Evaluation biophysique des services écosystémiques des sols cultivés - Adaptation de l’information pédologique pour la modélisation dynamique du fonctionnement des sols

Soils provide many ecosystem services essential to sustain human life and socio-economic development. However, soils are subjected to increasing pressure from current activities, including intensive land use to satisfy demands of a growing population for food and energy. To improve soil management, decision-support tools that consider soil diversity are required to assess impacts of human activities on soil dynamics. The objective of this study was to develop and validate a methodology to enhance existing pedological information (1: 250,000) using spatial disaggregation technique in order to estimate in space soil ecosystem services.By combining field observations, disaggregated soil data with known accuracy and dynamic modelling, six soil ecosystem services indicators were firstly assessed, to be then used to evaluate the sensitivity of estimated soil ecosystem services to the source of soil information. The main results highlight the contribution of digital mapping to produce relevant pedological information for assessing soil ecosystem services from cultivated soils. Future research must be performed to improve pedological information availability and soil ecosystem services assessment procedure by coupling dynamic coupling and proxies. Overall, this work emphasize the need to produce soils consideration in sustainable management strategies and territorial planning.Les sols rendent de nombreux services écosystémiques essentiels au maintien de la vie planétaire. Toutefois, ils sont soumis à une pression croissante pour satisfaire les demandes d’une population croissante en matière de production alimentaire et énergétique. En vue d’une meilleure gestion, des outils d’aide à la décision intégrant la diversité des sols sont nécessaires pour évaluer l’impact des activités anthropiques sur l’évolution des sols. Cette thèse se focalise notamment sur le développement d’une méthodologie de mise à disposition de l’information pédologique contenue dans les bases de données existantes (1/250 000) par une approche de désagrégation spatiale en vue de l’évaluation de six services écosystémiques des sols cultivés.Le couplage des données locales, des données spatialisées avec une précision connue et de la modélisation dynamique permet en premier temps de produire des indicateurs de services écosystémiques des sols cultivés et en deuxième temps d’évaluer l’effet de la source de l’information pédologique sur les services écosystémiques instruits. Les résultats obtenus montrent l’apport de la cartographie numérique pour la production de l’information pédologique nécessaire à l’évaluation des services écosystémiques. Ils permettent également d’identifier les voix d’amélioration possibles pour proposer une voie hybride d’évaluation des services écosystémiques couplant la modélisation et les proxies. Il s’agit ainsi de promouvoir la prise en compte des sols dans des stratégies de gestion durable et de planification territoriale

Axe thématique Collectivités et pouvoirs publics - Retour sur le séminaire ADEME Multifonctionnalité des sols

National audienc

Validation at regional scale of soil property maps derived from spatial disaggregation of large scale legacy soil maps

International audienc

Assessment of six soil ecosystem services by coupling simulation modelling and field measurement of soil properties

International audienc

Mapping soil properties at multiple depths from disaggregated legacy soil maps in the Brittany region, France

International audienc

The accuracy of soil information influences assessment of soil ecosystem services in Brittany, France

Soil is natural capital that provides several ecosystem services that contribute to human well-being and sustainable socioeconomic development. The scarcity of soil information constitutes the main shortcomings for assessing soil ecosystem services (SESs). The aim of this study was to assess effects of the accuracy of soil information on estimates of SES in agricultural systems using a modeling approach. To this end, three soil datasets that differed in accuracy were used: (i) legacy maps of soil properties, (ii) maps of disaggregated soil properties at 50 m spatial resolution and (iii) field observations (reference soil database). The supply of two regulating SESs (climate regulation and water quality regulation) and four provisioning SES (nitrogen (N)-to-plant provision, water to plant provision, plant biomass provision and groundwater recharge) over 30 years was estimated from daily outputs of 7437 simulations of the STICS soil-crop model. The main results showed that i) estimated SES supply, particularly of climate regulation and N-to-plant provision, depended on both inherent and manageable soil properties and was marginally sensitive to the accuracy of soil information, ii) using disaggregated soil maps marginally increased the accuracy of soil property predictions and thus partially compensated for the lack of soil information when assessing SESs over large areas, and iii) relationships among SESs (i.e. correlation coefficients) generally remained the same regardless of the soil dataset used. The results demonstrated that considering the accuracy of soil information in SES assessment approaches deserves more research

Analysis of extreme values of soil ecosystem services predicted from associated soil properties and weather conditions

International audienc

Validation of digital maps derived from spatial disaggregation of legacy soil maps

International audienceSpatial disaggregation of soil map units involves downscaling existing information to produce new information at a finer scale than that of the original source. Currently, it is becoming a powerful tool to address the spatial distribution of soil information over large areas, where legacy soil polygon maps are the only source of soil information. Because of the high expense of additional resampling, few studies have sought to validate disaggregated soil maps using independent sampling. This study implemented spatial disaggregation approach to measure the quality of soil property predictions derived from disaggregated soil maps, using stratified simple random sampling of a study area of 6848 km2 (11 strata and 135 soil profiles). In a previous study, the existing legacy soil polygon map of Brittany (France) at 1:250,000 scale was spatially disaggregated at 50 m resolution using an algorithm called Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART), which uses soil-landscape expert rules of soil distribution in space. By fitting equal-area spline functions, soil properties were then estimated at six depth intervals according to GlobalSoilMap specifications. To validate disaggregated soil maps, two approaches were developed according to the soil attribute nature (continuous or categorical). For categorical soil properties (soil parent material, soil drainage class, soil type and soil depth class), the overall strict purity (the degree to which all classification criterion are respected) by the most probable STU (Soil Typological Unit) map was estimated at 34%, while the overall average purity reached 70%. The overall partial soil-type purity reached 60%, the overall partial parent material purity reached 78% and the overall partial soil drainage class as well as soil depth class purities reached 65% and 78%, respectively. Continuous soil properties (clay content, fine silt content, coarse silt content, total silt content, fine sand content, coarse sand content, coarse fragments, Cation Exchange Capacity (CEC) and pH) were validated at two soil depth intervals (5–15 and 30–60 cm) using 260 soil samples. In general, soil property predictions were unbiased except for coarse fragments and CEC in the 5–15 cm layer. Validation statistics (R2, RMSE, RRMSE and ME) were better for the 30–60 cm layer except for soil particle-size distribution. Thus, differences in prediction accuracies among strata (the validation support) denote areas where more soil data or better soil prediction models are needed to improve the disaggregation process

Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

International audienceEnhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape