Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ~40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level

Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.Peer reviewe

Les données du GIS Sol pour appuyer les politiques d’atténuation et d’adaptation au changement climatique: Barrières et stratégies de valorisation

International audienceSoils play a key role in climate change mitigation and for the adaptation of the agricultural and forestry sectors. The development of public policies aimed at reducing our greenhouse gas emissions, at increasing the sink function of the soils with respect to atmospheric carbon dioxide, and strengthening our capacities of adaptation, particularly in terms of agriculture or forestry, is primarily based on knowledge of our soils and their management. The Soil Scientific Interest Group (Sol GIS) runs four major soil data acquisition programs and is responsible for their data management, processing and dissemination. Data from the GIS Sol have been, for several decades, at the heart of studies commissioned by the public authorities and research work aimed at better understanding and managing soils, for the mitigation of climate change or to adapt agriculture and the forest to it. These data make it possible to better understand and characterize the past, present and future role of soils in relation to climate change. The potential of the GIS Soil data is however under-exploited, for various reasons, and in particular the complexity of the soil data and its accessibility. We may reasonably recommend a strengthening of efforts to make it better known, particularly among the scientific communities working on climate change, for example by targeting certain national and international data dissemination portals. It will also be necessary to improve its accessibility, in particular by clarifying the related legal aspects, and its interoperability. These are the conditions for realizing the full potential of the ever-evolving GIS programs in research andpolicy support for climate change mitigation and adaptation.Los suelos desempeñan un papel importante en la atenuación del cambio climático y en la adaptación de los sectores agrícola y forestal. La elaboración de políticas públicas que tengan por objeto reducir nuestras emisiones de gases de efecto invernadero, aumentar la función de sumidero de los suelos frente al dióxido de carbono atmosférico y reforzar nuestra capacidad de adaptación, especialmente en materia de agricultura o silvicultura se basa en el conocimiento y la existencia de datos sobre nuestros suelosy su gestión. El Grupo de Interés Científico Suelo (GIS Sol) dirige cuatro grandes programas de adquisición de datos de suelo y es responsable de su gestión, tratamiento y difusión. Los datos del GIS Sol han sido, durante varias décadas, el centro de estudios encargados por las autoridades públicas y de trabajos de investigación para comprender mejor y gestionar los suelos, para la atenuación del cambio climático o para adaptar la agricultura y los bosques a las perturbaciones anunciadas. Estos datos permiten comprender y caracterizar mejor el papel pasado, presente y futuro de los suelos en relación con el cambio climático. Sin embargo, el potencial de los datos del GIS Sol no se aprovecha plenamente por diversas razones, entre ellas la complejidad de los datos de suelo y su accesibilidad. Parece razonable promover un fortalecimiento de los esfuerzos para dar a conocer esta cuestión, en particular entre las comunidades científicas que se ocupan del cambio climático, por ejemplo, centrándose en determinados portales nacionales e internacionales de difusión de datos. Asimismo, deberá mejorarse su accesibilidad, en particular mediante la aclaración de los aspectos jurídicos y su interoperabilidad. Estas son las condiciones para la realización del pleno potencial de los programas del GIS, de los programas en continua evolución, en materia de investigación y de apoyo a las políticas para la atenuación y la adaptación al cambio climático.Les sols jouent un rôle prépondérant en matière d’atténuation du changement climatique etd'adaptation des secteurs agricoles et forestiers. L’élaboration de politiques publiques quiviseraient à réduire nos émissions de gaz à effet de serre, à augmenter la fonction de puits dessols vis-à-vis du dioxyde de carbone atmosphérique, et à renforcer nos capacités d’adaptation,notamment en matière d’agriculture ou de sylviculture, repose sur la connaissance et l’existencede données concernant nos sols et leur gestion. Le Groupement d’Intérêt Scientifique Sol (le GISSol) pilote quatre grands programmes d’acquisition de la donnée sol, et est responsable de sagestion, de son traitement et de sa diffusion. Les données du GIS Sol ont été, depuis plusieursdécennies, au cœur d’études mandatées par les pouvoirs publics et de travaux de recherchevisant à mieux comprendre et gérer les sols, pour l’atténuation du changement climatique oupour adapter l’agriculture et la forêt aux perturbations annoncées. Ces données permettentde mieux comprendre et caractériser les rôles passé, présent et futur des sols en lien avecle changement climatique. Le potentiel des données du GIS Sol est toutefois sous-exploité,pour différentes raisons, et notamment la complexité de la donnée sol et son accessibilité. Ilsemble raisonnable de préconiser un renforcement des efforts pour la faire mieux connaître,notamment auprès des communautés scientifiques qui travaillent sur le changement climatique,par exemple en ciblant certains portails nationaux et internationaux de diffusion de la donnée.Il conviendra également d’améliorer son accessibilité, notamment en clarifiant les aspectsjuridiques s’y rapportant, et son inter-opérabilité. Ce sont là des conditions pour la réalisation du plein potentiel des programmes du GIS, des programmes en évolution permanente, en matière de recherche et d’appui aux politiques pour l’atténuation et l'adaptation au changement climatiqu

Evaluation report for Thunen institute, The German Agricultural Soil Inventory

The German Agricultural Soil Inventory (BZE-LW) is running a general project evaluation as this network is currently in the phase between the first and second sampling campaign, the latter starting in 2023. A one-day meeting was held in Braunschweig to discuss the general results of the first campaign and the organisation planned for the next campaign. The following scientists were present:●Bas van Wesemael, Earth and Life Institute, UCLouvain, Belgium●Nicolas Saby, unité Infosol, INRAE, Orléans, France●Claudy Jolivet, unité Infosol, INRAE, Orléans, France●Heikkinen Jaakko, Natural Resources Institute, Luke, Finland●Lars Elsgaard, Department of Agroecology, Aarhus University, Denmark●Axel Don and Christopher Poeplau, Thünen Institute This report gathers some general comments and ideas organised according to the list of questions raised before and during the workshop

Characterization of Environmental Health Inequalities Due to Polyaromatic Hydrocarbon Exposure in France

Characterization of Environmental Health Inequalities Due to Polyaromatic Hydrocarbon Exposure in Franc

Large-scale simultaneous hypothesis testing in monitoring carbon content from French soil database - a semi-parametric mixture approach

Investigating the information of the French National Soil Tests database for soil monitoring produces multiple hypothesis testing problems with hundreds or thousands of test responses to consider simultaneously. A largely used concept of error control in such multiple testing is the expected proportion of falsely rejected hypotheses, or False Discovery Rate (FOR). A related notion of local FDR (lFDR) can be appropriately represented by considering that the observed p-values come from a two-component mixture model where the component corresponding to the null hypothesis is known. In this work, we explore different solutions for FDR estimation. In particular, we introduce a specific version of a semi-parametric Expectation-Maximization (EM) algorithm for lFDR estimation, and compare it to classical 1FDR estimation using parametric mixtures, and conventional FDR approaches. The performances of the different models for estimating the FDR and related criteria are first illustrated on the results of simulated multiple comparison tests. These approaches are then applied to soil carbon content monitoring on our database. The results show that not taking into account the FDR estimation can lead to over-estimation of the number of cantons (locations) subject to a significant change. However, we have detected large numbers of significant changes in the database that occurred during the time period of this study. Globally, losses in organic carbon are observed in Northern France, along the Atlantic coastal regions, and to a lesser extent for the data collected over the North-Eastern regions. The OC increases are more scattered over the territory. We also use the data to estimate the minimum number of samples needed at each period to detect a given change

Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping

International audienceDigital soil mapping has been increasingly advocated as an efficient approach to deliver fine-resolution and up-to-date soil information in evaluating soil ecosystem services. Considering the great spatial heterogeneity of soils, it is widely recognized that more representative soil observations are needed for better capturing the soil spatial variation and thus to increase the accuracy of digital soil maps. In reality, the budget for the field work and soil laboratory analysis is commonly limited due to its high cost and low efficiency. In the last two decades, being an alternative to wet chemistry, soil spectroscopy, such as visible-near infrared (Vis-NIR), mid-infrared (MIR) spectroscopy has been developed in measuring soil information in a rapid and cost-effective manner and thus enable to collect more soil information for digital soil mapping (DSM). However, spectroscopically inferred (SI) data are subject to higher uncertainties than reference laboratory analysis. Many DSM practices integrated SI data with soil observations into spatial modelling while few studies addressed the key question that whether these non-errorless soil data improve map accuracy in DSM. In this study, French Soil Monitoring Network (RMQS) and Land Use and Coverage Area frame Survey Soil (LUCAS Soil) datasets were used to evaluate the potential of SI data from Vis-NIR and MIR in digital mapping of soil properties (i.e. soil organic carbon, clay, and pH) at a national scale. Cubist and quantile regression forests were used for spectral predictive modelling and DSM modelling, respectively. For both RMQS and LUCAS Soil dataset, different scenarios regarding varying proportions of SI data and laboratory observations were tested for spectral predictive models and DSM models. Repeated (50 times) external validation suggested that adding additional SI data can improve the performance of DSM models regardless of soil properties (gain of R2 proportion at 3–19%) when the laboratory observations are limited (≤50%). Lower proportion of SI data used in DSM model and higher accuracy of spectral predictive models led to greater improvement of DSM. Our results also showed that a greater proportion of SI data lowered the prediction intervals which may result in an underestimation of prediction uncertainty. The determination of accuracy threshold on SI data for the use in DSM needs to be explored in future studies