOC spectra. Further improvement of NIRS models could provide insight on SOC dynamics under potential conifer encroachment in semiarid montane forests.
Quacking aspen (Populus tremuloides Michx.) is an iconic species in western United States that offers multiple ecosystem services, including carbon sequestration. A shift in forest cover towards ...coniferous species due to natural succession, land management practices, or climate change may modify soil organic carbon (SOC) dynamics and CO2 emissions. The objectives of this study were to: (i) assess the effects of overstory composition on SOC storage and stability across the aspen-conifer ecotone, (ii) use Fourier transform infrared spectroscopy attenuated total reflectance (FTIR-ATR) to assess whether SOC storage is associated with preferential adsorption of certain organic molecules to the mineral surfaces, and (iii) develop models using near-infrared reflectance spectroscopy (NIRS) to predict aspen- and conifer-derived SOC concentration. Mineral soils (0 – 15 cm) were sampled in pure and mixed aspen and conifer stands in Utah and subjected to physical fractionation to characterize SOC stability (i.e., SOC protected against microbial decomposition), long term laboratory incubations (i.e., SOC decomposability), and hot water extractions (i.e., SOC solubility). Vegetation cover had no effect on SOC storage (47.0 ± 16.5 Mg C ha−1), SOC decomposability (cumulative released CO2-C of 93.2 ± 65.4 g C g−1 C), SOC solubility (9.8 ± 7.2 mg C g−1 C). Mineral-associated SOC (MoM) content was higher under aspen (31.2 ± 15.1 Mg C ha-1) than under mixed (25.7 ± 8.8 Mg C ha−1) and conifer cover (22.8 ± 9.0 Mg C ha−1), indicating that aspen favors long-term SOC storage. FTIR-ATR spectral analysis indicated that higher MoM content under aspen is not due to higher concentration of recalcitrant compounds (e.g., aliphatic and aromatic C), but rather to stabilization of simple molecules (e.g., polysaccharides) of plant or microbial origin. NIRS models performed well during calibration-validation stage (ratio of standard deviation of reference values to standard error of prediction (RPD) ≥ 2). However, model performance decreased during independent validation (RPD = 1.2 – 1.6), probably due to the influence of soil texture, mineralogy, understory vegetation, and land history on SOC spectra. Further improvement of NIRS models could provide insight on SOC dynamics under potential conifer encroachment in semiarid montane forests.
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